spce0038-machine-learning-with-big-data/week7/Lecture20_RNN.ipynb

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   "source": [
    "# Lecture 20: Recurrent Neural Networks"
   ]
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    "![](https://www.tensorflow.org/images/colab_logo_32px.png)\n",
    "[Run in colab](https://colab.research.google.com/drive/1vb_GwBXbMQwR8Kd0V0LTwvykwJP9FQlI)"
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     "text": [
      "Version: 2025-03-07 05:32:29\n"
     ]
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   "source": [
    "import datetime\n",
    "now = datetime.datetime.now()\n",
    "print(\"Version: \" + now.strftime(\"%Y-%m-%d %H:%M:%S\"))"
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     "text": [
      "2025-03-07 05:32:29.824955: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2025-03-07 05:32:29.828738: I external/local_xla/xla/tsl/cuda/cudart_stub.cc:32] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2025-03-07 05:32:29.838204: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1741325549.852575    7182 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1741325549.857068    7182 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2025-03-07 05:32:29.873822: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
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   "source": [
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "mpl.rc('axes', labelsize=14)\n",
    "mpl.rc('xtick', labelsize=12)\n",
    "mpl.rc('ytick', labelsize=12)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
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   "source": [
    "## Recurrent neurons and layers"
   ]
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   "source": [
    "Until now looked at feedforward networks where activations flow one way.  Recurrent neural networks (RNNs) have *connections pointing backward* as well."
   ]
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    "### Single recurrent neuron\n",
    "\n",
    "Simplest possible case shown on left.  At each time step $t$, this recurrent neuron receives the input $x_{(t)}$ as well as its own output from the previous time step $\\hat{y}_{(t-1)}$.\n",
    "\n",
    "Same network is illustrated *unrolled through time* on the right.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture20_Images/rnn.jpeg\" alt=\"Drawing\"  width=\"800px\" style=\"display:block; margin:auto\"/>\n",
    "\n",
    "[Source: Geron]"
   ]
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    "### Layer of recurrent neurons\n",
    "\n",
    "At each time step $t$, every neuron receives both the input vector $\\mathbf{x}_{(t)}$ and the ouput vector from the previous time step $\\hat{\\mathbf{y}}_{(t-1)}$, as shown on the left.\n",
    "\n",
    "Again, this can be unrolled over time, as shown on the right.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture20_Images/rnn_layer.jpeg\" alt=\"Drawing\"  width=\"800px\" style=\"display:block; margin:auto\"/>\n",
    "\n",
    "[Source: Geron]"
   ]
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    "Each recurrent neuron has two sets of weights: one for the inputs $\\mathbf{x}_{(t)}$ and one for the ouputs from the previous time step $\\hat{\\mathbf{y}}_{(t-1)}$.  Let's store weights for the entire recurrent layer in two weight matrices, $W_{\\mathbf{x}}$ and $W_{\\hat{\\mathbf{y}}}$, respectively.\n",
    "\n",
    "Output of a recurrent layer (for a single data instance) is given by\n",
    "\n",
    "$$\n",
    "\\hat{\\mathbf{y}}_{(t)} = \\varphi \\left(W_{\\mathbf{x}}^\\text{T} {\\mathbf{x}}_{(t)} + W_{\\hat{\\mathbf{y}}}^\\text{T} \\hat{\\mathbf{y}}_{(t-1)} + \\mathbf{b} \\right) ,\n",
    "$$\n",
    "\n",
    "where $\\mathbf{b}$ is the bias and $\\varphi(\\cdot)$ is the activation function."
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    "### Memory cells \n",
    "\n",
    "Since at time step $t$ there is some memory of previous states in the system.\n",
    "\n",
    "A part of a neual network that preserves some state across time steps is called a *memory cell*."
   ]
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    "A cell's hidden state $\\mathbf{h}_{(t)}$ that stores memory need be the same as the output of the cell.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture20_Images/rnn_hidden.png\" alt=\"Drawing\"  width=\"800px\" style=\"display:block; margin:auto\"/>\n",
    "\n",
    "[Source: Geron]"
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   "source": [
    "### Different types of RNN\n",
    "\n",
    "RNNs can be configured for different input and output sequences.\n",
    "\n",
    "- Sequence-to-sequence (top left), e.g. past stock prices to future stock prices\n",
    "- Sequence-to-vector (top right), e.g. sentiment score for movie review\n",
    "- Vector-to-sequence (bottom left), e.g. image to caption\n",
    "- Encoder-decoder (bottom right), e.g. language translation"
   ]
  },
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   "source": [
    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture20_Images/seqvec.jpeg\" alt=\"Drawing\"  width=\"800px\" style=\"display:block; margin:auto\"/>\n",
    "\n",
    "[Source: Geron]"
   ]
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   "source": [
    "### Training RNNs\n",
    "\n",
    "RNNs can be training by *backpropagation through time* (BPTT). Consists of unrolling through time (as we did above) and then training by regular backpropagation.\n",
    "\n",
    "- Consists of forward pass to compute cost function for outpus.\n",
    "- Then propagative gradients of cost function backwards through the unrolled network.\n",
    "- Update model parameters using gradients."
   ]
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   "source": [
    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture20_Images/backprop.jpeg\" alt=\"Drawing\"  width=\"800px\" style=\"display:block; margin:auto\"/>\n",
    "\n",
    "[Source: Geron]"
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    "Since the same weights and biases are used at each time step, backpropagation does the correct thing and sums over all time steps.\n",
    "\n",
    "TensorFlow can handle all of this for you."
   ]
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   "source": [
    "## Basic example of time-series forecasting"
   ]
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    "RNNs well-sutied for time series modelling, e.g. such as stock prices, air temperature, brain wave patterns, and so on. \n",
    "\n",
    "We will train an RNN to predict the next value in a time series. "
   ]
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   "source": [
    "### Generate dataset\n",
    "\n",
    "We will generate a mock dataset consisting of lots of time series, each with 51 samples.  \n",
    "\n",
    "We will attempt to predict the final value (hence the target will be the final time series values, which will be removed from the feature matrix)."
   ]
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   "source": [
    "def generate_time_series(batch_size, n_steps):\n",
    "    freq1, freq2, offsets1, offsets2 = np.random.rand(4, batch_size, 1)\n",
    "    time = np.linspace(0, 1, n_steps)\n",
    "    series = 0.5 * np.sin((time - offsets1) * (freq1 * 10 + 10))  #   wave 1\n",
    "    series += 0.2 * np.sin((time - offsets2) * (freq2 * 20 + 20)) # + wave 2\n",
    "    series += 0.1 * (np.random.rand(batch_size, n_steps) - 0.5)   # + noise\n",
    "    return series[..., np.newaxis].astype(np.float32)"
   ]
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   "source": [
    "np.random.seed(42)\n",
    "\n",
    "n_steps = 50\n",
    "series = generate_time_series(10000, n_steps + 1)\n",
    "X_train, y_train = series[:7000, :n_steps], series[:7000, -1]\n",
    "X_valid, y_valid = series[7000:9000, :n_steps], series[7000:9000, -1]\n",
    "X_test, y_test = series[9000:, :n_steps], series[9000:, -1]"
   ]
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    {
     "data": {
      "text/plain": [
       "((7000, 50, 1), (7000, 1))"
      ]
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   "source": [
    "X_train.shape, y_train.shape"
   ]
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      "text/plain": [
       "<Figure size 1200x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_series(series, y=None, y_pred=None, x_label=\"$t$\", y_label=\"$x(t)$\"):\n",
    "    plt.plot(series, \".-\")\n",
    "    if y is not None:\n",
    "        plt.plot(n_steps, y, \"bx\", markersize=10)\n",
    "    if y_pred is not None:\n",
    "        plt.plot(n_steps, y_pred, \"ro\")\n",
    "    plt.grid(True)\n",
    "    if x_label:\n",
    "        plt.xlabel(x_label, fontsize=16)\n",
    "    if y_label:\n",
    "        plt.ylabel(y_label, fontsize=16, rotation=0)\n",
    "    plt.hlines(0, 0, 100, linewidth=1)\n",
    "    plt.axis([0, n_steps + 1, -1, 1])\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, sharey=True, figsize=(12, 4))\n",
    "for col in range(3):\n",
    "    plt.sca(axes[col])\n",
    "    plot_series(X_valid[col, :, 0], y_valid[col, 0],\n",
    "                y_label=(\"$x(t)$\" if col==0 else None))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Compute baselines\n",
    "\n",
    "Let's consider naive forecasting to provide a baseline for comparison.\n",
    "\n",
    "We will just predict the last observed value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:32.585442Z",
     "iopub.status.busy": "2025-03-07T05:32:32.585236Z",
     "iopub.status.idle": "2025-03-07T05:32:32.589344Z",
     "shell.execute_reply": "2025-03-07T05:32:32.588954Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float32(0.020211367)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = X_valid[:, -1]\n",
    "np.mean((y_valid - y_pred)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Plot a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:32.591135Z",
     "iopub.status.busy": "2025-03-07T05:32:32.590958Z",
     "iopub.status.idle": "2025-03-07T05:32:32.698219Z",
     "shell.execute_reply": "2025-03-07T05:32:32.697571Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Let's consider a simple linear model as another baseline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:32.700154Z",
     "iopub.status.busy": "2025-03-07T05:32:32.699977Z",
     "iopub.status.idle": "2025-03-07T05:32:32.743673Z",
     "shell.execute_reply": "2025-03-07T05:32:32.742969Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-03-07 05:32:32.711495: E external/local_xla/xla/stream_executor/cuda/cuda_driver.cc:152] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[50, 1]),\n",
    "    keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:32.745522Z",
     "iopub.status.busy": "2025-03-07T05:32:32.745313Z",
     "iopub.status.idle": "2025-03-07T05:32:36.015539Z",
     "shell.execute_reply": "2025-03-07T05:32:36.014935Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:06\u001b[0m 305ms/step - loss: 0.7483"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 843us/step - loss: 0.6939  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m122/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 833us/step - loss: 0.5725"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m182/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 833us/step - loss: 0.4890"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.4493 - val_loss: 0.0651\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.0685"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 850us/step - loss: 0.0660"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m120/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 846us/step - loss: 0.0629"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m180/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 844us/step - loss: 0.0605"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0592 - val_loss: 0.0414\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.0495"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 852us/step - loss: 0.0433"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m120/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 849us/step - loss: 0.0417"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m180/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 847us/step - loss: 0.0404"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0397 - val_loss: 0.0296\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.0351"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 853us/step - loss: 0.0304"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m119/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 851us/step - loss: 0.0295"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m179/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 849us/step - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0283 - val_loss: 0.0224\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.0251"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 851us/step - loss: 0.0226"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m119/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 853us/step - loss: 0.0220"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m170/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 891us/step - loss: 0.0217"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0214 - val_loss: 0.0181\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0184"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 844us/step - loss: 0.0179"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m120/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 850us/step - loss: 0.0175"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m179/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 852us/step - loss: 0.0173"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0172 - val_loss: 0.0154\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.0140"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 858us/step - loss: 0.0149"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m119/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 860us/step - loss: 0.0147"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m178/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 858us/step - loss: 0.0146"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0145 - val_loss: 0.0134\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.0112"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 854us/step - loss: 0.0129"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m119/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 854us/step - loss: 0.0127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m178/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 852us/step - loss: 0.0127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0126 - val_loss: 0.0118\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 25ms/step - loss: 0.0092"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 59/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 875us/step - loss: 0.0114"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m118/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 866us/step - loss: 0.0112"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m177/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 864us/step - loss: 0.0112"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0112 - val_loss: 0.0105\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.0078"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 860us/step - loss: 0.0101"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m119/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 858us/step - loss: 0.0100"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m178/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 856us/step - loss: 0.0099"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0099 - val_loss: 0.0093\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=10,\n",
    "                    validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:36.017542Z",
     "iopub.status.busy": "2025-03-07T05:32:36.017336Z",
     "iopub.status.idle": "2025-03-07T05:32:36.123734Z",
     "shell.execute_reply": "2025-03-07T05:32:36.123122Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0106"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 969us/step - loss: 0.0096\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.009313036687672138"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "As expected, the simple linear model is better than naive forecasting (MSE of 0.006 versus 0.020)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "One could plot training and validation losses against epoch to check training ok."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:36.125750Z",
     "iopub.status.busy": "2025-03-07T05:32:36.125573Z",
     "iopub.status.idle": "2025-03-07T05:32:36.223297Z",
     "shell.execute_reply": "2025-03-07T05:32:36.222629Z"
    },
    "scrolled": true,
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_learning_curves(loss, val_loss):\n",
    "    plt.plot(np.arange(len(loss)) + 0.5, loss, \"b.-\", label=\"Training loss\")\n",
    "    plt.plot(np.arange(len(val_loss)) + 1, val_loss, \"r.-\", label=\"Validation loss\")\n",
    "    plt.gca().xaxis.set_major_locator(mpl.ticker.MaxNLocator(integer=True))\n",
    "    plt.axis([1, 10, 0, 0.05])\n",
    "    plt.legend(fontsize=14)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.grid(True)\n",
    "\n",
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Plot a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:36.225254Z",
     "iopub.status.busy": "2025-03-07T05:32:36.225077Z",
     "iopub.status.idle": "2025-03-07T05:32:36.450973Z",
     "shell.execute_reply": "2025-03-07T05:32:36.450233Z"
    },
    "slideshow": {
     "slide_type": "-"
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 24ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 910us/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_valid)\n",
    "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Not great in this case but this is just one example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Using a simple RNN\n",
    "\n",
    "By default, the hyperbolic tanget activation is used (more on activation functions for RNNs shortly...)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:36.453915Z",
     "iopub.status.busy": "2025-03-07T05:32:36.453718Z",
     "iopub.status.idle": "2025-03-07T05:32:36.494969Z",
     "shell.execute_reply": "2025-03-07T05:32:36.494328Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.SimpleRNN(1, input_shape=[None, 1])\n",
    "])\n",
    "\n",
    "optimizer = keras.optimizers.Adam(learning_rate=0.005)\n",
    "model.compile(loss=\"mse\", optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:36.497032Z",
     "iopub.status.busy": "2025-03-07T05:32:36.496845Z",
     "iopub.status.idle": "2025-03-07T05:32:42.071070Z",
     "shell.execute_reply": "2025-03-07T05:32:42.070494Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2:31\u001b[0m 696ms/step - loss: 0.0996"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 15/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1295    "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 29/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1281"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 44/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1265"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 58/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1245"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 72/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1226"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 86/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1204"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m101/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1180"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m115/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1156"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m130/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1131"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m145/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1106"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m160/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1082"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m174/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1059"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m189/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m203/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.1014"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m218/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0992"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - loss: 0.0989 - val_loss: 0.0199\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 17ms/step - loss: 0.0182"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 16/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0196 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 30/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0191"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 44/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0189"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 57/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0188"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 71/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0187"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 86/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0186"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m101/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0185"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m116/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0185"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m131/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0184"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m146/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0183"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m160/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0182"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m174/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0181"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m189/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0180"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m204/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0179"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0178"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0178 - val_loss: 0.0148\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 17ms/step - loss: 0.0158"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 16/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0153 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 30/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0149"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 44/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0148"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 58/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0147"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 73/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0147"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 86/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0147"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m101/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0146"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m115/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0146"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m130/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0146"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m145/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0145"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m160/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0145"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m175/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0144"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m190/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0144"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m205/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0143"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0143 - val_loss: 0.0126\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 19ms/step - loss: 0.0139"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 16/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0133 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0129"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 46/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0128"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 76/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 90/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m101/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m113/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m127/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m142/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m157/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0127"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m172/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m187/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m202/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m217/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0126"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0126 - val_loss: 0.0115\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 17ms/step - loss: 0.0132"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 16/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0124 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0121"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 45/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0120"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0119"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 75/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0119"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 90/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0119"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m105/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0119"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m120/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0119"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m132/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0119"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m147/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0118"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m162/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0118"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m177/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0118"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m191/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0118"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m206/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0118"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - loss: 0.0118 - val_loss: 0.0111\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=5,\n",
    "                    validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:42.073020Z",
     "iopub.status.busy": "2025-03-07T05:32:42.072845Z",
     "iopub.status.idle": "2025-03-07T05:32:42.233310Z",
     "shell.execute_reply": "2025-03-07T05:32:42.232739Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m32/63\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0114 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m62/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0112"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0112\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.01110600121319294"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "The RNN is not very good, with an MSE of 0.011 (better than naive forecasting but not as good as linear model).\n",
    "\n",
    "This is to be expected, since the simple RNN has very few parameters.\n",
    "\n",
    "- Linear model: one parameter per input and per time step, plus bias $\\Rightarrow$ 51 parameters.\n",
    "- Simple RNN: one parameter per input and per hidden state dimension, plus bias $\\Rightarrow$ 3 parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Again, could plot learning curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:42.235566Z",
     "iopub.status.busy": "2025-03-07T05:32:42.235193Z",
     "iopub.status.idle": "2025-03-07T05:32:42.350242Z",
     "shell.execute_reply": "2025-03-07T05:32:42.349589Z"
    },
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:42.352104Z",
     "iopub.status.busy": "2025-03-07T05:32:42.351932Z",
     "iopub.status.idle": "2025-03-07T05:32:42.746124Z",
     "shell.execute_reply": "2025-03-07T05:32:42.745441Z"
    },
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 79ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m37/63\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_valid)\n",
    "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Deep RNNs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Can also have multiple cells per time step, defining a deep RNNs.\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture20_Images/deeprnn.jpeg\" alt=\"Drawing\"  width=\"800px\" style=\"display:block; margin:auto\"/>\n",
    "\n",
    "[Source: Geron]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Back to the time-series forecasting example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:42.748140Z",
     "iopub.status.busy": "2025-03-07T05:32:42.747950Z",
     "iopub.status.idle": "2025-03-07T05:32:42.799237Z",
     "shell.execute_reply": "2025-03-07T05:32:42.798647Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True),\n",
    "    keras.layers.SimpleRNN(1)\n",
    "])\n",
    "\n",
    "optimizer = keras.optimizers.Adam(learning_rate=0.005)\n",
    "model.compile(loss=\"mse\", optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Set `return_sequences = True` to ensure it outputs all intermediate steps, not just the final."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:32:42.801568Z",
     "iopub.status.busy": "2025-03-07T05:32:42.801393Z",
     "iopub.status.idle": "2025-03-07T05:33:01.256448Z",
     "shell.execute_reply": "2025-03-07T05:33:01.255815Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m6:51\u001b[0m 2s/step - loss: 0.8050"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  5/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - loss: 0.8073"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  9/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.6887"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 13/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.6052"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 17/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.5398"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 21/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.4879"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 25/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.4463"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 29/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.4124"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 33/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.3842"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 37/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.3601"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 41/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.3394"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 45/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.3213"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 49/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.3053"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 53/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.2911"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 57/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.2784"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.2669"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 65/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.2564"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 69/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.2469"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 73/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.2382"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 77/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.2301"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 82/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.2209"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 87/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.2125"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 91/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.2063"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 95/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.2005"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 99/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1951"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m103/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1900"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m107/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1852"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m111/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1807"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m115/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1764"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m119/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1724"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m123/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1686"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m127/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m131/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1615"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m135/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1582"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m139/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1550"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m143/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.1520"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m147/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1491"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m151/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1464"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m156/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1431"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m161/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1400"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m165/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1376"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m169/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1354"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m173/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1332"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m177/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1310"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m181/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1290"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m185/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1270"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m189/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1251"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m193/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1233"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m197/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1215"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m201/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1198"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m205/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1182"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m209/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1166"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m213/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1150"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m217/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.1135"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - loss: 0.1124 - val_loss: 0.0050\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 25ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  5/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0050"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  9/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0049"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 13/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 17/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 21/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 25/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 29/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 33/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 37/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0048"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 41/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 45/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 49/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 53/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 57/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 65/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 69/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 73/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 77/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 81/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 85/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 89/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 93/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 97/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m101/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m105/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m109/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m113/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m117/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m121/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m125/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m129/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m133/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m137/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m141/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m145/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m149/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m153/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m157/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m161/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m165/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m169/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m173/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m177/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m181/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m185/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m189/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m193/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m197/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m201/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m205/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m209/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0046"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m213/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m217/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0045"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 15ms/step - loss: 0.0045 - val_loss: 0.0040\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 25ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  5/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - loss: 0.0042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  9/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0042"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 13/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 17/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 21/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 25/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 29/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 33/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 37/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 41/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 45/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 49/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 53/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 57/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 65/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 69/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 73/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 77/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 81/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 85/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 89/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 93/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 97/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m101/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m105/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m109/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m113/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m117/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m121/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m125/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m129/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m133/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m137/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m141/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m145/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m149/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m153/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m157/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m161/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m165/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m169/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m173/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m177/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m181/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m185/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m189/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m193/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m197/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m201/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m205/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m209/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m213/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m217/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 15ms/step - loss: 0.0040 - val_loss: 0.0037\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 26ms/step - loss: 0.0035"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  5/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  9/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 14/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 19/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 23/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 27/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 35/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 39/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 43/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 47/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 51/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 55/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 59/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 63/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 67/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 71/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 75/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 79/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 83/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 87/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 91/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 95/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 99/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m103/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m107/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m111/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m115/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m119/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m123/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m127/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m131/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m135/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m139/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m143/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m147/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m151/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m155/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m159/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m163/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m167/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m171/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m175/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m179/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m183/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m187/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m191/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m195/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m199/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m203/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m207/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m211/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m215/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 15ms/step - loss: 0.0038 - val_loss: 0.0040\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 26ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  5/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  9/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0040"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 13/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0039"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 17/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0038"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 21/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 25/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 29/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 33/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 37/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 41/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 45/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 49/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 53/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 57/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 65/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 69/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 73/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 77/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 81/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 85/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 89/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 93/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 97/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m101/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m105/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m109/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m113/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m117/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m121/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m125/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m129/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m133/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m137/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m141/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m145/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m149/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m153/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m158/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m163/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m167/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m171/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m175/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m179/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m183/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m187/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m191/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m195/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m199/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m203/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m207/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m211/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m215/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 15ms/step - loss: 0.0037 - val_loss: 0.0039\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, epochs=5,\n",
    "                    validation_data=(X_valid, y_valid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:01.258484Z",
     "iopub.status.busy": "2025-03-07T05:33:01.258291Z",
     "iopub.status.idle": "2025-03-07T05:33:01.602694Z",
     "shell.execute_reply": "2025-03-07T05:33:01.602093Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - loss: 0.0031"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m14/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0035 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m27/63\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0036"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m40/63\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m53/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0037"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0037\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.0038687658961862326"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_valid, y_valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Now achieve best MSE of 0.003."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "source": [
    "Again, can plot learning curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:01.604714Z",
     "iopub.status.busy": "2025-03-07T05:33:01.604546Z",
     "iopub.status.idle": "2025-03-07T05:33:01.696226Z",
     "shell.execute_reply": "2025-03-07T05:33:01.695621Z"
    },
    "slideshow": {
     "slide_type": "skip"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history.history[\"loss\"], history.history[\"val_loss\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:01.698076Z",
     "iopub.status.busy": "2025-03-07T05:33:01.697905Z",
     "iopub.status.idle": "2025-03-07T05:33:02.511179Z",
     "shell.execute_reply": "2025-03-07T05:33:02.510489Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m12s\u001b[0m 196ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m16/63\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m32/63\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 3ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m45/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m59/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = model.predict(X_valid)\n",
    "plot_series(X_valid[0, :, 0], y_valid[0, 0], y_pred[0, 0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Forecasting several steps ahead"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "So far, we just predicted the next time series sample.\n",
    "\n",
    "What if we want to predict the next, say, 10 samples?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Predict one step, multiple times\n",
    "\n",
    "Simplest approach is to predict next value, then add that to inputes (as if this value had actually occurred), then run model again to predict a next value, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:02.513270Z",
     "iopub.status.busy": "2025-03-07T05:33:02.513092Z",
     "iopub.status.idle": "2025-03-07T05:33:03.215733Z",
     "shell.execute_reply": "2025-03-07T05:33:03.215127Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 23ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 14ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 24ms/step\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(43) # not 42, as it would give the first series in the train set\n",
    "\n",
    "series = generate_time_series(1, n_steps + 10)\n",
    "X_new, Y_new = series[:, :n_steps], series[:, n_steps:]\n",
    "X = X_new\n",
    "for step_ahead in range(10):\n",
    "    y_pred_one = model.predict(X[:, step_ahead:])[:, np.newaxis, :]\n",
    "    X = np.concatenate([X, y_pred_one], axis=1)\n",
    "\n",
    "Y_pred = X[:, n_steps:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:03.217670Z",
     "iopub.status.busy": "2025-03-07T05:33:03.217497Z",
     "iopub.status.idle": "2025-03-07T05:33:03.221433Z",
     "shell.execute_reply": "2025-03-07T05:33:03.220835Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 10, 1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:03.223159Z",
     "iopub.status.busy": "2025-03-07T05:33:03.222998Z",
     "iopub.status.idle": "2025-03-07T05:33:03.355280Z",
     "shell.execute_reply": "2025-03-07T05:33:03.354619Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_multiple_forecasts(X, Y, Y_pred):\n",
    "    n_steps = X.shape[1]\n",
    "    ahead = Y.shape[1]\n",
    "    plot_series(X[0, :, 0])\n",
    "    plt.plot(np.arange(n_steps, n_steps + ahead), Y[0, :, 0], \"ro-\", label=\"Actual\")\n",
    "    plt.plot(np.arange(n_steps, n_steps + ahead), Y_pred[0, :, 0], \"bx-\", label=\"Forecast\", markersize=10)\n",
    "    plt.axis([0, n_steps + ahead, -1, 1])\n",
    "    plt.legend(fontsize=14)\n",
    "\n",
    "plot_multiple_forecasts(X_new, Y_new, Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Now let's use this model to predict the next 10 values. We first need to regenerate the sequences with 9 more time steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:03.357176Z",
     "iopub.status.busy": "2025-03-07T05:33:03.356999Z",
     "iopub.status.idle": "2025-03-07T05:33:03.386811Z",
     "shell.execute_reply": "2025-03-07T05:33:03.386192Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "n_steps = 50\n",
    "series = generate_time_series(10000, n_steps + 10)\n",
    "X_train, Y_train = series[:7000, :n_steps], series[:7000, -10:, 0]\n",
    "X_valid, Y_valid = series[7000:9000, :n_steps], series[7000:9000, -10:, 0]\n",
    "X_test, Y_test = series[9000:, :n_steps], series[9000:, -10:, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Now let's predict the next 10 values one by one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:03.389023Z",
     "iopub.status.busy": "2025-03-07T05:33:03.388711Z",
     "iopub.status.idle": "2025-03-07T05:33:07.019797Z",
     "shell.execute_reply": "2025-03-07T05:33:07.019209Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m15/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m29/63\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m43/63\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m57/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m13s\u001b[0m 214ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m14/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m28/63\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m40/63\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m53/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m14/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m27/63\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m40/63\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m53/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m14/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m27/63\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m40/63\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m53/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m14/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m25/63\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m38/63\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m51/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m14/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m27/63\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m40/63\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m53/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m13/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m25/63\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m37/63\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m50/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m62/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m13/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m25/63\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m36/63\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m48/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m60/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 16ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m13/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m25/63\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m37/63\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m49/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m61/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m 1/63\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m13/63\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m25/63\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m37/63\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m49/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m61/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m63/63\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step\n"
     ]
    }
   ],
   "source": [
    "X = X_valid\n",
    "for step_ahead in range(10):\n",
    "    y_pred_one = model.predict(X)[:, np.newaxis, :]\n",
    "    X = np.concatenate([X, y_pred_one], axis=1)\n",
    "\n",
    "Y_pred = X[:, n_steps:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:07.021844Z",
     "iopub.status.busy": "2025-03-07T05:33:07.021498Z",
     "iopub.status.idle": "2025-03-07T05:33:07.025235Z",
     "shell.execute_reply": "2025-03-07T05:33:07.024855Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 10)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Compute MSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:07.026898Z",
     "iopub.status.busy": "2025-03-07T05:33:07.026738Z",
     "iopub.status.idle": "2025-03-07T05:33:07.030864Z",
     "shell.execute_reply": "2025-03-07T05:33:07.030494Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float32(0.04106598)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean((Y_valid - Y_pred)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Let's compare this performance with some baselines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:07.032565Z",
     "iopub.status.busy": "2025-03-07T05:33:07.032378Z",
     "iopub.status.idle": "2025-03-07T05:33:07.036316Z",
     "shell.execute_reply": "2025-03-07T05:33:07.035940Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float32(0.22278848)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_naive_pred = Y_valid[:, -1:]\n",
    "np.mean((Y_valid - Y_naive_pred)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Predicting all next values at end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Consider a linear model that predicts next 10 time steps all at once.\n",
    "\n",
    "Simply need to set output layer to have 10 units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:07.038128Z",
     "iopub.status.busy": "2025-03-07T05:33:07.037966Z",
     "iopub.status.idle": "2025-03-07T05:33:07.072769Z",
     "shell.execute_reply": "2025-03-07T05:33:07.072139Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.Flatten(input_shape=[50, 1]),\n",
    "    keras.layers.Dense(10) # 10 output predictions\n",
    "])\n",
    "\n",
    "optimizer = keras.optimizers.Adam(learning_rate=0.001)\n",
    "model.compile(loss=\"mse\", optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:07.074764Z",
     "iopub.status.busy": "2025-03-07T05:33:07.074592Z",
     "iopub.status.idle": "2025-03-07T05:33:08.906347Z",
     "shell.execute_reply": "2025-03-07T05:33:08.905796Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:07\u001b[0m 312ms/step - loss: 0.4363"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 58/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 880us/step - loss: 0.3329  "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m118/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 860us/step - loss: 0.2801"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m177/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 859us/step - loss: 0.2465"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.2285 - val_loss: 0.0702\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - loss: 0.0788"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 854us/step - loss: 0.0678"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m119/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 857us/step - loss: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m178/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 855us/step - loss: 0.0630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0617 - val_loss: 0.0455\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - loss: 0.0517"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 59/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 865us/step - loss: 0.0454"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m118/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 861us/step - loss: 0.0440"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m172/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 886us/step - loss: 0.0433"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0428 - val_loss: 0.0365\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - loss: 0.0421"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 854us/step - loss: 0.0369"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m118/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 858us/step - loss: 0.0360"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m176/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 863us/step - loss: 0.0355"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0353 - val_loss: 0.0319\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - loss: 0.0368"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 855us/step - loss: 0.0323"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m119/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 856us/step - loss: 0.0316"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m178/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 855us/step - loss: 0.0313"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0311 - val_loss: 0.0289\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train, epochs=5,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Validation loss is 0.02."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Now let's create an RNN that predicts next 10 time steps all at once.\n",
    "\n",
    "Again, just need to set output layer to have 10 units."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:08.908463Z",
     "iopub.status.busy": "2025-03-07T05:33:08.908272Z",
     "iopub.status.idle": "2025-03-07T05:33:08.952364Z",
     "shell.execute_reply": "2025-03-07T05:33:08.951794Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
    "    keras.layers.SimpleRNN(20),\n",
    "    keras.layers.Dense(10) # 10 output predictions\n",
    "])\n",
    "\n",
    "optimizer = keras.optimizers.Adam(learning_rate=0.001)\n",
    "model.compile(loss=\"mse\", optimizer=optimizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:08.954170Z",
     "iopub.status.busy": "2025-03-07T05:33:08.954001Z",
     "iopub.status.idle": "2025-03-07T05:33:22.643619Z",
     "shell.execute_reply": "2025-03-07T05:33:22.643023Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5:13\u001b[0m 1s/step - loss: 0.3857"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  7/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 0.3303"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 13/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.2942 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 19/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.2699"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 25/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.2515"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.2367"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 37/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.2242"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 43/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.2138"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 49/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.2047"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 55/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.1967"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.1897"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 67/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.1834"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 73/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.1776"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 79/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.1724"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 85/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.1677"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 91/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.1633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 96/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.1599"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m102/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.1562"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m108/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.1527"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m114/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1494"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m120/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1463"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m126/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1435"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m132/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1408"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m138/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1383"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m144/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1359"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m150/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1337"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m156/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1315"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m162/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1295"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m168/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1276"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m174/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1257"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m180/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1240"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m186/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1223"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m192/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1207"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m198/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1192"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m204/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1177"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m209/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1165"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m215/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.1151"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 12ms/step - loss: 0.1140 - val_loss: 0.0340\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - loss: 0.0397"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  7/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0381 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 13/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0370"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 19/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0365"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 25/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0359"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0355"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 36/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0352"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 42/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0350"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 48/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0348"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 54/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0346"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0344"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 66/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0342"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 72/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0341"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 78/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0339"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 84/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0337"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 90/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0336"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 96/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0334"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m102/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0333"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m108/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0332"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m114/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0330"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m120/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0329"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m126/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0328"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m132/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0327"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m138/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0325"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m144/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0324"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m150/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0323"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m156/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0322"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m162/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0321"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m168/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m174/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0319"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m180/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0318"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m186/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0317"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m192/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0316"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m198/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0315"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m204/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0314"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m210/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0313"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m216/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0312"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - loss: 0.0312 - val_loss: 0.0235\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m54s\u001b[0m 250ms/step - loss: 0.0242"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  7/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0242   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 13/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0236"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 19/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0232"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 25/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0229"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0227"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 37/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0225"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 43/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0224"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 49/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0223"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 55/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0222"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0221"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 67/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0221"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 73/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0220"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 79/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0220"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 85/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0219"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 91/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0219"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 96/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0219"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m102/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0218"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m108/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0218"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m114/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0218"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m120/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0217"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m126/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0217"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m132/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0217"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m138/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0216"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m144/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0216"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m150/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0216"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m156/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0216"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m162/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0215"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m168/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0215"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m174/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0215"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m180/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0214"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m186/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0214"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m192/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0214"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m198/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0214"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m204/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0213"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m209/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0213"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m215/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0213"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 11ms/step - loss: 0.0213 - val_loss: 0.0173\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 22ms/step - loss: 0.0179"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  7/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0175 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 13/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0172"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 19/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0170"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 25/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0169"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0169"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 37/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 43/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 49/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 55/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 66/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 72/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 78/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 84/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 90/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 96/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m102/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m108/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m114/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m120/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m126/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m132/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m138/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m144/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m150/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m156/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m162/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m168/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m174/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m179/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m185/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0168"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m191/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0167"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m197/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0167"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m203/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0167"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m209/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0167"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m215/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0167"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 0.0167 - val_loss: 0.0140\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1:18\u001b[0m 362ms/step - loss: 0.0153"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  7/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - loss: 0.0134   "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 13/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 19/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0132 "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 25/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0132"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0132"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 37/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0132"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 43/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0132"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 49/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0132"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 55/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 67/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 73/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 79/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 85/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 91/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 97/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m103/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 9ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m109/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0133"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m115/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m121/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m128/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m135/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m141/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m147/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m153/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m159/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m165/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m171/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m177/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m183/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m189/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m195/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m201/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m207/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m213/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0134"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 10ms/step - loss: 0.0134 - val_loss: 0.0123\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train, epochs=5,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Validation loss is 0.01."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:22.645606Z",
     "iopub.status.busy": "2025-03-07T05:33:22.645431Z",
     "iopub.status.idle": "2025-03-07T05:33:22.833117Z",
     "shell.execute_reply": "2025-03-07T05:33:22.832554Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(43)\n",
    "\n",
    "series = generate_time_series(1, 50 + 10)\n",
    "X_new, Y_new = series[:, :50, :], series[:, -10:, :]\n",
    "Y_pred = model.predict(X_new)[..., np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:22.835108Z",
     "iopub.status.busy": "2025-03-07T05:33:22.834813Z",
     "iopub.status.idle": "2025-03-07T05:33:22.964881Z",
     "shell.execute_reply": "2025-03-07T05:33:22.964221Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multiple_forecasts(X_new, Y_new, Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Predict all next values at each time step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Now let's create an RNN that predicts the next 10 steps at each time step. \n",
    "\n",
    "To do this, we will replace the sequence-to-vector RNN with a sequence-to-sequence RNN.\n",
    "\n",
    "That is, instead of just forecasting time steps 50 to 59 based on time steps 0 to 49, it will forecast time steps 1 to 10 at time step 0, then time steps 2 to 11 at time step 1, and so on, and finally it will forecast time steps 50 to 59 at the last time step. \n",
    "\n",
    "Notice that the model is causal: when it makes predictions at any time step, it can only see past time steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:22.966834Z",
     "iopub.status.busy": "2025-03-07T05:33:22.966660Z",
     "iopub.status.idle": "2025-03-07T05:33:23.011776Z",
     "shell.execute_reply": "2025-03-07T05:33:23.011105Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "n_steps = 50\n",
    "series = generate_time_series(10000, n_steps + 10)\n",
    "X_train = series[:7000, :n_steps]\n",
    "X_valid = series[7000:9000, :n_steps]\n",
    "X_test = series[9000:, :n_steps]\n",
    "Y = np.empty((10000, n_steps, 10))\n",
    "for step_ahead in range(1, 10 + 1):\n",
    "    Y[..., step_ahead - 1] = series[..., step_ahead:step_ahead + n_steps, 0]\n",
    "Y_train = Y[:7000]\n",
    "Y_valid = Y[7000:9000]\n",
    "Y_test = Y[9000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:23.013922Z",
     "iopub.status.busy": "2025-03-07T05:33:23.013727Z",
     "iopub.status.idle": "2025-03-07T05:33:23.017912Z",
     "shell.execute_reply": "2025-03-07T05:33:23.017310Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7000, 50, 1), (7000, 50, 10))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:23.019863Z",
     "iopub.status.busy": "2025-03-07T05:33:23.019697Z",
     "iopub.status.idle": "2025-03-07T05:33:23.074046Z",
     "shell.execute_reply": "2025-03-07T05:33:23.073404Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "tf.random.set_seed(42)\n",
    "\n",
    "model = keras.models.Sequential([\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),\n",
    "    keras.layers.SimpleRNN(20, return_sequences=True),\n",
    "    keras.layers.TimeDistributed(keras.layers.Dense(10)) # Wrap dense layer to apply at every time step\n",
    "])\n",
    "\n",
    "def last_time_step_mse(Y_true, Y_pred):\n",
    "    return tf.reduce_mean(tf.square(Y_true[:, -1] - Y_pred[:, -1])) \n",
    "\n",
    "model.compile(loss=\"mse\", optimizer=keras.optimizers.Adam(learning_rate=0.01), metrics=[last_time_step_mse])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:23.076374Z",
     "iopub.status.busy": "2025-03-07T05:33:23.075893Z",
     "iopub.status.idle": "2025-03-07T05:33:39.248052Z",
     "shell.execute_reply": "2025-03-07T05:33:39.247479Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m8:50\u001b[0m 2s/step - last_time_step_mse: 0.2317 - loss: 0.2123"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  6/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.1838 - loss: 0.1762"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 11/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 12ms/step - last_time_step_mse: 0.1526 - loss: 0.1500"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 16/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.1343 - loss: 0.1342"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 21/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.1219 - loss: 0.1235"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 26/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.1129 - loss: 0.1155"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.1058 - loss: 0.1092"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 36/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.1001 - loss: 0.1041"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 41/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0954 - loss: 0.0999"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 46/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0915 - loss: 0.0964"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 51/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0881 - loss: 0.0934"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 56/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0851 - loss: 0.0907"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0825 - loss: 0.0883"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 66/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0802 - loss: 0.0863"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 71/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - last_time_step_mse: 0.0781 - loss: 0.0844"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 76/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - last_time_step_mse: 0.0763 - loss: 0.0828"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 81/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - last_time_step_mse: 0.0746 - loss: 0.0813"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 86/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - last_time_step_mse: 0.0731 - loss: 0.0799"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 91/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - last_time_step_mse: 0.0717 - loss: 0.0787"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 96/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - last_time_step_mse: 0.0703 - loss: 0.0775"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m101/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - last_time_step_mse: 0.0691 - loss: 0.0765"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m106/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0680 - loss: 0.0755"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m111/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0669 - loss: 0.0745"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m116/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0660 - loss: 0.0737"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m121/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0650 - loss: 0.0729"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m126/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0641 - loss: 0.0721"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m131/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0633 - loss: 0.0714"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m136/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0626 - loss: 0.0707"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m141/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0618 - loss: 0.0700"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m146/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0611 - loss: 0.0694"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m151/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0605 - loss: 0.0688"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m156/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0598 - loss: 0.0682"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m161/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0592 - loss: 0.0677"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m166/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0586 - loss: 0.0672"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m171/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0581 - loss: 0.0667"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m176/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0575 - loss: 0.0662"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m181/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0570 - loss: 0.0657"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m186/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0565 - loss: 0.0653"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m191/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0561 - loss: 0.0649"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m196/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0556 - loss: 0.0645"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m201/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0552 - loss: 0.0641"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m206/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0548 - loss: 0.0637"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m211/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0544 - loss: 0.0633"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m216/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0540 - loss: 0.0630"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 14ms/step - last_time_step_mse: 0.0537 - loss: 0.0627 - val_last_time_step_mse: 0.0243 - val_loss: 0.0374\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 26ms/step - last_time_step_mse: 0.0261 - loss: 0.0335"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  6/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0266 - loss: 0.0378"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 11/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0259 - loss: 0.0378"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 16/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0263 - loss: 0.0383"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 21/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - last_time_step_mse: 0.0274 - loss: 0.0390"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 26/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0282 - loss: 0.0396"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - last_time_step_mse: 0.0288 - loss: 0.0400"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 36/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - last_time_step_mse: 0.0291 - loss: 0.0402"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 41/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0293 - loss: 0.0403"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 46/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0295 - loss: 0.0404"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 51/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0296 - loss: 0.0405"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 56/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0296 - loss: 0.0405"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0296 - loss: 0.0405"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 66/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0296 - loss: 0.0405"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 71/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0296 - loss: 0.0405"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 76/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0296 - loss: 0.0405"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 81/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0295 - loss: 0.0404"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 86/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0295 - loss: 0.0404"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 91/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0294 - loss: 0.0404"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 96/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0294 - loss: 0.0404"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m101/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0294 - loss: 0.0403"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m106/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0293 - loss: 0.0403"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m111/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0292 - loss: 0.0402"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m116/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0292 - loss: 0.0402"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m121/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0291 - loss: 0.0402"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m126/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0291 - loss: 0.0401"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m131/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0290 - loss: 0.0401"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m136/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0289 - loss: 0.0400"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m141/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0289 - loss: 0.0400"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m146/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0288 - loss: 0.0399"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m151/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0288 - loss: 0.0399"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m156/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0287 - loss: 0.0398"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m161/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0286 - loss: 0.0398"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m166/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0286 - loss: 0.0397"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m171/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0285 - loss: 0.0397"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m176/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0284 - loss: 0.0396"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m181/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0284 - loss: 0.0395"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m186/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0283 - loss: 0.0395"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m191/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0282 - loss: 0.0394"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m195/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0282 - loss: 0.0394"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m200/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0281 - loss: 0.0393"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m205/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0280 - loss: 0.0393"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m210/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0279 - loss: 0.0392"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m215/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0279 - loss: 0.0392"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - last_time_step_mse: 0.0278 - loss: 0.0391 - val_last_time_step_mse: 0.0161 - val_loss: 0.0301\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m5s\u001b[0m 26ms/step - last_time_step_mse: 0.0157 - loss: 0.0271"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  6/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - last_time_step_mse: 0.0160 - loss: 0.0307"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 11/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - last_time_step_mse: 0.0171 - loss: 0.0314"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 16/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0175 - loss: 0.0316"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 21/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - last_time_step_mse: 0.0177 - loss: 0.0317"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 26/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0179 - loss: 0.0318"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0181 - loss: 0.0319"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 36/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0183 - loss: 0.0319"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 41/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0184 - loss: 0.0320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 46/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0185 - loss: 0.0320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 51/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0186 - loss: 0.0320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 56/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0187 - loss: 0.0320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0188 - loss: 0.0320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 66/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0189 - loss: 0.0321"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 71/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0189 - loss: 0.0321"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 77/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0190 - loss: 0.0321"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 82/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0190 - loss: 0.0321"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 87/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0190 - loss: 0.0321"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 92/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0190 - loss: 0.0321"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 97/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0190 - loss: 0.0321"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m102/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0190 - loss: 0.0320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m107/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0190 - loss: 0.0320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m112/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0190 - loss: 0.0320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m117/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0190 - loss: 0.0320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m122/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0190 - loss: 0.0320"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m127/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0189 - loss: 0.0319"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m132/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0189 - loss: 0.0319"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m137/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0189 - loss: 0.0319"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m142/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0189 - loss: 0.0319"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m147/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0189 - loss: 0.0318"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m152/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0188 - loss: 0.0318"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m157/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0188 - loss: 0.0318"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m162/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0188 - loss: 0.0318"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m167/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0188 - loss: 0.0317"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m172/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0187 - loss: 0.0317"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m177/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0187 - loss: 0.0317"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m182/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0187 - loss: 0.0317"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m187/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0187 - loss: 0.0316"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m192/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0186 - loss: 0.0316"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m197/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0186 - loss: 0.0316"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m202/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0186 - loss: 0.0316"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m207/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0186 - loss: 0.0315"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m212/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0185 - loss: 0.0315"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m217/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0185 - loss: 0.0315"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - last_time_step_mse: 0.0185 - loss: 0.0315 - val_last_time_step_mse: 0.0154 - val_loss: 0.0278\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - last_time_step_mse: 0.0156 - loss: 0.0253"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  6/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - last_time_step_mse: 0.0153 - loss: 0.0282"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 11/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - last_time_step_mse: 0.0158 - loss: 0.0288"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 16/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - last_time_step_mse: 0.0159 - loss: 0.0289"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 21/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - last_time_step_mse: 0.0158 - loss: 0.0289"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 26/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - last_time_step_mse: 0.0157 - loss: 0.0288"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 31/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - last_time_step_mse: 0.0157 - loss: 0.0288"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 36/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 10ms/step - last_time_step_mse: 0.0156 - loss: 0.0288"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 41/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0156 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 46/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0155 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 51/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0155 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 56/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0155 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 61/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0155 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 66/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0155 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 71/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0156 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 75/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0156 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 80/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0156 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 85/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0156 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 90/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0156 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 95/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0156 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m101/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0156 - loss: 0.0287"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m107/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0156 - loss: 0.0286"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m113/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0156 - loss: 0.0286"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m118/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0155 - loss: 0.0286"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m123/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0155 - loss: 0.0286"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m128/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0155 - loss: 0.0286"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m133/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0155 - loss: 0.0286"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m138/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0155 - loss: 0.0285"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m143/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0155 - loss: 0.0285"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m148/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0154 - loss: 0.0285"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m153/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0154 - loss: 0.0285"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m158/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0154 - loss: 0.0285"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m163/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0154 - loss: 0.0285"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m168/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0154 - loss: 0.0284"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m172/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0154 - loss: 0.0284"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m177/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0153 - loss: 0.0284"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m182/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0153 - loss: 0.0284"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m187/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0153 - loss: 0.0284"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m192/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0153 - loss: 0.0283"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m197/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0153 - loss: 0.0283"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m202/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0152 - loss: 0.0283"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m207/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0152 - loss: 0.0283"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m212/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0152 - loss: 0.0282"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m217/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0152 - loss: 0.0282"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - last_time_step_mse: 0.0151 - loss: 0.0282 - val_last_time_step_mse: 0.0106 - val_loss: 0.0239\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m  1/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m4s\u001b[0m 23ms/step - last_time_step_mse: 0.0095 - loss: 0.0228"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m  6/219\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0113 - loss: 0.0253"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 11/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0120 - loss: 0.0254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 16/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0122 - loss: 0.0253"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 21/219\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0123 - loss: 0.0253"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 25/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0124 - loss: 0.0253"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 30/219\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0125 - loss: 0.0254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 35/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 11ms/step - last_time_step_mse: 0.0126 - loss: 0.0254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 40/219\u001b[0m \u001b[32m━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0127 - loss: 0.0254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 45/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0127 - loss: 0.0254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 50/219\u001b[0m \u001b[32m━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0128 - loss: 0.0255"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 55/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0128 - loss: 0.0255"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 60/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0128 - loss: 0.0254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 65/219\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0128 - loss: 0.0254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 70/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0128 - loss: 0.0254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 75/219\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0128 - loss: 0.0254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 80/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0128 - loss: 0.0254"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 85/219\u001b[0m \u001b[32m━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0127 - loss: 0.0253"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 90/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0127 - loss: 0.0253"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m 95/219\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0127 - loss: 0.0253"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m100/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0126 - loss: 0.0252"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m105/219\u001b[0m \u001b[32m━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0126 - loss: 0.0252"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m110/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0126 - loss: 0.0252"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m115/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0125 - loss: 0.0251"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m119/219\u001b[0m \u001b[32m━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0125 - loss: 0.0251"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m124/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - last_time_step_mse: 0.0125 - loss: 0.0251"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m129/219\u001b[0m \u001b[32m━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0124 - loss: 0.0250"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m134/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0124 - loss: 0.0250"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m139/219\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0124 - loss: 0.0250"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m144/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0123 - loss: 0.0250"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m149/219\u001b[0m \u001b[32m━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0123 - loss: 0.0249"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m154/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0123 - loss: 0.0249"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m159/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0123 - loss: 0.0249"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m164/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0122 - loss: 0.0249"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m169/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0122 - loss: 0.0249"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m174/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0122 - loss: 0.0248"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m179/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0122 - loss: 0.0248"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m184/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0122 - loss: 0.0248"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m189/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0121 - loss: 0.0248"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m194/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0121 - loss: 0.0247"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m199/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0121 - loss: 0.0247"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m204/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0121 - loss: 0.0247"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m209/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0121 - loss: 0.0247"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m214/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0120 - loss: 0.0246"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step - last_time_step_mse: 0.0120 - loss: 0.0246"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m219/219\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 12ms/step - last_time_step_mse: 0.0120 - loss: 0.0246 - val_last_time_step_mse: 0.0103 - val_loss: 0.0222\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train, epochs=5,\n",
    "                    validation_data=(X_valid, Y_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "Last time step validation MSE is 0.007."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:39.250540Z",
     "iopub.status.busy": "2025-03-07T05:33:39.249971Z",
     "iopub.status.idle": "2025-03-07T05:33:39.649450Z",
     "shell.execute_reply": "2025-03-07T05:33:39.648857Z"
    },
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 369ms/step\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(43)\n",
    "\n",
    "series = generate_time_series(1, 50 + 10)\n",
    "X_new, Y_new = series[:, :50, :], series[:, 50:, :]\n",
    "Y_pred = model.predict(X_new)[:, -1][..., np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "editable": true,
    "execution": {
     "iopub.execute_input": "2025-03-07T05:33:39.651598Z",
     "iopub.status.busy": "2025-03-07T05:33:39.651258Z",
     "iopub.status.idle": "2025-03-07T05:33:39.783019Z",
     "shell.execute_reply": "2025-03-07T05:33:39.782344Z"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_multiple_forecasts(X_new, Y_new, Y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "## Handling long sequences"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "To train RNNs on long sequences, must run over many tmie steps, resulting in a very deep unrolled RNN.\n",
    "\n",
    "While the techniques discussed previously to train deep networks can often be considered for RNNs, there are some differences to feed-forward networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Unstable gradients\n",
    "\n",
    "Say weights result in outputs that are slightly larger than input at the first time step.  Then will be slightly larger again at next time step, since the same weights are used at every time step.  Feedback can lead to exploding output.\n",
    "\n",
    "Non-saturating activations are not always effective for RNNs since they do not avoid this and may cause to the RNN to be unstable during training.\n",
    "\n",
    "Thus, saturating activation functions, e.g. hyperbolic tangent, are often used (hence default)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Also, batch normalisation cannot be used as effectively with RNNs as with deep feed-forward neural networks.  \n",
    "\n",
    "Cannot use it between time steps (would effectively be the same batch normalisation layer at each time step).\n",
    "\n",
    "Alternatively, *layer normalisation* is often used, where instead of normalising across batch, normalise across feature dimensions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "### Short-term memory\n",
    "\n",
    "Deep RNNs may forget earlier inputs in the sequence, resulting in only short-term memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "#### Long-short-term-memory (LSTM) cell\n",
    "\n",
    "Alternative type of cell that learsn what to store in long-term state, what to throw away, and what to read from.\n",
    "\n",
    "Has not only a hidden state $\\mathbf{h}_{(t)}$ but also a long term context $\\mathbf{c}_{(t)}$, in addition to inputs and outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "\n",
    "\n",
    "<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture20_Images/lstm.png\" alt=\"Drawing\"  width=\"800px\" style=\"display:block; margin:auto\"/>\n",
    "\n",
    "[Source: Geron]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "- Main layer outputs $\\mathbf{g}_{(t)}$, which has usual role of analysing current inputs and previous hidden state.\n",
    "- Three gate controllers:\n",
    "  - Forget gate controls what part of long-term state should be erased.\n",
    "  - Input gate controls what part of $\\mathbf{g}_{(t)}$ should be added to the long-term state.\n",
    "  - Ouput gate controls what part of internal state should be read to hidden state and outputs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": "subslide"
    },
    "tags": []
   },
   "source": [
    "Can simply replace `SimpleRNN` layers with `LSTM` layers in architecture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "LSTM cell can learn to recognise important input, store it in long-term state and preserve it for as long as needed (before learned to forget via forget gate)."
   ]
  }
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