{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Lecture 14: Convolutional Neural Networks "
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"\n",
"[Run in colab](https://colab.research.google.com/drive/1CxeEInBOmkXyYx-vBjKzYCh8Uaoccgsd)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:25:43.908150Z",
"iopub.status.busy": "2024-01-10T00:25:43.907750Z",
"iopub.status.idle": "2024-01-10T00:25:43.919659Z",
"shell.execute_reply": "2024-01-10T00:25:43.919078Z"
},
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Version: 2024-01-10 00:25:43\n"
]
}
],
"source": [
"import datetime\n",
"now = datetime.datetime.now()\n",
"print(\"Version: \" + now.strftime(\"%Y-%m-%d %H:%M:%S\"))"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:25:43.958631Z",
"iopub.status.busy": "2024-01-10T00:25:43.957708Z",
"iopub.status.idle": "2024-01-10T00:25:44.374558Z",
"shell.execute_reply": "2024-01-10T00:25:44.373850Z"
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"slideshow": {
"slide_type": "skip"
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},
"outputs": [],
"source": [
"# Common imports\n",
"import numpy as np\n",
"import os\n",
"\n",
"# to make this notebook's output stable across runs\n",
"def reset_state(seed=42):\n",
" tf.keras.backend.clear_session()\n",
" tf.random.set_seed(seed)\n",
" np.random.seed(seed)\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)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"## Motivation"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Visual cortex\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/eye_rods_cones.jpg\" width=\"700px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[[Image source](https://www.blueconemonochromacy.org/wp-content/uploads/2011/02/1_EN.jpg)]\n",
"\n",
"\n",
"Neurons in the visual cortex have a small local receptive field, i.e. only react to limited region of visual field.\n",
"\n",
"Receptive fields of neurons overlap and together cover full visual field."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Architecture of the visual cortex\n",
"\n",
"Some neurons only sensitive to stimuli of certain structure, e.g. horizonal lines.\n",
"\n",
"Some neurons have larger receptive fields, and are sensitive to stimuli that are combinations of lower-level patterns.\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/localFov.jpeg\" width=\"700px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[Credit: Geron]\n",
"\n",
"Corresponding architecture can detect complex patterns in full visual field, which inspires design of convolutional neural networks (CNNs)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"## Convolution\n",
"\n",
"Core building block of CNNs is convolution."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Graphical description\n",
"\n",
"Convolution involves passing a filter (kernel) over an image and taking the sum of the product of terms for all positions.\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/convolution-operation-14.png\" width=\"700px\" style=\"display:block; margin:auto\"/>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/convolution-operation-24.png\" width=\"700px\" style=\"display:block; margin:auto\"/>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"#### Animation of convolution\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/convolution.gif\" width=\"700px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[[Animation source](https://miro.medium.com/max/1000/1*GcI7G-JLAQiEoCON7xFbhg.gif)]\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Mathematical description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convolution output is given by\n",
"\\begin{align*}\n",
"z_{i,j} = \\sum_{u,v} x_{u,v} w_{u-i,v-j},\n",
"\\end{align*}\n",
"where $x$ is the input image, $w$ is the filter (kernel) and $i$ ($u$) and $j$ ($v$) denote row and column indices, respectively."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"(Note that the kernel is not reflected as is typical in the usual mathematical definition of convolution.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Advantages\n",
"\n",
"- Localisation: Capture local structure.\n",
"- Efficiency: weight sharing results in dramatic reduction in number of weights (parameters) compared to fully-connected neural network.\n",
"- Translational equivariance: Feature space behavies \"nicely\" under a translation of the input. In a sense, learn general features rather than features for all locations of image.\n",
"- Composition: Can compose convolutions to extract more complex features."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"## Convolutional layers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define neural network layers using convolutions."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/geron_convolutional_layers.png\" width=\"700px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[Credit: Geron]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Allows the network to focus on small low-level features in the first layer and use these to constuct higher-level features in next layer, and so on. \n",
"\n",
"Results in a hierarchical representation that is common in images of the real-world."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Filters and features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Filters pull out corresponding features from image. \n",
"\n",
"Output of convolutional layer is typically called a *feature map*."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/geron_feature_maps.png\" width=\"700px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[Credit: Geron]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"During training, effective filters to extract representative features are learned."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Padding"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
},
"tags": []
},
"source": [
"Nominally, convolution results in an output image that is *smaller* than the input.\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/convolution-operation-24.png\" width=\"700px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"Various modifications to the nominal convolution are often considered.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"#### Animation of convolution (no padding, no stride)\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/conv_no_padding_no_strides.gif\" width=\"500px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[[Animation source](https://github.com/vdumoulin/conv_arithmetic/blob/master/gif/no_padding_no_strides.gif)]\n",
"\n",
"Blue pixels denote the input image, grey the convolutional kernel as it moves over the image, and green pixels denote the output."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"Padding often introduced to control output size."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"#### Animation of convolution (padding, no stride)\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/conv_arbitrary_padding_no_strides.gif\" width=\"500px\" style=\"display:block; margin:auto\">\n",
"\n",
"[[Animation source](https://github.com/vdumoulin/conv_arithmetic/blob/master/gif/arbitrary_padding_no_strides.gif)]\n",
"\n",
"Uncoloured pixels denote padding with zeros."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Stride"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Animation of convolution (no padding, stride)\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/conv_no_padding_strides.gif\" width=\"500px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[[Animation source](https://github.com/vdumoulin/conv_arithmetic/blob/master/gif/no_padding_strides.gif)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Padding and stride"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Animation of convolution (padding, stride)\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/conv_padding_strides.gif\" width=\"500px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[[Animation source](https://github.com/vdumoulin/conv_arithmetic/blob/master/gif/padding_strides.gif)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Channels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Multiple input channels\n",
"\n",
"Input images often have multiple channels, e.g. red, green and blue colour channels.\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/rgb_2.jpg\" width=\"700px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[[Image source](https://code.tutsplus.com/tutorials/create-a-retro-crt-distortion-effect-using-rgb-shifting--active-3359)]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"Support multiple input channels by defining a filter for each channel and summing result."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/convolution-operation-on-volume5.png\" width=\"700px\" style=\"display:block; margin:auto\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"#### Multiple input and output channels\n",
"\n",
"Add a set of filters for each desired output channel."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/convolution-with-multiple-filters2.png\" width=\"700px\" style=\"display:block; margin:auto\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"#### Mathematical description"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Convolution output is given by\n",
"\\begin{align*}\n",
"z_{i,j,k_\\text{out}} = \\sum_{u,v,k_\\text{in}} x_{u,v,k_\\text{in}} w_{u-i,v-j,k_\\text{in},k_\\text{out}},\n",
"\\end{align*}\n",
"where $x$ is the input image, $w$ is the filter (kernel) and $i$ ($u$) and $j$ ($v$) denote row and column indices, respectively. The input channel index is denoted $k_\\text{in}$ and the output channel index $k_\\text{out}$."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
},
"tags": []
},
"source": [
"Notice that the filter is now 4-dimensional. We quickly add a lot of additional parameters."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Non-linear activations\n",
"\n",
"Convolutions usually followed by pointwise activation functions to introduce non-linearity (cf. fully-connected neural networks).\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/activation_func.png\" width=\"500px\" style=\"display:block; margin:auto\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"### Full convolutional layer\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/one-convolution-layer1.png\" width=\"900px\" style=\"display:block; margin:auto\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"## Pooling layers"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pooling layers are used to downsample to reduce the computational load, memory usage and number of parameters.\n",
"\n",
"Retains equivariance to large translation and can introduce invariance to small translations."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Max pooling\n",
"\n",
"Take maximum over parent cell size, translating cell over image."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/pooling-layer3_max.png\" width=\"500px\" style=\"display:block; margin:auto\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Average pooling\n",
"\n",
"Take average over parent cell size, translating cell over image."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/pooling-layer3_average.png\" width=\"500px\" style=\"display:block; margin:auto\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Multiple input channels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When considering multiple input channels, pooling is performed separately for each channel."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/pooling-on-volume1.png\" width=\"500px\" style=\"display:block; margin:auto\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"## CNN architectures"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Stacking multiple feature maps\n",
"\n",
"Convolutional layers typically stacked with multiple input and output channels, leading to multiple feature maps.\n",
"\n",
"Neuron's receptor field extends across all previous layers' feature maps."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/stacked.jpeg\" width=\"700px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[Credit: Geron]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Basic CNN architecture\n",
"\n",
"Basic CNN architecture typically consists of combining the following layers:\n",
"- Convolutions\n",
"- Non-linear activations\n",
"- Pooling\n",
"\n",
"And repeating.\n",
"\n",
"Final layers are then added on that are tailored to the problem at hand (often fully connected layers)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/aCNN.jpeg\" alt=\"Drawing\" width=\"1100px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"[Credit: Geron]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### CNN explainer\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/cnn_explainer.png\" width=\"700px\" style=\"display:block; margin:auto\"/>\n",
"\n",
"The [CNN explainer](https://poloclub.github.io/cnn-explainer/) is a great visualisation tool, allowing you to look inside a CNN to visualise the layers making up a network."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### LeNet architecture\n",
"\n",
"Convolutional layers first introduced by [Lecun et al.](http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf) in 1998 for digital classification problem.\n",
"\n",
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/lenet-52.png\" width=\"900px\" style=\"display:block; margin:auto\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/lenet_summary.png\" width=\"700px\" style=\"display:block; margin:auto\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### AlexNet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[AlexNet](https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf), which helped to intitiate the deep learning revolution in 2012, was based on a CNN architecture and showed a significant improvement in performance on the [ImageNet](https://www.image-net.org/) benchmark problem compared to the state-of-the-art at the time."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/alexnet.png\" width=\"900px\" style=\"display:block; margin:auto\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/alexnet_summary.png\" width=\"700px\" style=\"display:block; margin:auto\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### VGG"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The [VGG network](https://arxiv.org/abs/1409.1556) followed soon afterwards, making another significant improvement in performance, while simplifying the architecture.\n",
"\n",
"VGG-16 uses 3x3 convolutions only and max pooling layers that step down by a factor of two at each stage."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src=\"https://raw.githubusercontent.com/astro-informatics/course_mlbd_images/master/Lecture14_Images/vgg16.png\" width=\"900px\" style=\"display:block; margin:auto\"/>\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
},
"tags": []
},
"source": [
"## Implementing CNNs in TensorFlow"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Load and set up data\n",
"\n",
"Let's consider fashion MNIST again."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:25:44.382339Z",
"iopub.status.busy": "2024-01-10T00:25:44.381587Z",
"iopub.status.idle": "2024-01-10T00:25:46.547911Z",
"shell.execute_reply": "2024-01-10T00:25:46.547190Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-10 00:25:44.549473: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
"2024-01-10 00:25:44.600874: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
"2024-01-10 00:25:44.601966: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-01-10 00:25:45.420709: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow import keras"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:25:46.552176Z",
"iopub.status.busy": "2024-01-10T00:25:46.551352Z",
"iopub.status.idle": "2024-01-10T00:25:47.403845Z",
"shell.execute_reply": "2024-01-10T00:25:47.403129Z"
}
},
"outputs": [],
"source": [
"# Load fashion MNIST data\n",
"(X_train_full, y_train_full), (X_test, y_test) = keras.datasets.fashion_mnist.load_data()\n",
"X_train, X_valid = X_train_full[:-30000], X_train_full[-30000:]\n",
"y_train, y_valid = y_train_full[:-30000], y_train_full[-30000:]\n",
"\n",
"# Standardize\n",
"X_mean = X_train.mean(axis=0, keepdims=True)\n",
"X_std = X_train.std(axis=0, keepdims=True) + 1e-7\n",
"X_train = (X_train - X_mean) / X_std\n",
"X_valid = (X_valid - X_mean) / X_std\n",
"X_test = (X_test - X_mean) / X_std\n",
"\n",
"# Add final channel axis (one channel)\n",
"X_train = X_train[..., np.newaxis]\n",
"X_valid = X_valid[..., np.newaxis]\n",
"X_test = X_test[..., np.newaxis]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Plot example data instance"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:25:47.407382Z",
"iopub.status.busy": "2024-01-10T00:25:47.406798Z",
"iopub.status.idle": "2024-01-10T00:25:47.465111Z",
"shell.execute_reply": "2024-01-10T00:25:47.464459Z"
}
},
"outputs": [
{
"data": {
"image/png": "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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"plt.imshow(X_train[0], cmap=\"binary\")\n",
"plt.axis('off')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Build CNN model"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:25:47.468394Z",
"iopub.status.busy": "2024-01-10T00:25:47.468154Z",
"iopub.status.idle": "2024-01-10T00:25:47.550288Z",
"shell.execute_reply": "2024-01-10T00:25:47.549569Z"
}
},
"outputs": [],
"source": [
"model = keras.models.Sequential([\n",
" \n",
" keras.layers.Conv2D(filters=4, kernel_size=7, activation=\"relu\", padding=\"same\", \n",
" input_shape=[28, 28, 1]),\n",
" keras.layers.MaxPooling2D(pool_size=2),\n",
"\n",
" keras.layers.Flatten(),\n",
" keras.layers.Dense(units=8, activation='relu'),\n",
" keras.layers.Dense(units=10, activation='softmax'),\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:25:47.553524Z",
"iopub.status.busy": "2024-01-10T00:25:47.553280Z",
"iopub.status.idle": "2024-01-10T00:25:47.567220Z",
"shell.execute_reply": "2024-01-10T00:25:47.566688Z"
},
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential\"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" Layer (type) Output Shape Param # \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"=================================================================\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" conv2d (Conv2D) (None, 28, 28, 4) 200 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" max_pooling2d (MaxPooling2 (None, 14, 14, 4) 0 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" D) \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" flatten (Flatten) (None, 784) 0 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" dense (Dense) (None, 8) 6280 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" dense_1 (Dense) (None, 10) 90 \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"=================================================================\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total params: 6570 (25.66 KB)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Trainable params: 6570 (25.66 KB)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Non-trainable params: 0 (0.00 Byte)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": []
},
"source": [
"### Compile and fit model"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2024-01-10T00:25:47.573511Z",
"iopub.status.busy": "2024-01-10T00:25:47.572888Z",
"iopub.status.idle": "2024-01-10T00:26:27.934056Z",
"shell.execute_reply": "2024-01-10T00:26:27.933483Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/3\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" 1/938 [..............................] - ETA: 14:52 - loss: 2.4321 - accuracy: 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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 7/938 [..............................] - ETA: 8s - loss: 2.3253 - accuracy: 0.1607 "
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 13/938 [..............................] - ETA: 8s - loss: 2.2660 - accuracy: 0.1779"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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" 96/938 [==>...........................] - ETA: 7s - loss: 1.6892 - accuracy: 0.4476"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"912/938 [============================>.] - ETA: 0s - loss: 0.7264 - accuracy: 0.7548"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"930/938 [============================>.] - ETA: 0s - loss: 0.7210 - accuracy: 0.7564"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"936/938 [============================>.] - ETA: 0s - loss: 0.7189 - accuracy: 0.7569"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"938/938 [==============================] - 14s 14ms/step - loss: 0.7182 - accuracy: 0.7572 - val_loss: 0.5206 - val_accuracy: 0.8157\n"
]
},
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"output_type": "stream",
"text": [
"Epoch 2/3\n"
]
},
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"output_type": "stream",
"text": [
"\r",
" 1/938 [..............................] - ETA: 10s - loss: 0.5667 - accuracy: 0.8438"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 67/938 [=>............................] - ETA: 8s - loss: 0.4350 - accuracy: 0.8535"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 73/938 [=>............................] - ETA: 8s - loss: 0.4350 - accuracy: 0.8515"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"891/938 [===========================>..] - ETA: 0s - loss: 0.4252 - accuracy: 0.8514"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"897/938 [===========================>..] - ETA: 0s - loss: 0.4256 - accuracy: 0.8513"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"903/938 [===========================>..] - ETA: 0s - loss: 0.4256 - accuracy: 0.8512"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"909/938 [============================>.] - ETA: 0s - loss: 0.4254 - accuracy: 0.8513"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"915/938 [============================>.] - ETA: 0s - loss: 0.4256 - accuracy: 0.8516"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"921/938 [============================>.] - ETA: 0s - loss: 0.4251 - accuracy: 0.8517"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"927/938 [============================>.] - ETA: 0s - loss: 0.4249 - accuracy: 0.8517"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"933/938 [============================>.] - ETA: 0s - loss: 0.4249 - accuracy: 0.8518"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"938/938 [==============================] - 13s 13ms/step - loss: 0.4250 - accuracy: 0.8519 - val_loss: 0.4190 - val_accuracy: 0.8567\n"
]
},
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"output_type": "stream",
"text": [
"Epoch 3/3\n"
]
},
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"output_type": "stream",
"text": [
"\r",
" 1/938 [..............................] - ETA: 9s - loss: 0.2851 - accuracy: 0.9062"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 7/938 [..............................] - ETA: 8s - loss: 0.3609 - accuracy: 0.8750"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 13/938 [..............................] - ETA: 8s - loss: 0.3731 - accuracy: 0.8678"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 19/938 [..............................] - ETA: 8s - loss: 0.3633 - accuracy: 0.8668"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 25/938 [..............................] - ETA: 8s - loss: 0.3762 - accuracy: 0.8700"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 31/938 [..............................] - ETA: 8s - loss: 0.3746 - accuracy: 0.8669"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 37/938 [>.............................] - ETA: 8s - loss: 0.3650 - accuracy: 0.8699"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 43/938 [>.............................] - ETA: 8s - loss: 0.3647 - accuracy: 0.8735"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 49/938 [>.............................] - ETA: 8s - loss: 0.3767 - accuracy: 0.8686"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 55/938 [>.............................] - ETA: 8s - loss: 0.3807 - accuracy: 0.8682"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 61/938 [>.............................] - ETA: 7s - loss: 0.3802 - accuracy: 0.8653"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 67/938 [=>............................] - ETA: 7s - loss: 0.3791 - accuracy: 0.8657"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 73/938 [=>............................] - ETA: 7s - loss: 0.3861 - accuracy: 0.8643"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 79/938 [=>............................] - ETA: 7s - loss: 0.3912 - accuracy: 0.8623"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 85/938 [=>............................] - ETA: 7s - loss: 0.3884 - accuracy: 0.8647"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 91/938 [=>............................] - ETA: 7s - loss: 0.3880 - accuracy: 0.8668"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
" 97/938 [==>...........................] - ETA: 7s - loss: 0.3873 - accuracy: 0.8660"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"103/938 [==>...........................] - ETA: 7s - loss: 0.3864 - accuracy: 0.8668"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"109/938 [==>...........................] - ETA: 7s - loss: 0.3846 - accuracy: 0.8673"
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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]
},
{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"text": [
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
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{
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"output_type": "stream",
"text": [
"\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r",
"313/313 [==============================] - 1s 4ms/step - loss: 0.4359 - accuracy: 0.8493\n"
]
},
{
"data": {
"text/plain": [
"[0.4358906149864197, 0.8493000268936157]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"nadam\", metrics=[\"accuracy\"])\n",
"history = model.fit(X_train, y_train, epochs=3, validation_data=(X_valid, y_valid))\n",
"model.evaluate(X_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
},
"tags": [
"exercise_pointer"
]
},
"source": [
"**Exercises:** *You can now complete Exercise 1 in the exercises associated with this lecture.*"
]
}
],
"metadata": {
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.18"
}
},
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"nbformat_minor": 4
}