slurm-result-73.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x4HI2mpwlrcn"
   },
   "source": [
    "##### Copyright 2019 The TensorFlow Authors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cellView": "form",
    "execution": {
     "iopub.execute_input": "2024-03-26T14:31:53.374244Z",
     "iopub.status.busy": "2024-03-26T14:31:53.373909Z",
     "iopub.status.idle": "2024-03-26T14:31:53.377552Z",
     "shell.execute_reply": "2024-03-26T14:31:53.377154Z"
    },
    "id": "679Lmwt3l1Bk"
   },
   "outputs": [],
   "source": [
    "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "# https://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DSPCom-KmApV"
   },
   "source": [
    "# Convolutional Neural Network (CNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "klAltGp8ycek"
   },
   "source": [
    "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/images/cnn\">\n",
    "    <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />\n",
    "    View on TensorFlow.org</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/images/cnn.ipynb\">\n",
    "    <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />\n",
    "    Run in Google Colab</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/images/cnn.ipynb\">\n",
    "    <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />\n",
    "    View source on GitHub</a>\n",
    "  </td>\n",
    "  <td>\n",
    "    <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/images/cnn.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
    "  </td>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qLGkt5qiyz4E"
   },
   "source": [
    "This tutorial demonstrates training a simple [Convolutional Neural Network](https://developers.google.com/machine-learning/glossary/#convolutional_neural_network) (CNN) to classify [CIFAR images](https://www.cs.toronto.edu/~kriz/cifar.html). Because this tutorial uses the [Keras Sequential API](https://www.tensorflow.org/guide/keras/overview), creating and training your model will take just a few lines of code.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m7KBpffWzlxH"
   },
   "source": [
    "### Import TensorFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:31:53.379639Z",
     "iopub.status.busy": "2024-03-26T14:31:53.379502Z",
     "iopub.status.idle": "2024-03-26T14:31:58.323818Z",
     "shell.execute_reply": "2024-03-26T14:31:58.323224Z"
    },
    "id": "iAve6DCL4JH4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-26 15:31:54.315284: I tensorflow/core/platform/cpu_feature_guard.cc:183] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: SSE3 SSE4.1 SSE4.2 AVX, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jRFxccghyMVo"
   },
   "source": [
    "### Download and prepare the CIFAR10 dataset\n",
    "\n",
    "\n",
    "The CIFAR10 dataset contains 60,000 color images in 10 classes, with 6,000 images in each class. The dataset is divided into 50,000 training images and 10,000 testing images. The classes are mutually exclusive and there is no overlap between them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:31:58.326486Z",
     "iopub.status.busy": "2024-03-26T14:31:58.326089Z",
     "iopub.status.idle": "2024-03-26T14:32:07.924202Z",
     "shell.execute_reply": "2024-03-26T14:32:07.923475Z"
    },
    "id": "JWoEqyMuXFF4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
      "170498071/170498071 [==============================] - 7s 0us/step\n"
     ]
    }
   ],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n",
    "\n",
    "# Normalize pixel values to be between 0 and 1\n",
    "train_images, test_images = train_images / 255.0, test_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7wArwCTJJlUa"
   },
   "source": [
    "### Verify the data\n",
    "\n",
    "To verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:32:07.926979Z",
     "iopub.status.busy": "2024-03-26T14:32:07.926684Z",
     "iopub.status.idle": "2024-03-26T14:32:08.482619Z",
     "shell.execute_reply": "2024-03-26T14:32:08.482126Z"
    },
    "id": "K3PAELE2eSU9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
    "               'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i])\n",
    "    # The CIFAR labels happen to be arrays, \n",
    "    # which is why you need the extra index\n",
    "    plt.xlabel(class_names[train_labels[i][0]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Oewp-wYg31t9"
   },
   "source": [
    "### Create the convolutional base"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3hQvqXpNyN3x"
   },
   "source": [
    "The 6 lines of code below define the convolutional base using a common pattern: a stack of [Conv2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Conv2D) and [MaxPooling2D](https://www.tensorflow.org/api_docs/python/tf/keras/layers/MaxPool2D) layers.\n",
    "\n",
    "As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure your CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument `input_shape` to your first layer.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:32:08.484841Z",
     "iopub.status.busy": "2024-03-26T14:32:08.484608Z",
     "iopub.status.idle": "2024-03-26T14:32:10.856221Z",
     "shell.execute_reply": "2024-03-26T14:32:10.855734Z"
    },
    "id": "L9YmGQBQPrdn"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-26 15:32:09.838247: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1636] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 78827 MB memory:  -> device: 0, name: NVIDIA A100-SXM4-80GB, pci bus id: 0000:87:00.0, compute capability: 8.0\n",
      "2024-03-26 15:32:09.842619: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1636] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 78827 MB memory:  -> device: 1, name: NVIDIA A100-SXM4-80GB, pci bus id: 0000:90:00.0, compute capability: 8.0\n",
      "2024-03-26 15:32:09.848350: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1636] Created device /job:localhost/replica:0/task:0/device:GPU:2 with 78827 MB memory:  -> device: 2, name: NVIDIA A100-SXM4-80GB, pci bus id: 0000:b7:00.0, compute capability: 8.0\n"
     ]
    }
   ],
   "source": [
    "model = models.Sequential()\n",
    "model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(layers.MaxPooling2D((2, 2)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation='relu'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lvDVFkg-2DPm"
   },
   "source": [
    "Let's display the architecture of your model so far:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:32:10.858534Z",
     "iopub.status.busy": "2024-03-26T14:32:10.858304Z",
     "iopub.status.idle": "2024-03-26T14:32:10.878905Z",
     "shell.execute_reply": "2024-03-26T14:32:10.878441Z"
    },
    "id": "8-C4XBg4UTJy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 15, 15, 32)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 6, 6, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 64)          36928     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 56320 (220.00 KB)\n",
      "Trainable params: 56320 (220.00 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_j-AXYeZ2GO5"
   },
   "source": [
    "Above, you can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically,  as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_v8sVOtG37bT"
   },
   "source": [
    "### Add Dense layers on top\n",
    "\n",
    "To complete the model, you will feed the last output tensor from the convolutional base (of shape (4, 4, 64)) into one or more Dense layers to perform classification. Dense layers take vectors as input (which are 1D), while the current output is a 3D tensor. First, you will flatten (or unroll) the 3D output to 1D,  then add one or more Dense layers on top. CIFAR has 10 output classes, so you use a final Dense layer with 10 outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:32:10.880954Z",
     "iopub.status.busy": "2024-03-26T14:32:10.880728Z",
     "iopub.status.idle": "2024-03-26T14:32:10.903130Z",
     "shell.execute_reply": "2024-03-26T14:32:10.902634Z"
    },
    "id": "mRs95d6LUVEi"
   },
   "outputs": [],
   "source": [
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(64, activation='relu'))\n",
    "model.add(layers.Dense(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ipGiQMcR4Gtq"
   },
   "source": [
    "Here's the complete architecture of your model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:32:10.905505Z",
     "iopub.status.busy": "2024-03-26T14:32:10.905277Z",
     "iopub.status.idle": "2024-03-26T14:32:10.922809Z",
     "shell.execute_reply": "2024-03-26T14:32:10.922270Z"
    },
    "id": "8Yu_m-TZUWGX"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 30, 30, 32)        896       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2  (None, 15, 15, 32)        0         \n",
      " D)                                                              \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 6, 6, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 4, 4, 64)          36928     \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 1024)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                65600     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                650       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 122570 (478.79 KB)\n",
      "Trainable params: 122570 (478.79 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xNKXi-Gy3RO-"
   },
   "source": [
    "The network summary shows that (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P3odqfHP4M67"
   },
   "source": [
    "### Compile and train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:32:10.928796Z",
     "iopub.status.busy": "2024-03-26T14:32:10.928528Z",
     "iopub.status.idle": "2024-03-26T14:33:02.113236Z",
     "shell.execute_reply": "2024-03-26T14:33:02.112734Z"
    },
    "id": "MdDzI75PUXrG"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-26 15:32:12.718684: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:432] Loaded cuDNN version 8905\n",
      "2024-03-26 15:32:13.291863: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:625] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n",
      "2024-03-26 15:32:13.469463: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x562b00237710 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2024-03-26 15:32:13.469504: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): NVIDIA A100-SXM4-80GB, Compute Capability 8.0\n",
      "2024-03-26 15:32:13.469510: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (1): NVIDIA A100-SXM4-80GB, Compute Capability 8.0\n",
      "2024-03-26 15:32:13.469514: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (2): NVIDIA A100-SXM4-80GB, Compute Capability 8.0\n",
      "2024-03-26 15:32:13.545089: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:255] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2024-03-26 15:32:14.090910: I ./tensorflow/compiler/jit/device_compiler.h:186] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1563/1563 [==============================] - 8s 3ms/step - loss: 1.5410 - accuracy: 0.4391 - val_loss: 1.2650 - val_accuracy: 0.5442\n",
      "Epoch 2/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 1.1631 - accuracy: 0.5864 - val_loss: 1.0864 - val_accuracy: 0.6132\n",
      "Epoch 3/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 1.0022 - accuracy: 0.6463 - val_loss: 0.9682 - val_accuracy: 0.6559\n",
      "Epoch 4/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 0.9058 - accuracy: 0.6840 - val_loss: 0.9302 - val_accuracy: 0.6723\n",
      "Epoch 5/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 0.8341 - accuracy: 0.7095 - val_loss: 0.8806 - val_accuracy: 0.6923\n",
      "Epoch 6/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 0.7677 - accuracy: 0.7310 - val_loss: 0.9016 - val_accuracy: 0.6918\n",
      "Epoch 7/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 0.7213 - accuracy: 0.7462 - val_loss: 0.8865 - val_accuracy: 0.6952\n",
      "Epoch 8/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 0.6764 - accuracy: 0.7625 - val_loss: 0.8611 - val_accuracy: 0.7106\n",
      "Epoch 9/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 0.6378 - accuracy: 0.7747 - val_loss: 0.8825 - val_accuracy: 0.6989\n",
      "Epoch 10/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 0.5967 - accuracy: 0.7900 - val_loss: 0.8610 - val_accuracy: 0.7163\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(train_images, train_labels, epochs=10, \n",
    "                    validation_data=(test_images, test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jKgyC5K_4O0d"
   },
   "source": [
    "### Evaluate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:33:02.115625Z",
     "iopub.status.busy": "2024-03-26T14:33:02.115427Z",
     "iopub.status.idle": "2024-03-26T14:33:02.962921Z",
     "shell.execute_reply": "2024-03-26T14:33:02.962428Z"
    },
    "id": "gtyDF0MKUcM7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 0s - loss: 0.8610 - accuracy: 0.7163 - 480ms/epoch - 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'], label='accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0.5, 1])\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-26T14:33:02.965036Z",
     "iopub.status.busy": "2024-03-26T14:33:02.964803Z",
     "iopub.status.idle": "2024-03-26T14:33:02.967824Z",
     "shell.execute_reply": "2024-03-26T14:33:02.967243Z"
    },
    "id": "0LvwaKhtUdOo"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7163000106811523\n"
     ]
    }
   ],
   "source": [
    "print(test_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8cfJ8AR03gT5"
   },
   "source": [
    "Your simple CNN has achieved a test accuracy of over 70%. Not bad for a few lines of code! For another CNN style, check out the [TensorFlow 2 quickstart for experts](https://www.tensorflow.org/tutorials/quickstart/advanced) example that uses the Keras subclassing API and `tf.GradientTape`."
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "cnn.ipynb",
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}