notebook/ImageClassification/Cats_vs_Dogs.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "MJPyDEzOqrKB"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/bin/bash: /home/danieldu/anaconda3/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n",
            "wget: /home/danieldu/anaconda3/lib/libuuid.so.1: no version information available (required by wget)\n",
            "--2023-10-12 10:26:59--  https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.163.59, 142.251.43.27, 142.251.42.251, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.163.59|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 68606236 (65M) [application/zip]\n",
            "Saving to: ‘/tmp/cats_and_dogs_filtered.zip’\n",
            "\n",
            "/tmp/cats_and_dogs_ 100%[===================>]  65.43M  9.94MB/s    in 11s     \n",
            "\n",
            "2023-10-12 10:27:09 (6.20 MB/s) - ‘/tmp/cats_and_dogs_filtered.zip’ saved [68606236/68606236]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip \\\n",
        "    -O /tmp/cats_and_dogs_filtered.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 2000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import zipfile\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import tensorflow_hub as hub\n",
        "from tensorflow.keras.layers import InputLayer\n",
        "\n",
        "local_zip = '/tmp/cats_and_dogs_filtered.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()\n",
        "\n",
        "base_dir = '/tmp/cats_and_dogs_filtered'\n",
        "\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "validation_dir = os.path.join(base_dir, 'validation')\n",
        "\n",
        "IMG_SIZE = (224, 224)\n",
        "INP_SHAPE = (*IMG_SIZE, 3)\n",
        "BATCH_SIZE = 64\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "      rescale=1./255,\n",
        "      rotation_range=40,\n",
        "      width_shift_range=0.2,\n",
        "      height_shift_range=0.2,\n",
        "      shear_range=0.2,\n",
        "      zoom_range=0.2,\n",
        "      horizontal_flip=True)\n",
        "\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_dir,\n",
        "        target_size=IMG_SIZE,\n",
        "        batch_size=20,\n",
        "        class_mode='binary')\n",
        "\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "        validation_dir,\n",
        "        target_size=IMG_SIZE,\n",
        "        batch_size=20,\n",
        "        class_mode='binary')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2023-10-12 10:27:11.813049: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:11.813254: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:11.838002: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:11.838196: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:11.838347: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:11.838492: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:11.838904: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2023-10-12 10:27:11.964875: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:11.965056: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:11.965206: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:11.965343: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:11.965478: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:11.965614: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:12.358097: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:12.358297: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:12.358440: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:12.358581: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:12.358719: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:12.358931: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 3812 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2080, pci bus id: 0000:01:00.0, compute capability: 7.5\n",
            "2023-10-12 10:27:12.359201: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:936] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2023-10-12 10:27:12.359319: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:1 with 6776 MB memory:  -> device: 1, name: NVIDIA GeForce RTX 2080, pci bus id: 0000:02:00.0, compute capability: 7.5\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " resizing (Resizing)         (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " keras_layer (KerasLayer)    (None, 2048)              23564800  \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 2049      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 23,566,849\n",
            "Trainable params: 2,049\n",
            "Non-trainable params: 23,564,800\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers.experimental.preprocessing import Resizing\n",
        "\n",
        "\n",
        "resnet_url = \"https://tfhub.dev/google/imagenet/resnet_v2_50/feature_vector/4\"\n",
        "\n",
        "resnet = Sequential()\n",
        "resnet.add(Resizing(224, 224, input_shape=INP_SHAPE))\n",
        "resnet.add(hub.KerasLayer(resnet_url, trainable=False))\n",
        "resnet.add(Dense(1, activation=\"sigmoid\"))\n",
        "resnet.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2023-10-12 10:27:16.367602: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8500\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "100/100 [==============================] - 20s 169ms/step - loss: 0.1786 - acc: 0.9305 - val_loss: 0.0438 - val_acc: 0.9850\n",
            "Epoch 2/10\n",
            "100/100 [==============================] - 17s 167ms/step - loss: 0.0750 - acc: 0.9725 - val_loss: 0.0530 - val_acc: 0.9810\n",
            "Epoch 3/10\n",
            "100/100 [==============================] - 17s 167ms/step - loss: 0.0631 - acc: 0.9705 - val_loss: 0.0288 - val_acc: 0.9920\n",
            "Epoch 4/10\n",
            "100/100 [==============================] - 17s 166ms/step - loss: 0.0582 - acc: 0.9770 - val_loss: 0.0263 - val_acc: 0.9910\n",
            "Epoch 5/10\n",
            "100/100 [==============================] - 17s 166ms/step - loss: 0.0558 - acc: 0.9790 - val_loss: 0.0307 - val_acc: 0.9920\n",
            "Epoch 6/10\n",
            "100/100 [==============================] - 17s 166ms/step - loss: 0.0596 - acc: 0.9800 - val_loss: 0.0321 - val_acc: 0.9910\n",
            "Epoch 7/10\n",
            "100/100 [==============================] - 17s 166ms/step - loss: 0.0459 - acc: 0.9840 - val_loss: 0.0260 - val_acc: 0.9920\n",
            "Epoch 8/10\n",
            "100/100 [==============================] - 17s 167ms/step - loss: 0.0449 - acc: 0.9860 - val_loss: 0.0257 - val_acc: 0.9880\n",
            "Epoch 9/10\n",
            "100/100 [==============================] - 17s 166ms/step - loss: 0.0391 - acc: 0.9850 - val_loss: 0.0279 - val_acc: 0.9860\n",
            "Epoch 10/10\n",
            "100/100 [==============================] - 17s 166ms/step - loss: 0.0391 - acc: 0.9855 - val_loss: 0.0267 - val_acc: 0.9920\n"
          ]
        }
      ],
      "source": [
        "\n",
        "resnet.compile(optimizer=\"rmsprop\", loss=\"binary_crossentropy\", metrics=[\"acc\"])\n",
        "\n",
        "history = resnet.fit(\n",
        "      train_generator,\n",
        "      epochs=10,\n",
        "      validation_data=validation_generator,\n",
        "      batch_size=32)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "height": 561
        },
        "colab_type": "code",
        "id": "bnyRnwopT5aW",
        "outputId": "8cdd3e7b-43f0-44de-ad69-30f08e1c5d4d"
      },
      "outputs": [],
      "source": [
        "resnet.save(\"cd.h5\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "include_colab_link": true,
      "name": "Course 2 - Part 4 - Lesson 2 - Notebook (Cats v Dogs Augmentation).ipynb",
      "provenance": [],
      "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": "3.8.18"
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  "nbformat": 4,
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