notebook/ImageClassification/RockPaperScissors.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "it1c0jCiNCIM"
      },
      "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",
            "--2023-10-11 17:17:50--  https://storage.googleapis.com/learning-datasets/rps.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.163.59, 142.251.43.27, 172.217.160.91, ...\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: 200682221 (191M) [application/zip]\n",
            "Saving to: ‘/tmp/rps.zip’\n",
            "\n",
            "/tmp/rps.zip        100%[===================>] 191.38M  27.5MB/s    in 7.2s    \n",
            "\n",
            "2023-10-11 17:17:57 (26.6 MB/s) - ‘/tmp/rps.zip’ saved [200682221/200682221]\n",
            "\n",
            "/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",
            "--2023-10-11 17:17:58--  https://storage.googleapis.com/learning-datasets/rps-test-set.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.160.91, 142.251.42.251, 172.217.163.59, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.160.91|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 29516758 (28M) [application/zip]\n",
            "Saving to: ‘/tmp/rps-test-set.zip’\n",
            "\n",
            "/tmp/rps-test-set.z 100%[===================>]  28.15M  12.9MB/s    in 2.2s    \n",
            "\n",
            "2023-10-11 17:18:00 (12.9 MB/s) - ‘/tmp/rps-test-set.zip’ saved [29516758/29516758]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/learning-datasets/rps.zip \\\n",
        "    -O /tmp/rps.zip\n",
        "  \n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/learning-datasets/rps-test-set.zip \\\n",
        "    -O /tmp/rps-test-set.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "PnYP_HhYNVUK"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "\n",
        "def extract_file(local_zip,dir):\n",
        "    zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "    zip_ref.extractall(dir)\n",
        "    zip_ref.close()\n",
        "    \n",
        "extract_file('/tmp/rps.zip','/tmp/')\n",
        "extract_file('/tmp/rps-test-set.zip','/tmp/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "LWTisYLQM1aM"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 2520 images belonging to 3 classes.\n",
            "Found 372 images belonging to 3 classes.\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\" \n",
        "import tensorflow as tf\n",
        "from keras_preprocessing.image import ImageDataGenerator\n",
        "\n",
        "TRAINING_DIR = \"/tmp/rps/\"\n",
        "training_datagen = ImageDataGenerator(\n",
        "      rescale = 1./255,\n",
        "\t    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",
        "      fill_mode='nearest')\n",
        "\n",
        "VALIDATION_DIR = \"/tmp/rps-test-set/\"\n",
        "validation_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "\n",
        "train_generator = training_datagen.flow_from_directory(\n",
        "\tTRAINING_DIR,\n",
        "\ttarget_size=(150,150),\n",
        "\tclass_mode='categorical',\n",
        "  batch_size=126\n",
        ")\n",
        "\n",
        "validation_generator = validation_datagen.flow_from_directory(\n",
        "\tVALIDATION_DIR,\n",
        "\ttarget_size=(150,150),\n",
        "\tclass_mode='categorical',\n",
        "  batch_size=126\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 148, 148, 64)      1792      \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 74, 74, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 72, 72, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 36, 36, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 34, 34, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 17, 17, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 15, 15, 128)       147584    \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 7, 7, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 6272)              0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 6272)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               3211776   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 1539      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,473,475\n",
            "Trainable params: 3,473,475\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2023-10-11 17:18:02.688650: 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",
            "2023-10-11 17:18:02.714786: 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",
            "2023-10-11 17:18:02.714966: 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",
            "2023-10-11 17:18:02.715532: 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",
            "2023-10-11 17:18:02.715879: 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",
            "2023-10-11 17:18:02.716047: 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",
            "2023-10-11 17:18:02.716325: 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",
            "2023-10-11 17:18:03.001796: 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",
            "2023-10-11 17:18:03.001993: 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",
            "2023-10-11 17:18:03.002150: 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",
            "2023-10-11 17:18:03.002282: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1525] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 6776 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2080, pci bus id: 0000:02:00.0, compute capability: 7.5\n"
          ]
        }
      ],
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(150, 150, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "2023-10-11 17:18:05.117687: I tensorflow/stream_executor/cuda/cuda_dnn.cc:368] Loaded cuDNN version 8500\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "20/20 [==============================] - 15s 632ms/step - loss: 1.2907 - accuracy: 0.3571 - val_loss: 1.0084 - val_accuracy: 0.5780\n",
            "Epoch 2/100\n",
            "20/20 [==============================] - 12s 619ms/step - loss: 1.3431 - accuracy: 0.3786 - val_loss: 1.0767 - val_accuracy: 0.4462\n",
            "Epoch 3/100\n",
            "20/20 [==============================] - 12s 616ms/step - loss: 1.1294 - accuracy: 0.4532 - val_loss: 1.0934 - val_accuracy: 0.3333\n",
            "Epoch 4/100\n",
            "20/20 [==============================] - 12s 608ms/step - loss: 0.9913 - accuracy: 0.4873 - val_loss: 0.6913 - val_accuracy: 0.7285\n",
            "Epoch 5/100\n",
            "20/20 [==============================] - 13s 630ms/step - loss: 0.8867 - accuracy: 0.6004 - val_loss: 0.5667 - val_accuracy: 0.6640\n",
            "Epoch 6/100\n",
            "20/20 [==============================] - 12s 620ms/step - loss: 0.7636 - accuracy: 0.6587 - val_loss: 0.7131 - val_accuracy: 0.6102\n",
            "Epoch 7/100\n",
            "20/20 [==============================] - 12s 618ms/step - loss: 0.6697 - accuracy: 0.7167 - val_loss: 0.2747 - val_accuracy: 0.9516\n",
            "Epoch 8/100\n",
            "20/20 [==============================] - 12s 609ms/step - loss: 0.5033 - accuracy: 0.7869 - val_loss: 0.2370 - val_accuracy: 0.9140\n",
            "Epoch 9/100\n",
            "20/20 [==============================] - 13s 627ms/step - loss: 0.4373 - accuracy: 0.8167 - val_loss: 0.8702 - val_accuracy: 0.6478\n",
            "Epoch 10/100\n",
            "20/20 [==============================] - 12s 610ms/step - loss: 0.3994 - accuracy: 0.8528 - val_loss: 0.1080 - val_accuracy: 0.9489\n",
            "Epoch 11/100\n",
            "20/20 [==============================] - 12s 617ms/step - loss: 0.3157 - accuracy: 0.8694 - val_loss: 0.1096 - val_accuracy: 0.9758\n",
            "Epoch 12/100\n",
            "20/20 [==============================] - 13s 635ms/step - loss: 0.2550 - accuracy: 0.9139 - val_loss: 0.1167 - val_accuracy: 0.9839\n",
            "Epoch 13/100\n",
            "20/20 [==============================] - 12s 622ms/step - loss: 0.2583 - accuracy: 0.9020 - val_loss: 0.1840 - val_accuracy: 0.8952\n",
            "Epoch 14/100\n",
            "20/20 [==============================] - 13s 626ms/step - loss: 0.2454 - accuracy: 0.9079 - val_loss: 0.0579 - val_accuracy: 0.9704\n",
            "Epoch 15/100\n",
            "20/20 [==============================] - 12s 622ms/step - loss: 0.1695 - accuracy: 0.9345 - val_loss: 0.0887 - val_accuracy: 0.9704\n",
            "Epoch 16/100\n",
            "20/20 [==============================] - 12s 618ms/step - loss: 0.1446 - accuracy: 0.9452 - val_loss: 0.0284 - val_accuracy: 1.0000\n",
            "Epoch 17/100\n",
            "20/20 [==============================] - 13s 625ms/step - loss: 0.2009 - accuracy: 0.9290 - val_loss: 0.0368 - val_accuracy: 1.0000\n",
            "Epoch 18/100\n",
            "20/20 [==============================] - 12s 615ms/step - loss: 0.1123 - accuracy: 0.9619 - val_loss: 0.0367 - val_accuracy: 0.9892\n",
            "Epoch 19/100\n",
            "20/20 [==============================] - 12s 622ms/step - loss: 0.1195 - accuracy: 0.9603 - val_loss: 0.0130 - val_accuracy: 1.0000\n",
            "Epoch 20/100\n",
            "20/20 [==============================] - 13s 622ms/step - loss: 0.1220 - accuracy: 0.9563 - val_loss: 0.0604 - val_accuracy: 0.9758\n",
            "Epoch 21/100\n",
            "20/20 [==============================] - 13s 621ms/step - loss: 0.1507 - accuracy: 0.9464 - val_loss: 0.0977 - val_accuracy: 0.9543\n",
            "Epoch 22/100\n",
            "20/20 [==============================] - 12s 615ms/step - loss: 0.0671 - accuracy: 0.9786 - val_loss: 0.1173 - val_accuracy: 0.9516\n",
            "Epoch 23/100\n",
            "20/20 [==============================] - 12s 613ms/step - loss: 0.1129 - accuracy: 0.9607 - val_loss: 0.0526 - val_accuracy: 0.9731\n",
            "Epoch 24/100\n",
            "20/20 [==============================] - 13s 624ms/step - loss: 0.1178 - accuracy: 0.9579 - val_loss: 0.0478 - val_accuracy: 0.9866\n",
            "Epoch 25/100\n",
            "20/20 [==============================] - 12s 617ms/step - loss: 0.0685 - accuracy: 0.9742 - val_loss: 0.0249 - val_accuracy: 1.0000\n",
            "Epoch 26/100\n",
            "20/20 [==============================] - 13s 628ms/step - loss: 0.0665 - accuracy: 0.9782 - val_loss: 0.5089 - val_accuracy: 0.8065\n",
            "Epoch 27/100\n",
            "20/20 [==============================] - 12s 619ms/step - loss: 0.1047 - accuracy: 0.9647 - val_loss: 0.0312 - val_accuracy: 0.9919\n",
            "Epoch 28/100\n",
            "20/20 [==============================] - 12s 610ms/step - loss: 0.1376 - accuracy: 0.9504 - val_loss: 0.0790 - val_accuracy: 0.9570\n",
            "Epoch 29/100\n",
            "20/20 [==============================] - 12s 611ms/step - loss: 0.0423 - accuracy: 0.9829 - val_loss: 0.0956 - val_accuracy: 0.9570\n",
            "Epoch 30/100\n",
            "20/20 [==============================] - 12s 611ms/step - loss: 0.1032 - accuracy: 0.9667 - val_loss: 0.0682 - val_accuracy: 0.9731\n",
            "Epoch 31/100\n",
            "20/20 [==============================] - 12s 609ms/step - loss: 0.0499 - accuracy: 0.9817 - val_loss: 0.1555 - val_accuracy: 0.9355\n",
            "Epoch 32/100\n",
            "20/20 [==============================] - 12s 609ms/step - loss: 0.0645 - accuracy: 0.9754 - val_loss: 0.0446 - val_accuracy: 0.9785\n",
            "Epoch 33/100\n",
            "20/20 [==============================] - 12s 620ms/step - loss: 0.0495 - accuracy: 0.9825 - val_loss: 0.0460 - val_accuracy: 0.9785\n",
            "Epoch 34/100\n",
            "20/20 [==============================] - 12s 617ms/step - loss: 0.0540 - accuracy: 0.9825 - val_loss: 0.0213 - val_accuracy: 1.0000\n",
            "Epoch 35/100\n",
            "20/20 [==============================] - 12s 616ms/step - loss: 0.0624 - accuracy: 0.9806 - val_loss: 0.0387 - val_accuracy: 0.9839\n",
            "Epoch 36/100\n",
            "20/20 [==============================] - 12s 613ms/step - loss: 0.0821 - accuracy: 0.9726 - val_loss: 0.1036 - val_accuracy: 0.9597\n",
            "Epoch 37/100\n",
            "20/20 [==============================] - 13s 624ms/step - loss: 0.0514 - accuracy: 0.9825 - val_loss: 0.0144 - val_accuracy: 1.0000\n",
            "Epoch 38/100\n",
            "20/20 [==============================] - 12s 616ms/step - loss: 0.0635 - accuracy: 0.9794 - val_loss: 0.0343 - val_accuracy: 0.9812\n",
            "Epoch 39/100\n",
            "20/20 [==============================] - 12s 607ms/step - loss: 0.0624 - accuracy: 0.9794 - val_loss: 0.0155 - val_accuracy: 1.0000\n",
            "Epoch 40/100\n",
            "20/20 [==============================] - 12s 612ms/step - loss: 0.0390 - accuracy: 0.9885 - val_loss: 0.0203 - val_accuracy: 0.9919\n",
            "Epoch 41/100\n",
            "20/20 [==============================] - 12s 595ms/step - loss: 0.0455 - accuracy: 0.9861 - val_loss: 0.3829 - val_accuracy: 0.8495\n",
            "Epoch 42/100\n",
            "20/20 [==============================] - 12s 593ms/step - loss: 0.0552 - accuracy: 0.9806 - val_loss: 0.0269 - val_accuracy: 0.9892\n",
            "Epoch 43/100\n",
            "20/20 [==============================] - 12s 613ms/step - loss: 0.0252 - accuracy: 0.9929 - val_loss: 0.0093 - val_accuracy: 1.0000\n",
            "Epoch 44/100\n",
            "20/20 [==============================] - 12s 603ms/step - loss: 0.0928 - accuracy: 0.9746 - val_loss: 0.0404 - val_accuracy: 0.9785\n",
            "Epoch 45/100\n",
            "20/20 [==============================] - 12s 591ms/step - loss: 0.0394 - accuracy: 0.9861 - val_loss: 0.0309 - val_accuracy: 0.9892\n",
            "Epoch 46/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0354 - accuracy: 0.9885 - val_loss: 0.0800 - val_accuracy: 0.9624\n",
            "Epoch 47/100\n",
            "20/20 [==============================] - 12s 597ms/step - loss: 0.0408 - accuracy: 0.9865 - val_loss: 0.0093 - val_accuracy: 1.0000\n",
            "Epoch 48/100\n",
            "20/20 [==============================] - 12s 591ms/step - loss: 0.0420 - accuracy: 0.9869 - val_loss: 0.2799 - val_accuracy: 0.8763\n",
            "Epoch 49/100\n",
            "20/20 [==============================] - 12s 587ms/step - loss: 0.0378 - accuracy: 0.9893 - val_loss: 0.4531 - val_accuracy: 0.8091\n",
            "Epoch 50/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0468 - accuracy: 0.9845 - val_loss: 0.0175 - val_accuracy: 1.0000\n",
            "Epoch 51/100\n",
            "20/20 [==============================] - 12s 587ms/step - loss: 0.0595 - accuracy: 0.9798 - val_loss: 0.0232 - val_accuracy: 0.9892\n",
            "Epoch 52/100\n",
            "20/20 [==============================] - 12s 591ms/step - loss: 0.0270 - accuracy: 0.9897 - val_loss: 0.1969 - val_accuracy: 0.9516\n",
            "Epoch 53/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0395 - accuracy: 0.9849 - val_loss: 0.0186 - val_accuracy: 0.9892\n",
            "Epoch 54/100\n",
            "20/20 [==============================] - 12s 590ms/step - loss: 0.1472 - accuracy: 0.9643 - val_loss: 0.0150 - val_accuracy: 0.9919\n",
            "Epoch 55/100\n",
            "20/20 [==============================] - 12s 592ms/step - loss: 0.0217 - accuracy: 0.9940 - val_loss: 0.0045 - val_accuracy: 1.0000\n",
            "Epoch 56/100\n",
            "20/20 [==============================] - 12s 590ms/step - loss: 0.0274 - accuracy: 0.9917 - val_loss: 0.0609 - val_accuracy: 0.9704\n",
            "Epoch 57/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0874 - accuracy: 0.9746 - val_loss: 0.1066 - val_accuracy: 0.9812\n",
            "Epoch 58/100\n",
            "20/20 [==============================] - 12s 590ms/step - loss: 0.0227 - accuracy: 0.9929 - val_loss: 0.1354 - val_accuracy: 0.9651\n",
            "Epoch 59/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0354 - accuracy: 0.9897 - val_loss: 0.0344 - val_accuracy: 0.9839\n",
            "Epoch 60/100\n",
            "20/20 [==============================] - 12s 598ms/step - loss: 0.0416 - accuracy: 0.9873 - val_loss: 0.0603 - val_accuracy: 0.9758\n",
            "Epoch 61/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0366 - accuracy: 0.9877 - val_loss: 0.0235 - val_accuracy: 0.9892\n",
            "Epoch 62/100\n",
            "20/20 [==============================] - 12s 592ms/step - loss: 0.0334 - accuracy: 0.9893 - val_loss: 0.0690 - val_accuracy: 0.9651\n",
            "Epoch 63/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0343 - accuracy: 0.9877 - val_loss: 0.0083 - val_accuracy: 1.0000\n",
            "Epoch 64/100\n",
            "20/20 [==============================] - 12s 586ms/step - loss: 0.0234 - accuracy: 0.9917 - val_loss: 0.0366 - val_accuracy: 0.9839\n",
            "Epoch 65/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0281 - accuracy: 0.9917 - val_loss: 0.0133 - val_accuracy: 1.0000\n",
            "Epoch 66/100\n",
            "20/20 [==============================] - 12s 590ms/step - loss: 0.0570 - accuracy: 0.9802 - val_loss: 0.2328 - val_accuracy: 0.9543\n",
            "Epoch 67/100\n",
            "20/20 [==============================] - 12s 590ms/step - loss: 0.0208 - accuracy: 0.9925 - val_loss: 0.1025 - val_accuracy: 0.9516\n",
            "Epoch 68/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0329 - accuracy: 0.9917 - val_loss: 0.0082 - val_accuracy: 1.0000\n",
            "Epoch 69/100\n",
            "20/20 [==============================] - 12s 593ms/step - loss: 0.0341 - accuracy: 0.9869 - val_loss: 0.0134 - val_accuracy: 0.9919\n",
            "Epoch 70/100\n",
            "20/20 [==============================] - 12s 594ms/step - loss: 0.0316 - accuracy: 0.9921 - val_loss: 0.0860 - val_accuracy: 0.9489\n",
            "Epoch 71/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0354 - accuracy: 0.9881 - val_loss: 0.0816 - val_accuracy: 0.9624\n",
            "Epoch 72/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0246 - accuracy: 0.9901 - val_loss: 0.0951 - val_accuracy: 0.9731\n",
            "Epoch 73/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0506 - accuracy: 0.9821 - val_loss: 0.1545 - val_accuracy: 0.9677\n",
            "Epoch 74/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0187 - accuracy: 0.9925 - val_loss: 0.0635 - val_accuracy: 0.9731\n",
            "Epoch 75/100\n",
            "20/20 [==============================] - 12s 591ms/step - loss: 0.0291 - accuracy: 0.9901 - val_loss: 0.0080 - val_accuracy: 1.0000\n",
            "Epoch 76/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0439 - accuracy: 0.9877 - val_loss: 0.0043 - val_accuracy: 1.0000\n",
            "Epoch 77/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0276 - accuracy: 0.9917 - val_loss: 0.0231 - val_accuracy: 0.9866\n",
            "Epoch 78/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0393 - accuracy: 0.9909 - val_loss: 0.0182 - val_accuracy: 0.9946\n",
            "Epoch 79/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0181 - accuracy: 0.9940 - val_loss: 0.0068 - val_accuracy: 1.0000\n",
            "Epoch 80/100\n",
            "20/20 [==============================] - 12s 591ms/step - loss: 0.0528 - accuracy: 0.9869 - val_loss: 0.0442 - val_accuracy: 0.9731\n",
            "Epoch 81/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0097 - accuracy: 0.9960 - val_loss: 0.0076 - val_accuracy: 1.0000\n",
            "Epoch 82/100\n",
            "20/20 [==============================] - 12s 591ms/step - loss: 0.0788 - accuracy: 0.9794 - val_loss: 0.0288 - val_accuracy: 0.9866\n",
            "Epoch 83/100\n",
            "20/20 [==============================] - 12s 590ms/step - loss: 0.0107 - accuracy: 0.9976 - val_loss: 0.0344 - val_accuracy: 0.9785\n",
            "Epoch 84/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0171 - accuracy: 0.9944 - val_loss: 0.0135 - val_accuracy: 0.9946\n",
            "Epoch 85/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0713 - accuracy: 0.9837 - val_loss: 0.0256 - val_accuracy: 0.9892\n",
            "Epoch 86/100\n",
            "20/20 [==============================] - 12s 590ms/step - loss: 0.0162 - accuracy: 0.9933 - val_loss: 0.0385 - val_accuracy: 0.9839\n",
            "Epoch 87/100\n",
            "20/20 [==============================] - 12s 587ms/step - loss: 0.0183 - accuracy: 0.9940 - val_loss: 0.0219 - val_accuracy: 0.9866\n",
            "Epoch 88/100\n",
            "20/20 [==============================] - 12s 587ms/step - loss: 0.1159 - accuracy: 0.9762 - val_loss: 0.0513 - val_accuracy: 0.9731\n",
            "Epoch 89/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0222 - accuracy: 0.9925 - val_loss: 0.2307 - val_accuracy: 0.8844\n",
            "Epoch 90/100\n",
            "20/20 [==============================] - 12s 590ms/step - loss: 0.0192 - accuracy: 0.9948 - val_loss: 0.0480 - val_accuracy: 0.9839\n",
            "Epoch 91/100\n",
            "20/20 [==============================] - 12s 590ms/step - loss: 0.0152 - accuracy: 0.9964 - val_loss: 0.0136 - val_accuracy: 0.9946\n",
            "Epoch 92/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0320 - accuracy: 0.9917 - val_loss: 0.0091 - val_accuracy: 1.0000\n",
            "Epoch 93/100\n",
            "20/20 [==============================] - 12s 591ms/step - loss: 0.0430 - accuracy: 0.9885 - val_loss: 0.0246 - val_accuracy: 0.9866\n",
            "Epoch 94/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0135 - accuracy: 0.9964 - val_loss: 0.0739 - val_accuracy: 0.9731\n",
            "Epoch 95/100\n",
            "20/20 [==============================] - 12s 590ms/step - loss: 0.0588 - accuracy: 0.9837 - val_loss: 0.0264 - val_accuracy: 0.9866\n",
            "Epoch 96/100\n",
            "20/20 [==============================] - 12s 588ms/step - loss: 0.0223 - accuracy: 0.9937 - val_loss: 0.0269 - val_accuracy: 0.9866\n",
            "Epoch 97/100\n",
            "20/20 [==============================] - 12s 591ms/step - loss: 0.0230 - accuracy: 0.9940 - val_loss: 0.0373 - val_accuracy: 0.9839\n",
            "Epoch 98/100\n",
            "20/20 [==============================] - 12s 587ms/step - loss: 0.0392 - accuracy: 0.9893 - val_loss: 0.0032 - val_accuracy: 1.0000\n",
            "Epoch 99/100\n",
            "20/20 [==============================] - 12s 589ms/step - loss: 0.0223 - accuracy: 0.9925 - val_loss: 0.0186 - val_accuracy: 0.9946\n",
            "Epoch 100/100\n",
            "20/20 [==============================] - 12s 590ms/step - loss: 0.0249 - accuracy: 0.9929 - val_loss: 0.0671 - val_accuracy: 0.9758\n"
          ]
        }
      ],
      "source": [
        "model.compile(loss = 'categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
        "history = model.fit(\n",
        "  train_generator,\n",
        "  epochs=100,\n",
        "  steps_per_epoch=20,\n",
        "  validation_data = validation_generator,\n",
        "  verbose = 1,\n",
        "  batch_size=64,\n",
        "  validation_steps=3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "aeTRVCr6aosw"
      },
      "outputs": [],
      "source": [
        "model.save(\"rps.h5\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "include_colab_link": true,
      "name": "Course 2 - Part 8 - Lesson 2 - Notebook (RockPaperScissors).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,
  "nbformat_minor": 0
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