catdog_mltest.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "catdog_mltest.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1tAj74ozMH0BopfCbdJ9VOr-_wDFVyfoo",
      "authorship_tag": "ABX9TyOFwja38u8ka0JP/t2RLBvF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/taegukang35/ML-study/blob/main/catdog_mltest.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZ02RlBHZ0oM"
      },
      "source": [
        "from google.colab import files\n",
        "#drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qC99M9B8u8u6"
      },
      "source": [
        "!rm -rf \"/content/cats_and_dogs_small\"\n",
        "!rm -rf \"/content/dataset\"\n",
        "!rm -rf \"/content/dogs-cats-images.zip\"\n",
        "!rm -rf \"/content/dog vs cat\""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KCRJ_PUnaYHo",
        "outputId": "72033d67-9c8a-4152-e865-494a3bcc1647"
      },
      "source": [
        "!pip install -q kaggle\n",
        "uploaded = files.upload()\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!ls ~/.kaggle\n",
        "!chmod 600 /root/.kaggle/kaggle.json\n",
        "!kaggle datasets download -d chetankv/dogs-cats-images\n",
        "!unzip '/content/dogs-cats-images.zip'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-97821584-3b88-4760-b283-d4c78e919f7d\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-97821584-3b88-4760-b283-d4c78e919f7d\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.37.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.370.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3700.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3701.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3702.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3703.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3704.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3705.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3706.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3707.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3708.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3709.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.371.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3710.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3711.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3712.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3713.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3714.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3715.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3716.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3717.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3718.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3719.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.372.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3720.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3721.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3722.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3723.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3724.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3725.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3726.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3727.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3728.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3729.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.373.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3730.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3731.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3732.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3733.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3734.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3735.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3736.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3737.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3738.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3739.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.374.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3740.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3741.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3742.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3743.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3744.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3745.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3746.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3747.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3748.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3749.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.375.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3750.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3751.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3752.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3753.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3754.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3755.jpg  \n",
            "  inflating: dog vs cat/dataset/training_set/cats/cat.3756.jpg  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAt4DjzfcMnb"
      },
      "source": [
        "import os,shutil\n",
        "\n",
        "original_dataset_dir = '/content/dog vs cat/dataset/training_set/'\n",
        "base_dir = '/content/cats_and_dogs_small'\n",
        "os.mkdir(base_dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2GlbataSfyl4"
      },
      "source": [
        "train_dir = os.path.join(base_dir,'train')\n",
        "os.mkdir(train_dir)\n",
        "validation_dir = os.path.join(base_dir,'validation')\n",
        "os.mkdir(validation_dir)\n",
        "test_dir = os.path.join(base_dir,'test')\n",
        "os.mkdir(test_dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2YfuHiDgJQs"
      },
      "source": [
        "train_cats_dir = os.path.join(train_dir,'cats')\n",
        "os.mkdir(train_cats_dir)\n",
        "train_dogs_dir = os.path.join(train_dir,'dogs')\n",
        "os.mkdir(train_dogs_dir)\n",
        "\n",
        "validation_cats_dir = os.path.join(validation_dir,'cats')\n",
        "os.mkdir(validation_cats_dir)\n",
        "validation_dogs_dir = os.path.join(validation_dir,'dogs')\n",
        "os.mkdir(validation_dogs_dir)\n",
        "\n",
        "test_cats_dir = os.path.join(test_dir,'cats')\n",
        "os.mkdir(test_cats_dir)\n",
        "test_dogs_dir = os.path.join(test_dir,'dogs')\n",
        "os.mkdir(test_dogs_dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ei1MXu80g4i1"
      },
      "source": [
        "fnames = ['cat.{}.jpg'.format(i) for i in range(1,1000)]\n",
        "for fname in fnames:\n",
        "    src = os.path.join(original_dataset_dir,'cats',fname)\n",
        "    dst = os.path.join(train_cats_dir,fname)\n",
        "    shutil.copyfile(src,dst)\n",
        "\n",
        "fnames = ['cat.{}.jpg'.format(i) for i in range(1000,1500)]\n",
        "for fname in fnames:\n",
        "    src = os.path.join(original_dataset_dir,'cats',fname)\n",
        "    dst = os.path.join(validation_cats_dir,fname)\n",
        "    shutil.copyfile(src,dst)\n",
        "\n",
        "fnames = ['cat.{}.jpg'.format(i) for i in range(1500,2000)]\n",
        "for fname in fnames:\n",
        "    src = os.path.join(original_dataset_dir,'cats',fname)\n",
        "    dst = os.path.join(test_cats_dir,fname)\n",
        "    shutil.copyfile(src,dst)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oD36f25bp1pK"
      },
      "source": [
        "fnames = ['dog.{}.jpg'.format(i) for i in range(1,1000)]\n",
        "for fname in fnames:\n",
        "    src = os.path.join(original_dataset_dir,'dogs',fname)\n",
        "    dst = os.path.join(train_dogs_dir,fname)\n",
        "    shutil.copyfile(src,dst)\n",
        "fnames = ['dog.{}.jpg'.format(i) for i in range(1000,1500)]\n",
        "for fname in fnames:\n",
        "    src = os.path.join(original_dataset_dir,'dogs',fname)\n",
        "    dst = os.path.join(validation_dogs_dir,fname)\n",
        "    shutil.copyfile(src,dst)\n",
        "fnames = ['dog.{}.jpg'.format(i) for i in range(1500,2000)]\n",
        "for fname in fnames:\n",
        "    src = os.path.join(original_dataset_dir,'dogs',fname)\n",
        "    dst = os.path.join(test_dogs_dir,fname)\n",
        "    shutil.copyfile(src,dst)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "moPJy5LnqVQN",
        "outputId": "1d4e875b-e0bf-4b60-b5d6-9c895a2766e5"
      },
      "source": [
        "len(os.listdir(train_cats_dir))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "999"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z7N7MyzzqcA8",
        "outputId": "0471f7fc-a0b8-42d7-ccc7-b3c156d53d9a"
      },
      "source": [
        "len(os.listdir(train_cats_dir))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "999"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y6ZzRo0lqe2c",
        "outputId": "435a177f-1aa5-4014-eaf5-c87c7307b078"
      },
      "source": [
        "len(os.listdir(validation_cats_dir))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "500"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N6x6y_T4gVdN",
        "outputId": "b442ca67-cef0-45aa-a64a-2ba5fb34c2f9"
      },
      "source": [
        "len(os.listdir(test_cats_dir))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "500"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BQNiPmiuqpa"
      },
      "source": [
        "from keras import layers\n",
        "from keras import models\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(150,150,3)))\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(128,(3,3),activation = 'relu'))\n",
        "model.add(layers.MaxPooling2D((2,2)))\n",
        "model.add(layers.Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(layers.MaxPooling2D((2,2)))\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(512,activation = 'relu'))\n",
        "model.add(layers.Dense(1,activation = 'sigmoid'))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "it141IAAzaJ6",
        "outputId": "77a6d225-330e-42df-c16b-e4b1fe48f6b6"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_8 (Conv2D)            (None, 148, 148, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 74, 74, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 72, 72, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2 (None, 36, 36, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 34, 34, 128)       73856     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 17, 17, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 15, 15, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 6272)              0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 512)               3211776   \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 3,453,121\n",
            "Trainable params: 3,453,121\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7PP_aCTU0aON"
      },
      "source": [
        "from keras import optimizers\n",
        "model.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(learning_rate=1e-4),metrics=['acc'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jlcstlcy4eLE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xh0akX783y_S",
        "outputId": "bd73626b-885a-447d-f20a-2f5a6a81b0f9"
      },
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=(150,150),\n",
        "    batch_size = 20,\n",
        "    class_mode = 'binary'\n",
        ")\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "    validation_dir,\n",
        "    target_size = (150,150),\n",
        "    batch_size = 20,\n",
        "    class_mode = 'binary'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1998 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eMgN4qqj7oD5",
        "outputId": "bc7d27d3-e5d5-4127-f35d-15931c77d8f7"
      },
      "source": [
        "for data_batch, labels_batch in train_generator:\n",
        "    print(\"배치 데이터 크기\",data_batch.shape)\n",
        "    print(\"배치 레이블 크기: \",labels_batch.shape)\n",
        "    break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "배치 데이터 크기 (20, 150, 150, 3)\n",
            "배치 레이블 크기:  (20,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 882
        },
        "id": "Kzuerflp8IIG",
        "outputId": "1ba03ee6-0bf2-4cf1-df16-c4a77c673be9"
      },
      "source": [
        "history = model.fit_generator(\n",
        "    train_generator,\n",
        "    steps_per_epoch = 100,\n",
        "    epochs = 30,\n",
        "    validation_data = validation_generator,\n",
        "    validation_steps=50\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1915: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "100/100 [==============================] - 12s 108ms/step - loss: 0.6980 - acc: 0.5224 - val_loss: 0.6703 - val_acc: 0.5940\n",
            "Epoch 2/30\n",
            "100/100 [==============================] - 10s 102ms/step - loss: 0.6714 - acc: 0.5847 - val_loss: 0.6438 - val_acc: 0.6150\n",
            "Epoch 3/30\n",
            "100/100 [==============================] - 10s 101ms/step - loss: 0.6130 - acc: 0.6878 - val_loss: 0.6293 - val_acc: 0.6270\n",
            "Epoch 4/30\n",
            "100/100 [==============================] - 10s 103ms/step - loss: 0.5803 - acc: 0.6855 - val_loss: 0.6099 - val_acc: 0.6620\n",
            "Epoch 5/30\n",
            "100/100 [==============================] - 10s 103ms/step - loss: 0.5459 - acc: 0.7184 - val_loss: 0.5908 - val_acc: 0.6820\n",
            "Epoch 6/30\n",
            "100/100 [==============================] - 10s 103ms/step - loss: 0.5132 - acc: 0.7476 - val_loss: 0.6147 - val_acc: 0.6750\n",
            "Epoch 7/30\n",
            "100/100 [==============================] - 10s 103ms/step - loss: 0.4795 - acc: 0.7651 - val_loss: 0.6345 - val_acc: 0.6650\n",
            "Epoch 8/30\n",
            "100/100 [==============================] - 10s 103ms/step - loss: 0.4674 - acc: 0.7838 - val_loss: 0.5909 - val_acc: 0.6930\n",
            "Epoch 9/30\n",
            "100/100 [==============================] - 10s 103ms/step - loss: 0.4584 - acc: 0.7875 - val_loss: 0.5939 - val_acc: 0.6830\n",
            "Epoch 10/30\n",
            "100/100 [==============================] - 10s 102ms/step - loss: 0.4091 - acc: 0.8069 - val_loss: 0.6007 - val_acc: 0.7060\n",
            "Epoch 11/30\n",
            "100/100 [==============================] - 10s 102ms/step - loss: 0.3974 - acc: 0.8206 - val_loss: 0.5336 - val_acc: 0.7280\n",
            "Epoch 12/30\n",
            " 46/100 [============>.................] - ETA: 3s - loss: 0.3725 - acc: 0.8488"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-102-cff90531affa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m )\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1930\u001b[0m         \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1931\u001b[0m         \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1932\u001b[0;31m         initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1933\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1934\u001b[0m   def evaluate_generator(self,\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1156\u001b[0m                 _r=1):\n\u001b[1;32m   1157\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1158\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1159\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1160\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    915\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 917\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    918\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    919\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3022\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   3023\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 3024\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   3025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1959\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1960\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1961\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1962\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1963\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    594\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 596\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    597\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HlIWIra8-kiI"
      },
      "source": [
        "model.save('model1.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iDry323B8tnq"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "history_dict = history.history\n",
        "loss = history_dict['loss']\n",
        "val_loss = history_dict['val_loss']\n",
        "epochs = range(1,len(loss)+1)\n",
        "\n",
        "plt.plot(epochs,loss,'bo',label='Training loss')\n",
        "plt.plot(epochs,val_loss,'b',label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-idW04ME-GC1"
      },
      "source": [
        "acc = history_dict['acc']\n",
        "val_acc = history_dict['val_acc']\n",
        "\n",
        "plt.plot(epochs,acc,'bo',label ='Training acc')\n",
        "plt.plot(epochs,val_acc,'b',label='Validation acc')\n",
        "plt.title('Training and Validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3ni1F0O-NtK"
      },
      "source": [
        "model.metrics_names"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abL4H40P_Rig"
      },
      "source": [
        "from keras.applications.vgg16 import VGG16 \n",
        "conv_base = VGG16(weights = 'imagenet',include_top=False,input_shape = (150,150,3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYCre7K6I2Cn"
      },
      "source": [
        "conv_base.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W4EcOTHQLHg9",
        "outputId": "0b17e2a9-e9d2-436a-b03d-28f3478f6df9"
      },
      "source": [
        "import os \n",
        "import numpy as np\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "base_dir = '/content/cats_and_dogs_small'\n",
        "train_dir = os.path.join(base_dir,'train')\n",
        "validation_dir = os.path.join(base_dir,'validation')\n",
        "test_dir = os.path.join(base_dir,'test')\n",
        "\n",
        "datagen = ImageDataGenerator(rescale=1./255)\n",
        "batch_size =20\n",
        "\n",
        "def extract_features(directory,sample_count):\n",
        "    features = np.zeros(shape=(sample_count,4,4,512))\n",
        "    labels = np.zeros(shape=(sample_count))\n",
        "    generator = datagen.flow_from_directory(\n",
        "        directory,\n",
        "        target_size=(150,150),\n",
        "        batch_size= batch_size,\n",
        "        class_mode = 'binary'\n",
        "    )\n",
        "    i=0\n",
        "    for inputs_batch,labels_batch in generator:\n",
        "        features_batch = conv_base.predict(inputs_batch)\n",
        "        features[i*batch_size:(i+1)*batch_size] = features_batch\n",
        "        labels[i*batch_size:(i+1)*batch_size] = labels_batch\n",
        "        i += 1\n",
        "        if i*batch_size >= sample_count:\n",
        "            break\n",
        "    return features,labels\n",
        "\n",
        "train_features,train_labels = extract_features(train_dir,1998)\n",
        "validation_features,validation_labels = extract_features(validation_dir,1000)\n",
        "test_features, test_labels = extract_features(test_dir,1000)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 1998 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dwsZnq6bW9UV",
        "outputId": "c9fb1f50-d98a-4030-d81a-7e225fc5d2d2"
      },
      "source": [
        "train_features.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1998, 4, 4, 512)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g53vSqR-eywC",
        "outputId": "a5752163-88ed-4161-c247-7aff258e3c16"
      },
      "source": [
        "train_labels.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1998,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNX0n1-ahx7Q",
        "outputId": "9a183a25-6847-4de8-c111-593981972a13"
      },
      "source": [
        "train_labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1., 1., ..., 1., 0., 1.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bw838t2CodHW"
      },
      "source": [
        "train_features = np.reshape(train_features,(1998,4*4*512))\n",
        "validation_features = np.reshape(validation_features,(1000,4*4*512))\n",
        "test_features = np.reshape(test_features,(1000,4*4*512))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w4X0pEENtnwc",
        "outputId": "36b559b4-650f-400d-fa92-1cc224cce869"
      },
      "source": [
        "test_features.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 8192)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vqK286PkiCSm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ddd28f1c-36b4-499b-9225-b0321bd9e91d"
      },
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "\n",
        "model = models.Sequential()\n",
        "model.add(layers.Dense(256,activation='relu',input_dim=4*4*512))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(1,activation='sigmoid'))\n",
        "\n",
        "model.compile(optimizer = optimizers.RMSprop(learning_rate = 2e-5),\n",
        "              loss = 'binary_crossentropy',\n",
        "              metrics = ['acc'])\n",
        "\n",
        "history = model.fit(train_features,train_labels,epochs = 30, batch_size = 20, validation_data = (validation_features,validation_labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "100/100 [==============================] - 2s 8ms/step - loss: 0.7341 - acc: 0.5475 - val_loss: 0.4588 - val_acc: 0.8390\n",
            "Epoch 2/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.4734 - acc: 0.7794 - val_loss: 0.3702 - val_acc: 0.8760\n",
            "Epoch 3/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.3940 - acc: 0.8268 - val_loss: 0.3376 - val_acc: 0.8620\n",
            "Epoch 4/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.3295 - acc: 0.8567 - val_loss: 0.3027 - val_acc: 0.8850\n",
            "Epoch 5/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.2916 - acc: 0.8854 - val_loss: 0.2822 - val_acc: 0.8940\n",
            "Epoch 6/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.2770 - acc: 0.8902 - val_loss: 0.2773 - val_acc: 0.8900\n",
            "Epoch 7/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.2479 - acc: 0.9026 - val_loss: 0.2603 - val_acc: 0.8980\n",
            "Epoch 8/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.2216 - acc: 0.9140 - val_loss: 0.2625 - val_acc: 0.8940\n",
            "Epoch 9/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.2323 - acc: 0.9085 - val_loss: 0.2492 - val_acc: 0.9040\n",
            "Epoch 10/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.2057 - acc: 0.9272 - val_loss: 0.2575 - val_acc: 0.8960\n",
            "Epoch 11/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.2081 - acc: 0.9286 - val_loss: 0.2427 - val_acc: 0.9010\n",
            "Epoch 12/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.1883 - acc: 0.9345 - val_loss: 0.2410 - val_acc: 0.9000\n",
            "Epoch 13/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.2039 - acc: 0.9257 - val_loss: 0.2403 - val_acc: 0.9020\n",
            "Epoch 14/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.1648 - acc: 0.9405 - val_loss: 0.2360 - val_acc: 0.9030\n",
            "Epoch 15/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.1661 - acc: 0.9396 - val_loss: 0.2341 - val_acc: 0.9030\n",
            "Epoch 16/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.1608 - acc: 0.9478 - val_loss: 0.2323 - val_acc: 0.9050\n",
            "Epoch 17/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.1550 - acc: 0.9444 - val_loss: 0.2334 - val_acc: 0.8990\n",
            "Epoch 18/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.1413 - acc: 0.9556 - val_loss: 0.2467 - val_acc: 0.8990\n",
            "Epoch 19/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.1384 - acc: 0.9556 - val_loss: 0.2298 - val_acc: 0.9050\n",
            "Epoch 20/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.1500 - acc: 0.9510 - val_loss: 0.2296 - val_acc: 0.9040\n",
            "Epoch 21/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.1344 - acc: 0.9566 - val_loss: 0.2358 - val_acc: 0.9010\n",
            "Epoch 22/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.1269 - acc: 0.9552 - val_loss: 0.2303 - val_acc: 0.9050\n",
            "Epoch 23/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.1328 - acc: 0.9519 - val_loss: 0.2394 - val_acc: 0.9010\n",
            "Epoch 24/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.1197 - acc: 0.9605 - val_loss: 0.2347 - val_acc: 0.9030\n",
            "Epoch 25/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.1139 - acc: 0.9589 - val_loss: 0.2294 - val_acc: 0.9030\n",
            "Epoch 26/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.1133 - acc: 0.9589 - val_loss: 0.2307 - val_acc: 0.9030\n",
            "Epoch 27/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.1047 - acc: 0.9667 - val_loss: 0.2301 - val_acc: 0.9050\n",
            "Epoch 28/30\n",
            "100/100 [==============================] - 1s 6ms/step - loss: 0.0972 - acc: 0.9703 - val_loss: 0.2364 - val_acc: 0.9010\n",
            "Epoch 29/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.0878 - acc: 0.9719 - val_loss: 0.2431 - val_acc: 0.8980\n",
            "Epoch 30/30\n",
            "100/100 [==============================] - 1s 5ms/step - loss: 0.0906 - acc: 0.9718 - val_loss: 0.2332 - val_acc: 0.9070\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XUmKRWu3ivob"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}