5_6.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "5_6.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyOsof08Bth+wbY5Mff5LRYx",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hoo0681/raspi4withTF/blob/master/5_6.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tMp-x2MDu1R5",
        "colab_type": "code",
        "outputId": "53556887-caf2-4103-e70a-274860188d33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4Rjdz-bvEDm",
        "colab_type": "code",
        "outputId": "44bd22d5-876f-45df-ecea-69c39b2b79cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        " %tensorflow_version 2.x\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "import IPython.display as display\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import pathlib\n",
        "print(\"Module Loaded.\")\n",
        "print(\"TensorFlow Version :{}\".format(tf.__version__))\n",
        "print(\"NumPy Version :{}\".format(np.__version__))\n",
        "print(\"Matplotlib Version :{}\".format(plt.matplotlib.__version__))\n",
        "\n",
        "# set auto tune\n",
        "AUTOTUNE = tf.data.experimental.AUTOTUNE"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "Module Loaded.\n",
            "TensorFlow Version :2.1.0-rc1\n",
            "NumPy Version :1.17.5\n",
            "Matplotlib Version :3.1.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oEUYaDimvFYj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# 지정 폴더 아래에 있는 모든 *.jpg 파일의 수\n",
        "#  및 폴더명 목록을 리턴  \n",
        "def check_dir(d_path):\n",
        "    img_count = len(list(d_path.glob('*/*.jpg')))\n",
        "    c_name = np.array([item.name for item in d_path.glob('*') if item.name != \"LICENSE.txt\"])\n",
        "    return img_count, c_name"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMQqVh1dxBJw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip /content/drive/My\\ Drive/tf2/z_data/asirra_fixed.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7X5i-5PGxBuf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pathlib\n",
        "train_data_dir_cwd=pathlib.Path('/content/asirra/train')\n",
        "test_data_dir_cwd=pathlib.Path('/content/asirra/test')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fxjnlQPTxEBB",
        "colab_type": "code",
        "outputId": "67b496fd-1f68-4cd1-850d-5412122f8453",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "image_count_cwd, CLASS_NAMES_cwd = check_dir(train_data_dir_cwd)\n",
        "test_image_count_cwd, CLASS_NAMES_cwd = check_dir(test_data_dir_cwd)\n",
        "print('image_count: {}\\nclasses: {}'.format(image_count_cwd, CLASS_NAMES_cwd))\n",
        "print('image_count: {}\\nclasses: {}'.format(test_image_count_cwd, CLASS_NAMES_cwd))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "image_count: 12500\n",
            "classes: ['dog' 'cat']\n",
            "image_count: 12500\n",
            "classes: ['dog' 'cat']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5AMw2fjk_6af",
        "colab_type": "text"
      },
      "source": [
        "# 변형X 데이터"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CRIRJ3WpxOOb",
        "colab_type": "code",
        "outputId": "eb9049bf-bd23-4b6b-9e74-0271fbca7ba1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "datagen_2=tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)\n",
        "generator_2=datagen_2.flow_from_directory(\n",
        "    directory=str(train_data_dir_cwd),\n",
        "    batch_size=12500,\n",
        "    shuffle=False,\n",
        "    target_size=(150, 150)\n",
        ")\n",
        "generator_2_test=datagen_2.flow_from_directory(\n",
        "    directory=str(test_data_dir_cwd),\n",
        "    batch_size=125,\n",
        "    shuffle=True,\n",
        "    target_size=(150, 150)\n",
        ")\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 12500 images belonging to 2 classes.\n",
            "Found 12500 images belonging to 2 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J9ElEvLvADpE",
        "colab_type": "text"
      },
      "source": [
        "# 변형데이터"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-IbTU9RoAgMV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_x=np.load(file='/content/drive/My Drive/train_data_150_img.npy',allow_pickle=True)\n",
        "train_y=np.load(file='/content/drive/My Drive/train_data_150_label.npy',allow_pickle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8yslVbTwAlJZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#width_shift_range=0.3,height_shift_range=0.3,horizontal_flip=True,vertical_flip=True,rotation_range=45,zoom_range=0.2\n",
        "gen_arg=tf.keras.preprocessing.image.ImageDataGenerator(width_shift_range=0.3,height_shift_range=0.3)\n",
        "arg_train=gen_arg.flow(x=train_x,y=train_y,batch_size=12500,shuffle=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QYJ1cUamoAQ2",
        "colab_type": "text"
      },
      "source": [
        "# techer_student"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yQfE_5rRoE5c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vgg_model_ts=tf.keras.applications.VGG16(include_top=False,weights='imagenet',input_tensor=tf.keras.layers.Input(shape=(150,150,3)))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EcGbdReoLZW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "b7c25a16-c932-4742-eee1-18745c1c75a4"
      },
      "source": [
        "%%time\n",
        "img_train,label_train=generator_2.next()\n",
        "bottleneck_train=vgg_model_2.predict(img_train)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 1h 30min 1s, sys: 16 s, total: 1h 30min 17s\n",
            "Wall time: 23min 18s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zjfn29cToiFn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save(file='bottleneck_train_label',arr=label_train)\n",
        "np.save(file='bottleneck_train',arr=bottleneck_train)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZUdQfZvoQv-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d5f4b283-c82a-4153-a50a-1ae16ec63665"
      },
      "source": [
        "%%time\n",
        "img_val,label_val=generator_2_test.next()\n",
        "bottleneck_val=vgg_model_2.predict(img_val)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 1h 30min 3s, sys: 16.9 s, total: 1h 30min 20s\n",
            "Wall time: 23min 20s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsDwI-tEozMi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save(file='bottleneck_val_label',arr=label_val)\n",
        "np.save(file='bottleneck_val',arr=bottleneck_val)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NT0ZOY8Lo7je",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cp bottleneck_val_label.npy /content/drive/My\\ Drive\n",
        "%cp bottleneck_val.npy /content/drive/My\\ Drive\n",
        "%cp bottleneck_train_label.npy /content/drive/My\\ Drive\n",
        "%cp bottleneck_train.npy /content/drive/My\\ Drive\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uLHIFTmrCWOa",
        "colab_type": "text"
      },
      "source": [
        "# 병목만들기\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HaZc_IdixQiV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "f502f2e8-4257-4ae0-ce41-c61b1fbf798d"
      },
      "source": [
        "vgg_model_2=tf.keras.applications.VGG16(include_top=False,weights='imagenet',input_tensor=tf.keras.layers.Input(shape=(150,150,3)))\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-iv9fjn4sG4",
        "colab_type": "code",
        "outputId": "fecbf933-22cc-4ba2-9b18-9df77faa6c2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#변형없음\n",
        "%%time\n",
        "img,label=generator_2.next()\n",
        "bottleneck_test=vgg_model_2.predict(img)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 1h 29min 45s, sys: 17.8 s, total: 1h 30min 3s\n",
            "Wall time: 23min 14s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qc3xj7peBNSX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#변형있음\n",
        "%%time\n",
        "arg_img,arg_label=arg_train.ne \n",
        "\n",
        "xt()\n",
        "bottleneck_arg_train=vgg_model_2.predict(arg_img)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aQXuDmpg9ZsD",
        "colab_type": "code",
        "outputId": "a29d6c25-fced-443d-a5b6-64e254ca22dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "keKHQ0PfDr-2",
        "colab_type": "text"
      },
      "source": [
        "# 모델정의"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uz7jZyoYx2mK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_bt = tf.keras.Sequential()\n",
        "model_bt.add(tf.keras.layers.Flatten())\n",
        "model_bt.add(tf.keras.layers.Dense(256, activation='relu'))\n",
        "model_bt.add(tf.keras.layers.Dropout(0.5))\n",
        "model_bt.add(tf.keras.layers.Dense(2, activation='softmax'))\n",
        "\n",
        "model_bt.compile(optimizer='adam',loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HfN2lkHyyPKY",
        "colab_type": "code",
        "outputId": "78d90fd0-6936-45da-9bb0-15b8e2db1e41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        }
      },
      "source": [
        "model_bt.fit(bottleneck_train, label_train,\n",
        "          epochs=5,\n",
        "          batch_size=512,validation_data=(bottleneck_val,label_val)\n",
        "          )"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 12500 samples, validate on 12500 samples\n",
            "Epoch 1/5\n",
            "12500/12500 [==============================] - 2s 150us/sample - loss: 0.1414 - accuracy: 0.9467 - val_loss: 0.2129 - val_accuracy: 0.9115\n",
            "Epoch 2/5\n",
            "12500/12500 [==============================] - 2s 152us/sample - loss: 0.1357 - accuracy: 0.9494 - val_loss: 0.2157 - val_accuracy: 0.9121\n",
            "Epoch 3/5\n",
            "12500/12500 [==============================] - 2s 150us/sample - loss: 0.1294 - accuracy: 0.9511 - val_loss: 0.2221 - val_accuracy: 0.9123\n",
            "Epoch 4/5\n",
            "12500/12500 [==============================] - 2s 147us/sample - loss: 0.1179 - accuracy: 0.9554 - val_loss: 0.2167 - val_accuracy: 0.9114\n",
            "Epoch 5/5\n",
            "12500/12500 [==============================] - 2s 148us/sample - loss: 0.1050 - accuracy: 0.9615 - val_loss: 0.2171 - val_accuracy: 0.9127\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f84e5fa6550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-nVc_YDEzeZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_bt.save_weights('/content/save_weight')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HQiU737UFEWW",
        "colab_type": "text"
      },
      "source": [
        "# 평가"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NgDQZ26GttX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vgg_model_2=tf.keras.applications.VGG16(include_top=False,weights='imagenet',input_tensor=tf.keras.layers.Input(shape=(150,150,3)))\n",
        "\n",
        "existing_model = tf.keras.Sequential()\n",
        "existing_model.add(tf.keras.layers.Flatten())\n",
        "existing_model.add(tf.keras.layers.Dense(256, activation='relu'))\n",
        "existing_model.add(tf.keras.layers.Dropout(0.5))\n",
        "existing_model.add(tf.keras.layers.Dense(2, activation='softmax'))\n",
        "\n",
        "existing_model.build(vgg_model_2.output_shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oNYNMO6DFDe_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "existing_model(vgg_model_2.output)\n",
        "\n",
        "exvgg_model=tf.keras.Model(inputs=vgg_model_2.input,outputs=existing_model.output)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DG_bo3A8fiRn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "outputId": "236089da-8d0c-415c-fe00-58e1a3969a90"
      },
      "source": [
        "exvgg_model.summary()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_5 (InputLayer)         [(None, 150, 150, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
            "_________________________________________________________________\n",
            "sequential_1 (Sequential)    (None, 2)                 2097922   \n",
            "=================================================================\n",
            "Total params: 16,812,610\n",
            "Trainable params: 16,812,610\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flHeFxgjFnMy",
        "colab_type": "text"
      },
      "source": [
        "# 실험중"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CZBGnKuXbXg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vgg_model_for_test=tf.keras.applications.VGG16(include_top=False,weights='imagenet',input_tensor=tf.keras.layers.Input(shape=(150,150,3)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTg7sl_0Ebpf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2a92175f-6f1e-4ca2-f343-1151ae6fc174"
      },
      "source": [
        "vgg_model_for_test.output_shape"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(None, 4, 4, 512)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J7jKtcj7CmOx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "inputs_=tf.keras.layers.Input(shape=vgg_model_for_test.output_shape[1:])\n",
        "x= tf.keras.layers.Flatten()(inputs_)\n",
        "x= tf.keras.layers.Dense(256, activation='relu')(x)\n",
        "x= tf.keras.layers.Dropout(0.5)(x)\n",
        "outputs_= tf.keras.layers.Dense(2, activation='softmax')(x)\n",
        "existing_model2=tf.keras.Model(inputs=inputs_,outputs=outputs_)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c_d0q5VRYah3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "4a672e3f-894a-40f6-a740-35c6b07a06b5"
      },
      "source": [
        "existing_model2.summary()\n"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         [(None, 4, 4, 512)]       0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 256)               2097408   \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 2)                 514       \n",
            "=================================================================\n",
            "Total params: 2,097,922\n",
            "Trainable params: 2,097,922\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2iJBYM0sbQR9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "d6c6778d-3f35-42b9-b705-6d7753b8112f"
      },
      "source": [
        "vgg_model_for_test.output"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor 'block5_pool/Identity:0' shape=(None, 4, 4, 512) dtype=float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JV92FA9TFI9K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inputs=tf.keras.layers.Input(shape=(150,150,3))\n",
        "model1=tf.keras.applications.VGG16(include_top=False,weights='imagenet',input_tensor=inputs)\n",
        "model2=existing_model2(model1.output)\n",
        "exvgg_model2=tf.keras.Model(inputs=model1.input,outputs=model2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q8l0QxnJ2KRV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "exvgg_model2.compile(optimizer='rmsprop',loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AovwPoRW3EnR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "92cde6f0-e955-4aae-d5a0-ccfad56b3a96"
      },
      "source": [
        "existing_model2.load_weights('/content/save_weight')"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f84e4bb5e10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6VBFpaXeiZ1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "c59dfebd-6e73-4d95-a4ca-ec4d11d3a17a"
      },
      "source": [
        "exvgg_model2.evaluate(generator_2_test)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:sample_weight modes were coerced from\n",
            "  ...\n",
            "    to  \n",
            "  ['...']\n",
            "  5/100 [>.............................] - ETA: 21:57 - loss: 0.7607 - accuracy: 0.5440"
          ],
          "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-48-af0fa9b39b44>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mexvgg_model2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_2_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m    928\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    929\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 930\u001b[0;31m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[1;32m    931\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    932\u001b[0m   def predict(self,\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, model, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    488\u001b[0m         \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 490\u001b[0;31m         use_multiprocessing=use_multiprocessing, **kwargs)\n\u001b[0m\u001b[1;32m    491\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    492\u001b[0m   def predict(self, model, x, batch_size=None, verbose=0, steps=None,\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36m_model_iteration\u001b[0;34m(self, model, mode, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m    473\u001b[0m               \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    474\u001b[0m               \u001b[0mtraining_context\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_context\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 475\u001b[0;31m               total_epochs=1)\n\u001b[0m\u001b[1;32m    476\u001b[0m           \u001b[0mcbks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_logs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepoch_logs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    477\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2.py\u001b[0m in \u001b[0;36mrun_one_epoch\u001b[0;34m(model, iterator, execution_function, dataset_size, batch_size, strategy, steps_per_epoch, num_samples, mode, training_context, total_epochs)\u001b[0m\n\u001b[1;32m    126\u001b[0m         step=step, mode=mode, size=current_batch_size) as batch_logs:\n\u001b[1;32m    127\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 128\u001b[0;31m         \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexecution_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    129\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mStopIteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOutOfRangeError\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    130\u001b[0m         \u001b[0;31m# TODO(kaftan): File bug about tf function and errors.OutOfRangeError?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36mexecution_function\u001b[0;34m(input_fn)\u001b[0m\n\u001b[1;32m     96\u001b[0m     \u001b[0;31m# `numpy` translates Tensors to values in Eager mode.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     97\u001b[0m     return nest.map_structure(_non_none_constant_value,\n\u001b[0;32m---> 98\u001b[0;31m                               distributed_function(input_fn))\n\u001b[0m\u001b[1;32m     99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mexecution_function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    566\u001b[0m         \u001b[0mxla_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mExit\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    567\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[0;32m--> 568\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    569\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    570\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtracing_count\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_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[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    604\u001b[0m       \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m       \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 606\u001b[0;31m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_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;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    607\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    608\u001b[0m         raise ValueError(\"Creating variables on a non-first call to a function\"\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2361\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2362\u001b[0m       \u001b[0mgraph_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_define_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2363\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mgraph_function\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2365\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/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_filtered_call\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1609\u001b[0m          if isinstance(t, (ops.Tensor,\n\u001b[1;32m   1610\u001b[0m                            resource_variable_ops.BaseResourceVariable))),\n\u001b[0;32m-> 1611\u001b[0;31m         self.captured_inputs)\n\u001b[0m\u001b[1;32m   1612\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1613\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\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/tensorflow-2.1.0/python3.6/tensorflow_core/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   1690\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   1691\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1692\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1693\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1694\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/tensorflow-2.1.0/python3.6/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    543\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    544\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"executor_type\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"config_proto\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\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--> 545\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    546\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    547\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/tensorflow-2.1.0/python3.6/tensorflow_core/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     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[1;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\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---> 61\u001b[0;31m                                                num_outputs)\n\u001b[0m\u001b[1;32m     62\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     63\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": "4iJcZN8ZfDjb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.keras.utils.plot_model(exvgg_model2, 'modle1_test.png', show_shapes=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "luGjpVo4Fq4Q",
        "colab_type": "text"
      },
      "source": [
        "# layer 잠그기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2txib9OgPD1M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for layer in vgg_model_2.layers :\n",
        "  layer.trainable=False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KII18DR4FN0f",
        "colab_type": "code",
        "outputId": "a9ead0c0-03f3-4bfb-bed8-f4777f5f3034",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "existing_model.load_weights('/content/save_weight')"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7f3cbca9b978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DJT0nd0rQ3TW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "exvgg_model.compile(optimizer='rmsprop',loss='binary_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "52GmI-F7rwcq",
        "colab_type": "code",
        "outputId": "d371f74b-e988-47c0-e175-aca0042eb4f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        }
      },
      "source": [
        "exvgg_model.summary()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_7 (InputLayer)         [(None, 150, 150, 3)]     0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 150, 150, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 150, 150, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 75, 75, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 75, 75, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 75, 75, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 37, 37, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 37, 37, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 37, 37, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 18, 18, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 18, 18, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 18, 18, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 9, 9, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 9, 9, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
            "_________________________________________________________________\n",
            "sequential_3 (Sequential)    (None, 2)                 2097922   \n",
            "=================================================================\n",
            "Total params: 16,812,610\n",
            "Trainable params: 2,097,922\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MVNZ1KjsHL0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_img,test_label=generator_2_test.next()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TP7iWvfsNin",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_image = np.expand_dims(test_img[1], 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrKhrZS4FpHZ",
        "colab_type": "code",
        "outputId": "e59dab10-af4a-45d0-9372-76c7b9fb9cef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "exvgg_model.predict(batch_image)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.05833877, 0.9416612 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e7ECE3xzP567",
        "colab_type": "code",
        "outputId": "acc456b6-445c-40ae-a495-0ab64a2ce215",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168
        }
      },
      "source": [
        "exvgg_model.evaluate(generator_2_test)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-e047ab94da77>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mexvgg_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_2_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'exvgg_model' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BUG1EfglSNl3",
        "colab_type": "text"
      },
      "source": [
        "# 모델로드"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5Wfe-sQs2j3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "exvgg_model.save('/content/5_6.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tgo2gWyVL30J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cp 5_6.h5 /content/drive/My Drive"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lP-l9xrOM2tG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "model2 = load_model('/content/drive/My Drive/5_6.h5')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KCDHWMbQtzE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "98efc301-e827-49e0-b699-968e23167aa1"
      },
      "source": [
        "model2.predict(batch_image)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.9456012 , 0.05439877]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Psp2MpXFRrW6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}