Transfer_Learning.ipynb

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      "name": "Transfer Learning.ipynb",
      "version": "0.3.2",
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      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RegaipKURT/Advanced_AI_with_PYTHON/blob/master/Transfer_Learning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jh0rvNLjrzwS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "from keras.applications import VGG19\n",
        "from keras.applications import VGG16\n",
        "from keras.applications import ResNet50V2\n",
        "from keras.models import load_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_y8RyXrHclh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pouuxu7dJcDz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = VGG19(weights='imagenet', \n",
        "                      include_top=False, \n",
        "                      input_shape=(32, 32, 3))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "omo83gMZKycx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Dense, Activation, Flatten, Dropout\n",
        "from keras.models import Sequential, Model\n",
        "import tensorflow as tf\n",
        "\n",
        "for layer in model.layers[:-1]:\n",
        "    layer.trainable = False\n",
        "\n",
        "x = model.output\n",
        "x = Flatten()(x)\n",
        "x = Dense(1024, activation='relu')(x) \n",
        "predictions = Dense(10, activation='softmax')(x) \n",
        "model = Model(inputs=model.input, outputs=predictions)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OdMFKR5BUews",
        "colab_type": "code",
        "outputId": "ccd7f076-86c9-4995-d35c-1fa874c76a08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         (None, 32, 32, 3)         0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 32, 32, 64)        1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 32, 32, 64)        36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 16, 16, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 16, 16, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 8, 8, 256)         295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "block3_conv4 (Conv2D)        (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 4, 4, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv4 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv4 (Conv2D)        (None, 2, 2, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 512)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1024)              525312    \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 10)                10250     \n",
            "=================================================================\n",
            "Total params: 20,559,946\n",
            "Trainable params: 535,562\n",
            "Non-trainable params: 20,024,384\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wtQT-oxUhu7",
        "colab_type": "code",
        "outputId": "fb54190e-5965-4f59-97c4-11cfc4443ee8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "from keras.datasets import cifar10\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "print(y_train[10])\n",
        "train_y = np.zeros((50000,10), dtype=\"uint8\")\n",
        "print(train_y[2])\n",
        "for sira, deger in enumerate(y_train):\n",
        "    train_y[sira, deger] = 1"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4]\n",
            "[0 0 0 0 0 0 0 0 0 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VWZ9af7JjZ-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "outputId": "1c9c596a-2915-4cb7-8a93-762724bc5ace"
      },
      "source": [
        "model.compile(optimizer=\"adam\", loss=\"categorical_crossentropy\", metrics=[\"mse\"])\n",
        "history = model.fit(x_train, train_y, epochs=20, validation_split=0.2)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 40000 samples, validate on 10000 samples\n",
            "Epoch 1/20\n",
            "40000/40000 [==============================] - 37s 929us/step - loss: 10.9331 - mean_squared_error: 0.1369 - val_loss: 10.5125 - val_mean_squared_error: 0.1312\n",
            "Epoch 2/20\n",
            "40000/40000 [==============================] - 37s 916us/step - loss: 10.6367 - mean_squared_error: 0.1327 - val_loss: 10.4749 - val_mean_squared_error: 0.1306\n",
            "Epoch 3/20\n",
            "40000/40000 [==============================] - 36s 912us/step - loss: 10.6566 - mean_squared_error: 0.1327 - val_loss: 10.4797 - val_mean_squared_error: 0.1306\n",
            "Epoch 4/20\n",
            "40000/40000 [==============================] - 36s 912us/step - loss: 10.4873 - mean_squared_error: 0.1304 - val_loss: 10.5214 - val_mean_squared_error: 0.1309\n",
            "Epoch 5/20\n",
            "40000/40000 [==============================] - 37s 918us/step - loss: 10.5279 - mean_squared_error: 0.1309 - val_loss: 10.5038 - val_mean_squared_error: 0.1305\n",
            "Epoch 6/20\n",
            "40000/40000 [==============================] - 36s 911us/step - loss: 10.4655 - mean_squared_error: 0.1301 - val_loss: 10.1735 - val_mean_squared_error: 0.1266\n",
            "Epoch 7/20\n",
            "40000/40000 [==============================] - 37s 916us/step - loss: 10.0094 - mean_squared_error: 0.1246 - val_loss: 9.9466 - val_mean_squared_error: 0.1238\n",
            "Epoch 8/20\n",
            "40000/40000 [==============================] - 37s 914us/step - loss: 9.7729 - mean_squared_error: 0.1216 - val_loss: 9.9674 - val_mean_squared_error: 0.1239\n",
            "Epoch 9/20\n",
            "40000/40000 [==============================] - 36s 912us/step - loss: 9.6695 - mean_squared_error: 0.1202 - val_loss: 9.7894 - val_mean_squared_error: 0.1217\n",
            "Epoch 10/20\n",
            "40000/40000 [==============================] - 37s 913us/step - loss: 9.7059 - mean_squared_error: 0.1206 - val_loss: 9.7019 - val_mean_squared_error: 0.1207\n",
            "Epoch 11/20\n",
            "40000/40000 [==============================] - 37s 916us/step - loss: 9.6508 - mean_squared_error: 0.1199 - val_loss: 9.7092 - val_mean_squared_error: 0.1207\n",
            "Epoch 12/20\n",
            "40000/40000 [==============================] - 36s 911us/step - loss: 9.6831 - mean_squared_error: 0.1203 - val_loss: 9.7408 - val_mean_squared_error: 0.1210\n",
            "Epoch 13/20\n",
            "40000/40000 [==============================] - 37s 915us/step - loss: 9.7136 - mean_squared_error: 0.1207 - val_loss: 9.8870 - val_mean_squared_error: 0.1228\n",
            "Epoch 14/20\n",
            "40000/40000 [==============================] - 37s 917us/step - loss: 9.6243 - mean_squared_error: 0.1195 - val_loss: 9.7049 - val_mean_squared_error: 0.1205\n",
            "Epoch 15/20\n",
            "40000/40000 [==============================] - 36s 911us/step - loss: 9.7038 - mean_squared_error: 0.1205 - val_loss: 9.8700 - val_mean_squared_error: 0.1226\n",
            "Epoch 16/20\n",
            "40000/40000 [==============================] - 36s 910us/step - loss: 9.7679 - mean_squared_error: 0.1213 - val_loss: 9.9359 - val_mean_squared_error: 0.1234\n",
            "Epoch 17/20\n",
            "40000/40000 [==============================] - 36s 912us/step - loss: 9.7362 - mean_squared_error: 0.1209 - val_loss: 9.7109 - val_mean_squared_error: 0.1206\n",
            "Epoch 18/20\n",
            "40000/40000 [==============================] - 36s 911us/step - loss: 9.7194 - mean_squared_error: 0.1207 - val_loss: 9.7353 - val_mean_squared_error: 0.1209\n",
            "Epoch 19/20\n",
            "40000/40000 [==============================] - 37s 915us/step - loss: 9.6363 - mean_squared_error: 0.1197 - val_loss: 9.6929 - val_mean_squared_error: 0.1204\n",
            "Epoch 20/20\n",
            "40000/40000 [==============================] - 36s 909us/step - loss: 9.6676 - mean_squared_error: 0.1201 - val_loss: 9.7803 - val_mean_squared_error: 0.1214\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0R2zLWrbIjvd",
        "colab_type": "code",
        "outputId": "427998e4-0892-4584-ce0e-9b64e697a0f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plt.subplot(2, 1, 1)\n",
        "plt.plot(history.history[\"mean_squared_error\"], 'o-')\n",
        "plt.title('Epoch Sayısı')\n",
        "plt.ylabel('MSE')\n",
        "\n",
        "plt.subplot(2, 1, 2)\n",
        "plt.plot(history.history[\"loss\"], '.-')\n",
        "plt.xlabel('Epoch Sayısı')\n",
        "plt.ylabel('Loss')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oh2OMCUCobkg8NETxmB5uSE4ql04ZxBOfbeTyqYMZ2S8t2iEZY0LkgFVbqlrsPQF7cBrc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\nMSYolkiMMcYExRKJMcaYoPx/hoIQyENJd1kAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}