Transfer_Learning.ipynb

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  "metadata": {
    "colab": {
      "name": "Transfer Learning.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyOeaLhPCDsYA5SriMpwQ/aU",
      "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/mvadrev/Tensorflow/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": "rOsjzDXV7pIk",
        "colab_type": "code",
        "outputId": "2e1f737d-c8b4-4a24-fa93-c7e80b67a4fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "!unzip images.zip"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "unzip:  cannot find or open chest.zip, chest.zip.zip or chest.zip.ZIP.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AdMZSgZVDNXR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import os\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import tensorflow_hub as hub\n",
        "from PIL import Image\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fSPZDE2sDU1y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        },
        "outputId": "dde3a2cc-b053-46c9-b7ed-1fdc66414e7a"
      },
      "source": [
        "base_dir = \"images\"\n",
        "train_dir = os.path.join(base_dir, \"train\")\n",
        "validation_dir = os.path.join(base_dir, \"validation\")\n",
        "\n",
        "train_PNEUMONIA = os.path.join(train_dir, \"PNEUMONIA\")\n",
        "val_PNEUMONIA = os.path.join(validation_dir, \"PNEUMONIA\")\n",
        "train_NORMAL = os.path.join(train_dir, \"NORMAL\")\n",
        "val_NORMAL = os.path.join(validation_dir, \"NORMAL\")\n",
        "\n",
        "num_PNEUMONIA_train = len(os.listdir(train_PNEUMONIA))\n",
        "num_NORMAL_train = len(os.listdir(train_NORMAL))\n",
        "num_PNEUMONIA_val = len(os.listdir(val_PNEUMONIA))\n",
        "num_NORMAL_val = len(os.listdir(val_NORMAL))\n",
        "\n",
        "total_train = num_PNEUMONIA_train+num_NORMAL_train\n",
        "total_val = num_PNEUMONIA_val + num_NORMAL_val"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-a6d033488ed5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mval_NORMAL\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"NORMAL\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mnum_PNEUMONIA_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_PNEUMONIA\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[0m\u001b[1;32m     11\u001b[0m \u001b[0mnum_NORMAL_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_NORMAL\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     12\u001b[0m \u001b[0mnum_PNEUMONIA_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_PNEUMONIA\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;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'images/train/PNEUMONIA'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IVBiWJyWU6wA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 32\n",
        "img_shape = 224  # Mobile Net image dimensions"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0S8veNtU9MY",
        "colab_type": "code",
        "outputId": "7f9c6b99-8774-48ca-9d1b-8e4ae477dea8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        }
      },
      "source": [
        "train_image_generator = ImageDataGenerator(rescale=1./255)\n",
        "validation_image_generator = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_image_gen = train_image_generator.flow_from_directory(\n",
        "    batch_size=batch_size, directory=train_dir, shuffle=True, target_size=(img_shape, img_shape), class_mode=\"binary\")\n",
        "\n",
        "val_image_gen = validation_image_generator.flow_from_directory(\n",
        "    batch_size=batch_size, directory=validation_dir, shuffle=False, target_size=(img_shape, img_shape), class_mode=\"binary\")\n",
        "\n",
        "URL = \"https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\"\n",
        "\n",
        "mobile_net = hub.KerasLayer(URL, input_shape=(img_shape, img_shape, 3))\n",
        "mobile_net.trainable = False\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "    mobile_net,\n",
        "    tf.keras.layers.Dense(2, activation=\"softmax\")\n",
        "])\n",
        "\n",
        "model.compile(optimizer=\"adam\",\n",
        "              loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 2000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer (KerasLayer)     (None, 1280)              2257984   \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 2)                 2562      \n",
            "=================================================================\n",
            "Total params: 2,260,546\n",
            "Trainable params: 2,562\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpyDYbxPVE1A",
        "colab_type": "code",
        "outputId": "7d2718ce-4ffa-4e96-b2db-4c090d547707",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        }
      },
      "source": [
        "epochs = 10\n",
        "\n",
        "history = model.fit_generator(\n",
        "    train_image_gen,\n",
        "    steps_per_epoch=int(np.ceil(total_train/float(batch_size))),\n",
        "    epochs=epochs,\n",
        "    validation_data=val_image_gen,\n",
        "    validation_steps=int(np.ceil(total_val/float(batch_size)))\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "62/63 [============================>.] - ETA: 0s - loss: 0.0411 - acc: 0.9878Epoch 1/10\n",
            "63/63 [==============================] - 11s 167ms/step - loss: 0.0406 - acc: 0.9880 - val_loss: 0.0445 - val_acc: 0.9860\n",
            "Epoch 2/10\n",
            "62/63 [============================>.] - ETA: 0s - loss: 0.0318 - acc: 0.9894Epoch 1/10\n",
            "63/63 [==============================] - 9s 151ms/step - loss: 0.0317 - acc: 0.9895 - val_loss: 0.0450 - val_acc: 0.9840\n",
            "Epoch 3/10\n",
            "62/63 [============================>.] - ETA: 0s - loss: 0.0233 - acc: 0.9939Epoch 1/10\n",
            "63/63 [==============================] - 10s 155ms/step - loss: 0.0234 - acc: 0.9940 - val_loss: 0.0430 - val_acc: 0.9860\n",
            "Epoch 4/10\n",
            "62/63 [============================>.] - ETA: 0s - loss: 0.0195 - acc: 0.9964Epoch 1/10\n",
            "63/63 [==============================] - 10s 155ms/step - loss: 0.0195 - acc: 0.9965 - val_loss: 0.0432 - val_acc: 0.9860\n",
            "Epoch 5/10\n",
            "62/63 [============================>.] - ETA: 0s - loss: 0.0165 - acc: 0.9970Epoch 1/10\n",
            "63/63 [==============================] - 9s 148ms/step - loss: 0.0163 - acc: 0.9970 - val_loss: 0.0432 - val_acc: 0.9860\n",
            "Epoch 6/10\n",
            "62/63 [============================>.] - ETA: 0s - loss: 0.0134 - acc: 0.9990Epoch 1/10\n",
            "63/63 [==============================] - 10s 154ms/step - loss: 0.0133 - acc: 0.9990 - val_loss: 0.0444 - val_acc: 0.9850\n",
            "Epoch 7/10\n",
            "62/63 [============================>.] - ETA: 0s - loss: 0.0111 - acc: 0.9990Epoch 1/10\n",
            "63/63 [==============================] - 10s 153ms/step - loss: 0.0110 - acc: 0.9990 - val_loss: 0.0456 - val_acc: 0.9850\n",
            "Epoch 8/10\n",
            "62/63 [============================>.] - ETA: 0s - loss: 0.0096 - acc: 0.9995Epoch 1/10\n",
            "63/63 [==============================] - 10s 152ms/step - loss: 0.0096 - acc: 0.9995 - val_loss: 0.0470 - val_acc: 0.9850\n",
            "Epoch 9/10\n",
            "62/63 [============================>.] - ETA: 0s - loss: 0.0092 - acc: 0.9990Epoch 1/10\n",
            "63/63 [==============================] - 10s 156ms/step - loss: 0.0092 - acc: 0.9990 - val_loss: 0.0451 - val_acc: 0.9840\n",
            "Epoch 10/10\n",
            "62/63 [============================>.] - ETA: 0s - loss: 0.0073 - acc: 0.9995Epoch 1/10\n",
            "63/63 [==============================] - 9s 150ms/step - loss: 0.0073 - acc: 0.9995 - val_loss: 0.0459 - val_acc: 0.9860\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7bOGMzGnVM0D",
        "colab_type": "code",
        "outputId": "e1a67d53-ac3e-4cc2-dbfc-7b064213520d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "IMG_SIZE = 224\n",
        "\n",
        "img = Image.open('./g.jpg')  # image extension *.png,*.jpg\n",
        "new_width = 224\n",
        "new_height = 224\n",
        "img = img.resize((new_width, new_height), Image.ANTIALIAS)\n",
        "# format may what u want ,*.png,*jpg,*.gif\n",
        "img.save('gs.jpg')\n",
        "\n",
        "img = plt.imread(\"./gs.jpg\")\n",
        "imgplot = plt.imshow(img, cmap='gray', vmin=0, vmax=255)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5QbdtZdhXAPsiWqpvWxSDQcRiTEUChhAoQ8/BwQG536KqxCGSxi2tNeAFZy2lKDklckoI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\nQlQQAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPqMzUgSViJJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_AQNf6pGW64o",
        "colab_type": "code",
        "outputId": "e33142f2-c010-49dc-f363-99760cfe2994",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(img.shape)\n",
        "print(type(img))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(224, 224, 3)\n",
            "<class 'numpy.ndarray'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f-WXAxN7XCQ5",
        "colab_type": "code",
        "outputId": "337d0e85-9eb8-4501-c7f6-a21e16435f76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "print(img.shape)\n",
        "img2 = np.expand_dims(img, axis=0)\n",
        "\n",
        "print(img2.shape)\n",
        "predictions = model.predict(img2)\n",
        "print(predictions)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(224, 224, 3)\n",
            "(1, 224, 224, 3)\n",
            "[[0.946846   0.05315401]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AkTk3nM7XxiE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install -q pyyaml h5py"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JAPW6yhZYExK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('mymodel.h5') "
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}