Train_the_skin_cancer.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Train the skin cancer.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "toc_visible": true,
      "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/sayedmohamedscu/skin-lesion-classifier-/blob/master/Train_the_skin_cancer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4X-UvhuhD37K",
        "colab_type": "text"
      },
      "source": [
        "# main libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIobDvDgDBjg",
        "colab_type": "code",
        "outputId": "c6787656-2a26-4eab-cb46-4dbe97d4990f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "from keras.applications import VGG16\n",
        "from keras.applications import MobileNet\n",
        "from keras.applications.inception_v3 import InceptionV3\n",
        "import keras\n",
        "from keras import models\n",
        "from keras import layers\n",
        "from keras.layers import Dense,GlobalAveragePooling2D,Flatten,Dropout,BatchNormalization\n",
        "from keras.layers import Conv2D, MaxPooling2D,Input\n",
        "from keras import optimizers\n",
        "from keras.models import Model\n",
        "from keras.models import Sequential"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUUbKZ-xFeSn",
        "colab_type": "code",
        "outputId": "f2c558d6-43a7-48d4-da91-8abced14b975",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        }
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "      rescale=1./255,\n",
        "rotation_range=20,\n",
        "    \n",
        "    horizontal_flip=True)\n",
        "\n",
        "# Note that the validation data should not be augmented!\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "        # This is the target directory\n",
        "        '/content/train',\n",
        "        # All images will be resized to 240*240\n",
        "        target_size=(160, 160),\n",
        "        batch_size=80,\n",
        "        shuffle=True,\n",
        "        class_mode='categorical')\n",
        "train_generator2 = train_datagen.flow_from_directory(\n",
        "        # This is the target directory\n",
        "        '/content/hsv_train',\n",
        "        # All images will be resized to 240*240\n",
        "        target_size=(160, 160),\n",
        "        batch_size=80,\n",
        "        shuffle=True,\n",
        "        class_mode='categorical')\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "    '/content/test',\n",
        "        \n",
        "        target_size=(160, 160),\n",
        "        batch_size=30,\n",
        "        shuffle=True,\n",
        "        class_mode='categorical')\n",
        "validation_generator2 = test_datagen.flow_from_directory(\n",
        "    '/content/hsv_test',\n",
        "        \n",
        "        target_size=(160, 160),\n",
        "        batch_size=30,\n",
        "        shuffle=True,\n",
        "        class_mode='categorical')"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 8022 images belonging to 7 classes.\n",
            "Found 8022 images belonging to 7 classes.\n",
            "Found 1993 images belonging to 7 classes.\n",
            "Found 1993 images belonging to 7 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JYtpEgusD1eR",
        "colab_type": "text"
      },
      "source": [
        "# VGG16 MODEL (transfer learning)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z_2BpNzHD0rq",
        "colab_type": "code",
        "outputId": "20c40fd1-6e33-406e-bf38-39733738f44c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        }
      },
      "source": [
        "base_model = VGG16(weights='imagenet',\n",
        "                  include_top=False,\n",
        "                  input_shape=(160, 160, 3))\n",
        "base_model.summary()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         (None, 160, 160, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 160, 160, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 160, 160, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 80, 80, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 80, 80, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 80, 80, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 40, 40, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 40, 40, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 40, 40, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 40, 40, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 20, 20, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 20, 20, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 5, 5, 512)         0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rtLZjw6xEpNf",
        "colab_type": "code",
        "outputId": "4d452dae-d32d-4e57-b681-1469488f29d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "x=base_model.output\n",
        "from keras.models import Model\n",
        "x = Conv2D(100, kernel_size = (3,3), padding = 'valid')(x)\n",
        "#x=MaxPooling2D(pool_size=(2, 2))(x)\n",
        "#x = keras.layers.GlobalAveragePooling2D()(x)\n",
        "x = Flatten()(x)\n",
        "x=Dropout(0.5)(x)\n",
        "\n",
        "\n",
        "x = Dense(50, activation='relu')(x)\n",
        "x=Dropout(0.2)(x)\n",
        "\n",
        "x = Dense(7 , activation='softmax')(x)\n",
        "model=Model(inputs=base_model.input,outputs=x)\n",
        "model.summary()\n"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         (None, 160, 160, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 160, 160, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 160, 160, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 80, 80, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 80, 80, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 80, 80, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 40, 40, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 40, 40, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 40, 40, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 40, 40, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 20, 20, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 20, 20, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 5, 5, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 3, 3, 100)         460900    \n",
            "_________________________________________________________________\n",
            "flatten_8 (Flatten)          (None, 900)               0         \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 900)               0         \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 50)                45050     \n",
            "_________________________________________________________________\n",
            "dropout_17 (Dropout)         (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 7)                 357       \n",
            "=================================================================\n",
            "Total params: 15,220,995\n",
            "Trainable params: 15,220,995\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzPbN4dEEbYr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.trainable = True\n",
        "\n",
        "set_trainable = False\n",
        "for layer in model.layers:\n",
        "    if layer.name == 'conv2d_5':\n",
        "        set_trainable = True\n",
        "    if set_trainable:\n",
        "        layer.trainable = True\n",
        "    else:\n",
        "        layer.trainable = False"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ojXJCFHRFESd",
        "colab_type": "code",
        "outputId": "d8a9e896-d748-4323-ee98-1ec002b73c57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.summary()\n"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_3 (InputLayer)         (None, 160, 160, 3)       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 160, 160, 64)      1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 160, 160, 64)      36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 80, 80, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 80, 80, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 80, 80, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 40, 40, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 40, 40, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 40, 40, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 40, 40, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 20, 20, 256)       0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 20, 20, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 20, 20, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 10, 10, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 5, 5, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 3, 3, 100)         460900    \n",
            "_________________________________________________________________\n",
            "flatten_8 (Flatten)          (None, 900)               0         \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 900)               0         \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 50)                45050     \n",
            "_________________________________________________________________\n",
            "dropout_17 (Dropout)         (None, 50)                0         \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 7)                 357       \n",
            "=================================================================\n",
            "Total params: 15,220,995\n",
            "Trainable params: 15,220,995\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sg7RU8qEFIUq",
        "colab_type": "code",
        "outputId": "b029fae2-31d0-472a-e7c3-2e48b97bbb45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "source": [
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=optimizers.SGD(lr=0.1),\n",
        "              metrics=['acc'])\n",
        "history = model.fit_generator(\n",
        "      train_generator,\n",
        "      steps_per_epoch=102,\n",
        "      epochs=15,\n",
        "      \n",
        "      validation_data=validation_generator,\n",
        "      validation_steps=70)\n"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n"
          ],
          "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-64-e0998a58e8c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\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---> 10\u001b[0;31m       validation_steps=70)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/legacy/interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[1;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[0;32m---> 91\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\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[0mkwargs\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     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/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, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m   1416\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   1417\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-> 1418\u001b[0;31m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[0;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0mbatch_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0msteps_done\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m                 \u001b[0mgenerator_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput_generator\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    182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenerator_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'__len__'\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/python3.6/dist-packages/keras/utils/data_utils.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    683\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[1;32m    684\u001b[0m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\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--> 685\u001b[0;31m                 \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\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    686\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mqueue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtask_done\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    687\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0minputs\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;32m/usr/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\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--> 638\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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    639\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mready\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[1;32m    640\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    633\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    634\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\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--> 635\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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    636\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    637\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\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/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    550\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 551\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\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    552\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    553\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.6/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m    \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\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;32m--> 295\u001b[0;31m                 \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\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    296\u001b[0m                 \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    297\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;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yrQUaKeKbK_1",
        "colab_type": "text"
      },
      "source": [
        "the lose is very high and the accurcy still not usefull"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aMHQfU3vUe7G",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "outputId": "1fc1b1e5-a008-4076-a81d-01c0df1dfa60"
      },
      "source": [
        "import numpy as np\n",
        "%matplotlib inline\n",
        "from keras.preprocessing import image\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "img_path = \"/content/test/vasc/ISIC_0025197.jpg\"\n",
        "img = image.load_img(img_path, target_size=(160, 160))\n",
        "print(type(img))\n",
        "plt.imshow(img)\n",
        "\n",
        "\n",
        "x = image.img_to_array(img)\n",
        "x=x/255\n",
        "print(type(x))\n",
        "print(x.shape)\n",
        "x = np.expand_dims(x, axis=0)\n",
        "x=x.reshape(1,160,160,3)\n",
        "#k\n",
        "predictions = model.predict(x)     # Vector of probabilities\n",
        "print(predictions)\n",
        "print(np.argmax(predictions))\n",
        "#5 akiec"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'PIL.Image.Image'>\n",
            "<class 'numpy.ndarray'>\n",
            "(160, 160, 3)\n",
            "[[4.2647123e-05 2.7084351e-04 4.5431554e-03 6.6757202e-05 2.0494895e-02\n",
            "  1.0000000e+00 6.1719483e-03]]\n",
            "5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Um2o6UfHbzNZ",
        "colab_type": "text"
      },
      "source": [
        "#MobileNet VGG16 MODEL (transfer learning)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aavr9F9GWnB5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "base_model=MobileNet(weights='imagenet',include_top=False,input_shape=(160, 160, 3)) #imports the mobilenet model and discards the last 1000 neuron layer.\n",
        "\n",
        "x=base_model.output\n",
        "x=GlobalAveragePooling2D()(x)\n",
        "x=Dropout(0.4)(x)\n",
        "\n",
        "x=Dense(300,activation='relu')(x) #we add dense layers so that the model can learn more complex functions and classify for better result\n",
        "x=Dropout(0.4)(x)\n",
        "x=BatchNormalization()(x)\n",
        "x=Dense(100,activation='relu')(x) #dense layer 2\n",
        "x=Dropout(0.4)(x)\n",
        "\n",
        "x=Dense(50,activation='relu')(x) #dense layer 3\n",
        "preds=Dense(7,activation='softmax')(x) #final layer with softmax activation"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRlrj82CW9M1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f9d57fbf-d6d3-44f5-965c-d50e9ca0045e"
      },
      "source": [
        "from keras.models import Model\n",
        "\n",
        "model=Model(inputs=base_model.input,outputs=preds)\n",
        "print(len(model.layers[:]))\n",
        "for layer in model.layers[:85]:\n",
        "    layer.trainable=False\n",
        "for layer in model.layers[85:]:\n",
        "    layer.trainable=True\n",
        "model.summary()"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "96\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_4 (InputLayer)         (None, 160, 160, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)    (None, 161, 161, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv1 (Conv2D)               (None, 80, 80, 32)        864       \n",
            "_________________________________________________________________\n",
            "conv1_bn (BatchNormalization (None, 80, 80, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv1_relu (ReLU)            (None, 80, 80, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_dw_1 (DepthwiseConv2D)  (None, 80, 80, 32)        288       \n",
            "_________________________________________________________________\n",
            "conv_dw_1_bn (BatchNormaliza (None, 80, 80, 32)        128       \n",
            "_________________________________________________________________\n",
            "conv_dw_1_relu (ReLU)        (None, 80, 80, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv_pw_1 (Conv2D)           (None, 80, 80, 64)        2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_1_bn (BatchNormaliza (None, 80, 80, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv_pw_1_relu (ReLU)        (None, 80, 80, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv_pad_2 (ZeroPadding2D)   (None, 81, 81, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv_dw_2 (DepthwiseConv2D)  (None, 40, 40, 64)        576       \n",
            "_________________________________________________________________\n",
            "conv_dw_2_bn (BatchNormaliza (None, 40, 40, 64)        256       \n",
            "_________________________________________________________________\n",
            "conv_dw_2_relu (ReLU)        (None, 40, 40, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv_pw_2 (Conv2D)           (None, 40, 40, 128)       8192      \n",
            "_________________________________________________________________\n",
            "conv_pw_2_bn (BatchNormaliza (None, 40, 40, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_pw_2_relu (ReLU)        (None, 40, 40, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_3 (DepthwiseConv2D)  (None, 40, 40, 128)       1152      \n",
            "_________________________________________________________________\n",
            "conv_dw_3_bn (BatchNormaliza (None, 40, 40, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_dw_3_relu (ReLU)        (None, 40, 40, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_3 (Conv2D)           (None, 40, 40, 128)       16384     \n",
            "_________________________________________________________________\n",
            "conv_pw_3_bn (BatchNormaliza (None, 40, 40, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_pw_3_relu (ReLU)        (None, 40, 40, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_4 (ZeroPadding2D)   (None, 41, 41, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_4 (DepthwiseConv2D)  (None, 20, 20, 128)       1152      \n",
            "_________________________________________________________________\n",
            "conv_dw_4_bn (BatchNormaliza (None, 20, 20, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv_dw_4_relu (ReLU)        (None, 20, 20, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_4 (Conv2D)           (None, 20, 20, 256)       32768     \n",
            "_________________________________________________________________\n",
            "conv_pw_4_bn (BatchNormaliza (None, 20, 20, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_pw_4_relu (ReLU)        (None, 20, 20, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_5 (DepthwiseConv2D)  (None, 20, 20, 256)       2304      \n",
            "_________________________________________________________________\n",
            "conv_dw_5_bn (BatchNormaliza (None, 20, 20, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_dw_5_relu (ReLU)        (None, 20, 20, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_5 (Conv2D)           (None, 20, 20, 256)       65536     \n",
            "_________________________________________________________________\n",
            "conv_pw_5_bn (BatchNormaliza (None, 20, 20, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_pw_5_relu (ReLU)        (None, 20, 20, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_6 (ZeroPadding2D)   (None, 21, 21, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_6 (DepthwiseConv2D)  (None, 10, 10, 256)       2304      \n",
            "_________________________________________________________________\n",
            "conv_dw_6_bn (BatchNormaliza (None, 10, 10, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv_dw_6_relu (ReLU)        (None, 10, 10, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_6 (Conv2D)           (None, 10, 10, 512)       131072    \n",
            "_________________________________________________________________\n",
            "conv_pw_6_bn (BatchNormaliza (None, 10, 10, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_6_relu (ReLU)        (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_7 (DepthwiseConv2D)  (None, 10, 10, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_7_bn (BatchNormaliza (None, 10, 10, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_7_relu (ReLU)        (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_7 (Conv2D)           (None, 10, 10, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_7_bn (BatchNormaliza (None, 10, 10, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_7_relu (ReLU)        (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_8 (DepthwiseConv2D)  (None, 10, 10, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_8_bn (BatchNormaliza (None, 10, 10, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_8_relu (ReLU)        (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_8 (Conv2D)           (None, 10, 10, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_8_bn (BatchNormaliza (None, 10, 10, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_8_relu (ReLU)        (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_9 (DepthwiseConv2D)  (None, 10, 10, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_9_bn (BatchNormaliza (None, 10, 10, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_9_relu (ReLU)        (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_9 (Conv2D)           (None, 10, 10, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_9_bn (BatchNormaliza (None, 10, 10, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_9_relu (ReLU)        (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_10 (DepthwiseConv2D) (None, 10, 10, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_10_bn (BatchNormaliz (None, 10, 10, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_10_relu (ReLU)       (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_10 (Conv2D)          (None, 10, 10, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_10_bn (BatchNormaliz (None, 10, 10, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_10_relu (ReLU)       (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_11 (DepthwiseConv2D) (None, 10, 10, 512)       4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_11_bn (BatchNormaliz (None, 10, 10, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_11_relu (ReLU)       (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pw_11 (Conv2D)          (None, 10, 10, 512)       262144    \n",
            "_________________________________________________________________\n",
            "conv_pw_11_bn (BatchNormaliz (None, 10, 10, 512)       2048      \n",
            "_________________________________________________________________\n",
            "conv_pw_11_relu (ReLU)       (None, 10, 10, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_pad_12 (ZeroPadding2D)  (None, 11, 11, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv_dw_12 (DepthwiseConv2D) (None, 5, 5, 512)         4608      \n",
            "_________________________________________________________________\n",
            "conv_dw_12_bn (BatchNormaliz (None, 5, 5, 512)         2048      \n",
            "_________________________________________________________________\n",
            "conv_dw_12_relu (ReLU)       (None, 5, 5, 512)         0         \n",
            "_________________________________________________________________\n",
            "conv_pw_12 (Conv2D)          (None, 5, 5, 1024)        524288    \n",
            "_________________________________________________________________\n",
            "conv_pw_12_bn (BatchNormaliz (None, 5, 5, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_pw_12_relu (ReLU)       (None, 5, 5, 1024)        0         \n",
            "_________________________________________________________________\n",
            "conv_dw_13 (DepthwiseConv2D) (None, 5, 5, 1024)        9216      \n",
            "_________________________________________________________________\n",
            "conv_dw_13_bn (BatchNormaliz (None, 5, 5, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_dw_13_relu (ReLU)       (None, 5, 5, 1024)        0         \n",
            "_________________________________________________________________\n",
            "conv_pw_13 (Conv2D)          (None, 5, 5, 1024)        1048576   \n",
            "_________________________________________________________________\n",
            "conv_pw_13_bn (BatchNormaliz (None, 5, 5, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "conv_pw_13_relu (ReLU)       (None, 5, 5, 1024)        0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_2 ( (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dropout_18 (Dropout)         (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_21 (Dense)             (None, 300)               307500    \n",
            "_________________________________________________________________\n",
            "dropout_19 (Dropout)         (None, 300)               0         \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 300)               1200      \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 100)               30100     \n",
            "_________________________________________________________________\n",
            "dropout_20 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 50)                5050      \n",
            "_________________________________________________________________\n",
            "dense_24 (Dense)             (None, 7)                 357       \n",
            "=================================================================\n",
            "Total params: 3,573,071\n",
            "Trainable params: 345,655\n",
            "Non-trainable params: 3,227,416\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KIWveUVXXRDM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "outputId": "3473e17d-34eb-4f66-cb30-b27dbe159711"
      },
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "import keras\n",
        "\n",
        "from keras import backend as K\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='sgd',\n",
        "              metrics=['acc'])\n",
        "\n",
        "history = model.fit_generator(\n",
        "      train_generator,\n",
        "      steps_per_epoch=102,\n",
        "      epochs=15,\n",
        "      \n",
        "      validation_data=validation_generator,\n",
        "      validation_steps=70)\n",
        "\n"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "102/102 [==============================] - 126s 1s/step - loss: 1.3360 - acc: 0.5977 - val_loss: 1.1612 - val_acc: 0.6769\n",
            "Epoch 2/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 1.1475 - acc: 0.6630 - val_loss: 1.0536 - val_acc: 0.6759\n",
            "Epoch 3/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 1.0764 - acc: 0.6650 - val_loss: 1.0004 - val_acc: 0.6702\n",
            "Epoch 4/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 1.0247 - acc: 0.6714 - val_loss: 0.9952 - val_acc: 0.6702\n",
            "Epoch 5/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9969 - acc: 0.6731 - val_loss: 0.9610 - val_acc: 0.6793\n",
            "Epoch 6/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9739 - acc: 0.6742 - val_loss: 0.9776 - val_acc: 0.6759\n",
            "Epoch 7/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9656 - acc: 0.6777 - val_loss: 1.0092 - val_acc: 0.6687\n",
            "Epoch 8/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9643 - acc: 0.6709 - val_loss: 0.9759 - val_acc: 0.6774\n",
            "Epoch 9/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9198 - acc: 0.6862 - val_loss: 1.0356 - val_acc: 0.6697\n",
            "Epoch 10/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9398 - acc: 0.6770 - val_loss: 1.0245 - val_acc: 0.6745\n",
            "Epoch 11/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9078 - acc: 0.6905 - val_loss: 1.0581 - val_acc: 0.6591\n",
            "Epoch 12/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9057 - acc: 0.6856 - val_loss: 1.0282 - val_acc: 0.6846\n",
            "Epoch 13/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9052 - acc: 0.6890 - val_loss: 1.0629 - val_acc: 0.6735\n",
            "Epoch 14/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9039 - acc: 0.6854 - val_loss: 1.0573 - val_acc: 0.6711\n",
            "Epoch 15/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.8817 - acc: 0.6927 - val_loss: 1.0806 - val_acc: 0.6750\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccwlmb2FXqg6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 855
        },
        "outputId": "0b149fe6-123e-4a4b-9ca3-22a2e30944ee"
      },
      "source": [
        "import numpy as np\n",
        "%matplotlib inline\n",
        "from keras.preprocessing import image\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "img_path = \"/content/test/df/ISIC_0029039.jpg\"\n",
        "img = image.load_img(img_path, target_size=(160, 160))\n",
        "print(type(img))\n",
        "plt.imshow(img)\n",
        "\n",
        "\n",
        "x = image.img_to_array(img)\n",
        "#x=x/255\n",
        "print(type(x))\n",
        "print(x.shape)\n",
        "#x = np.expand_dims(x, axis=0)\n",
        "x=x.reshape(1,160,160,3)\n",
        "#k\n",
        "predictions = model.predict(x)     # Vector of probabilities\n",
        "print(predictions)\n",
        "print(np.argmax(predictions))\n",
        "#5 akiec"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'PIL.Image.Image'>\n",
            "<class 'numpy.ndarray'>\n",
            "(160, 160, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-105-37dd9487d259>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m160\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m160\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m#k\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m     \u001b[0;31m# Vector of probabilities\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\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/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m   1147\u001b[0m                              'argument.')\n\u001b[1;32m   1148\u001b[0m         \u001b[0;31m# Validate user data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1149\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_standardize_user_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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   1150\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstateful\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1151\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\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/python3.6/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[1;32m    749\u001b[0m             \u001b[0mfeed_input_shapes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    750\u001b[0m             \u001b[0mcheck_batch_axis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0;31m# Don't enforce the batch size.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 751\u001b[0;31m             exception_prefix='input')\n\u001b[0m\u001b[1;32m    752\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    753\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0my\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;32m/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[0;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[1;32m    100\u001b[0m                 \u001b[0;34m'Expected to see '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' array(s), '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m                 \u001b[0;34m'but instead got the following list of '\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m                 str(len(data)) + ' arrays: ' + str(data)[:200] + '...')\n\u001b[0m\u001b[1;32m    103\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m             raise ValueError(\n",
            "\u001b[0;31mValueError\u001b[0m: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 2 array(s), but instead got the following list of 1 arrays: [array([[[[175., 158., 151.],\n         [176., 159., 152.],\n         [176., 157., 151.],\n         ...,\n         [184., 168., 168.],\n         [186., 168., 168.],\n         [185., 167., 167.]],\n\n        [..."
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhx222DEpHHbYYYsdk8Jhhx222P8Hgm99SBBn4e0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7D-ueTwhXRZT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 605
        },
        "outputId": "a96e02a6-e34a-4f97-89eb-c4de64beb75a"
      },
      "source": [
        "model_input = Input(shape=(160, 160,3))\n",
        "#x = BatchNormalization()(model_input)\n",
        "#model_input2 = Input(shape=(160, 160,3))\n",
        "\n",
        "x = Conv2D(32, (3, 3), activation='relu', padding='same')(model_input)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x) \n",
        "    \n",
        "x = Conv2D(32, (3, 3), activation='relu')(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)    \n",
        "x = Dropout(0.2)(x)\n",
        "\n",
        "x = Conv2D(64, (3, 3), activation='relu')(x)  \n",
        "x = MaxPooling2D(pool_size=(2, 2))(x)    \n",
        "x = Dropout(0.2)(x)\n",
        "\n",
        "x = Conv2D(64, (3, 3), activation='relu')(x)\n",
        "x = MaxPooling2D(pool_size=(2, 2))(x) \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "    \n",
        "x = Dropout(0.5)(x)\n",
        "\n",
        "              \n",
        "x = Flatten()(x)\n",
        "x = Dense(20, activation='linear')(x)    \n",
        "x = Dropout(0.2)(x)\n",
        "\n",
        "#y1 = Dense(7, activation='')(x)\n",
        "\n",
        "y5 = Dense(7, activation='softmax')(x)\n",
        "    \n",
        "model2 = Model(input=model_input, output=y5)\n",
        "model2.summary()\n",
        "\n"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_19 (InputLayer)        (None, 160, 160, 3)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_56 (Conv2D)           (None, 160, 160, 32)      896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d_43 (MaxPooling (None, 80, 80, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_57 (Conv2D)           (None, 78, 78, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_44 (MaxPooling (None, 39, 39, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_58 (Conv2D)           (None, 37, 37, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_45 (MaxPooling (None, 18, 18, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_59 (Conv2D)           (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_46 (MaxPooling (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_12 (Flatten)         (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_29 (Dense)             (None, 20)                81940     \n",
            "_________________________________________________________________\n",
            "dense_30 (Dense)             (None, 7)                 147       \n",
            "=================================================================\n",
            "Total params: 147,655\n",
            "Trainable params: 147,655\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:31: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jP4MqoTOXdT6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        },
        "outputId": "aad52815-b27e-486b-8f9b-51900ff9f543"
      },
      "source": [
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "import keras\n",
        "\n",
        "from keras import backend as K\n",
        "model2.compile(loss='categorical_crossentropy',\n",
        "              optimizer='sgd',\n",
        "              metrics=['acc'])\n",
        "\n",
        "history = model2.fit_generator(\n",
        "      train_generator,\n",
        "      steps_per_epoch=102,\n",
        "      epochs=15,\n",
        "      \n",
        "      validation_data=validation_generator,\n",
        "      validation_steps=70)\n",
        "\n"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "102/102 [==============================] - 128s 1s/step - loss: 1.1514 - acc: 0.6652 - val_loss: 1.0909 - val_acc: 0.6721\n",
            "Epoch 2/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 1.1008 - acc: 0.6690 - val_loss: 1.0470 - val_acc: 0.6774\n",
            "Epoch 3/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 1.0564 - acc: 0.6670 - val_loss: 1.0120 - val_acc: 0.6764\n",
            "Epoch 4/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 1.0155 - acc: 0.6700 - val_loss: 1.0162 - val_acc: 0.6620\n",
            "Epoch 5/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9992 - acc: 0.6701 - val_loss: 0.9898 - val_acc: 0.6707\n",
            "Epoch 6/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9762 - acc: 0.6684 - val_loss: 0.9418 - val_acc: 0.6740\n",
            "Epoch 7/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9617 - acc: 0.6710 - val_loss: 0.9019 - val_acc: 0.6860\n",
            "Epoch 8/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9521 - acc: 0.6717 - val_loss: 0.9356 - val_acc: 0.6692\n",
            "Epoch 9/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9204 - acc: 0.6782 - val_loss: 0.9064 - val_acc: 0.6692\n",
            "Epoch 10/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9355 - acc: 0.6707 - val_loss: 0.8834 - val_acc: 0.6841\n",
            "Epoch 11/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9133 - acc: 0.6820 - val_loss: 0.8855 - val_acc: 0.6764\n",
            "Epoch 12/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.8992 - acc: 0.6882 - val_loss: 0.8867 - val_acc: 0.6740\n",
            "Epoch 13/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9024 - acc: 0.6828 - val_loss: 0.8435 - val_acc: 0.7019\n",
            "Epoch 14/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.9051 - acc: 0.6808 - val_loss: 0.8824 - val_acc: 0.6807\n",
            "Epoch 15/15\n",
            "102/102 [==============================] - 113s 1s/step - loss: 0.8839 - acc: 0.6925 - val_loss: 0.8705 - val_acc: 0.6928\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZqLDPp55tj0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        },
        "outputId": "74921796-1aeb-49bc-b68a-1660cef37c9c"
      },
      "source": [
        "import numpy as np\n",
        "%matplotlib inline\n",
        "from keras.preprocessing import image\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "img_path = \"/content/test/df/ISIC_0029039.jpg\"\n",
        "img = image.load_img(img_path, target_size=(160, 160))\n",
        "print(type(img))\n",
        "plt.imshow(img)\n",
        "\n",
        "\n",
        "x = image.img_to_array(img)\n",
        "x=x/255\n",
        "print(type(x))\n",
        "print(x.shape)\n",
        "#x = np.expand_dims(x, axis=0)\n",
        "x=x.reshape(1,160,160,3)\n",
        "#k\n",
        "predictions = model2.predict(x)     # Vector of probabilities\n",
        "print(predictions)\n",
        "print(np.argmax(predictions))\n",
        "#5 akiec"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'PIL.Image.Image'>\n",
            "<class 'numpy.ndarray'>\n",
            "(160, 160, 3)\n",
            "[[0.04724947 0.21487764 0.20293675 0.02537245 0.04120496 0.44573402\n",
            "  0.02262477]]\n",
            "5\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhx222DEpHHbYYYsdk8Jhhx222P8Hgm99SBBn4e0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}