Steel_Sheet_Defect_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "238msiobGC9E"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1433,
     "status": "ok",
     "timestamp": 1524145763688,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "eC3MS439KolG",
    "outputId": "702006bb-b263-4275-9011-e73479b52cf8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['datalab', '.rnd', '.config', 'model_only.h5', '.local', '.ipython', 'Steel', 'model_with_weights.h5', '.nv', '.forever', 'data_cls.zip', '.keras', '.cache']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "print( os.listdir('/content') )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "p_IaBfBTGNPs"
   },
   "outputs": [],
   "source": [
    "from keras import initializers\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.optimizers import adam\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.layers import Dropout\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C35eCdL8GSs_"
   },
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eA-iOu4MGTXU"
   },
   "outputs": [],
   "source": [
    "tf.set_random_seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RKIXUveGGVOD"
   },
   "outputs": [],
   "source": [
    "classifier = Sequential()\n",
    " \n",
    "# Step 1 - Convolution\n",
    "classifier.add(Conv2D(64, (5, 5), input_shape = (200, 200, 3), activation = 'relu', kernel_initializer=initializers.uniform(seed=42)))\n",
    " \n",
    "# Step 2 - Pooling (first Batch normalization)\n",
    "classifier.add(BatchNormalization())\n",
    "classifier.add(MaxPooling2D(pool_size = (4, 4)))\n",
    " \n",
    "# Adding a second convolutional layer\n",
    " \n",
    "classifier.add(Conv2D(64, (4, 4), activation = 'relu', kernel_initializer=initializers.uniform(seed=42)))\n",
    " \n",
    "classifier.add(BatchNormalization())\n",
    " \n",
    "classifier.add(MaxPooling2D(pool_size = (2, 2)))\n",
    " \n",
    "# Adding a third convolutional layer\n",
    " \n",
    "classifier.add(Conv2D(32, (4, 4), activation = 'relu', kernel_initializer=initializers.uniform(seed=42)))\n",
    " \n",
    "classifier.add(BatchNormalization())\n",
    " \n",
    "classifier.add(MaxPooling2D(pool_size = (2, 2)))\n",
    " \n",
    "# Adding a fourth convolutional layer\n",
    " \n",
    "classifier.add(Conv2D(32, (4, 4), activation = 'relu', kernel_initializer=initializers.uniform(seed=42)))\n",
    " \n",
    "classifier.add(BatchNormalization())\n",
    " \n",
    "classifier.add(MaxPooling2D(pool_size = (2, 2)))\n",
    " \n",
    "# Step 3 - Flattening\n",
    "classifier.add(Flatten())\n",
    " \n",
    "# Step 4 - Full connection 1\n",
    " \n",
    "classifier.add(Dense(units = 4096, activation = 'relu'))\n",
    " \n",
    "classifier.add(BatchNormalization())\n",
    " \n",
    "classifier.add(Dropout(rate=0.3, seed=42))\n",
    " \n",
    "# Step 6 - Full connection 2\n",
    " \n",
    "classifier.add(Dense(units = 1024, activation = 'relu'))\n",
    " \n",
    "classifier.add(BatchNormalization())\n",
    " \n",
    "classifier.add(Dense(units = 6, activation = 'softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 773
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1298,
     "status": "ok",
     "timestamp": 1524127925045,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "FGD0rDltGkZV",
    "outputId": "0788b0f2-e5a4-4a9d-f817-f174c738e886"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 196, 196, 64)      4864      \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 196, 196, 64)      256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 49, 49, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 46, 46, 64)        65600     \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 46, 46, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 23, 23, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 20, 20, 32)        32800     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 20, 20, 32)        128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 10, 10, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 7, 7, 32)          16416     \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 7, 7, 32)          128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 3, 3, 32)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 288)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4096)              1183744   \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 4096)              16384     \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1024)              4195328   \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 6)                 6150      \n",
      "=================================================================\n",
      "Total params: 5,526,150\n",
      "Trainable params: 5,515,526\n",
      "Non-trainable params: 10,624\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "classifier.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 72,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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",
       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3089901,
     "status": "ok",
     "timestamp": 1524131128310,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "qx1KJRY1Gn3u",
    "outputId": "57b620b2-68b1-41e4-b648-ece12ddbb2f6"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-6e02887a-e5cd-4d7e-b09c-6e56ae9f67b8\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-6e02887a-e5cd-4d7e-b09c-6e56ae9f67b8\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving data_cls.zip to data_cls.zip\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files\n",
    "uploaded = files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 30828
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 3227,
     "status": "ok",
     "timestamp": 1524131217054,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "sTk722wSTEP7",
    "outputId": "48d8a0a0-a147-4079-b9d3-c0126c06cedc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  data_cls.zip\n",
      "   creating: Steel/data_cls/\n",
      "   creating: Steel/data_cls/test/\n",
      "   creating: Steel/data_cls/test/crazing/\n",
      "  inflating: Steel/data_cls/test/crazing/Cr_100.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_101.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_103.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_105.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_106.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_107.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_108.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_109.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_110.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_111.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_112.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_113.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_114.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_115.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_116.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_117.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_118.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_119.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_120.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_121.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_122.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_123.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_124.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_125.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_126.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_127.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_128.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_129.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_130.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_131.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_132.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_133.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_134.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_135.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_136.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_137.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_138.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_139.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_140.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_25.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_26.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_38.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_41.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_65.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_67.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_78.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_79.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_91.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_92.bmp  \n",
      "  inflating: Steel/data_cls/test/crazing/Cr_99.bmp  \n",
      "   creating: Steel/data_cls/test/inclusion/\n",
      "  inflating: Steel/data_cls/test/inclusion/desktop.ini  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_100.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_103.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_106.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_107.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_109.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_111.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_116.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_118.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_119.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_123.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_124.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_127.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_128.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_129.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_130.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_131.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_132.bmp  \n",
      "  inflating: Steel/data_cls/test/inclusion/In_134.bmp  \n",
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      "  inflating: Steel/data_cls/train/scratches/Sc_300.bmp  \n",
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      "  inflating: Steel/data_cls/train/scratches/Sc_8.bmp  \n",
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      "  inflating: Steel/data_cls/train/scratches/Sc_9.bmp  \n",
      "  inflating: Steel/data_cls/train/scratches/Sc_94.bmp  \n",
      "  inflating: Steel/data_cls/train/scratches/Sc_96.bmp  \n",
      "  inflating: Steel/data_cls/train/scratches/Sc_98.bmp  \n",
      "  inflating: Steel/data_cls/train/scratches/Sc_99.bmp  \n",
      "   creating: Steel/data_cls/validation/\n",
      "  inflating: Steel/data_cls/validation/1.bmp  \n",
      "  inflating: Steel/data_cls/validation/1.png  \n",
      "  inflating: Steel/data_cls/validation/2.bmp  \n",
      "  inflating: Steel/data_cls/validation/3.bmp  \n",
      "  inflating: Steel/data_cls/validation/4.bmp  \n",
      "  inflating: Steel/data_cls/validation/5.bmp  \n",
      "  inflating: Steel/data_cls/validation/6.bmp  \n",
      "  inflating: Steel/data_cls/validation/desktop.ini  \n"
     ]
    }
   ],
   "source": [
    "!mkdir Steel && unzip data_cls.zip -d Steel/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eaqsTJ-XGwfZ"
   },
   "outputs": [],
   "source": [
    "train_data_dir ='Steel/data_cls/train/'\n",
    "validation_data_dir = 'Steel/data_cls/validation'\n",
    "test_data_dir = 'Steel/data_cls/test/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 942,
     "status": "ok",
     "timestamp": 1524131223416,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "vTiC5jwqHI6F",
    "outputId": "3c270b56-cb1d-4ec0-d971-b9dab4fb5fd7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "augmentation configuration for training and testing\n",
      "augmentation configuration completed\n"
     ]
    }
   ],
   "source": [
    "# Augmentation configuration Of images\n",
    "print(\"augmentation configuration for training and testing\")\n",
    "train_datagen = ImageDataGenerator(rescale=1./255,\n",
    "                                   shear_range=0.2,\n",
    "                                   rotation_range=20,\n",
    "                                   width_shift_range=0.2,\n",
    "                                   height_shift_range=0.2,\n",
    "                                   horizontal_flip=True,\n",
    "                                   zoom_range=0.2,\n",
    "                                   vertical_flip=True)\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "print(\"augmentation configuration completed\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1483,
     "status": "ok",
     "timestamp": 1524131487455,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "BRrGE3vhHORg",
    "outputId": "9c7eedc3-3091-4420-8390-6ad92d194102"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1500 images belonging to 6 classes.\n",
      "Found 300 images belonging to 6 classes.\n"
     ]
    }
   ],
   "source": [
    "# Generating Dataset\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        train_data_dir,\n",
    "        target_size=(200,200),\n",
    "        batch_size=64,\n",
    "        class_mode='binary')\n",
    "\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "        test_data_dir,\n",
    "        target_size=(200, 200),\n",
    "        batch_size=64,\n",
    "        class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XzL3FnD7HRaw"
   },
   "outputs": [],
   "source": [
    "optimizer_adam = adam(lr = 0.0005, decay = 0.0001)\n",
    " \n",
    "classifier.compile(optimizer = optimizer_adam, loss = 'sparse_categorical_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hbwBsb_MHU_h"
   },
   "outputs": [],
   "source": [
    "train = True\n",
    "nb_test_samples = 300\n",
    "nb_test_samples = 1500 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 5820246,
     "status": "ok",
     "timestamp": 1524137316129,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "UX1AthZQHXgm",
    "outputId": "1a2422b2-7ff2-460a-9c10-6c69de135661"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting the model\n",
      "Epoch 1/10\n",
      "500/500 [==============================] - 585s 1s/step - loss: 0.3144 - acc: 0.8986 - val_loss: 1.4964 - val_acc: 0.6067\n",
      "Epoch 2/10\n",
      "184/500 [==========>...................] - ETA: 3:52 - loss: 0.1648 - acc: 0.9434500/500 [==============================] - 582s 1s/step - loss: 0.1446 - acc: 0.9499 - val_loss: 9.2146 - val_acc: 0.3400\n",
      "Epoch 3/10\n",
      "346/500 [===================>..........] - ETA: 1:53 - loss: 0.1235 - acc: 0.9571500/500 [==============================] - 582s 1s/step - loss: 0.1226 - acc: 0.9574 - val_loss: 8.7996 - val_acc: 0.2433\n",
      "Epoch 4/10\n",
      "408/500 [=======================>......] - ETA: 1:08 - loss: 0.1012 - acc: 0.9646500/500 [==============================] - 585s 1s/step - loss: 0.1009 - acc: 0.9648 - val_loss: 0.2854 - val_acc: 0.9333\n",
      "Epoch 5/10\n",
      "431/500 [========================>.....] - ETA: 50s - loss: 0.0941 - acc: 0.9690500/500 [==============================] - 581s 1s/step - loss: 0.0912 - acc: 0.9701 - val_loss: 2.3685 - val_acc: 0.6633\n",
      "Epoch 6/10\n",
      "440/500 [=========================>....] - ETA: 44s - loss: 0.0877 - acc: 0.9721500/500 [==============================] - 582s 1s/step - loss: 0.0853 - acc: 0.9727 - val_loss: 5.2169 - val_acc: 0.4600\n",
      "Epoch 7/10\n",
      "443/500 [=========================>....] - ETA: 41s - loss: 0.0627 - acc: 0.9793500/500 [==============================] - 579s 1s/step - loss: 0.0606 - acc: 0.9799 - val_loss: 0.1696 - val_acc: 0.9467\n",
      "Epoch 8/10\n",
      "444/500 [=========================>....] - ETA: 41s - loss: 0.0556 - acc: 0.9807500/500 [==============================] - 583s 1s/step - loss: 0.0553 - acc: 0.9809 - val_loss: 5.1920 - val_acc: 0.5667\n",
      "Epoch 9/10\n",
      "445/500 [=========================>....] - ETA: 40s - loss: 0.0537 - acc: 0.9823500/500 [==============================] - 579s 1s/step - loss: 0.0576 - acc: 0.9816 - val_loss: 1.3013 - val_acc: 0.8300\n",
      "Epoch 10/10\n",
      "443/500 [=========================>....] - ETA: 41s - loss: 0.0514 - acc: 0.9827500/500 [==============================] - 575s 1s/step - loss: 0.0501 - acc: 0.9831 - val_loss: 0.0351 - val_acc: 0.9900\n",
      "Saving the weights\n"
     ]
    }
   ],
   "source": [
    " if(train == True):\n",
    "    #Using the early stopping technique to prevent overfitting\n",
    "    earlyStopping= keras.callbacks.EarlyStopping(monitor='val_loss', patience=50, verbose=1, mode='auto')\n",
    "    print(\"Fitting the model\")\n",
    "    history = classifier.fit_generator(\n",
    "            train_generator,\n",
    "            steps_per_epoch=500,\n",
    "            callbacks=[earlyStopping],\n",
    "            epochs=10,\n",
    "            validation_data=test_generator,\n",
    "            validation_steps=nb_test_samples)\n",
    "\n",
    " #Saving the Model\n",
    "print(\"Saving the weights\")\n",
    "classifier.save('model_only.h5')\n",
    "classifier.save_weights('model_with_weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3Va7c53bqKAz"
   },
   "outputs": [],
   "source": [
    "IMAGE_WIDTH=200\n",
    "IMAGE_HEIGHT=200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 971,
     "status": "ok",
     "timestamp": 1524137380306,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "ICQlLFPGTUxU",
    "outputId": "21d38bda-1674-42b8-f803-0c7193d8e895"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'patches'"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Predicting the result1\n",
    "from keras.preprocessing import image\n",
    "test_image = image.load_img('Steel/data_cls/validation/1.bmp', target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = classifier.predict(test_image)\n",
    "cls = train_generator.class_indices\n",
    "cls = train_generator.class_indices\n",
    "pred_cls = np.argmax(result)\n",
    "cls_name = list(cls.keys())\n",
    "cls_name[pred_cls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 871,
     "status": "ok",
     "timestamp": 1524137401323,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "r_slj6g2qkib",
    "outputId": "f6d98304-6ebf-4332-9546-bf6130b6d007"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'patches'"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Predicting the result2\n",
    "from keras.preprocessing import image\n",
    "test_image = image.load_img('Steel/data_cls/validation/2.bmp', target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = classifier.predict(test_image)\n",
    "cls = train_generator.class_indices\n",
    "cls = train_generator.class_indices\n",
    "pred_cls = np.argmax(result)\n",
    "cls_name = list(cls.keys())\n",
    "cls_name[pred_cls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 903,
     "status": "ok",
     "timestamp": 1524137414673,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "9UR-Ika8qxgE",
    "outputId": "e68918d8-7c42-4836-990f-c74a1edef886"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'patches'"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Predicting the result3\n",
    "from keras.preprocessing import image\n",
    "test_image = image.load_img('Steel/data_cls/validation/3.bmp', target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = classifier.predict(test_image)\n",
    "cls = train_generator.class_indices\n",
    "cls = train_generator.class_indices\n",
    "pred_cls = np.argmax(result)\n",
    "cls_name = list(cls.keys())\n",
    "cls_name[pred_cls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 920,
     "status": "ok",
     "timestamp": 1524137422994,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "JdxfvhkLq0wo",
    "outputId": "ee94f3e6-9ab7-41dd-dd81-873a912a2b5a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'patches'"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Predicting the result3\n",
    "from keras.preprocessing import image\n",
    "test_image = image.load_img('Steel/data_cls/validation/4.bmp', target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = classifier.predict(test_image)\n",
    "cls = train_generator.class_indices\n",
    "cls = train_generator.class_indices\n",
    "pred_cls = np.argmax(result)\n",
    "cls_name = list(cls.keys())\n",
    "cls_name[pred_cls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 941,
     "status": "ok",
     "timestamp": 1524137441900,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "Xi4ANSqGq2ye",
    "outputId": "99583d94-2570-4762-936f-d27b80c05464"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'patches'"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Predicting the result5\n",
    "from keras.preprocessing import image\n",
    "test_image = image.load_img('Steel/data_cls/validation/5.bmp', target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = classifier.predict(test_image)\n",
    "cls = train_generator.class_indices\n",
    "cls = train_generator.class_indices\n",
    "pred_cls = np.argmax(result)\n",
    "cls_name = list(cls.keys())\n",
    "cls_name[pred_cls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 934,
     "status": "ok",
     "timestamp": 1524137459241,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "ORAYiTklq7Zu",
    "outputId": "fa7ce0e0-6bd1-44c8-ba6d-5fdf046baa25"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'patches'"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Predicting the result6\n",
    "from keras.preprocessing import image\n",
    "test_image = image.load_img('Steel/data_cls/validation/6.bmp', target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = classifier.predict(test_image)\n",
    "cls = train_generator.class_indices\n",
    "cls = train_generator.class_indices\n",
    "pred_cls = np.argmax(result)\n",
    "cls_name = list(cls.keys())\n",
    "cls_name[pred_cls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 953,
     "status": "ok",
     "timestamp": 1524139186442,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "NDo6QoKlq_om",
    "outputId": "ec6ff0d6-ee8c-425d-bfe8-2b2102520534"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'patches'"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image = image.load_img('Steel/data_cls/train/crazing/Cr_161.bmp', target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = classifier.predict(test_image)\n",
    "cls = train_generator.class_indices\n",
    "cls = train_generator.class_indices\n",
    "pred_cls = np.argmax(result)\n",
    "cls_name = list(cls.keys())\n",
    "cls_name[pred_cls]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1119,
     "status": "ok",
     "timestamp": 1524139248251,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "YiQko6qhxfNX",
    "outputId": "f41e7515-8fa4-462b-82b5-01adbfd740e7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image = image.load_img('Steel/data_cls/train/crazing/Cr_160.bmp', target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = classifier.predict(test_image)\n",
    "cls = train_generator.class_indices\n",
    "cls = train_generator.class_indices\n",
    "pred_cls = np.argmax(result)\n",
    "cls_name = list(cls.keys())\n",
    "# cls_name[pred_cls]\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1025,
     "status": "ok",
     "timestamp": 1524139299350,
     "user": {
      "displayName": "Nagesh Waghmare",
      "photoUrl": "//lh5.googleusercontent.com/-3U7uS8i4S88/AAAAAAAAAAI/AAAAAAAAUpM/ZjYL_9-uGcs/s50-c-k-no/photo.jpg",
      "userId": "113349633299993715993"
     },
     "user_tz": -330
    },
    "id": "Muos9T2ExoXE",
    "outputId": "44a979d4-9bb8-4f67-8a14-b3d01beca43b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., 0., 0., 0.]], dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image = image.load_img('Steel/data_cls/train/patches/Pa_162.bmp', target_size=(IMAGE_WIDTH, IMAGE_HEIGHT))\n",
    "test_image = image.img_to_array(test_image)\n",
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "result = classifier.predict(test_image)\n",
    "cls = train_generator.class_indices\n",
    "cls = train_generator.class_indices\n",
    "pred_cls = np.argmax(result)\n",
    "cls_name = list(cls.keys())\n",
    "cls_name[pred_cls]\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h2Jr1Usxx4i-"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "Steel_1.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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