VGG19_Oversampling.ipynb

{
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   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 92,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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    "colab_type": "code",
    "id": "E0oRRLu3SGOp",
    "outputId": "7e948d91-de8c-4842-d994-9e63fc5e252e"
   },
   "outputs": [],
   "source": [
    "! pip install -q kaggle\n",
    "from google.colab import files\n",
    "\n",
    "files.upload()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 63
    },
    "colab_type": "code",
    "id": "wVW8vZfsSbGl",
    "outputId": "e3242b60-35d9-4f81-d096-2c53b145a65e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
     ]
    }
   ],
   "source": [
    "! mkdir ~/.kaggle\n",
    "\n",
    "! cp kaggle.json ~/.kaggle/\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "H-HeV07VSfDP",
    "outputId": "897e8661-cdfe-480a-c22b-25781d1a11f2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
      "Downloading train_images.zip to /content\n",
      "100% 8.00G/8.01G [02:59<00:00, 38.7MB/s]\n",
      "100% 8.01G/8.01G [02:59<00:00, 47.8MB/s]\n",
      "Downloading test_images.zip to /content\n",
      "100% 1.50G/1.50G [00:28<00:00, 38.0MB/s]\n",
      "100% 1.50G/1.50G [00:28<00:00, 56.8MB/s]\n",
      "Downloading train.csv to /content\n",
      "  0% 0.00/53.7k [00:00<?, ?B/s]\n",
      "100% 53.7k/53.7k [00:00<00:00, 41.6MB/s]\n",
      "Downloading test.csv to /content\n",
      "  0% 0.00/24.5k [00:00<?, ?B/s]\n",
      "100% 24.5k/24.5k [00:00<00:00, 22.1MB/s]\n",
      "Downloading sample_submission.csv to /content\n",
      "  0% 0.00/28.3k [00:00<?, ?B/s]\n",
      "100% 28.3k/28.3k [00:00<00:00, 23.3MB/s]\n"
     ]
    }
   ],
   "source": [
    "! kaggle competitions download -c 'aptos2019-blindness-detection'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 46
    },
    "colab_type": "code",
    "id": "eoZ7bth5Ssb4",
    "outputId": "a464a7f9-e5f7-4bb3-e19f-75aa5ac82e0e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘train1’: File exists\n"
     ]
    }
   ],
   "source": [
    "! mkdir train1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "k06Sebn0Sxh9"
   },
   "outputs": [],
   "source": [
    "! unzip train_images.zip -d train1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "elrX7VAMS2PH",
    "outputId": "6ccb42da-6b12-42e0-ad97-f869b196b4ae"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.14.0\n"
     ]
    }
   ],
   "source": [
    "from numpy.random import seed\n",
    "seed(2)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(2)\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from IPython import display\n",
    "from matplotlib import cm\n",
    "from matplotlib import gridspec\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tensorflow.python.data import Dataset\n",
    "from sklearn import preprocessing\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "print(tf.__version__)\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Activation\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D,AveragePooling2D\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3qGqugtAXcb_"
   },
   "outputs": [],
   "source": [
    "import copy\n",
    "import random\n",
    "import cv2\n",
    "from keras.applications import VGG16, VGG19, InceptionV3, Xception\n",
    "from keras.datasets import cifar10\n",
    "from keras.layers import Dense, Dropout, GlobalAveragePooling2D\n",
    "from keras.models import Model\n",
    "from keras.optimizers import SGD\n",
    "from keras.utils import to_categorical\n",
    "import numpy as np\n",
    "from keras.layers.convolutional import ZeroPadding2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oj-m0dvwc59y"
   },
   "outputs": [],
   "source": [
    "from keras.callbacks import History\n",
    "from keras.callbacks import ModelCheckpoint, TensorBoard\n",
    "from keras.datasets import cifar10\n",
    "from keras.engine import training\n",
    "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, Dropout, Activation, Average\n",
    "from keras.losses import categorical_crossentropy\n",
    "from keras.models import Model, Input\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import to_categorical\n",
    "from tensorflow.python.framework.ops import Tensor\n",
    "from typing import Tuple, List\n",
    "import glob\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "VmAnDlg2S_yD",
    "outputId": "277fb6f7-21f0-42d9-8c81-a6487abcfe02"
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_code</th>\n",
       "      <th>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000c1434d8d7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>001639a390f0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0024cdab0c1e</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>002c21358ce6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>005b95c28852</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id_code  diagnosis\n",
       "0  000c1434d8d7          2\n",
       "1  001639a390f0          4\n",
       "2  0024cdab0c1e          1\n",
       "3  002c21358ce6          0\n",
       "4  005b95c28852          0"
      ]
     },
     "execution_count": 16,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "blnd_img=list()\n",
    "for file in os.listdir(\"/content/train1\"):\n",
    "    #if file.endswith(\".jpg\"):\n",
    "      #print(1)\n",
    "\n",
    "        #print(os.path.join('/content/jpg', file))\n",
    "        img=blnd_img.append(os.path.join(\"/content/train1\",file))\n",
    "        #print(blnd_img)\n",
    "blnd_df = pd.DataFrame(blnd_img)\n",
    "blnd_df['colFromIndex'] = blnd_df.index\n",
    "blnd_df = blnd_df.sort_values([0, 'colFromIndex'])\n",
    "blnd_df=blnd_df.reset_index()\n",
    "len(blnd_df)\n",
    "label_dataframe = pd.read_csv('/content/train.csv')\n",
    "label_dataframe.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "2sEgHQVXTKTl",
    "outputId": "a15531ba-7fbb-46fd-c5e2-55913d28a571"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "#from keras.preprocessing.image import img_to_array\n",
    "i=0\n",
    "import cv2\n",
    "import numpy as np\n",
    "while i<len(blnd_df[0]):\n",
    "  a=cv2.imread(blnd_df[0][i])\n",
    "  a=cv2.resize(a,(256,256))\n",
    "  #a=img_to_array(a)\n",
    "  #a=np.expand_dims(a,axis=0) \n",
    "  #a=a[np.newaxis,:,:]\n",
    "  #a = a.reshape((1,) + a.shape)\n",
    "  blnd_df[0][i]=a\n",
    "  print(i)\n",
    "  i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "4nTBDM-VTTRg",
    "outputId": "8e72fa42-d8c3-4d48-f4ba-d9d3c603d6ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   index                                                  0  ...             1  2\n",
      "0      9  [[[2, 1, 3], [2, 1, 3], [2, 1, 3], [1, 1, 1], ...  ...  000c1434d8d7  2\n",
      "1   2507  [[[2, 1, 3], [1, 0, 2], [1, 0, 2], [2, 1, 3], ...  ...  001639a390f0  4\n",
      "2    552  [[[2, 1, 3], [2, 1, 3], [2, 1, 3], [2, 1, 3], ...  ...  0024cdab0c1e  1\n",
      "3   1782  [[[0, 1, 1], [0, 1, 1], [0, 1, 1], [0, 1, 1], ...  ...  002c21358ce6  0\n",
      "4   2703  [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...  ...  005b95c28852  0\n",
      "\n",
      "[5 rows x 5 columns]\n",
      "      index                                                  0  ...             1  2\n",
      "3657    821  [[[0, 0, 0], [1, 1, 1], [1, 1, 1], [1, 1, 1], ...  ...  ffa47f6a7bf4  2\n",
      "3658   3058  [[[0, 0, 0], [0, 0, 0], [0, 0, 0], [0, 0, 0], ...  ...  ffc04fed30e6  0\n",
      "3659   3485  [[[2, 1, 3], [2, 1, 3], [2, 1, 3], [2, 1, 3], ...  ...  ffcf7b45f213  2\n",
      "3660   1752  [[[0, 1, 2], [0, 1, 2], [2, 4, 5], [1, 3, 4], ...  ...  ffd97f8cd5aa  0\n",
      "3661   1229  [[[6, 4, 4], [6, 3, 5], [6, 4, 4], [6, 4, 4], ...  ...  ffec9a18a3ce  2\n",
      "\n",
      "[5 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "blnd_df[1]=label_dataframe['id_code']\n",
    "blnd_df[2]=label_dataframe['diagnosis']\n",
    "print(blnd_df.head(5))\n",
    "print(blnd_df.tail(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "euRvjOndTjKf"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split #  train,test = train_test_split(dataset,test_size=0.20,stratify=True)\n",
    "train_img_blnd, test_img_blnd, train_label_blnd, test_label_blnd = train_test_split(blnd_df[0], blnd_df[2], test_size=0.2,stratify=blnd_df[2],random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6EIfdp3ZHd8O"
   },
   "outputs": [],
   "source": [
    "np.save(\"/content/label_test\",test_label_blnd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Esq1f1muhp4t"
   },
   "outputs": [],
   "source": [
    "train=np.save('/content/train', np.stack(train_img_blnd,axis=0))\n",
    "test=np.save('/content/test', np.stack(test_img_blnd,axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "yRrQBoTWTpOX",
    "outputId": "20e52117-a5e8-4a07-8cf7-0b135658f0f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(733, 256, 256, 3)"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_img_blnd=np.load('/content/train.npy')\n",
    "test_img_blnd = np.load('/content/test.npy')\n",
    "test_img_blnd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "G2kd94mfTtXQ"
   },
   "outputs": [],
   "source": [
    "train_img_blnd=train_img_blnd.astype('float32')/255\n",
    "test_img_blnd=test_img_blnd.astype('float32')/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "9v9iqS-mTwQm",
    "outputId": "2d3c15cd-9570-4164-ac49-20024ce3e049"
   },
   "outputs": [
    {
     "data": {
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       "      0  1  2  3  4\n",
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     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_label_blnd=train_label_blnd.astype('category')\n",
    "dummies_train= pd.get_dummies(train_label_blnd)\n",
    "dummies_train.head()\n",
    "test_label_blnd=test_label_blnd.astype('category')\n",
    "dummies_test= pd.get_dummies(test_label_blnd)\n",
    "dummies_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JWcPZK5OMPua"
   },
   "source": [
    "#vgg19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "Pba3f9F0Yubk",
    "outputId": "437c3cf8-3bdb-474c-b83d-a062bfabdbc4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "2929/2929 [==============================] - 21s 7ms/step - loss: 1.1124\n"
     ]
    }
   ],
   "source": [
    "    vgg19_model = VGG19(weights='imagenet', include_top=False,input_shape=(256, 256, 3))\n",
    "    x = vgg19_model.output\n",
    "    x = GlobalAveragePooling2D()(x)\n",
    "    x = Dense(128, activation='relu')(x)\n",
    "    x = Dropout(0.2)(x)\n",
    "\n",
    "    predictions = Dense(5, activation='softmax')(x)\n",
    "\n",
    "    model = Model(inputs=vgg19_model.input, outputs=predictions)\n",
    "    for layer in vgg19_model.layers:\n",
    "       layer.trainable = False\n",
    "\n",
    "    model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
    "    model.fit(train_img_blnd, dummies_train)\n",
    "\n",
    "    for i, layer in enumerate(model.layers):\n",
    "\n",
    "        if i < 14:\n",
    "            layer.trainable = False\n",
    "        else:\n",
    "            layer.trainable = True\n",
    "\n",
    "    model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JwpGno__NQhG"
   },
   "outputs": [],
   "source": [
    "model.save(\"vgg19_82%.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "colab_type": "code",
    "id": "vPhwVadTIjg8",
    "outputId": "559a2449-c55c-4962-f050-c79d91eba7b6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).\n",
      "  \"(https://pypi.org/project/six/).\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before OverSampling, counts of label '0': 1444\n",
      "Before OverSampling, counts of label '1': 296 \n",
      "\n",
      "Before OverSampling, counts of label '2': 799\n",
      "Before OverSampling, counts of label '3': 154 \n",
      "\n",
      "Before OverSampling, counts of label '4': 236 \n",
      "\n",
      "After OverSampling, the shape of train_X: (4219, 196608)\n",
      "After OverSampling, the shape of train_y: (4219,) \n",
      "\n",
      "After OverSampling, counts of label '0': 1444\n",
      "After OverSampling, counts of label '1': 296\n",
      "After OverSampling, counts of label '2': 799\n",
      "After OverSampling, counts of label '3': 1444\n",
      "After OverSampling, counts of label '4': 236\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "print(\"Before OverSampling, counts of label '0': {}\".format(sum(train_label_blnd.values==0)))\n",
    "print(\"Before OverSampling, counts of label '1': {} \\n\".format(sum(train_label_blnd.values==1)))\n",
    "print(\"Before OverSampling, counts of label '2': {}\".format(sum(train_label_blnd.values==2)))\n",
    "print(\"Before OverSampling, counts of label '3': {} \\n\".format(sum(train_label_blnd.values==3)))\n",
    "print(\"Before OverSampling, counts of label '4': {} \\n\".format(sum(train_label_blnd.values==4)))\n",
    "\n",
    "sm = SMOTE(\"minority\")\n",
    "X_res, y_res = sm.fit_sample(train_img_blnd.reshape(train_img_blnd.shape[0], -1), train_label_blnd.ravel())\n",
    "#train_img_blnd = np.reshape(train_img_blnd, (-1, 2))\n",
    "print('After OverSampling, the shape of train_X: {}'.format(X_res.shape))\n",
    "print('After OverSampling, the shape of train_y: {} \\n'.format( y_res.shape))\n",
    "\n",
    "print(\"After OverSampling, counts of label '0': {}\".format(sum( y_res==0)))\n",
    "print(\"After OverSampling, counts of label '1': {}\".format(sum( y_res==1)))\n",
    "print(\"After OverSampling, counts of label '2': {}\".format(sum( y_res==2)))\n",
    "print(\"After OverSampling, counts of label '3': {}\".format(sum( y_res==3)))\n",
    "print(\"After OverSampling, counts of label '4': {}\".format(sum( y_res==4)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZglGSt-II4gl"
   },
   "outputs": [],
   "source": [
    "X_res = X_res.reshape(X_res.shape[0], 256, 256, 3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "u158PZ2nN49k"
   },
   "outputs": [],
   "source": [
    " model.compile(optimizer=SGD(lr=0.001, momentum=0.9), loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "XJvXUnSANRBc",
    "outputId": "6f201bb1-ef7f-4657-b1e9-97ee57fe84e9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 4219 samples, validate on 733 samples\n",
      "Epoch 1/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3844 - acc: 0.8613 - val_loss: 0.6279 - val_acc: 0.7790\n",
      "Epoch 2/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3937 - acc: 0.8561 - val_loss: 0.6657 - val_acc: 0.7885\n",
      "Epoch 3/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3653 - acc: 0.8675 - val_loss: 0.6169 - val_acc: 0.7913\n",
      "Epoch 4/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3573 - acc: 0.8642 - val_loss: 0.6134 - val_acc: 0.7954\n",
      "Epoch 5/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3434 - acc: 0.8744 - val_loss: 0.6088 - val_acc: 0.7981\n",
      "Epoch 6/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3437 - acc: 0.8730 - val_loss: 0.6188 - val_acc: 0.7954\n",
      "Epoch 7/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3282 - acc: 0.8760 - val_loss: 0.7148 - val_acc: 0.7653\n",
      "Epoch 8/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3168 - acc: 0.8855 - val_loss: 0.6757 - val_acc: 0.7735\n",
      "Epoch 9/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3331 - acc: 0.8732 - val_loss: 0.7895 - val_acc: 0.7749\n",
      "Epoch 10/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3255 - acc: 0.8801 - val_loss: 0.7995 - val_acc: 0.7572\n",
      "Epoch 11/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3045 - acc: 0.8862 - val_loss: 0.6301 - val_acc: 0.8008\n",
      "Epoch 12/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2951 - acc: 0.8874 - val_loss: 0.6818 - val_acc: 0.7899\n",
      "Epoch 13/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2884 - acc: 0.8929 - val_loss: 0.6786 - val_acc: 0.7844\n",
      "Epoch 14/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2892 - acc: 0.8884 - val_loss: 0.6817 - val_acc: 0.8035\n",
      "Epoch 15/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.3031 - acc: 0.8893 - val_loss: 0.6513 - val_acc: 0.7913\n",
      "Epoch 16/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2887 - acc: 0.8879 - val_loss: 0.7878 - val_acc: 0.7735\n",
      "Epoch 17/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2754 - acc: 0.8976 - val_loss: 0.7257 - val_acc: 0.7954\n",
      "Epoch 18/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2587 - acc: 0.9016 - val_loss: 0.6949 - val_acc: 0.7735\n",
      "Epoch 19/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2567 - acc: 0.9054 - val_loss: 0.6661 - val_acc: 0.7899\n",
      "Epoch 20/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2575 - acc: 0.9035 - val_loss: 0.7547 - val_acc: 0.7572\n",
      "Epoch 21/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2367 - acc: 0.9142 - val_loss: 0.6287 - val_acc: 0.8172\n",
      "Epoch 22/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2332 - acc: 0.9144 - val_loss: 0.7427 - val_acc: 0.7981\n",
      "Epoch 23/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2700 - acc: 0.8971 - val_loss: 0.7462 - val_acc: 0.7735\n",
      "Epoch 24/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2488 - acc: 0.9099 - val_loss: 0.7517 - val_acc: 0.8022\n",
      "Epoch 25/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2372 - acc: 0.9142 - val_loss: 0.6882 - val_acc: 0.7954\n",
      "Epoch 26/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2152 - acc: 0.9204 - val_loss: 0.6991 - val_acc: 0.7954\n",
      "Epoch 27/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2328 - acc: 0.9151 - val_loss: 0.6919 - val_acc: 0.7967\n",
      "Epoch 28/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2149 - acc: 0.9154 - val_loss: 0.8061 - val_acc: 0.7804\n",
      "Epoch 29/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2135 - acc: 0.9218 - val_loss: 0.7702 - val_acc: 0.8117\n",
      "Epoch 30/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1981 - acc: 0.9284 - val_loss: 0.7764 - val_acc: 0.7926\n",
      "Epoch 31/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1957 - acc: 0.9277 - val_loss: 0.7046 - val_acc: 0.8049\n",
      "Epoch 32/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.2035 - acc: 0.9206 - val_loss: 0.7691 - val_acc: 0.7858\n",
      "Epoch 33/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1991 - acc: 0.9282 - val_loss: 0.7315 - val_acc: 0.8063\n",
      "Epoch 34/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1872 - acc: 0.9303 - val_loss: 0.7382 - val_acc: 0.7817\n",
      "Epoch 35/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1936 - acc: 0.9287 - val_loss: 0.7482 - val_acc: 0.7954\n",
      "Epoch 36/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1851 - acc: 0.9336 - val_loss: 0.7958 - val_acc: 0.7995\n",
      "Epoch 37/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1725 - acc: 0.9403 - val_loss: 0.7325 - val_acc: 0.7995\n",
      "Epoch 38/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1756 - acc: 0.9348 - val_loss: 0.7417 - val_acc: 0.8104\n",
      "Epoch 39/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1732 - acc: 0.9393 - val_loss: 0.7399 - val_acc: 0.8008\n",
      "Epoch 40/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1614 - acc: 0.9436 - val_loss: 0.8206 - val_acc: 0.7763\n",
      "Epoch 41/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1746 - acc: 0.9374 - val_loss: 0.8145 - val_acc: 0.7926\n",
      "Epoch 42/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1501 - acc: 0.9464 - val_loss: 0.8135 - val_acc: 0.8117\n",
      "Epoch 43/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1437 - acc: 0.9509 - val_loss: 0.7129 - val_acc: 0.8104\n",
      "Epoch 44/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1622 - acc: 0.9441 - val_loss: 1.1105 - val_acc: 0.7599\n",
      "Epoch 45/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1620 - acc: 0.9405 - val_loss: 0.8034 - val_acc: 0.8131\n",
      "Epoch 46/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1432 - acc: 0.9467 - val_loss: 0.7480 - val_acc: 0.7831\n",
      "Epoch 47/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1607 - acc: 0.9443 - val_loss: 0.9418 - val_acc: 0.7967\n",
      "Epoch 48/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1359 - acc: 0.9495 - val_loss: 0.8775 - val_acc: 0.7899\n",
      "Epoch 49/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1379 - acc: 0.9528 - val_loss: 0.7431 - val_acc: 0.8145\n",
      "Epoch 50/50\n",
      "4219/4219 [==============================] - 33s 8ms/step - loss: 0.1448 - acc: 0.9490 - val_loss: 1.0321 - val_acc: 0.6944\n"
     ]
    }
   ],
   "source": [
    "history_sm1 = model.fit(X_res, y_res, batch_size=32, validation_data = (test_img_blnd, test_label_blnd), epochs=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 545
    },
    "colab_type": "code",
    "id": "E4PhhkzzWg-Y",
    "outputId": "ab4f154b-10b1-48ef-b5f9-06ce1bee3b1b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1jx3rA6+c2CQcpb8eODFkvVpg21FUdYOq9lDV5sDwwLad4RwbKDtOVVupaqsq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\n7c8C/ULKvQz0TOUcg4C5wNz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LE//HvXsnToISCerXt+/ff0/cFpwXN7eJonun4Cj92rXt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\nIbxHtgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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D06bQqZNNzBwJVC1a69AhizUP5i+Pi7M+oPfeOzYrUU759lvz5+fGag9y0UXW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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = history_sm1.history['acc']\n",
    "val_acc = history_sm1.history['val_acc']\n",
    "loss = history_sm1.history['loss']\n",
    "val_loss = history_sm1.history['val_loss']\n",
    " \n",
    "epochs = range(len(acc))\n",
    " \n",
    "plt.plot(epochs, acc, 'b', label='Training acc')\n",
    "plt.plot(epochs, val_acc, 'r', label='Validation acc')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    " \n",
    "plt.figure()\n",
    " \n",
    "plt.plot(epochs, loss, 'b', label='Training loss')\n",
    "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EIcGftad8Gi8"
   },
   "outputs": [],
   "source": [
    "model.save(\"smote82%.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Oqh7DiC-vPjx"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "import numpy as np\n",
    "\n",
    "y_proba = model.predict(test_img_blnd)\n",
    "y_classes = y_proba.argmax(axis=-1)\n",
    "#y_classes = keras.np_utils.probas_to_classes(y_proba)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 595
    },
    "colab_type": "code",
    "id": "3kaNcJEsyBdA",
    "outputId": "5b73b393-5f00-4263-e82f-888a9187e7ce"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 1, 3, 4, 0, 0, 0, 4, 2, 1, 0, 4, 4, 0, 0, 0, 0, 4, 2, 0, 2, 0,\n",
       "       0, 0, 0, 0, 3, 2, 0, 0, 0, 0, 2, 0, 3, 0, 2, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 2, 2, 2, 3, 2, 0, 0, 2, 0, 1, 0, 0, 4, 2, 0, 0, 3, 4, 0,\n",
       "       0, 2, 0, 2, 0, 2, 3, 1, 4, 2, 4, 3, 3, 3, 0, 4, 0, 3, 4, 4, 0, 2,\n",
       "       4, 0, 3, 0, 0, 0, 0, 0, 0, 4, 3, 3, 0, 0, 0, 1, 0, 0, 0, 2, 4, 0,\n",
       "       2, 2, 0, 0, 3, 2, 3, 0, 2, 4, 0, 0, 1, 3, 0, 0, 0, 2, 1, 0, 0, 0,\n",
       "       0, 3, 1, 3, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 0, 2, 4, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 4, 0, 0, 4, 0, 0, 3, 0, 4, 0, 1, 0, 4, 4, 0, 4, 0, 0,\n",
       "       0, 3, 0, 0, 3, 0, 2, 3, 3, 1, 0, 2, 1, 4, 0, 3, 0, 2, 4, 3, 0, 3,\n",
       "       0, 4, 2, 2, 2, 4, 4, 3, 4, 0, 2, 1, 3, 3, 0, 0, 0, 0, 2, 2, 0, 4,\n",
       "       0, 3, 0, 0, 4, 2, 2, 0, 1, 2, 0, 2, 2, 0, 3, 0, 4, 3, 3, 4, 0, 0,\n",
       "       0, 2, 0, 0, 0, 3, 0, 0, 3, 4, 0, 0, 3, 4, 0, 4, 2, 0, 4, 0, 2, 4,\n",
       "       0, 0, 0, 3, 0, 4, 0, 4, 4, 3, 3, 0, 0, 2, 2, 2, 2, 2, 3, 0, 0, 4,\n",
       "       2, 2, 2, 0, 2, 0, 0, 0, 4, 1, 0, 3, 4, 1, 0, 0, 3, 2, 4, 4, 0, 0,\n",
       "       3, 4, 2, 0, 0, 0, 3, 0, 0, 3, 4, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 4,\n",
       "       0, 4, 3, 2, 0, 0, 0, 2, 4, 0, 0, 0, 2, 0, 0, 2, 4, 2, 2, 4, 3, 2,\n",
       "       4, 0, 0, 3, 0, 2, 3, 2, 4, 0, 0, 0, 0, 0, 2, 2, 3, 0, 0, 1, 4, 0,\n",
       "       4, 2, 4, 0, 4, 1, 4, 3, 3, 0, 0, 2, 0, 4, 0, 0, 0, 2, 0, 3, 2, 2,\n",
       "       0, 0, 0, 0, 0, 0, 2, 4, 0, 0, 0, 0, 0, 3, 1, 3, 0, 2, 3, 0, 2, 0,\n",
       "       2, 2, 0, 2, 0, 0, 0, 4, 0, 0, 0, 0, 2, 0, 4, 4, 0, 0, 1, 0, 3, 0,\n",
       "       2, 0, 0, 0, 4, 3, 1, 2, 0, 2, 2, 0, 2, 3, 2, 0, 0, 3, 3, 0, 0, 0,\n",
       "       0, 0, 0, 4, 4, 3, 3, 2, 2, 4, 0, 0, 2, 0, 0, 2, 0, 1, 0, 0, 3, 0,\n",
       "       2, 2, 2, 2, 2, 0, 3, 1, 0, 4, 0, 0, 3, 0, 2, 1, 0, 3, 2, 3, 2, 0,\n",
       "       2, 1, 1, 0, 0, 3, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 3, 0, 1, 4, 4,\n",
       "       3, 0, 4, 3, 4, 1, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 2, 0, 1, 2, 0, 4,\n",
       "       2, 4, 3, 2, 0, 0, 0, 0, 4, 1, 2, 4, 4, 0, 2, 2, 2, 3, 0, 3, 0, 0,\n",
       "       0, 2, 0, 1, 0, 2, 2, 0, 3, 3, 3, 2, 2, 0, 0, 2, 2, 3, 2, 0, 0, 0,\n",
       "       0, 0, 0, 2, 0, 3, 4, 0, 0, 1, 0, 0, 0, 3, 0, 0, 2, 1, 0, 2, 0, 0,\n",
       "       0, 0, 3, 2, 2, 0, 2, 2, 0, 2, 2, 2, 3, 3, 3, 0, 4, 0, 4, 3, 0, 3,\n",
       "       3, 1, 2, 4, 2, 0, 3, 3, 0, 2, 3, 2, 3, 0, 2, 4, 0, 3, 3, 0, 0, 0,\n",
       "       1, 2, 2, 2, 0, 0, 0, 0, 3, 0, 3, 3, 3, 2, 0, 0, 0, 0, 1, 0, 3, 0,\n",
       "       3, 4, 3, 0, 0, 0, 2, 0, 1, 0, 0, 0, 0, 3, 0, 3, 2, 0, 2, 0, 4, 0,\n",
       "       4, 2, 3, 4, 0, 2, 0, 0, 2, 4, 2, 4, 0, 2, 2, 4, 0, 0, 0, 0, 2, 0,\n",
       "       4, 0, 3, 0, 2, 2, 1])"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "cBs4nwDgvrmc",
    "outputId": "df5c51d4-2c2d-4328-af56-c52f327bb5a7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[340,   4,  12,   0,   5],\n",
       "       [  3,  27,  26,   6,  12],\n",
       "       [  3,   6,  93,  58,  40],\n",
       "       [  0,   0,   6,  23,  10],\n",
       "       [  2,   2,   9,  20,  26]])"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mlxtend.evaluate import confusion_matrix\n",
    "\n",
    "cm = confusion_matrix(y_target=test_label_blnd, \n",
    "                      y_predicted=y_classes, \n",
    "                      binary=False)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "y0N0RQ4FXnLp",
    "outputId": "256172eb-dee9-4047-f5f6-2331591c40be"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ahw4eoGeHu2kZU5eHRwwmPT3dq3ncKYYM13+PikgXEWkEVMjvQSJSXURWiMhuEdklIo/d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\nLmJcP5cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.evaluate import confusion_matrix\n",
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "\n",
    "fig, ax = plot_confusion_matrix(conf_mat=cm)\n",
    "plt.show()\n",
    "plot_confusion_matrix(conf_mat=cm, normalize=True,\n",
    "                      title='Normalized confusion matrix')\n"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Copy of Oversampling_Deeplearning_Project.ipynb",
   "provenance": [],
   "toc_visible": true,
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}