AV-Intel-Scene-Classification-Challenge/scene-classification-cnn-data-aug-inception-resnet.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"scene-classification-cnn-data-aug-inception-resnet.ipynb","version":"0.3.2","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"metadata":{"id":"ZgiJmF-3pe3s","colab_type":"text"},"cell_type":"markdown","source":["{'buildings' -> 0, \n","\n","'forest' -> 1,\n","\n","'glacier' -> 2,\n","\n","'mountain' -> 3,\n","\n","'sea' -> 4,\n","\n","'street' -> 5 }"]},{"metadata":{"id":"n3RYV4j1pM_s","colab_type":"text"},"cell_type":"markdown","source":[""]},{"metadata":{"id":"Z0Clg3m3nxeM","colab_type":"code","colab":{}},"cell_type":"code","source":["labels = ['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street']"],"execution_count":0,"outputs":[]},{"metadata":{"id":"XPLyRQHKmdUq","colab_type":"code","colab":{}},"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"f2b1b45d7222c7109884df0029385d81718f7209","id":"CNui6my2ipD2","colab_type":"code","colab":{}},"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import json\n","import os\n","from tqdm import tqdm, tqdm_notebook\n","\n","import tensorflow as tf\n","from tensorflow.keras.models import Sequential, Model\n","from tensorflow.keras.layers import *\n","from tensorflow.keras.optimizers import *\n","from tensorflow.keras.applications import *\n","from tensorflow.keras.callbacks import *\n","from tensorflow.keras.initializers import *\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator"],"execution_count":0,"outputs":[]},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","id":"mK84XzEvipEJ","colab_type":"code","outputId":"ed7111ab-9d5c-4faf-de22-963b443cebe4","executionInfo":{"status":"ok","timestamp":1553878858839,"user_tz":-330,"elapsed":2040,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":54}},"cell_type":"code","source":["home_dir = '/content/gdrive/My Drive/data-science-my-projects/AV-Intel-Scene-Classification-Challenge'\n","print(os.listdir(home_dir))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["['dataset', 'submissions', 'scene-classification-cnn-data-aug-vgg16.ipynb', 'models', 'scene-classification-cnn-vgg16-hybrid1365.ipynb', 'scene-classification-cnn-vgg16-places365.ipynb', 'scene-classification-cnn-data-aug-resnet50.ipynb', 'scene-classification-cnn-data-aug-inception-resnet.ipynb']\n"],"name":"stdout"}]},{"metadata":{"_uuid":"4d3374968aa7f3881f1b83408f272367f0e63619","id":"cwFAsswOipEY","colab_type":"text"},"cell_type":"markdown","source":["## Read and set up data"]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","id":"e-JCRYi3ipEb","colab_type":"code","outputId":"47af6650-1f72-4d68-829d-841a53963993","executionInfo":{"status":"ok","timestamp":1553878859563,"user_tz":-330,"elapsed":1702,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":204}},"cell_type":"code","source":["# Read data\n","dataset_dir = os.path.join(home_dir, \"dataset\")\n","train_dir = os.path.join(dataset_dir, \"train\")\n","\n","\n","train_df = pd.read_csv(dataset_dir + '/train.csv')\n","train_df.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>image_name</th>\n","      <th>label</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1.jpg</td>\n","      <td>4</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2.jpg</td>\n","      <td>5</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7.jpg</td>\n","      <td>4</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  image_name  label\n","0      0.jpg      0\n","1      1.jpg      4\n","2      2.jpg      5\n","3      4.jpg      0\n","4      7.jpg      4"]},"metadata":{"tags":[]},"execution_count":5}]},{"metadata":{"_uuid":"2ffaa5aba1e6197e0f2ced15187c06aa87cb4726","id":"BXPM-y_kipEo","colab_type":"code","outputId":"844c37e2-ef62-4cbe-fc77-3d4a150decda","executionInfo":{"status":"ok","timestamp":1553879007439,"user_tz":-330,"elapsed":2132,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":367}},"cell_type":"code","source":["# Read and display an image\n","image = plt.imread(os.path.join(train_dir, os.listdir(train_dir)[150]))\n","print(\"Image shape =\", image.shape)\n","train_input_shape = image.shape\n","plt.imshow(image)\n","plt.show()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Image shape = (150, 150, 3)\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAVEAAAFNCAYAAAC5YlyiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncR4sBFuz3gAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 576x396 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"metadata":{"id":"B7VO2ODbtTeG","colab_type":"code","outputId":"2e827c14-1a55-409d-cbb4-d366be37a41d","executionInfo":{"status":"ok","timestamp":1553879013321,"user_tz":-330,"elapsed":1581,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["# Number of unique classes\n","n_classes = len(train_df.label.unique())\n","print(\"Number of unique classes =\", n_classes)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Number of unique classes = 6\n"],"name":"stdout"}]},{"metadata":{"_uuid":"9afc1b8533ad9c6167e3a50432127e4c6e9f44e0","id":"R7b2hbCpipEy","colab_type":"text"},"cell_type":"markdown","source":["## Image Augmentation"]},{"metadata":{"_uuid":"5bdbbb3b523d3e82c9c83508842c8536ddb645a9","id":"Hf2H6y0ZipE1","colab_type":"code","outputId":"c6f5224c-545e-44fe-ac5c-53884c3b6905","executionInfo":{"status":"ok","timestamp":1553879100099,"user_tz":-330,"elapsed":87924,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":68}},"cell_type":"code","source":["# Augment data\n","batch_size = 16\n","#train_input_shape = (75, 75, 3)\n","\n","train_datagen = ImageDataGenerator(validation_split=0.2,\n","                                   rescale=1./255.,\n","                                   rotation_range=45,\n","                                   width_shift_range=0.2,\n","                                   height_shift_range=0.2,\n","                                   shear_range=0.7,\n","                                   zoom_range=0.7,\n","                                   horizontal_flip=True,\n","                                   #vertical_flip=True,\n","                                  )\n","\n","train_generator = train_datagen.flow_from_dataframe(dataframe=train_df, \n","                                                    directory=train_dir, \n","                                                    x_col=\"image_name\", \n","                                                    y_col=\"label\", \n","                                                    class_mode=\"other\",\n","                                                    subset=\"training\",\n","                                                    target_size=train_input_shape[0:2],\n","                                                    shuffle=True,\n","                                                    batch_size=batch_size)\n","\n","valid_generator = train_datagen.flow_from_dataframe(dataframe=train_df, \n","                                                    directory=train_dir, \n","                                                    x_col=\"image_name\", \n","                                                    y_col=\"label\", \n","                                                    class_mode=\"other\",\n","                                                    subset=\"validation\",\n","                                                    target_size=train_input_shape[0:2],\n","                                                    shuffle=True,\n","                                                    batch_size=batch_size)\n","\n","STEP_SIZE_TRAIN = train_generator.n//train_generator.batch_size\n","STEP_SIZE_VALID = valid_generator.n//valid_generator.batch_size\n","print(\"Total number of batches =\", STEP_SIZE_TRAIN, \"and\", STEP_SIZE_VALID)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Found 13628 images.\n","Found 3406 images.\n","Total number of batches = 851 and 212\n"],"name":"stdout"}]},{"metadata":{"_uuid":"14162cea761f425a19a7f1a0589e445c8f0b06c7","id":"FUjYGRNdipFB","colab_type":"text"},"cell_type":"markdown","source":["## Build model"]},{"metadata":{"_uuid":"0015c78383b92457b131f2f79379847503f01833","id":"vBmJPc8yipFE","colab_type":"code","outputId":"03119fd1-7763-4be0-b222-55e5208804f6","executionInfo":{"status":"ok","timestamp":1553879116766,"user_tz":-330,"elapsed":102865,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":88}},"cell_type":"code","source":["# Load pre-trained model\n","base_model = InceptionResNetV2(weights=None, include_top=False, input_shape=train_input_shape)\n","\n","#for layer in resnet50.layers:\n","#    layer.trainable = False\n","\n","#resnet50.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Colocations handled automatically by placer.\n"],"name":"stdout"}]},{"metadata":{"_uuid":"e0a60b2a913c2d77e9af8580e65af63c08b136af","id":"pkPMPx20ipFQ","colab_type":"code","outputId":"25e68f28-6274-4d2a-8446-1c2f8eb0a121","executionInfo":{"status":"ok","timestamp":1553879116776,"user_tz":-330,"elapsed":99717,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":88}},"cell_type":"code","source":["# Add layers at the end\n","X = base_model.output\n","X = Flatten()(X)\n","\n","X = Dense(16, kernel_initializer='he_uniform')(X)\n","X = Dropout(0.5)(X)\n","X = BatchNormalization()(X)\n","X = Activation('relu')(X)\n","\n","output = Dense(n_classes, activation='softmax')(X)\n","\n","model = Model(inputs=base_model.input, outputs=output)\n","\n","#model.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"],"name":"stdout"}]},{"metadata":{"id":"WX93DE4QxIin","colab_type":"code","outputId":"f2017863-d826-4009-e1fa-d6d25a1103bf","executionInfo":{"status":"ok","timestamp":1553879130680,"user_tz":-330,"elapsed":111347,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"cell_type":"code","source":["model_dir = os.path.join(home_dir, \"models\")\n","MY_MODEL = os.path.join(model_dir, \"InceptionResNetV2_model.h5\")\n","MY_MODEL_WEIGHTS = os.path.join(model_dir, \"InceptionResNetV2_weights.h5\")\n","\n","from keras.models import load_model\n","\n","#model = load_model(MY_MODEL)\n","model.load_weights(MY_MODEL_WEIGHTS)\n","#model.summary()"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n"],"name":"stderr"}]},{"metadata":{"_uuid":"6ce184ce000b658fc951097822dc1c5605152d47","id":"xPxVLN3YipFk","colab_type":"code","colab":{}},"cell_type":"code","source":["#optimizer = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n","optimizer=Adam()\n","\n","model.compile(loss='sparse_categorical_crossentropy',\n","              optimizer=optimizer, \n","              metrics=['accuracy'])"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"4206d90e8c4dd006d25c29ff9c318fb937826ba2","id":"mDZtRHTiipFu","colab_type":"code","colab":{}},"cell_type":"code","source":["early_stop = EarlyStopping(monitor='val_acc', patience=20, verbose=1, \n","                           mode='auto', restore_best_weights=True)\n","\n","reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.3, patience=5, \n","                              verbose=1, mode='auto')"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"b2cd4cfb400f9d849029593191dd283667cfae9c","id":"Ai4nSL66ipF0","colab_type":"code","outputId":"1a53929d-247b-4242-f4a2-0359d621b0da","executionInfo":{"status":"ok","timestamp":1553855819237,"user_tz":-330,"elapsed":12188751,"user":{"displayName":"Sup H","photoUrl":"","userId":"04337592286741421479"}},"colab":{"base_uri":"https://localhost:8080/","height":1241}},"cell_type":"code","source":["%%time\n","n_epoch = 25\n","\n","history = model.fit_generator(generator=train_generator, steps_per_epoch=STEP_SIZE_TRAIN,\n","                              validation_data=valid_generator, validation_steps=STEP_SIZE_VALID,\n","                              epochs=n_epoch,\n","                              shuffle=True,\n","                              verbose=1,\n","                              callbacks=[reduce_lr],\n","                              #use_multiprocessing=True,\n","                              #workers=6\n","                             )"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epoch 1/25\n","213/213 [==============================] - 56s 262ms/step - loss: 0.3706 - acc: 0.8705\n","852/852 [==============================] - 600s 704ms/step - loss: 0.4535 - acc: 0.8505 - val_loss: 0.3706 - val_acc: 0.8705\n","Epoch 2/25\n","213/213 [==============================] - 55s 259ms/step - loss: 0.3799 - acc: 0.8711\n","852/852 [==============================] - 600s 704ms/step - loss: 0.4451 - acc: 0.8532 - val_loss: 0.3799 - val_acc: 0.8711\n","Epoch 3/25\n","213/213 [==============================] - 56s 261ms/step - loss: 0.3533 - acc: 0.8655\n","852/852 [==============================] - 601s 705ms/step - loss: 0.4471 - acc: 0.8522 - val_loss: 0.3533 - val_acc: 0.8655\n","Epoch 4/25\n","213/213 [==============================] - 55s 257ms/step - loss: 0.3861 - acc: 0.8623\n","852/852 [==============================] - 599s 703ms/step - loss: 0.4305 - acc: 0.8604 - val_loss: 0.3861 - val_acc: 0.8623\n","Epoch 5/25\n","213/213 [==============================] - 56s 262ms/step - loss: 0.3873 - acc: 0.8623\n","852/852 [==============================] - 600s 704ms/step - loss: 0.4316 - acc: 0.8560 - val_loss: 0.3873 - val_acc: 0.8623\n","Epoch 6/25\n","213/213 [==============================] - 54s 252ms/step - loss: 0.3442 - acc: 0.8796\n","852/852 [==============================] - 598s 702ms/step - loss: 0.4229 - acc: 0.8587 - val_loss: 0.3442 - val_acc: 0.8796\n","Epoch 7/25\n","213/213 [==============================] - 55s 259ms/step - loss: 0.3306 - acc: 0.8829\n","852/852 [==============================] - 597s 701ms/step - loss: 0.4430 - acc: 0.8529 - val_loss: 0.3306 - val_acc: 0.8829\n","Epoch 8/25\n","213/213 [==============================] - 54s 255ms/step - loss: 0.3701 - acc: 0.8667\n","852/852 [==============================] - 596s 700ms/step - loss: 0.4237 - acc: 0.8613 - val_loss: 0.3701 - val_acc: 0.8667\n","Epoch 9/25\n","213/213 [==============================] - 51s 237ms/step - loss: 0.3760 - acc: 0.8814\n","852/852 [==============================] - 591s 693ms/step - loss: 0.4290 - acc: 0.8600 - val_loss: 0.3760 - val_acc: 0.8814\n","Epoch 10/25\n","213/213 [==============================] - 52s 244ms/step - loss: 0.3364 - acc: 0.8876\n","852/852 [==============================] - 592s 695ms/step - loss: 0.4157 - acc: 0.8656 - val_loss: 0.3364 - val_acc: 0.8876\n","Epoch 11/25\n","213/213 [==============================] - 53s 251ms/step - loss: 0.3627 - acc: 0.8782\n","852/852 [==============================] - 596s 699ms/step - loss: 0.4138 - acc: 0.8643 - val_loss: 0.3627 - val_acc: 0.8782\n","Epoch 12/25\n","213/213 [==============================] - 53s 250ms/step - loss: 0.3323 - acc: 0.8808\n","\n","Epoch 00012: ReduceLROnPlateau reducing learning rate to 0.0003000000142492354.\n","852/852 [==============================] - 599s 704ms/step - loss: 0.4151 - acc: 0.8617 - val_loss: 0.3323 - val_acc: 0.8808\n","Epoch 13/25\n","213/213 [==============================] - 53s 248ms/step - loss: 0.2807 - acc: 0.9011\n","852/852 [==============================] - 595s 698ms/step - loss: 0.3488 - acc: 0.8871 - val_loss: 0.2807 - val_acc: 0.9011\n","Epoch 14/25\n","213/213 [==============================] - 52s 244ms/step - loss: 0.2756 - acc: 0.9043\n","852/852 [==============================] - 594s 697ms/step - loss: 0.3419 - acc: 0.8905 - val_loss: 0.2756 - val_acc: 0.9043\n","Epoch 15/25\n","213/213 [==============================] - 53s 248ms/step - loss: 0.2745 - acc: 0.9093\n","852/852 [==============================] - 594s 697ms/step - loss: 0.3373 - acc: 0.8899 - val_loss: 0.2745 - val_acc: 0.9093\n","Epoch 16/25\n","213/213 [==============================] - 51s 242ms/step - loss: 0.2862 - acc: 0.8996\n","852/852 [==============================] - 594s 697ms/step - loss: 0.3206 - acc: 0.8977 - val_loss: 0.2862 - val_acc: 0.8996\n","Epoch 17/25\n","213/213 [==============================] - 54s 251ms/step - loss: 0.2714 - acc: 0.9069\n","852/852 [==============================] - 596s 700ms/step - loss: 0.3182 - acc: 0.8954 - val_loss: 0.2714 - val_acc: 0.9069\n","Epoch 18/25\n","213/213 [==============================] - 53s 249ms/step - loss: 0.2777 - acc: 0.9060\n","852/852 [==============================] - 595s 698ms/step - loss: 0.3076 - acc: 0.8997 - val_loss: 0.2777 - val_acc: 0.9060\n","Epoch 19/25\n","213/213 [==============================] - 54s 254ms/step - loss: 0.2901 - acc: 0.9028\n","852/852 [==============================] - 596s 700ms/step - loss: 0.3105 - acc: 0.9000 - val_loss: 0.2901 - val_acc: 0.9028\n","Epoch 20/25\n","213/213 [==============================] - 52s 244ms/step - loss: 0.2880 - acc: 0.8993\n","852/852 [==============================] - 594s 697ms/step - loss: 0.2963 - acc: 0.9032 - val_loss: 0.2880 - val_acc: 0.8993\n","Epoch 21/25\n","213/213 [==============================] - 54s 252ms/step - loss: 0.2792 - acc: 0.9008\n","852/852 [==============================] - 597s 700ms/step - loss: 0.3111 - acc: 0.8994 - val_loss: 0.2792 - val_acc: 0.9008\n","Epoch 22/25\n","213/213 [==============================] - 53s 251ms/step - loss: 0.2895 - acc: 0.9002\n","\n","Epoch 00022: ReduceLROnPlateau reducing learning rate to 9.000000427477062e-05.\n","852/852 [==============================] - 598s 702ms/step - loss: 0.3036 - acc: 0.9031 - val_loss: 0.2895 - val_acc: 0.9002\n","Epoch 23/25\n"," 19/852 [..............................] - ETA: 8:49 - loss: 0.2935 - acc: 0.9079"],"name":"stdout"}]},{"metadata":{"id":"x8uIuuit8paW","colab_type":"code","colab":{}},"cell_type":"code","source":["model_dir = os.path.join(home_dir, \"models\")\n","#model.save(model_dir + '/InceptionResNetV2_model1.h5')\n","model.save_weights(model_dir + '/InceptionResNetV2_weights2.h5')"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"898075f768db94965133df52fe650334f744541d","id":"8-bDEvFOipF-","colab_type":"code","colab":{}},"cell_type":"code","source":["# Plot the training graph\n","def plot_training(history):\n","    acc = history.history['acc']\n","    val_acc = history.history['val_acc']\n","    loss = history.history['loss']\n","    val_loss = history.history['val_loss']\n","    epochs = range(len(acc))\n","\n","    fig, axes = plt.subplots(1, 2, figsize=(15,5))\n","    \n","    axes[0].plot(epochs, acc, 'r-', label='Training Accuracy')\n","    axes[0].plot(epochs, val_acc, 'b--', label='Validation Accuracy')\n","    axes[0].set_title('Training and Validation Accuracy')\n","    axes[0].legend(loc='best')\n","\n","    axes[1].plot(epochs, loss, 'r-', label='Training Loss')\n","    axes[1].plot(epochs, val_loss, 'b--', label='Validation Loss')\n","    axes[1].set_title('Training and Validation Loss')\n","    axes[1].legend(loc='best')\n","    \n","    plt.show()\n","    \n","plot_training(history)"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"cd4bf4945a12d11ce13c9cda6befe55c392c0d8d","id":"NQtv0MCgipGI","colab_type":"code","colab":{}},"cell_type":"code","source":["# Evaluate on validation set\n","result = model.evaluate_generator(generator=valid_generator, verbose=1)\n","result"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"cd427b2643d0a1e94227977ecf9b579415f9f7d4","id":"ATjwrbViipGR","colab_type":"code","colab":{}},"cell_type":"code","source":["# Classification report and confusion matrix\n","from sklearn.metrics import *\n","import seaborn as sns\n","\n","def showClassficationReport_Generator(model, valid_generator, STEP_SIZE_VALID):\n","    # Loop on each generator batch and predict\n","    y_pred, y_true = [], []\n","    for i in range(STEP_SIZE_VALID):\n","        (X,y) = next(valid_generator)\n","        y_pred.append(model.predict(X))\n","        y_true.append(y)\n","    \n","    # Create a flat list for y_true and y_pred\n","    y_pred = [subresult for result in y_pred for subresult in result]\n","    y_true = [subresult for result in y_true for subresult in result]\n","    \n","    y_true = np.asarray(y_true).ravel()\n","    \n","    # Update Prediction vector based on argmax\n","    #y_pred = np.asarray(y_pred).astype('float32').ravel()\n","    #y_pred = y_pred >= 0.5\n","    #y_pred = y_pred.astype('int').ravel()\n","    y_pred = np.argmax(y_pred, axis=1)\n","    y_pred = np.asarray(y_pred).ravel()\n","    \n","    # Confusion Matrix\n","    conf_matrix = confusion_matrix(y_true, y_pred, labels=[0,1,2,3,4,5])\n","    sns.heatmap(conf_matrix, annot=True, fmt=\"d\", square=True, cbar=False, \n","                cmap=plt.cm.gray, xticklabels=labels, yticklabels=labels)\n","    plt.ylabel('Actual')\n","    plt.xlabel('Predicted')\n","    plt.title('Confusion Matrix')\n","    plt.show()\n","    \n","    print(classification_report(y_true, y_pred))\n","    #print(\"\\nAUC: \", roc_auc_score(y_true, y_pred, average='micro'))\n","\n","showClassficationReport_Generator(model, valid_generator, STEP_SIZE_VALID)"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"c841e3ab0b59e0a3bf9ae1885d99533b05f06fc4","id":"YQSdReezipGd","colab_type":"text"},"cell_type":"markdown","source":["## Prepare data for prediction on test set"]},{"metadata":{"id":"6e7bQQBIqtBu","colab_type":"code","colab":{}},"cell_type":"code","source":["test_df = pd.read_csv(dataset_dir + \"/test_WyRytb0.csv\")\n","test_df.shape"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"33c7560628f0fe446b6355d26f9fee67da4fc58a","id":"9WSL1H-WipGf","colab_type":"code","colab":{}},"cell_type":"code","source":["test_datagen = ImageDataGenerator(rescale=1./255.)\n","\n","test_generator = test_datagen.flow_from_dataframe(dataframe=test_df, \n","                                                    directory=train_dir, \n","                                                    x_col=\"image_name\", \n","                                                    #y_col=\"label\", \n","                                                    class_mode=None,\n","                                                    target_size=train_input_shape[0:2], \n","                                                    batch_size=1,\n","                                                    shuffle=False\n","                                                 )"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"08afdd8c3000a63d5846644d83bebd55bb2e7fdb","id":"6hixVyr-ipGw","colab_type":"text"},"cell_type":"markdown","source":["## Predict and Submit"]},{"metadata":{"_uuid":"067733fbfd186fa1d65d4110165b345a6c9f1898","id":"xX7gSEVUipG5","colab_type":"code","colab":{}},"cell_type":"code","source":["# Predict on test data\n","test_generator.reset()\n","predictions = model.predict_generator(test_generator,verbose=1)\n","predictions = np.argmax(predictions, axis=1)\n","#predictions = predictions.astype('int').ravel()\n","predictions.shape"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"3c52db7624172d624771f0a00205568676fcc853","id":"QK9ubPZwipHB","colab_type":"code","colab":{}},"cell_type":"code","source":["# Retrieve filenames\n","import re\n","#test_img_ids = [re.split(\"/\", val)[1] for val in test_generator.filenames]\n","test_img_ids = test_generator.filenames\n","len(test_img_ids)"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"5785b0499ce1d7425bce81e98c2d20d29dc7cc7e","id":"QpsDW_baipHL","colab_type":"code","colab":{}},"cell_type":"code","source":["# Create dataframe for submission\n","submission_df = pd.DataFrame({'image_name' : test_img_ids,\n","                              'label' : predictions })\n","submission_df.head()"],"execution_count":0,"outputs":[]},{"metadata":{"_uuid":"f2b7c10b6dd4103cf2477fe6f492a635f312b1e1","id":"O16A0M70ipHW","colab_type":"code","colab":{}},"cell_type":"code","source":["# Create submission file\n","submission_dir = os.path.join(home_dir, \"submissions\")\n","submission_df.to_csv(submission_dir + '/submission_InceptionResnetV2_3.csv', index=False)"],"execution_count":0,"outputs":[]}]}