Training GoogLeNet Model
\n","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.callbacks import EarlyStopping\n\n# GoogLeNet_bestWeights = save_bestWeights('val_main_accuracy') #save weights for higher validation accuracy.\nGoogLeNet_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nes = EarlyStopping(patience = 4,mode='max',restore_best_weights=True)\n\n\nhistory = GoogLeNet_model.fit(train_generator,\n validation_data=valid_generator,\n steps_per_epoch = train_generator.samples//128,\n validation_steps=valid_generator.samples//128,\n epochs = 20,\n callbacks=[es])\n \n \n# callback 함수 오류, 이상한건 지워도 오류 (?)\n#train_generator (데이터 확장) 부분에서 outputs의 값이 3개 들어가게 만들어야함\n\n#weights = GoogLeNet_bestWeights.get_bestWeights()\n#GoogLeNet_model.set_weights(weights) \n\n#datagen = datagen.flow(X, [Y, Y, Y], batch_size=batch)\n'''\nepochs = 20 \nfor e in range(epochs):\n batches = 0\n for x_batch, y_batch in datagen.flow(x_train, y_train, batch_size=32):\n GoogLeNet_model = GoogLeNet_model.train_on_batch(X_batch, [Y_batch,Y_batch,Y_batch])\n batches += 1\n if batches >= len(x_train) / 32:\n # we need to break the loop by hand because\n # the generator loops indefinitely\n break\n'''","metadata":{"id":"kGgd5iaci6G7","execution":{"iopub.status.busy":"2021-12-13T21:47:42.173246Z","iopub.execute_input":"2021-12-13T21:47:42.17356Z","iopub.status.idle":"2021-12-13T21:47:42.989432Z","shell.execute_reply.started":"2021-12-13T21:47:42.173532Z","shell.execute_reply":"2021-12-13T21:47:42.98811Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"교육 중 보조 네트워크 0 정확도 및 손실 기능 플로팅.","metadata":{}},{"cell_type":"code","source":"plot('Aux 0', (history.history['aux_0_loss'], history.history['val_aux_0_loss']),(\n history.history['aux_0_accuracy'], history.history['val_aux_0_accuracy'] ))","metadata":{"id":"mWm3kP7YC0aG","execution":{"iopub.status.busy":"2021-12-13T21:46:23.854192Z","iopub.status.idle":"2021-12-13T21:46:23.854539Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"교육 중 보조 네트워크 1 정확도 및 손실 기능 플로팅.","metadata":{}},{"cell_type":"code","source":"plot('Aux 1', (history.history['aux_1_loss'], history.history['val_aux_1_loss']),(\n history.history['aux_1_accuracy'], history.history['val_aux_1_accuracy'] ))\n","metadata":{"id":"VpNj-kO9Mtod","execution":{"iopub.status.busy":"2021-12-13T21:46:23.855499Z","iopub.status.idle":"2021-12-13T21:46:23.855879Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Plotting 기본 네트워크 정확도 및 교육 중 손실 기능.","metadata":{}},{"cell_type":"code","source":"plot('Main', (history.history['main_loss'], history.history['val_main_loss']),(\n history.history['main_accuracy'], history.history['val_main_accuracy'] ))\n","metadata":{"id":"hR5rHha6D5Nm","execution":{"iopub.status.busy":"2021-12-13T21:46:23.856898Z","iopub.status.idle":"2021-12-13T21:46:23.857262Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Google Net prediction.
","metadata":{}},{"cell_type":"code","source":"batch = 32\nGoogLeNet_prediction = GoogLeNet_model.predict_generator(data_predictGenerator(X_prediction, batch),steps=(len(X_prediction)//batch)+1, verbose=1)\nGoogLeNet_prediction = GoogLeNet_prediction[0] + GoogLeNet_prediction[1] + GoogLeNet_prediction[2]\nGoogLeNet_prediction /= 3\nGoogLeNet_sub = pd.read_csv('../input/plant-pathology-2021-fgvc8/sample_submission.csv')\nGoogLeNet_sub.iloc[:,1:] = GoogLeNet_prediction\nGoogLeNet_sub.to_csv('googleNet_submission.csv', index=False)\nGoogLeNet_sub.head()","metadata":{"id":"fdsYoyQlQcIJ","execution":{"iopub.status.busy":"2021-12-13T21:46:23.858149Z","iopub.status.idle":"2021-12-13T21:46:23.858463Z"},"trusted":true},"execution_count":null,"outputs":[]}]}