from model import CreateModel from data import LoadData from keras.callbacks import ModelCheckpoint import tensorflowjs as tfjs import matplotlib.pyplot as plt model = CreateModel() model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['binary_accuracy']) training_data, validation_data = LoadData() # ## train model #save_model = ModelCheckpoint('model.h5', save_best_only=True, save_weights_only=True, verbose=1) #print(f'data generator length: {len(training_data)}') #model.fit_generator( # training_data, # validation_data=validation_data, # epochs=2, # callbacks=[save_model]) # ## fine tuning #for layer in base_model.layers[:-5]: # layer.trainable = False #model.compile(optimizer='sgd', loss='binary_crossentropy', metrics=['binary_accuracy']) #model.fit_generator( # training_data, # validation_data=validation_data, # epochs=2, # callbacks=[save_model]) ## save model