optim = Adam(1e-4) # optim = SGD(1e-4, momentum=0.99, nesterov=True) loss = PGCE # model._weights('../models/deform_cnn.h5') model.compile(optim, loss=[loss, lambda y_true, y_pred: K.sum(y_pred)], loss_weights=[1., .001], metrics={'output': 'accuracy'}) checkpoint = ModelCheckpoint("deform_center_best.h5", monitor='val_output_acc', save_best_only=True) checkpoint_tl = ModelCheckpoint("deform_center_trainbest.h5", monitor='output_loss', save_best_only=True) spreadsheet = SpreadSheet("1nu6AFqzeYc2rNFAjtUtem-CFYKiRI4HCmXkxWsGglRg", "DeformFaceAgeML3") if args.img_dir is None: try: model.fit_generator( train_scaled_gen, steps_per_epoch=steps_per_epoch, epochs=1000, verbose=1, validation_data=test_scaled_gen, validation_steps=validation_steps, callbacks=[checkpoint, checkpoint_tl], ) val_loss, val_acc = model.evaluate_generator( test_scaled_gen, steps=validation_steps)
model.load_weights(args.weight) # sys.exit(0) # input("Press enter to start training...") optim = Adam(1e-4) loss = categorical_crossentropy model.compile(optim, [loss], metrics=['accuracy']) checkpoint = ModelCheckpoint("deform_cnn_pg_best.h5", monitor='val_acc', save_best_only=True) checkpoint_tl = ModelCheckpoint("deform_cnn_pg_trainbest.h5", monitor='loss', save_best_only=True) spreadsheet = SpreadSheet( "1nu6AFqzeYc2rNFAjtUtem-CFYKiRI4HCmXkxWsGglRg", "DeformPersonGenderML3") if args.img_dir is None: (x_train, y_train), (x_test, y_test) = cifar100.load_data(label_mode='fine') # x_train = x_train.astype('float32') x_test = x_test.astype('float32') # x_train /= 255 x_test /= 255 datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True, rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True)