def shoubi_model(): model = load_model(os.path.join(modelPath, shoubi)) model = Model(inputs=model.input, outputs=model.layers[1].output, name="shoubi_model") model = Sequential(name="shoubi_model") model.add(LSTM(64, input_shape=(30, 9), return_sequences=True, kernel_regularizer=tf.keras.regularizers.l2(0.0001))) model.add(LSTM(64, kernel_regularizer=tf.keras.regularizers.l2(0.0001))) # model.trainable = False return model
dense2 = Dense(512)(dense1) dense2 = LeakyReLU(0.1)(dense2) dense3 = Dense(10)(dense2) dense3 = LeakyReLU(0.1)(dense3) output_position = Dense(1)(dense3) model = Model(inputs=input_image1, outputs=output_position) opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0001) model.compile(loss='mse', optimizer=opt) ## Discriminator model = Sequential() model.add( Conv2D(1, (8, 8), 8, padding='valid', input_shape=(image_size, image_size, 1), name='conv1')) model.add(Activation('relu')) model.add(Conv2D(1, (8, 8), 8, padding='valid', name='conv2')) model.add(Activation('relu')) model.add(Flatten()) model.add(Dense(10, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(2, 'softmax', name='output')) opt = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0001,