模型-MLP數字廻歸 – 柯博文老師

    # 建立模型
    model = tf.keras.models.Sequential()
    model.add(tf.keras.layers.Dense(320, activation='tanh', input_shape=[x_train.shape[1]]))
    model.add(tf.keras.layers.Dense(640, activation='tanh'))
    model.add(tf.keras.layers.Dense(640, activation='tanh'))
    model.add(tf.keras.layers.Dense(1))
    learning_rate = 0.0001
    opt1 = tf.keras.optimizers.Nadam(lr=learning_rate)
    model.compile(loss='mse', optimizer=opt1, metrics=['mae'])

    checkpoint = tf.keras.callbacks.ModelCheckpoint("model2.h5", monitor='loss', verbose=1,
                                                    save_best_only=True, mode='auto', period=1)
    history = model.fit(x_train, y_train,
                        epochs=4000,
                        callbacks=[checkpoint],
                        batch_size=100)
Python
