TF/Keras:如何堆叠模型

import tensorflow as tf
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten

# Sequential模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(MaxPooling2D((2, 2)))
model.add(Flatten())
model.add(Dense(10, activation='softmax'))

# Functional API
input_layer = tf.keras.Input(shape=(32, 32, 3))
x = Conv2D(32, (3, 3), activation='relu')(input_layer)
x = MaxPooling2D((2, 2))(x)
x = Flatten()(x)
output_layer = Dense(10, activation='softmax')(x)
model = Model(inputs=input_layer, outputs=output_layer)

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_val, y_val))

predictions = model.predict(x_test)