Keras 常用函数

1 特征提取

initial_model = keras.Sequential(
    [
        keras.Input(shape=(250, 250, 3)),
        layers.Conv2D(32, 5, strides=2, activation="relu"),
        layers.Conv2D(32, 3, activation="relu", name="my_intermediate_layer"),
        layers.Conv2D(32, 3, activation="relu"),
    ]
)

feature_extractor = keras.Model(
    inputs=initial_model.inputs,
    outputs=[layer.output for layer in initial_model.layers],
)

feature_extractor = keras.Model(
    inputs=initial_model.inputs,
    outputs=initial_model.get_layer(name="my_intermediate_layer").output,
)

# Call feature extractor on test input.
x = tf.ones((1, 250, 250, 3))
features = feature_extractor(x)

2 网络冻结

# 冻结某一层 
layer.trainable = False

# Load a convolutional base with pre-trained weights
base_model = keras.applications.Xception(
    weights='imagenet',
    include_top=False,
    pooling='avg')

# 冻结某一部分模型
base_model.trainable = False

# Use a Sequential model to add a trainable classifier on top
model = keras.Sequential([
    base_model,
    layers.Dense(1000),
])

# Compile & train
model.compile(...)
model.fit(...)

3 函数式API

# 创建一个简单模型
inputs = keras.Input(shape=(784,))
dense = layers.Dense(64, activation="relu")
x = dense(inputs)
x = layers.Dense(64, activation="relu")(x)
outputs = layers.Dense(10)(x)
model = keras.Model(inputs=inputs, outputs=outputs, name="mnist_model")
# 常用函数
inputs.shape 
inputs.dtype
model.summary()
keras.utils.plot_model(model, "my_first_model.png")
keras.utils.plot_model(model, "my_first_model_with_shape_info.png", show_shapes=True)

# 编译训练 
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

x_train = x_train.reshape(60000, 784).astype("float32") / 255
x_test = x_test.reshape(10000, 784).astype("float32") / 255

model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=keras.optimizers.RMSprop(),
    metrics=["accuracy"],
)

history = model.fit(x_train, y_train, batch_size=64, epochs=2, validation_split=0.2)

test_scores = model.evaluate(x_test, y_test, verbose=2)
print("Test loss:", test_scores[0])
print("Test accuracy:", test_scores[1])

# 保存和序列化 
保存的文件包括：
  模型架构
  模型权重值（在训练过程中得知）
  模型训练配置（如果有的话，如传递给 compile）
  优化器及其状态（如果有的话，用来从上次中断的地方重新开始训练）
model.save("path_to_my_model")
del model
# Recreate the exact same model purely from the file:
model = keras.models.load_model("path_to_my_model")