TF2 Keras (1)： 用序列（Sequence ）创建模

本文是对官方文档 的学习笔记。

Keras 支持2种定义Model 的方式 Sequence 和 Functional API 这篇文章主要讨论Sequence 模式。 Sequence 的优点是简单，用一个简单的数组就可以定义一个Model。虽然方便， 但是 Sequence 也有其缺点

不合适使用Sequence 方式构建模型的场景

• 模型有多个输入和输出
• 有任何一层是多输入，或者多输出
• 有layer sharing
• 存在非线性拓扑 (例如：ResNet， Multi-Branch Model)

用 Squence 构建一个Model 注意事项

• 可以用 activation 设定激活函数
• 每层可以设置 name 【可选】

# Define Sequential model with 3 layers
model = keras.Sequential(
    [
        layers.Dense(2, activation="relu", name="layer1"),
        layers.Dense(3, activation="relu", name="layer2"),
        layers.Dense(4, name="layer3"),
    ]
)
# Call model on a test input
x = tf.ones((3, 3))
y = model(x)

添加/删除层

对于已经build 好的model ，可以通过 add， pop函数对layer 进行添加、删除。

add

model = keras.Sequential()
model.add(layers.Dense(2, activation="relu"))
model.add(layers.Dense(3, activation="relu"))
model.add(layers.Dense(4))

pop

model.pop()
print(len(model.layers))  # 2

利用 add() + summary() 进行 Debug

summary（）可以打印整个Model 的结构， 进行调试

model = keras.Sequential()
model.add(keras.Input(shape=(250, 250, 3)))  # 250x250 RGB images
model.add(layers.Conv2D(32, 5, strides=2, activation="relu"))
model.add(layers.Conv2D(32, 3, activation="relu"))
model.add(layers.MaxPooling2D(3))

# Can you guess what the current output shape is at this point? Probably not.
# Let's just print it:
model.summary()

# The answer was: (40, 40, 32), so we can keep downsampling...

model.add(layers.Conv2D(32, 3, activation="relu"))
model.add(layers.Conv2D(32, 3, activation="relu"))
model.add(layers.MaxPooling2D(3))
model.add(layers.Conv2D(32, 3, activation="relu"))
model.add(layers.Conv2D(32, 3, activation="relu"))
model.add(layers.MaxPooling2D(2))

# And now?
model.summary()

# Now that we have 4x4 feature maps, time to apply global max pooling.
model.add(layers.GlobalMaxPooling2D())

# Finally, we add a classification layer.
model.add(layers.Dense(10))

提取Featrue

每一层的输出都可以单独提取出来。

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=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)

基于Sequence 的迁移学习

2种常见的迁移学习的方式

1 把除了最后一层（输出层）以外的层全部冻住

model = keras.Sequential([
    keras.Input(shape=(784))
    layers.Dense(32, activation='relu'),
    layers.Dense(32, activation='relu'),
    layers.Dense(32, activation='relu'),
    layers.Dense(10),
])

# Presumably you would want to first load pre-trained weights.
model.load_weights(...)

# Freeze all layers except the last one.
for layer in model.layers[:-1]:
  layer.trainable = False

# Recompile and train (this will only update the weights of the last layer).
model.compile(...)
model.fit(...)

2 在已有模型上叠加新的层

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

# Freeze the base model
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(...)