Question

我有一个由自定义图层组成的模型。每个自定义层都包含许多tf.keras.layers。问题是，如果我想在定义模型后冻结这些层，则循环：

for i, layer in enumerate(model.layers):
    print(i, layer.name)

仅打印“外部”自定义图层，而不打印其中的自定义图层。有什么方法可以访问内层，以便冻结它们？

来自official tf docs的自定义图层的示例：

class MLPBlock(layers.Layer):

  def __init__(self):
    super(MLPBlock, self).__init__()
    self.linear_1 = Linear(32)
    self.linear_2 = Linear(32)
    self.linear_3 = Linear(1)

  def call(self, inputs):
    x = self.linear_1(inputs)
    x = tf.nn.relu(x)
    x = self.linear_2(x)
    x = tf.nn.relu(x)
    return self.linear_3(x)

Answer 1

在更新功能中，您要做的是将第一Dense()层替换为另一个Dense()层，这次设置为trainable = false。

虽然可行，但我将更新“ update”功能如下：

 def updt(self):
     self.dense1.trainable = False

Answer 2

好吧，我想出了一个解决方案。必须在自定义层内部实现“更新”功能，该功能会更新内部层，使它们变得不可训练。这是示例代码：

import tensorflow as tf
import numpy as np

layers = tf.keras.layers

seq_model = tf.keras.models.Sequential


class MDBlock(layers.Layer):

    def __init__(self):
        super(MDBlock, self).__init__()
        self.dense1 = layers.Dense(784, name="first")
        self.dense2 = layers.Dense(32, name="second")
        self.dense3 = layers.Dense(32, name="third")
        self.dense4 = layers.Dense(1, activation='sigmoid', name="outp")

    def call(self, inputs):
        x = self.dense1(inputs)
        x = tf.nn.relu(x)
        x = self.dense2(x)
        x = tf.nn.relu(x)
        x = self.dense3(x)
        x = tf.nn.relu(x)
        x = self.dense4(x)
        return x

    def updt(self):
        self.dense1.trainable = False

    def __str__(self):
        return "\nd1:{0}\nd2:{1}\nd3:{2}\nd4:{3}".format(self.dense1.trainable, self.dense2.trainable,
                                                         self.dense3.trainable, self.dense4.trainable)


# define layer block
layer = MDBlock()

model = seq_model()
model.add(layers.Input(shape=(784,)))
model.add(layer)

# Use updt function to make layers non-trainable
for i, layer in enumerate(model.layers):
    layer.updt()

model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['accuracy'])

# Generate dummy data
data = np.random.random((1000, 784))
labels = np.random.randint(2, size=(1000, 1))

# Train the model, iterating on the data in batches of 32 samples
model.fit(data, labels, epochs=10, batch_size=32)

# print block's layers state
for i, layer in enumerate(model.layers):
    print(i, layer)

Answer 3

您可以使用keras回调。如果要在经过一定时间后冻结第一层，请添加此回调


class FreezeCallback(tf.keras.callbacks.Callback):
    def __init__(self, n_epochs=10):
        super().__init__()
        self.n_epochs = n_epochs

    def on_epoch_end(self, epoch, logs=None):
        if epoch == self.n_epochs:
            l = self.model.get_layer('first')
            l.trainable = False

在tensorflow 2中冻结子层

3 个答案: