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
,
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)
,
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
本文链接:https://www.f2er.com/3152132.html