最佳答案
您可以只创建一个自定义层。
class DenseEQ(Dense):
"""
Standard dense layer but includes learning rate equilization
at runtime as per Karras et al. 2017.
Inherits Dense layer and overides the call method.
"""
def __init__(self, **kwargs):
if 'kernel_initializer' in kwargs:
raise Exception("Cannot override kernel_initializer")
super().__init__(kernel_initializer=normal(0,1), **kwargs)
def build(self, input_shape):
super().build(input_shape)
# The number of inputs
n = np.product([int(val) for val in input_shape[1:]])
# He initialisation constant
self.c = np.sqrt(2/n)
def call(self, inputs):
output = K.dot(inputs, self.kernel*self.c) # scale kernel
if self.use_bias:
output = K.bias_add(output, self.bias, data_format='channels_last')
if self.activation is not None:
output = self.activation(output)
return output
然后像往常一样创建一个模型......
model_in = Input(shape(12,))
x = DenseEq(name="whatever_1")(model_in)
x = LeakyRelu(0.2)(x)
x = DenseEq(name="whatever_2")(model_in)
model_out = LeakyRelu(0.2)(x)
model = Model(model_in, model_out)
你可以对卷积做同样的事情。
class Conv2DEQ(Conv2D):
"""
Standard Conv2D layer but includes learning rate equilization
at runtime as per Karras et al. 2017.
Inherits Conv2D layer and overrides the call method, following
https://github.com/keras-team/keras/blob/master/keras/layers/convolutional.py
"""
def __init__(self, **kwargs):
if 'kernel_initializer' in kwargs:
raise Exception("Cannot override kernel_initializer")
super().__init__(kernel_initializer=normal(0,1), **kwargs)
def build(self, input_shape):
super().build(input_shape)
# The number of inputs
n = np.product([int(val) for val in input_shape[1:]])
# He initialisation constant
self.c = np.sqrt(2/n)
def call(self, inputs):
if self.rank == 2:
outputs = K.conv2d(
inputs,
self.kernel*self.c, # scale kernel
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate)
if self.use_bias:
outputs = K.bias_add(
outputs,
self.bias,
data_format=self.data_format)
if self.activation is not None:
return self.activation(outputs)
return outputs
关于python - 我如何使用 tensorflow 2 进行均衡学习率?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/60012406/