lasagne定义两个用于正则化的具有batchnorm和Drop的反卷积层

下面是使用Lasagne定义两个具有BatchNorm和Dropout的反卷积层的示例代码：

import lasagne
import numpy as np

# 定义反卷积层的函数
def deconv_layer(input_layer, num_filters, filter_size, stride, pad, nonlinearity=None, W=lasagne.init.Normal()):
    deconv = lasagne.layers.TransposedConv2DLayer(input_layer, num_filters, filter_size, stride, pad, W=W, nonlinearity=nonlinearity)
    return deconv

# 定义具有BatchNorm和Dropout的反卷积层的函数
def deconv_layer_with_bn_and_drop(input_layer, num_filters, filter_size, stride, pad, nonlinearity=None):
    deconv = deconv_layer(input_layer, num_filters, filter_size, stride, pad, nonlinearity=nonlinearity)
    bn = lasagne.layers.batch_norm(deconv)
    dropout = lasagne.layers.DropoutLayer(bn)
    return dropout


# 创建一个输入层
input_layer = lasagne.layers.InputLayer(shape=(None, 3, 32, 32))

# 创建第一个具有BatchNorm和Dropout的反卷积层
deconv1 = deconv_layer_with_bn_and_drop(input_layer, num_filters=16, filter_size=3, stride=2, pad=1, nonlinearity=lasagne.nonlinearities.rectify)

# 创建第二个具有BatchNorm和Dropout的反卷积层
deconv2 = deconv_layer_with_bn_and_drop(deconv1, num_filters=32, filter_size=3, stride=2, pad=1, nonlinearity=lasagne.nonlinearities.rectify)

# 输出第二个反卷积层的形状
print(lasagne.layers.get_output_shape(deconv2))

这里我们定义了两个函数，`deconv_layer`用于创建一个反卷积层，`deconv_layer_with_bn_and_drop`则在此基础上增加了BatchNorm和Dropout。我们使用这两个函数来创建两个具有BatchNorm和Dropout的反卷积层，并且输出第二个反卷积层的形状。注意，我们在这里使用的是Lasagne的`TransposedConv2DLayer`来创建反卷积层。