深度可分离卷积模块的代码：
```python
import tensorflow as tf
def depthwise_separable_conv(inputs, num_filters, kernel_size, strides=(1, 1), padding='SAME', activation=tf.nn.selu, use_bn=True, use_winograd=False):
if use_winograd:
# Use Winograd algorithm for small kernel size
if kernel_size == (3, 3):
strides = (1, 1)
padding = 'VALID'
elif kernel_size == (5, 5):
strides = (1, 1)
padding = 'SAME'
# Depthwise Convolution
net = tf.keras.layers.DepthwiseConv2D(kernel_size=kernel_size, strides=strides, padding=padding, use_bias=not use_bn)(inputs)
net = tf.keras.layers.BatchNormalization()(net) if use_bn else net
net = activation(net)
# Pointwise Convolution (1x1 Convolution)
net = tf.keras.layers.Conv2D(filters=num_filters, kernel_size=(1, 1), strides=(1, 1), padding='SAME', use_bias=not use_bn)(net)
net = tf.keras.layers.BatchNormalization()(net) if use_bn else net
net = activation(net)
return net
```
这个深度可分离卷积模块包含了深度卷积、批量归一化、激活函数和点卷积等操作，并且根据需要提供了是否使用Winograd算法的选项。
接下来是搭建一个简单的神经网络的代码：
```python
import tensorflow as tf
def simple_cnn(input_shape, num_classes):
inputs = tf.keras.layers.Input(shape=input_shape)
# Block 1
net = depthwise_separable_conv(inputs, num_filters=32, kernel_size=(3, 3), use_winograd=True)
net = depthwise_separable_conv(net, num_filters=64, kernel_size=(3, 3), strides=(2, 2), use_winograd=True)
net = depthwise_separable_conv(net, num_filters=64, kernel_size=(3, 3), use_winograd=True)
# Block 2
net = depthwise_separable_conv(net, num_filters=128, kernel_size=(3, 3), strides=(2, 2), use_winograd=True)
net = depthwise_separable_conv(net, num_filters=128, kernel_size=(3, 3), use_winograd=True)
# Block 3
net = depthwise_separable_conv(net, num_filters=256, kernel_size=(3, 3), strides=(2, 2), use_winograd=True)
net = depthwise_separable_conv(net, num_filters=256, kernel_size=(3, 3), use_winograd=True)
# Global Average Pooling
net = tf.keras.layers.GlobalAveragePooling2D()(net)
# Output
outputs = tf.keras.layers.Dense(num_classes, activation='softmax')(net)
model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
return model
```
这个简单的神经网络由3个深度可分离卷积块和一个全局平均池化层组成，最后接一个全连接层输出预测结果。在深度可分离卷积块中使用了Winograd算法来加速计算。
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