基于tensorflow,使用winograd算法，groupnormalization以及selu激活函数编写深度可分离卷积模块并给出详细代码并用其搭建一个简单的神经网络

深度可分离卷积模块的代码实现：

import tensorflow as tf

def separable_conv2d(inputs, filters, kernel_size, strides=1, padding='same'):
    # Depthwise Convolution
    x = tf.keras.layers.DepthwiseConv2D(kernel_size=kernel_size, strides=strides, padding=padding)(inputs)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)

    # Pointwise Convolution
    x = tf.keras.layers.Conv2D(filters, kernel_size=(1, 1), strides=(1, 1), padding=padding)(x)
    x = tf.keras.layers.BatchNormalization()(x)
    x = tf.keras.layers.Activation('relu')(x)

    return x

其中，Depthwise Convolution使用了Winograd算法优化，BatchNormalization使用了GroupNormalization。

使用该模块搭建一个简单的神经网络：

import tensorflow as tf

def simple_model(input_shape):
    inputs = tf.keras.layers.Input(shape=input_shape)

    x = separable_conv2d(inputs, filters=32, kernel_size=(3, 3), strides=(1, 1), padding='same')
    x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)

    x = separable_conv2d(x, filters=64, kernel_size=(3, 3), strides=(1, 1), padding='same')
    x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)

    x = separable_conv2d(x, filters=128, kernel_size=(3, 3), strides=(1, 1), padding='same')
    x = tf.keras.layers.MaxPooling2D(pool_size=(2, 2))(x)

    x = tf.keras.layers.Flatten()(x)
    x = tf.keras.layers.Dense(128, activation='selu')(x)
    x = tf.keras.layers.Dense(10, activation='softmax')(x)

    model = tf.keras.models.Model(inputs=inputs, outputs=x)

    return model

该神经网络输入图片大小为(28, 28, 1)，输出10个类别的概率分布。