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
时间: 2024-06-10 09:10:41 浏览: 15
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。