首先，祝我自己生日快乐
其次，今天想要编写的是卷积神经网络函数，其实就是改写一下之前那个数字训练的神经网络
代码如下：
import paddle.fluid as fluid def conv_bn_layer(input, filter_size, num_filters, stride, padding, channels=None, num_groups=1, act='relu', use_cudnn=True): conv = fluid.layers.conv2d(input=input, num_filters=num_filters, filter_size=filter_size, stride=stride, padding=padding, groups=num_groups, act=None, use_cudnn=use_cudnn, bias_attr=False) return fluid.layers.batch_norm(input=conv, act=act) def depthwise_separable(input, num_filters1, num_filters2, num_groups, stride, scale): depthwise_conv = conv_bn_layer(input=input, filter_size=3, num_filters=int(num_filters1 * scale), stride=stride, padding=1, num_groups=int(num_groups * scale), use_cudnn=False) pointwise_conv = conv_bn_layer(input=depthwise_conv, filter_size=1, num_filters=int(num_filters2 * scale), stride=1, padding=0) return pointwise_conv def net(input, class_dim, scale=1.0): # conv1: 112x112 input = conv_bn_layer(input=input, filter_size=3, channels=3, num_filters=int(32 * scale), stride=2, padding=1) # 56x56 input = depthwise_separable(input=input, num_filters1=32, num_filters2=64, num_groups=32, stride=1, scale=scale) input = depthwise_separable(input=input, num_filters1=64, num_filters2=128, num_groups=64, stride=2, scale=scale) # 28x28 input = depthwise_separable(input=input, num_filters1=128, num_filters2=128, num_groups=128, stride=1, scale=scale) input = depthwise_separable(input=input, num_filters1=128, num_filters2=256, num_groups=128, stride=2, scale=scale) # 14x14 input = depthwise_separable(input=input, num_filters1=256, num_filters2=256, num_groups=256, stride=1, scale=scale) input = depthwise_separable(input=input, num_filters1=256, num_filters2=512, num_groups=256, stride=2, scale=scale) # 14x14 for i in range(5): input = depthwise_separable(input=input, num_filters1=512, num_filters2=512, num_groups=512, stride=1, scale=scale) # 7x7 input = depthwise_separable(input=input, num_filters1=512, num_filters2=1024, num_groups=512, stride=2, scale=scale) input = depthwise_separable(input=input, num_filters1=1024, num_filters2=1024, num_groups=1024, stride=1, scale=scale) feature = fluid.layers.pool2d(input=input, pool_size=0, pool_stride=1, pool_type='avg', global_pooling=True) net = fluid.layers.fc(input=feature, size=class_dim, act='softmax') return net
好，就是这样