vgg16代码实现paddlepaddle

VGG16是一种经典的卷积神经网络，它在ImageNet数据集上取得了很好的表现。以下是VGG16的PaddlePaddle实现代码：

import paddle.fluid as fluid

class VGG(fluid.dygraph.Layer):
    def __init__(self, num_classes=10):
        super(VGG, self).__init__()
        self.conv1_1 = fluid.dygraph.Conv2D(num_channels=3, num_filters=64, filter_size=3, padding=1, act='relu')
        self.conv1_2 = fluid.dygraph.Conv2D(num_channels=64, num_filters=64, filter_size=3, padding=1, act='relu')
        self.pool1 = fluid.dygraph.Pool2D(pool_size=2, pool_type='max', pool_stride=2)
        self.conv2_1 = fluid.dygraph.Conv2D(num_channels=64, num_filters=128, filter_size=3, padding=1, act='relu')
        self.conv2_2 = fluid.dygraph.Conv2D(num_channels=128, num_filters=128, filter_size=3, padding=1, act='relu')
        self.pool2 = fluid.dygraph.Pool2D(pool_size=2, pool_type='max', pool_stride=2)
        self.conv3_1 = fluid.dygraph.Conv2D(num_channels=128, num_filters=256, filter_size=3, padding=1, act='relu')
        self.conv3_2 = fluid.dygraph.Conv2D(num_channels=256, num_filters=256, filter_size=3, padding=1, act='relu')
        self.conv3_3 = fluid.dygraph.Conv2D(num_channels=256, num_filters=256, filter_size=3, padding=1, act='relu')
        self.pool3 = fluid.dygraph.Pool2D(pool_size=2, pool_type='max', pool_stride=2)
        self.conv4_1 = fluid.dygraph.Conv2D(num_channels=256, num_filters=512, filter_size=3, padding=1, act='relu')
        self.conv4_2 = fluid.dygraph.Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1, act='relu')
        self.conv4_3 = fluid.dygraph.Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1, act='relu')
        self.pool4 = fluid.dygraph.Pool2D(pool_size=2, pool_type='max', pool_stride=2)
        self.conv5_1 = fluid.dygraph.Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1, act='relu')
        self.conv5_2 = fluid.dygraph.Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1, act='relu')
        self.conv5_3 = fluid.dygraph.Conv2D(num_channels=512, num_filters=512, filter_size=3, padding=1, act='relu')
        self.pool5 = fluid.dygraph.Pool2D(pool_size=2, pool_type='max', pool_stride=2)
        self.fc1 = fluid.dygraph.Linear(input_dim=512*7*7, output_dim=4096, act='relu')
        self.drop1 = fluid.dygraph.Dropout(p=0.5)
        self.fc2 = fluid.dygraph.Linear(input_dim=4096, output_dim=4096, act='relu')
        self.drop2 = fluid.dygraph.Dropout(p=0.5)
        self.fc3 = fluid.dygraph.Linear(input_dim=4096, output_dim=num_classes)

    def forward(self, inputs, label=None):
        x = self.conv1_1(inputs)
        x = self.conv1_2(x)
        x = self.pool1(x)
        x = self.conv2_1(x)
        x = self.conv2_2(x)
        x = self.pool2(x)
        x = self.conv3_1(x)
        x = self.conv3_2(x)
        x = self.conv3_3(x)
        x = self.pool3(x)
        x = self.conv4_1(x)
        x = self.conv4_2(x)
        x = self.conv4_3(x)
        x = self.pool4(x)
        x = self.conv5_1(x)
        x = self.conv5_2(x)
        x = self.conv5_3(x)
        x = self.pool5(x)
        x = fluid.layers.reshape(x, [-1, 512*7*7])
        x = self.fc1(x)
        x = self.drop1(x)
        x = self.fc2(x)
        x = self.drop2(x)
        x = self.fc3(x)
        if label is not None:
            acc = fluid.layers.accuracy(input=x, label=label)
            return x, acc
        else:
            return x

其中，VGG类继承自fluid.dygraph.Layer，包含了VGG16网络的所有层。在forward方法中，我们按照VGG16的结构依次调用各个层，并返回最后的输出。在训练时，我们可以通过调用accuracy函数计算准确率。