tf实现vgg网络的代码

以下是TensorFlow实现VGG网络的代码：

import tensorflow as tf

def vgg_net(inputs):
    # 定义卷积层函数
    def conv_layer(inputs, filters, kernel_size, strides, padding, activation):
        conv = tf.layers.conv2d(inputs=inputs, filters=filters, kernel_size=kernel_size,
                                strides=strides, padding=padding, activation=activation)
        return conv
    
    # 定义最大池化层函数
    def max_pool(inputs, pool_size, strides, padding):
        pool = tf.layers.max_pooling2d(inputs=inputs, pool_size=pool_size,
                                       strides=strides, padding=padding)
        return pool
    
    # 定义输入层
    input_layer = tf.reshape(inputs, [-1, 224, 224, 3])
    
    # 定义卷积层
    conv1_1 = conv_layer(input_layer, 64, 3, 1, 'same', tf.nn.relu)
    conv1_2 = conv_layer(conv1_1, 64, 3, 1, 'same', tf.nn.relu)
    pool1 = max_pool(conv1_2, 2, 2, 'valid')
    
    conv2_1 = conv_layer(pool1, 128, 3, 1, 'same', tf.nn.relu)
    conv2_2 = conv_layer(conv2_1, 128, 3, 1, 'same', tf.nn.relu)
    pool2 = max_pool(conv2_2, 2, 2, 'valid')
    
    conv3_1 = conv_layer(pool2, 256, 3, 1, 'same', tf.nn.relu)
    conv3_2 = conv_layer(conv3_1, 256, 3, 1, 'same', tf.nn.relu)
    conv3_3 = conv_layer(conv3_2, 256, 3, 1, 'same', tf.nn.relu)
    pool3 = max_pool(conv3_3, 2, 2, 'valid')
    
    conv4_1 = conv_layer(pool3, 512, 3, 1, 'same', tf.nn.relu)
    conv4_2 = conv_layer(conv4_1, 512, 3, 1, 'same', tf.nn.relu)
    conv4_3 = conv_layer(conv4_2, 512, 3, 1, 'same', tf.nn.relu)
    pool4 = max_pool(conv4_3, 2, 2, 'valid')
    
    conv5_1 = conv_layer(pool4, 512, 3, 1, 'same', tf.nn.relu)
    conv5_2 = conv_layer(conv5_1, 512, 3, 1, 'same', tf.nn.relu)
    conv5_3 = conv_layer(conv5_2, 512, 3, 1, 'same', tf.nn.relu)
    pool5 = max_pool(conv5_3, 2, 2, 'valid')
    
    # 将卷积层输出展开
    flatten = tf.layers.flatten(pool5)
    
    # 定义全连接层
    fc6 = tf.layers.dense(flatten, 4096, activation=tf.nn.relu)
    fc7 = tf.layers.dense(fc6, 4096, activation=tf.nn.relu)
    fc8 = tf.layers.dense(fc7, 1000, activation=None)
    
    # 定义输出层
    output = tf.nn.softmax(fc8)
    
    return output

注意，该代码实现了VGG16网络的结构，最后输出1000个类别的概率。如果需要输出其他数量的类别概率，可以修改最后一层全连接层的输出大小。同时，如果需要使用VGG19网络，只需要在上述代码中增加4个卷积层和2个全连接层即可。