tensorflow实现vggnet16结构的python代码

以下是tensorflow实现vggnet16结构的python代码：

import tensorflow as tf

# 定义vggnet16结构
def VGG16(inputs, num_classes=1000, is_training=True, dropout_keep_prob=0.5, spatial_squeeze=True, scope='vgg_16'):
    with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:
        end_points_collection = sc.name + '_end_points'
        # 使用 collection 将所有的端点收集起来，方便最后输出
        with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                            outputs_collections=end_points_collection):
            net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
            net = slim.max_pool2d(net, [2, 2], scope='pool1')
            net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
            net = slim.max_pool2d(net, [2, 2], scope='pool2')
            net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
            net = slim.max_pool2d(net, [2, 2], scope='pool3')
            net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
            net = slim.max_pool2d(net, [2, 2], scope='pool4')
            net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
            net = slim.max_pool2d(net, [2, 2], scope='pool5')
            # 将卷积层输出的结果转化为全连接层的输入格式，以便后面的全连接层对全部的features做预测
            net = slim.flatten(net)
            net = slim.fully_connected(net, 4096, scope='fc6')
            net = slim.dropout(net, dropout_keep_prob, is_training=is_training, scope='dropout6')
            net = slim.fully_connected(net, 4096, scope='fc7')
            net = slim.dropout(net, dropout_keep_prob, is_training=is_training, scope='dropout7')
            logits = slim.fully_connected(net, num_classes, activation_fn=None, scope='fc8')
            end_points = slim.utils.convert_collection_to_dict(end_points_collection)
            if spatial_squeeze:
                logits = tf.squeeze(logits, [1, 2], name='fc8/squeezed')
                end_points = {sc.name + '/fc8': logits}
            return logits, end_points