VGG16--slim

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network

模型参数：

https://github.com/tensorflow/models/tree/master/research/slim

slim实现

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import tensorflow.contrib.slim as slim

def vgg_16(inputs,
           num_classes=1000,
           is_training=True,
           dropout_keep_prob=0.5,
           spatial_squeeze=True,
           scope='vgg_16',
           fc_conv_padding='VALID',
           global_pool=False):
  """Oxford Net VGG 16-Layers version D Example.
  Note: All the fully_connected layers have been transformed to conv2d layers.
        To use in classification mode, resize input to 224x224.
  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    
    spatial_squeeze: whether or not should squeeze the spatial dimensions of the outputs. Useful to remove unnecessary dimensions for classification.
  
    global_pool: Optional boolean flag. If True, the input to the classification layer is avgpooled to size 1x1, for any input size. (This is not part of the original VGG architecture.)
    
  Returns:
    end_points: a dict of tensors with intermediate activations.
  """
  with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:
    end_points_collection = sc.original_name_scope + '_end_points'
    # Collect outputs for conv2d, fully_connected and max_pool2d.
    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')

      # Use conv2d instead of fully_connected layers.
      net = slim.conv2d(net, 4096, [7, 7], padding=fc_conv_padding, scope='fc6')
      net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                         scope='dropout6')
      net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
      # Convert end_points_collection into a end_point dict.
      end_points = slim.utils.convert_collection_to_dict(end_points_collection)
      if global_pool:
        net = tf.reduce_mean(net, [1, 2], keep_dims=True, name='global_pool')
        end_points['global_pool'] = net
      if num_classes:
        net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                           scope='dropout7')
        net = slim.conv2d(net, num_classes, [1, 1],
                          activation_fn=None,
                          normalizer_fn=None,
                          scope='fc8')
        if spatial_squeeze:
          net = tf.squeeze(net, [1, 2], name='fc8/squeezed')
        end_points[sc.name + '/fc8'] = net
      return net, end_points
vgg_16.default_image_size = 224


with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      activation_fn=tf.nn.relu,
                      weights_regularizer=slim.l2_regularizer(weight_decay),
                      biases_initializer=tf.zeros_initializer()):
    with slim.arg_scope([slim.conv2d], padding='SAME') as arg_sc:
      return arg_sc

Usage:

with slim.arg_scope(vgg.vgg_arg_scope()):
    outputs, end_points = vgg.vgg_16(inputs)