iclr_mnr.py 文件源码

def cnn_layers(inputs, scope, end_points_collection, dropout_keep_prob=0.8, is_training=True):
  with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                      outputs_collections=[end_points_collection]):
    net = slim.conv2d(inputs, 48, [5, 5], scope='conv1')
    net = slim.max_pool2d(net, [2, 2], 2, scope='pool1')
    net = slim.conv2d(net, 64, [5, 5], scope='conv2')
    net = slim.max_pool2d(net, [2, 2], 2, scope='pool2')
    net = slim.conv2d(net, 128, [5, 5], scope='conv3')
    net = slim.max_pool2d(net, [2, 2], 2, scope='pool3')
    net = slim.conv2d(net, 160, [5, 5], scope='conv4')
    net = slim.conv2d(net, 192, [5, 5], scope='conv5')
    net = slim.conv2d(net, 192, [5, 5], scope='conv6')
    net = slim.conv2d(net, 192, [5, 5], scope='conv7')
    net = slim.flatten(net)

    # By removing the fc layer, we'll get much smaller model with almost the same performance
    # net = slim.fully_connected(net, 3072, scope='fc8')

  return net, end_points_collection