alexnet.py 文件源码

def cnn_layers(inputs, scope, end_points_collection, dropout_keep_prob=0.8, is_training=True):
  # Collect outputs for conv2d and max_pool2d.
  with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                      outputs_collections=[end_points_collection]):
    net = slim.conv2d(inputs, 64, [11, 11], 4, padding='VALID',
                      scope='conv1')
    net = slim.max_pool2d(net, [3, 3], 2, scope='pool1')
    net = slim.conv2d(net, 192, [5, 5], scope='conv2')
    net = slim.max_pool2d(net, [3, 3], 2, scope='pool2')
    net = slim.conv2d(net, 384, [3, 3], scope='conv3')
    net = slim.conv2d(net, 384, [3, 3], scope='conv4')
    net = slim.conv2d(net, 256, [3, 3], scope='conv5')
    net = slim.max_pool2d(net, [3, 3], 2, scope='pool5')

  with slim.arg_scope([slim.conv2d],
                      weights_initializer=trunc_normal(0.005),
                      biases_initializer=tf.constant_initializer(0.1),
                      outputs_collections=[end_points_collection]):
    net = slim.conv2d(net, 4096, [5, 5], padding='VALID',
                      scope='fc6')
    net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                       scope='dropout6')
    net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
    net = slim.dropout(net, dropout_keep_prob, is_training=is_training,
                       scope='dropout7')

  return net, end_points_collection