common.py 文件源码

def _conv_layer(self, bottom, filter_size, filter_num, scope_name, bottom_channel=None, padding='SAME'):
    if not bottom_channel:
      _, _, _, bottom_channel = bottom.get_shape().as_list()
    with tf.variable_scope(scope_name):
      kernel = tf.Variable(
        tf.truncated_normal([*filter_size, bottom_channel, filter_num], dtype=tf.float32, stddev=1e-1),
        trainable=False,
        name='weights'
      )

      conv = tf.nn.conv2d(bottom, kernel, [1, 1, 1, 1], padding=padding)
      biases = tf.Variable(
        tf.constant(0.0, shape=[filter_num], dtype=tf.float32),
        trainable=True,
        name='bias'
      )
      out = tf.nn.bias_add(conv, biases)

      return out