手写 fully_connected全连接层

fully_connected用于生成全连接层，总体思路就是

用_variable_with_weight_decay生成w×x+b中的w，并加上偏置biases。

最后通过参数提供batch_norm和activation等功能

def fully_connected(inputs,
                    num_outputs,
                    scope,
                    use_xavier=True,
                    stddev=1e-3,
                    weight_decay=0.0,
                    activation_fn=tf.nn.relu,
                    bn=False,
                    bn_decay=None,
                    is_training=None):
  """ Fully connected layer with non-linear operation.

  Args:
    inputs: 2-D tensor BxN
    num_outputs: int

  Returns:
    Variable tensor of size B x num_outputs.
  """
  with tf.variable_scope(scope) as sc:

    num_input_units = inputs.get_shape()[-1].value
    weights = _variable_with_weight_decay('weights',
                                          shape=[num_input_units, num_outputs],
                                          use_xavier=use_xavier,
                                          stddev=stddev,
                                          wd=weight_decay)
    outputs = tf.matmul(inputs, weights)
    biases = _variable_on_cpu('biases', [num_outputs],
                             tf.constant_initializer(0.0))
    outputs = tf.nn.bias_add(outputs, biases)


    if bn:
      outputs = batch_norm_for_fc(outputs, is_training, bn_decay, 'bn')

    if activation_fn is not None:
      outputs = activation_fn(outputs)

    return outputs