layers.py 文件源码

python
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项目:lsdc 作者: febert 项目源码 文件源码
def bias_add(inputs,
             activation_fn=None,
             initializer=init_ops.zeros_initializer,
             regularizer=None,
             reuse=None,
             variables_collections=None,
             outputs_collections=None,
             trainable=True,
             scope=None):
  """Adds a bias to the inputs.

  Can be used as a normalizer function for conv2d and fully_connected.

  Args:
    inputs: a tensor of with at least rank 2 and value for the last dimension,
      e.g. `[batch_size, depth]`, `[None, None, None, depth]`.
    activation_fn: activation function, default set to None to skip it and
      maintain a linear activation.
    initializer: An initializer for the bias, defaults to 0.
    regularizer: A regularizer like the result of
      `l1_regularizer` or `l2_regularizer`.
    reuse: whether or not the layer and its variables should be reused. To be
      able to reuse the layer scope must be given.
    variables_collections: optional collections for the variables.
    outputs_collections: collections to add the outputs.
    trainable: If `True` also add variables to the graph collection
      `GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
    scope: Optional scope for variable_scope.

  Returns:
    a tensor representing the result of adding biases to the inputs.
  """
  with variable_scope.variable_scope(scope, 'BiasAdd', [inputs],
                                     reuse=reuse) as sc:
    inputs = ops.convert_to_tensor(inputs)
    dtype = inputs.dtype.base_dtype
    num_features = utils.last_dimension(inputs.get_shape(), min_rank=2)
    biases_collections = utils.get_variable_collections(variables_collections,
                                                        'biases')
    biases = variables.model_variable('biases',
                                      shape=[num_features,],
                                      dtype=dtype,
                                      initializer=initializer,
                                      regularizer=regularizer,
                                      collections=biases_collections,
                                      trainable=trainable)
    outputs = nn.bias_add(inputs, biases)
    if activation_fn is not None:
      outputs = activation_fn(outputs)
    return utils.collect_named_outputs(outputs_collections,
                                       sc.original_name_scope, outputs)
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