layers.py 文件源码

def __init__(self, num_units_out, activation=tf.nn.relu, initializer=None,
               input_keep_prob=None, output_keep_prob=None,
               normalization_fn=None, weight_norm=False, name=None):
    """Initializes the layer.

    Args:
      num_units_out: The number of output units in the layer.
      activation: The activation function. Default is ReLU. Use `None` to get a
        linear layer.
      initializer: The initializer for the weights. Defaults to uniform unit
        scaling with factor derived in <http://arxiv.org/pdf/1412.6558v3.pdf>
        if activation is ReLU, ReLU6, tanh, or linear. Otherwise defaults to
        truncated normal initialization with a standard deviation of 0.01.
      input_keep_prob: Optional scalar float32 tensor for dropout on input.
        Feed 1.0 at serving to disable dropout.
      output_keep_prob: Optional scalar float32 tensor for dropout on output.
        Feed 1.0 at serving to disable dropout.
      normalization_fn: Optional normalization function that will be inserted
        before nonlinearity.
      weight_norm: A bool to control whether weight normalization is used. See
        https://arxiv.org/abs/1602.07868 for how it works.
      name: An optional string name. Defaults to `FC_%d % num_units_out`. Used
        to name the variable scope where the variables for the layer live.
    """
    self.set_constructor_args('td.FC', *get_local_arguments(FC.__init__, True))

    if not initializer:
      # TODO(SamEisenstat): This constant is calibrated for ReLU, something else
      # might be better for ReLU6.
      if activation in [tf.nn.relu, tf.nn.relu6]:
        initializer = tf.uniform_unit_scaling_initializer(1.43)
      elif activation == tf.tanh:
        initializer = tf.uniform_unit_scaling_initializer(1.15)
      elif not activation:
        initializer = tf.uniform_unit_scaling_initializer(1.0)
      else:
        initializer = tf.truncated_normal_initializer(stddev=0.01)
    self._activation = activation
    self._initializer = initializer
    self._input_keep_prob = input_keep_prob
    self._output_keep_prob = output_keep_prob
    self._normalization_fn = normalization_fn
    self._weight_norm = weight_norm
    if name is None: name = 'FC_%d' % num_units_out
    super(FC, self).__init__(
        output_type=tdt.TensorType([num_units_out]), name_or_scope=name)