lstm1d.py 文件源码

def sequence_softmax(inputs, noutput, scope=None, name=None, linear_name=None):
  """Run a softmax layer over all the time steps of an input sequence.

  Args:
    inputs: (length, batch_size, depth) tensor
    noutput: output depth
    scope: optional scope name
    name: optional name for output tensor
    linear_name: name for linear (pre-softmax) output

  Returns:
    A tensor of size (length, batch_size, noutput).

  """
  length, _, ninputs = _shape(inputs)
  inputs_u = array_ops.unstack(inputs)
  output_u = []
  with variable_scope.variable_scope(scope, "SequenceSoftmax", [inputs]):
    initial_w = random_ops.truncated_normal([0 + ninputs, noutput], stddev=0.1)
    initial_b = constant_op.constant(0.1, shape=[noutput])
    w = variables.model_variable("weights", initializer=initial_w)
    b = variables.model_variable("biases", initializer=initial_b)
    for i in xrange(length):
      with variable_scope.variable_scope(scope, "SequenceSoftmaxStep",
                                         [inputs_u[i]]):
        # TODO(tmb) consider using slim.fully_connected(...,
        # activation_fn=tf.nn.softmax)
        linear = nn_ops.xw_plus_b(inputs_u[i], w, b, name=linear_name)
        output = nn_ops.softmax(linear)
        output_u += [output]
    outputs = array_ops.stack(output_u, name=name)
  return outputs