(x, n_inputs, n_outputs, name)
return y
Simple linear layer.

  Args:
    x: The [mb, time, channels] tensor input.
    n_inputs: The input number of channels.
    n_outputs: The output number of channels.
    name: The variable scope to provide to W and biases.

  Returns:
    y: The output of the operation.
Simple linear layer.
Simple linear layer .
def linear(x, n_inputs, n_outputs, name):
  """Simple linear layer.

  Args:
    x: The [mb, time, channels] tensor input.
    n_inputs: The input number of channels.
    n_outputs: The output number of channels.
    name: The variable scope to provide to W and biases.

  Returns:
    y: The output of the operation.
  """
  w = tf.get_variable(
      name=name + "/W", shape=[1, 1, n_inputs, n_outputs], dtype=tf.float32)
  b = tf.get_variable(
      name=name + "/biases", shape=[n_outputs], dtype=tf.float32)
  y = tf.nn.bias_add(tf.matmul(x[:, 0, :], w[0][0]), b)
  y = tf.expand_dims(y, 1)
  return y
def linear ( x , n_inputs , n_outputs , name ) : w = tf . get_variable ( name = name + "/W" , shape = [ 1 , 1 , n_inputs , n_outputs ] , dtype = tf . float32 ) b = tf . get_variable ( name = name + "/biases" , shape = [ n_outputs ] , dtype = tf . float32 ) y = tf . nn . bias_add ( tf . matmul ( x [ : , 0 , : ] , w [ 0 ] [ 0 ] ) , b ) y = tf . expand_dims ( y , 1 ) return y