def leaky_relu(x, name=None):
  """Creates a leaky_relu.

  This is an alternate non-linearity to relu. The leaky part of the relu may
  prevent dead Neurons in a model since the gradient doesn't go completely to
  0.

  Args:
    x: The input tensor.
    name: Optional name for this op.
  Returns:
    x if x > 0 otherwise 0.01 * x.
  """
  with tf.name_scope(name, 'leaky_relu', [x]) as scope:
    x = tf.convert_to_tensor(x, name='x')
    return tf.where(tf.less(x, 0.0), 0.01 * x, x, name=scope)
def leaky_relu ( x , name = None ) : with tf . name_scope ( name , 'leaky_relu' , [ x ] ) as scope : x = tf . convert_to_tensor ( x , name = 'x' ) return tf . where ( tf . less ( x , 0.0 ) , 0.01 * x , x , name = scope )
Creates a leaky_relu.

  This is an alternate non-linearity to relu. The leaky part of the relu may
  prevent dead Neurons in a model since the gradient doesn't go completely to
  0.

  Args:
    x: The input tensor.
    name: Optional name for this op.
  Returns:
    x if x > 0 otherwise 0.01 * x.