(X, std=1.0)
return tf.add(X, tf.random_normal(shape=tf.shape(X),
                                      mean=0.0,
                                      stddev=std))
Take an input tensor and add Gaussian noise.

    Parameters
    ----------
    X : Tensor/Placeholder
        Input to corrupt.
    std: float
        Desired standard deviation of the noise.

    Returns
    -------
    x_corrupted : Tensor
        Input tensor plus random Gaussian noise with mean 0.0 and standard
        deviation `std`.
Take an input tensor and add Gaussian noise.
Take an input tensor and add Gaussian noise .
def gaussian_noise(X, std=1.0):
    """Take an input tensor and add Gaussian noise.

    Parameters
    ----------
    X : Tensor/Placeholder
        Input to corrupt.
    std: float
        Desired standard deviation of the noise.

    Returns
    -------
    x_corrupted : Tensor
        Input tensor plus random Gaussian noise with mean 0.0 and standard
        deviation `std`.
    """
    return tf.add(X, tf.random_normal(shape=tf.shape(X),
                                      mean=0.0,
                                      stddev=std))
def gaussian_noise ( X , std = 1.0 ) : return tf . add ( X , tf . random_normal ( shape = tf . shape ( X ) , mean = 0.0 , stddev = std ) )