

def random_normal(shape, mean=0.0, std=1.0, dtype=_FLOATX, seed=None):
    if (seed is None):
        seed = np.random.randint(10000000.0)
    return tf.random_normal(shape, mean=mean, stddev=mean, dtype=dtype, seed=seed)

dataset/ETHPy150Open fchollet/keras/keras/backend/tensorflow_backend.py random_normal/VarMisuse@60/67 `std`->`mean`