def random_normal_variable(shape, mean, scale, dtype=None, name=None, seed=None):
 if (dtype is None):
  dtype = floatx()
 shape = tuple(map(int, shape))
 tf_dtype = _convert_string_dtype(dtype)
 if (seed is None):
  seed = np.random.randint(1000000000.0)
 value = tf.random_normal_initializer(mean, scale, dtype=tf_dtype, seed=seed)(shape)
 return variable(value, dtype=dtype, name=name)

def random_normal_variable ( shape , mean , scale , dtype = None , name = None , seed = None ) : if ( dtype is None ) : dtype = floatx ( ) shape = tuple ( map ( int , shape ) ) tf_dtype = _convert_string_dtype ( dtype ) if ( seed is None ) : seed = np . random . randint ( 1000000000.0 ) value = tf . random_normal_initializer ( mean , scale , dtype = tf_dtype , seed = seed ) ( shape ) return variable ( value , dtype = dtype , name = name )
instantiates an keras variable filled with samples drawn from a normal distribution and returns it .