def _bin_op(tf_fun, a, b, promote=True):
if promote:
a, b = array_ops._promote_dtype(a, b) # pylint: disable=protected-access
else:
a = array_ops.array(a)
b = array_ops.array(b)
return utils.tensor_to_ndarray(tf_fun(a.data, b.data))
def kron(a, b):
# pylint: disable=protected-access,g-complex-comprehension
a, b = array_ops._promote_dtype(a, b)
ndim = max(a.ndim, b.ndim)
if a.ndim < ndim:
a = array_ops.reshape(a, array_ops._pad_left_to(ndim, a.shape))
if b.ndim < ndim:
b = array_ops.reshape(b, array_ops._pad_left_to(ndim, b.shape))
a_reshaped = array_ops.reshape(a, [i for d in a.shape for i in (d, 1)])
b_reshaped = array_ops.reshape(b, [i for d in b.shape for i in (1, d)])
out_shape = tuple(np.multiply(a.shape, b.shape))
return array_ops.reshape(a_reshaped * b_reshaped, out_shape)
def clip(a, a_min, a_max): # pylint: disable=missing-docstring
if a_min is None and a_max is None:
raise ValueError(
'Not more than one of `a_min` and `a_max` may be `None`.')
if a_min is None:
return minimum(a, a_max)
elif a_max is None:
return maximum(a, a_min)
else:
a, a_min, a_max = array_ops._promote_dtype(a, a_min, a_max) # pylint: disable=protected-access
return utils.tensor_to_ndarray(
tf.clip_by_value(
*utils.tf_broadcast(a.data, a_min.data, a_max.data)))
