def kron(a, b):
a, b = array_creation._promote_dtype(a, b)
ndim = max(a.ndim, b.ndim)
if a.ndim < ndim:
a = array_methods.reshape(a, _pad_left_to(ndim, a.shape))
if b.ndim < ndim:
b = array_methods.reshape(b, _pad_left_to(ndim, b.shape))
a_reshaped = array_methods.reshape(a, [i for d in a.shape for i in (d, 1)])
b_reshaped = array_methods.reshape(b, [i for d in b.shape for i in (1, d)])
out_shape = tuple(np.multiply(a.shape, b.shape))
return array_methods.reshape(a_reshaped * b_reshaped, out_shape)
def f(x):
# pylint: disable=g-long-lambda
x = array_creation.asarray(x)
return array_creation.asarray(
utils.cond(
utils.greater(n, tf.rank(x)), lambda: array_methods.reshape(
x, new_shape(n, tf.shape(x.data))).data, lambda: x.data))
def run_test(arr, newshape, *args, **kwargs):
for fn1 in self.array_transforms:
for fn2 in self.array_transforms:
arr_arg = fn1(arr)
newshape_arg = fn2(newshape)
self.match(
array_methods.reshape(arr_arg, newshape_arg, *args, **kwargs),
np.reshape(arr_arg, newshape, *args, **kwargs))
def reshape(x, num_devices=2):
x_shape = list(x.shape)
batch_size = x_shape[0]
batch_size_per_device = batch_size // num_devices
# New shape.
new_shape_prefix = [num_devices, batch_size_per_device]
return array_methods.reshape(x, new_shape_prefix + x_shape[1:])
def f(x):
x = array_creation.asarray(x)
if x.ndim < n:
x = array_methods.reshape(x, new_shape(n, x.shape))
return x
