def randint(low, high, size, dtype="int64", requires_grad=False):
return Tensor(
fluid.layers.randint(low,
high=high,
shape=size,
out=None,
dtype=dtype,
device=None,
stop_gradient=not requires_grad))
def rand(*shape):
if isinstance(shape, int):
shape = [shape]
if isinstance(shape[0], Iterable):
shape = shape[0]
return Tensor(paddle.rand(shape))
def matmul(x, y):
return Tensor(fluid.layers.matmul(x, y))
def dot(x, y):
return Tensor(fluid.layers.dot(x, y))
def sigmoid(x):
return Tensor(fluid.layers.sigmoid(x))
def bmm(x, y):
return Tensor(fluid.layers.bmm(x, y))
def arange(*args, **kwargs):
return Tensor(np.arange(*args, **kwargs).astype("int32"))
def flip(self, dim):
return Tensor(paddle.flip(self, dims=[dim]))
def trace(x, offset=0, dim1=0, dim2=1, out=None):
return Tensor(paddle.trace(x, offset, dim1, dim2, out))
def bmm(x, y, transpose=False):
if transpose:
y = y.transpose(len(y.shape) - 1, len(y.shape) - 2)
return Tensor(paddle.bmm(x, y))
def eye(n, m=None):
if m is None:
m = n
return Tensor(paddle.eye(n, m))
def from_numpy(x):
return Tensor(x)
def LongTensor(x):
if isinstance(x, int):
return Tensor(paddle.to_tensor([x]))
if isinstance(x, list):
x = paddle.to_tensor(x, dtype="int64")
return convertTensor(x.astype("int64"))
def floor(x):
return Tensor(paddle.floor(x))
def flip(self, dim):
return Tensor(fluid.layers.flip(self, dims=[dim]))
def bmm(x, y):
return Tensor(paddle.bmm(x, y))
def linspace(start, stop, num, dtype="float32"):
return Tensor(fluid.layers.linspace(start, stop, num, dtype))
def eye(n, m):
return Tensor(paddle.eye(n, m))
def LongTensor(x):
if isinstance(x, int):
return Tensor(fluid.Tensor)
if isinstance(x, list):
x = np.array(x, dtype=np.int32)
return Tensor(x)
def dot(x, y):
return Tensor(paddle.dot(x, y))
def trace(x, offset=0, dim1=0, dim2=1, out=None):
return Tensor(fluid.layers.trace(x, offset, dim1, dim2, out))
def squeeze(x, axes=[-1]):
return Tensor(paddle.squeeze(x, axes))
def eye(n, m):
return Tensor(fluid.layers.eye(n, m))
def matmul(x, y):
return Tensor(paddle.matmul(x, y))
def tanh(x):
return Tensor(fluid.layers.tanh(x))
def full_like(x, fill_value):
return Tensor.new_full(x, x.shape, fill_value)
def squeeze(x, axes=[-1]):
return Tensor(fluid.layers.squeeze(x, axes))
def norm(input, p="fro", dim=None, keepdim=False, out=None, dtype=None):
from . import linalg
return Tensor(linalg.norm(input, p=p, axis=dim, keepdim=keepdim,
name=None))
def tensor(x, dtype=np.float32):
if isinstance(x, list):
x = np.array(x, dtype=dtype)
if isinstance(x, int) or isinstance(x, np.int64):
return zeros(x)
return Tensor(x)
def randint(low, high, size=[1], dtype="int32", requires_grad=False):
return Tensor(
paddle.randint(low=low, high=high, shape=size, dtype=dtype, name=None))