def __init__(self, data, dtype=None, device=None):
if isinstance(data, (tuple, list, int, float)):
data = np.array(data)
if not isinstance(data, np.ndarray):
raise ValueError("data must be a list, tuple, or Numpy array.")
if dtype is None:
dtype = _numpy_dtype_to_dtype(data.dtype)
if device is None:
device = Device("CPU:0")
super(Tensor, self).__init__(data, dtype, device)
def eye(n, dtype=Dtype.Float64, device=Device("CPU:0")):
"""
Create an identity square matrix.
Args:
n (int): size of square matrix
dtype (Dtype): Data type of the tensor.
device (Device): Device where the tensor is created.
"""
return super(Tensor, Tensor).eye(n, dtype, device)
def ones(shape, dtype, device=Device("CPU:0")):
"""
Create a tensor with fill with ones.
Args:
shape (list, tuple, SizeVector): Shape of the tensor.
dtype (Dtype): Data type of the tensor.
device (Device): Device where the tensor is created.
"""
if not isinstance(shape, SizeVector):
shape = SizeVector(shape)
return super(Tensor, Tensor).ones(shape, dtype, device)
def empty(shape, dtype, device=Device("CPU:0")):
"""
Create a tensor with uninitilized values.
Args:
shape (list, tuple, SizeVector): Shape of the tensor.
dtype (Dtype): Data type of the tensor.
device (Device): Device where the tensor is created.
"""
if not isinstance(shape, SizeVector):
shape = SizeVector(shape)
return super(Tensor, Tensor).empty(shape, dtype, device)
def full(shape, fill_value, dtype, device=Device("CPU:0")):
"""
Create a tensor with fill with the specified value.
Args:
shape (list, tuple, SizeVector): Shape of the tensor.
fill_value (scalar): The value to be filled.
dtype (Dtype): Data type of the tensor.
device (Device): Device where the tensor is created.
"""
if not isinstance(shape, SizeVector):
shape = SizeVector(shape)
return super(Tensor, Tensor).full(shape, fill_value, dtype, device)
def __init__(self, shape, dtype=None, device=None, size=None):
if isinstance(shape, list) or isinstance(shape, tuple):
shape = SizeVector(shape)
elif isinstance(shape, SizeVector):
pass
else:
raise ValueError('shape must be a list, tuple, or SizeVector')
if dtype is None:
dtype = Dtype.Float32
if device is None:
device = Device("CPU:0")
if size is None:
size = 0
super(TensorList, self).__init__(shape, dtype, device, size)
def from_tensors(tensors, device=None):
"""
Returns a TensorList from a list of existing tensors.
Args:
tensors: The list of Tensor to construct from.
The tensors' shapes should be compatible.
device: The device where the tensorlist is targeted.
"""
if not isinstance(tensors, list) and not isinstance(tensors, tuple):
raise ValueError('tensors must be a list or tuple')
for tensor in tensors:
if not isinstance(tensor, Tensor):
raise ValueError(
'every element of the input list must be a valid tensor')
if device is None:
device = Device("CPU:0")
return super(TensorList, TensorList).from_tensors(tensors, device)