Interface: Torch
torchlive/torch.Torch
Properties
channelsLast
• channelsLast: "channelsLast"
contiguousFormat
• contiguousFormat: "contiguousFormat"
double
• double: "double"
float
• float: "float"
float32
• float32: "float32"
float64
• float64: "float64"
int
• int: "int"
int16
• int16: "int16"
int32
• int32: "int32"
int64
• int64: "int64"
int8
• int8: "int8"
jit
• jit: JIT
JIT module
long
• long: "long"
preserveFormat
• preserveFormat: "preserveFormat"
short
• short: "short"
uint8
• uint8: "uint8"
Methods
arange
▸ arange(end, options?): Tensor
Returns a 1-D tensor of size (end - 0) / 1 with values from the interval
[0, end) taken with common difference step beginning from start.
https://pytorch.org/docs/1.12/generated/torch.arange.html
Parameters
| Name | Type | Description |
|---|---|---|
end | number | The ending value for the set of points. |
options? | TensorOptions |
Returns
▸ arange(start, end, options?): Tensor
Returns a 1-D tensor of size (end - start) / 1 with values from the
interval [start, end) taken with common difference 1 beginning from
start.
https://pytorch.org/docs/1.12/generated/torch.arange.html
Parameters
| Name | Type | Description |
|---|---|---|
start | number | The starting value for the set of points. |
end | number | The ending value for the set of points. |
options? | TensorOptions |
Returns
▸ arange(start, end, step, options?): Tensor
Returns a 1-D tensor of size (end - start) / step with values from the
interval [start, end) taken with common difference step beginning from
start.
https://pytorch.org/docs/1.12/generated/torch.arange.html
Parameters
| Name | Type | Description |
|---|---|---|
start | number | The starting value for the set of points. |
end | number | The ending value for the set of points. |
step | number | The gap between each pair of adjacent points. |
options? | TensorOptions |
Returns
cat
▸ cat(tensors, options?): Tensor
Concatenate a list of tensors along the specified axis, which default to be axis 0
https://pytorch.org/docs/1.12/generated/torch.cat.html
Parameters
| Name | Type | Description |
|---|---|---|
tensors | Tensor[] | A sequence of Tensor to be concatenated. |
options? | Object | used to specify the dimenstion to concate. |
options.dim? | number | - |
Returns
empty
▸ empty(size, options?): Tensor
Returns a tensor filled with uninitialized data. The shape of the tensor is defined by the variable argument size.
https://pytorch.org/docs/1.12/generated/torch.empty.html
Parameters
| Name | Type | Description |
|---|---|---|
size | number[] | A sequence of integers defining the shape of the output tensor. |
options? | TensorOptions | - |
Returns
eye
▸ eye(n, m?, options?): Tensor
Returns a tensor filled with ones on the diagonal, and zeroes elsewhere. The shape of the tensor is defined by the arguments n and m.
https://pytorch.org/docs/1.12/generated/torch.eye.html
Parameters
| Name | Type | Description |
|---|---|---|
n | number | An integer defining the number of rows in the result. |
m? | number | An integer defining the number of columns in the result. Optional, defaults to n. |
options? | TensorOptions | - |
Returns
fromBlob
▸ fromBlob(blob, sizes?, options?): Tensor
Exposes the given data as a Tensor without taking ownership of the original data.
The function exists in JavaScript and C++ (torch::from_blob).
Parameters
| Name | Type | Description |
|---|---|---|
blob | any | The blob holding the data. |
sizes? | number[] | Should specify the shape of the tensor, strides the stride |
options? | TensorOptions | Tensor options in each dimension. |
Returns
full
▸ full(size, fillValue, options?): Tensor
Creates a tensor of size size filled with fillValue. The tensor’s dtype is default to be torch.float32,
unless specified with options.
https://pytorch.org/docs/1.12/generated/torch.full.html
Parameters
| Name | Type | Description |
|---|---|---|
size | number[] | A list of integers defining the shape of the output tensor. |
fillValue | number | The value to fill the output tensor with. |
options? | TensorOptions | Object to customizing dtype, etc. default to be {dtype: torch.float32} |
Returns
linspace
▸ linspace(start, end, steps, options?): Tensor
Creates a one-dimensional tensor of size steps whose values are evenly spaced from start to end,
inclusive.
https://pytorch.org/docs/1.12/generated/torch.linspace.html
Parameters
| Name | Type | Description |
|---|---|---|
start | number | Starting value for the set of points |
end | number | Ending value for the set of points |
steps | number | Size of the constructed tensor |
options? | TensorOptions | Object to customizing dtype. default to be {dtype: torch.float32} |
Returns
logspace
▸ logspace(start, end, steps, options?): Tensor
Returns a one-dimensional tensor of size steps whose values are evenly spaced from base^start to base^end, inclusive, on a logarithmic scale with base.
https://pytorch.org/docs/1.12/generated/torch.logspace.html
Parameters
| Name | Type | Description |
|---|---|---|
start | number | Starting value for the set of points |
end | number | Ending value for the set of points |
steps | number | Size of the constructed tensor |
options? | TensorOptions & { base: number } | Object to customizing base and dtype. default to be {base: 10, dtype: torch.float32} |
Returns
ones
▸ ones(size, options?): Tensor
Returns a tensor filled with the scalar value 1, with the shape defined
by the argument size.
https://pytorch.org/docs/1.12/generated/torch.ones.html
Parameters
| Name | Type | Description |
|---|---|---|
size | number[] | A sequence of integers defining the shape of the output tensor. |
options? | TensorOptions | Tensor options. |
Returns
rand
▸ rand(size, options?): Tensor
Returns a tensor filled with random numbers from a uniform distribution on
the interval [0, 1).
Parameters
| Name | Type | Description |
|---|---|---|
size | number[] | A sequence of integers defining the shape of the output tensor. |
options? | TensorOptions | Tensor options. |
Returns
randint
▸ randint(high, size): Tensor
Returns a tensor filled with random integers generated uniformly between
0 (inclusive) and high (exclusive).
https://pytorch.org/docs/1.12/generated/torch.randint.html
Parameters
| Name | Type | Description |
|---|---|---|
high | number | One above the highest integer to be drawn from the distribution. |
size | number[] | A tuple defining the shape of the output tensor. |
Returns
▸ randint(low, high, size): Tensor
Returns a tensor filled with random integers generated uniformly between
low (inclusive) and high (exclusive).
https://pytorch.org/docs/1.12/generated/torch.randint.html
Parameters
| Name | Type | Description |
|---|---|---|
low | number | Lowest integer to be drawn from the distribution. |
high | number | One above the highest integer to be drawn from the distribution. |
size | number[] | A tuple defining the shape of the output tensor. |
Returns
randn
▸ randn(size, options?): Tensor
Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution).
https://pytorch.org/docs/1.12/generated/torch.randn.html
Parameters
| Name | Type | Description |
|---|---|---|
size | number[] | A sequence of integers defining the shape of the output tensor. |
options? | TensorOptions | Tensor options. |
Returns
randperm
▸ randperm(n, options?): Tensor
Returns a random permutation of integers from 0 to n - 1
https://pytorch.org/docs/1.12/generated/torch.randperm.html
Parameters
| Name | Type | Description |
|---|---|---|
n | number | The upper bound (exclusive) |
options? | TensorOptions | Object to customizing dtype, etc. default to be {dtype: torch.int64}. |
Returns
tensor
▸ tensor(data, options?): Tensor
Constructs a tensor with no autograd history.
Parameters
| Name | Type | Description |
|---|---|---|
data | number | ItemArray | Tensor data as multi-dimensional array. |
options? | TensorOptions | Tensor options. |
Returns
zeros
▸ zeros(size, options?): Tensor
Returns a tensor filled with the scalar value 0, with the shape defined
by the argument size.
https://pytorch.org/docs/1.12/generated/torch.zeros.html
Parameters
| Name | Type | Description |
|---|---|---|
size | number[] | A sequence of integers defining the shape of the output tensor. |
options? | TensorOptions | Tensor options. |