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Tensor

The Tensor is the main object of this library. In this section are listed all of the Tensor methods.

torch.tensor

torch.tensor(data,
            requires_grad=false,
            device='cpu') → Tensor

Returns a tensor filled with the data in the argument data.

Parameters

  • data (Array) - Javascript Array containing the data to be stored in the Tensor. This array can have any number of dimensions, but must have a homogenous shape, and only numbers.
  • requires_grad (boolean) - Whether to keep track of this tensor's gradients. Set this to true if you want to learn this parameter in your model. Default: false.
  • device (string) - Device to store Tensor. Either "gpu" or "cpu". If your device has a gpu, large models will train faster on it.

Example

>>> let a = torch.tensor([[1,2,3],[4,5,6]], false, 'gpu');
>>> console.log(a.data);
//[[1,2,3],
// [4,5,6]]


torch.zeros

torch.zeros(*shape,
            requires_grad=false,
            device='cpu') → Tensor

Returns a tensor filled with zeros with dimensions like shape.

Parameters

  • shape (Array) - Javascript Array containing the shape of the Tensor.
  • requires_grad (boolean) - Whether to keep track of this tensor's gradients. Set this to true if you want to learn this parameter in your model. Default: false.
  • device (string) - Device to store Tensor. Either "gpu" or "cpu". If your device has a gpu, large models will train faster on it.

Example

>>> let a = torch.zeros([3,2], false, 'gpu');
>>> console.log(a.data);
//[[0, 0],
// [0, 0]
// [0, 0]]


torch.ones

torch.ones(*shape,
            requires_grad=false,
            device='cpu') → Tensor

Returns a tensor filled with ones with dimensions like shape.

Parameters

  • shape (Array) - Javascript Array containing the shape of the Tensor.
  • requires_grad (boolean) - Whether to keep track of this tensor's gradients. Set this to true if you want to learn this parameter in your model. Default: false.
  • device (string) - Device to store Tensor. Either "gpu" or "cpu". If your device has a gpu, large models will train faster on it.

Example

>>> let a = torch.ones([3,2], false, 'gpu');
>>> console.log(a.data);
//[[1, 1],
// [1, 1]
// [1, 1]]


torch.tril

torch.tril(*shape,
            requires_grad=false,
            device='cpu') → Tensor

Returns a 2D lower triangular tensor.

Parameters

  • shape (Array) - Javascript Array containing the shape of the Tensor.
  • requires_grad (boolean) - Whether to keep track of this tensor's gradients. Set this to true if you want to learn this parameter in your model. Default: false.
  • device (string) - Device to store Tensor. Either "gpu" or "cpu". If your device has a gpu, large models will train faster on it.

Example

>>> let a = torch.tril([4,3], false, 'gpu');
>>> console.log(a.data);
//[[1, 0, 0],
// [1, 1, 0]
// [1, 1, 1]
// [1, 1, 1]]
>>> let b = torch.tril([3,4], false, 'gpu');
>>> console.log(b.data);
//[[1, 0, 0, 0],
// [1, 1, 0, 0]
// [1, 1, 1, 0]


torch.randn

torch.randn(*shape,
            requires_grad=false,
            device='cpu',
            xavier=false) → Tensor

Returns a tensor filled with randomly sampled data with dimensions like shape. The sample is from a normal distribution.

Parameters

  • shape (Array) - Javascript Array containing the shape of the Tensor.
  • requires_grad (boolean) - Whether to keep track of this tensor's gradients. Set this to true if you want to learn this parameter in your model. Default: false.
  • device (string) - Device to store Tensor. Either "gpu" or "cpu". If your device has a gpu, large models will train faster on it.
  • xavier (boolean) - Whether to use Xavier Initialization on this tensor. Default: false.

Example

>>> let a = torch.randn([3,2], false, 'gpu');
>>> console.log(a.data);
//[[1.001, 0.122],
// [-0.93, 0.125]
// [0.123,-0.001]]


torch.rand

torch.rand(*shape,
            requires_grad=false,
            device='cpu') → Tensor

Creates new instance of the Tensor class filled with numbers in a uniform distribution in ]0,1[.

Parameters

  • shape (Array) - Javascript Array containing the shape of the Tensor.
  • requires_grad (boolean) - Whether to keep track of this tensor's gradients. Set this to true if you want to learn this parameter in your model. Default: false.
  • device (string) - Device to store Tensor. Either "gpu" or "cpu". If your device has a gpu, large models will train faster on it.

Example

>>> let a = torch.rand([3,2], false, 'gpu');
>>> console.log(a.data);
//[[0.011, 0.122],
// [-0.03, 0.105]
// [-0.90,-0.202]]


torch.randint

torch.rand(low,
            high,
            *shape,
            requires_grad=false,
            device='cpu') → Tensor

Creates new instance of the Tensor class filled with random integers between low and high.

Parameters

  • low - Lowest integer that can be sampled.
  • high - One above highest integer that can be sampled.
  • shape (Array) - Javascript Array containing the shape of the Tensor.
  • requires_grad (boolean) - Whether to keep track of this tensor's gradients. Set this to true if you want to learn this parameter in your model. Default: false.
  • device (string) - Device to store Tensor. Either "gpu" or "cpu". If your device has a gpu, large models will train faster on it.

Example

>>> let a = torch.rand([3,2], false, 'gpu');
>>> console.log(a.data);
//[[0.011, 0.122],
// [-0.03, 0.105]
// [-0.90,-0.202]]


tensor.backward

Performs backpropagation from this tensor backwards. It fills the gradients of every tensor that led to this one with gradients relative to this tensor.

Note: This only happens to tensors that have requires_grad set to true.

Example

>>> let a = torch.randn([3,2], true, 'gpu');
>>> let b = torch.randn([2,4], false, 'gpu');
>>> let c = torch.matmul(a,b);
>>> c.backward();
>>> console.log(a.grad);
//[[0.001, -0.99],
// [-0.13, 0.333]
// [-0.91,-0.044]]


tensor.zero_grad

Clears the gradients stored in this tensor. Sets the gadients to zero. This is used after the gradients have been used to update the parameters of a model, to set the model up for the next iteration.

Example

>>> let a = torch.randn([3,2], true, 'gpu');
>>> let b = torch.randn([2,4], false, 'gpu');
>>> let c = torch.matmul(a,b);
>>> c.backward();
>>> console.log(a.grad);
//[[0.001, -0.99],
// [-0.13, 0.333]
// [-0.91,-0.044]]
>>> a.zero_grad();
>>> console.log(a.grad);
//[[0, 0],
// [0, 0]
// [0, 0]]


tensor.zero_grad_graph

Clears the gradients stored in this tensor, setting the gadients to zero, and does the same for every tensor that led to this one. This is used after the gradients have been used to update the parameters of a model, to set the model up for the next iteration.

Example

>>> let a = torch.randn([3,2], true, 'gpu');
>>> let b = torch.randn([2,4], true, 'gpu');
>>> let c = torch.matmul(a,b);
>>> c.backward();
>>> console.log(a.grad);
//[[0.001, -0.99],
// [-0.13, 0.333]
// [-0.91,-0.044]]
>>> c.zero_grad_graph(); // Clears gradients of c, b, and a.
>>> console.log(a.grad);
//[[0, 0],
// [0, 0]
// [0, 0]]


tensor.tolist

Returns an Array with the tensor's data.

Example

>>> let a = torch.randn([3,2], true, 'gpu');
>>> let aArray = a.tolist();
>>> console.log(aArray);
//[[0.001, -0.99],
// [-0.13, 0.333]
// [-0.91,-0.044]]


tensor.data

Returns the tensor's data as a javascript Array.

Example

>>> let a = torch.randn([3,2], true, 'gpu');
>>> console.log(a.data);
//[[0.001, -0.99],
// [-0.13, 0.333]
// [-0.91,-0.044]]


tensor.length

Returns the tensor's length (size of first dimension).

Example

>>> let a = torch.randn([3,2], true, 'gpu');
>>> console.log(a.length);
// 3


tensor.ndims

Returns the number of dimensions in the Tensor.

Example

>>> let a = torch.randn([3,2,1,4], true, 'gpu');
>>> console.log(a.ndims);
// 4


tensor.grad

Returns the gradients currently stored in the Tensor.

Example

>>> let a = torch.randn([3,2], true, 'gpu');
>>> let b = torch.randn([2,4], true, 'gpu');
>>> let c = torch.matmul(a,b);
>>> c.backward();
>>> console.log(a.grad);
//[[0.001, -0.99],
// [-0.13, 0.333]
// [-0.91,-0.044]]