PyTorch is an open-source Python-based library. It provides high flexibility and speed while building, training, and deploying deep learning models.

At its core, PyTorch involves operations involving *tensors. *A tensor is a number, vector, matrix, or any n-dimensional array.

In this article, we will see different ways of creating tensors using PyTorch tensor methods (functions).

## Topics we’ll cover

- tensor
- zeros
- ones
- full
- arange
- linspace
- rand
- randint
- eye
- complex

### The tensor() method

This method returns a tensor when `data`

is passed to it. `data`

can be a scalar, tuple, a list or a *NumPy* array.

In the above example, a NumPy array that was created using `np.arange()`

was passed to the `tensor()`

method, resulting in a 1-D tensor.

We can create a multi-dimensional tensor by passing a tuple of tuples, a list of lists, or a multi-dimensional NumPy array.

When an empty tuple or list is passed into `tensor()`

, it creates an empty tensor.

### The zeros() method

This method returns a tensor where all elements are zeros, of specified `size`

(shape). The `size`

** can** be given as a tuple or a list or neither.

We could have passed `3, 2`

inside a tuple or a list as well. It is self-explainable that passing negative numbers or a float would result in a run time error.

Passing an empty tuple or an empty list gives a tensor of size (dimension) 0, having 0 as its only element, whose data type is float.

### The ones() method

Similar to `zeros()`

, `ones()`

returns a tensor where all elements are 1, of specified `size`

(shape). The `size`

** can** be given as a tuple or a list or neither.

Like `zeros()`

, passing an empty tuple or list gives a tensor of 0 dimension, having 1 as the sole element, whose data type is float.

### The full() method

What if you want all the elements of a tensor to be equal to some value but not only 0 and 1? Maybe 2.9?

`full()`

returns a tensor of a shape given by the `size`

argument, with all its elements equal to the `fill_value`

.

Here, we have created a tensor of shape `3, 2`

with the `fill_value`

as 3. Here again, passing an empty tuple or list creates a scalar tensor of zero dimension.

While using `full`

, it is ** necessary** to give

`size`

as a tuple or a list.### The arange() method

This method returns a 1-D tensor, with elements from `start`

(inclusive) to `end`

(exclusive) with a common difference `step`

. The default value for `start`

is 0 while that for `step`

is 1.

The elements of the tensor can be said to be in ** Arithmetic Progression**, with

`step`

as common difference.Here, we created a tensor which starts from 2 and goes until 20 with a `step`

(common difference) of 2.

All the three parameters, `start`

, `end`

and `step`

can be positive, negative or float.

While choosing `start`

, `end`

, and `step`

, we need to ensure that `start`

and `end`

are consistent with the `step`

sign.

Since `step`

is set as -2, there is no way -42 can reach -22 (exclusive). Hence, it gives an error.

### The linspace() method

This method returns a 1-D dimensional tensor, with elements from `start`

(inclusive) to `end`

(inclusive). However, unlike `arange()`

, here, `steps`

isn’t the common difference but the number of elements to be in the tensor.

PyTorch automatically decides the common difference based on the `steps`

given.

Not providing a value for `steps`

is deprecated. For * backwards compatibility*, not providing a value for steps creates a tensor with

**elements. According to the official documentation, in a future PyTorch release, failing to provide a value for steps will throw a runtime error.**

**100**Unlike `arange()`

, `linspace`

can have a `start`

greater than `end`

since the common difference is automatically calculated.

Since `steps`

here is not a common difference, but the number of elements, it can only be a non-negative integer.

### The rand() method

This method returns a tensor filled with random numbers from a uniform distribution on the interval 0 (inclusive) to 1 (exclusive). The shape is given by the `size`

argument. The `size`

argument can be given as a tuple or list or neither.

Passing an empty tuple or list creates a scalar tensor of zero dimension.

### The randint() method

This method returns a tensor filled with random integers generated uniformly between `low`

(inclusive) and `high`

(exclusive). The shape is given by the `size`

argument. The default value for `low`

is 0.

When only one `int`

argument is passed, `low`

gets the value 0, by default, and `high`

gets the passed value.

The `size`

argument only takes a tuple or a list. An empty tuple or list creates a tensor with zero dimension.

### The eye() method

This method returns a 2-D tensor with ones on the diagonal and zeros elsewhere. The number of rows is given by `n`

and columns is given by `m`

.

The default value for `m`

is the value of `n`

. When only `n`

is passed, it creates a tensor in the form of an ** identity matrix**. An identity matrix has its diagonal elements as 1 and all others as 0.

### The complex() method

This method returns a complex tensor with its real part equal to `real`

and its imaginary part equal to `imag`

. Both `real`

and `imag`

are tensors.

The data type of both the `real`

and `imag`

tensors should be either `float`

or `double`

.

Also, the `size`

of both tensors, `real`

and `imag`

, should be the same, since the corresponding elements of the two matrices form a complex number.

## Conclusion

We’ve covered ten different ways to create tensors using PyTorch methods. You can go through the official documentation to know more about other PyTorch methods.

You can click here to go to the Jupyter notebook where you can play around with these methods.

If you want to learn more about PyTorch, check out this amazing course on freeCodeCamp’s YouTube channel.

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