The **reshape -1 Python** is a **NumPy function** used to reshape the input in a specific order and modify it. This is usually used for various reasons, like matching input shapes, reducing computational complexity, or improving accuracy. This guide will give you a walkthrough to understand the reshape -1 Python and find ways to use it.

Contents

## What is reshape -1 python?

The **reshape() in Python is a NumPy function used to reshape an array, which means changing the shape of an array.** Reshaping an array allows you to edit the array by adding or removing its dimensions. It can also change the number of elements of the dimensions. Also, the “-1” in the reshape -1 python function means there is one unknown dimension in the function that needs to be calculated by the reshape() function.

Syntax: –

Here, the reshape() function is used as the numpy.reshape function. Here, three parameters are taken. An input array, Integers for shape, and C-contagious order are accepted. The C-contagious order means the operating row rise will be quicker than the other present one.

Syntax: –

```
import numpy as np
x = np.arange(20)
print(x)
print()
print(x.shape)
print()
print(x.ndim)
```

This is a fundamental step for machine learning as it is a crucial step involved in changing the shape or size of the input for the machine learning model as well as in descreasin g the computational complexity and improving the performance and accuracy of the model.

## How to use reshape -1 python?

It’s effortless to use the reshape -1 python. This can be understood by the example given below.

Syntax: –

```
import numpy as np
a = np.arange(10)
print(a)
print()
print(x.shape)
print()
print(x.ndim)
```

Here, in this example, the output will give the numbers from 0 to 9 as the elements of the array “a,” which will have ten integers in content.

## How to reshape a tensor?

The * reshape function in Python can also be used to shape the tensors* into the required size. To do so, you can use the following methods.

### Reshape Tensor into two dimensional Tensor

One of the methods to reshape the Tensor is to convert the one-dimensional Tensor into a two-dimensional Tensor.

Syntax:

```
import torch
a = torch.tensor([4, 5, 6, 7, 8, 9])
print(a.shape)
print(a)
print(a.reshape([3, 2]))
print(a.shape)
```

The reshaping of Tensor from one dimension to two dimensions is relatively easy. It can be done in my ways, like three rows to two columns.

Syntax:

```
import torch
a = torch.tensor([4, 5, 6, 7, 8, 9])
print(a.shape)
print(a)
print(a.reshape([3, 2]))
print(a.shape)
```

Another way of reshaping the Tensor is converting them into 6 columns and 1 row.

Syntax:

```
import torch
a = torch.tensor([4, 5, 6, 7, 8, 9])
print(a.shape)
print(a)
print(a.reshape([6, 1]))
print(a.shape)
```

## FAQs

### What does the shape of an array mean?

In Python, the shape of an array is the number of elements in each dimension. It returns a tuple with each index with the number of corresponding elements.

Syntax: –

### What is an N-dimension array?

The N-dimension array is a * multidimensional array* that can contain items of the same size and type. In arrays, the number of dimensions is defined by the shape, which is a tuple of N numbers of non-negative integers that specify each dimension’s sizes.

Syntax: –

### What is Tensor?

The Tensor in Python is a multidimensional arrays that is of uniform type. It is a generalization of vector and matrices used for numerical computations and machine learning.

### What is a tuple?

In Python, Tuples is a type of built-in data type and is an ordered and unchangeable collection that can store multiple items in a single variable.

Syntax: –

## Conclusion

This guide has all the information regarding the reshape -1 python and instructions on how to use it. This guide will also help you understand the numpy reshape -1 python function can reshape an array to shape.

## Reference

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