In this Python tutorial, we will learn about the ** PyTorch Reshape Tensor** in Python. The reshape permits us to convert the shape with similar data and the number of elements and that means it returns the identical data as the identified array but with different recognized dimension sizes.

And additionally, we will also cover different examples related to **PyTorch** **Reshape Tensor**. And we will cover these topics.

- PyTorch reshape tensor
- PyTorch reshape tensor example
- PyTorch reshape tensor view
- PyTorch reshape one-dimension tensor into two dimension tensor
- PyTorch reshape tensor into 4 rows and 2 columns
- PyTorch reshape tensor into 8 rows and 1 column

**Table of Contents**show

## PyTorch reshape tensor

In this section, we will learn about the **PyTorch reshape tensor** in python.

Reshape permit us to convert the shape with similar data and the number of elements and that means it returns the identical data as the identified array but with different recognized dimension sizes.

**Syntax:**

The syntax of PyTorch reshape tensor:

`torch.reshape(input,shape)`

Parameter:

The following are the parameters of PyTorch reshape tensor

**input:**The input parameter is used as a tensor to be reshaped.**shape:**The shape parameter is used as the new shape.

So, with this, we have learned about the PyTorch reshape tensor.

Read: PyTorch Hyperparameter Tuning

## PyTorch reshape tensor example

In this section, we will learn about **how to implement the PyTorch reshape** **tensor with the help of an example** in python.

The **reshape()** function returns the tensor with the similar data and the number of elements as input but with the identified shape.

When feasible the returned tensor will be a view of input. The constant inputs with consistent strides can be reshaped without copying.

**Code:**

In the following code, we will import all the necessary libraries such as import torch and import torch.nn.

**a = torch.arange(6.)**is used to declaring a variable by using torch.arange() function.**torch.reshape(a, (3, 2)):**Here we are using the reshape() function and within this function, we are using some parameters such as input and shape.

```
#Importing Libraries
import torch
import torch. nn as nn
# Describing the variable
a = torch.arange(6.)
# Using reshaping() method
torch.reshape(a, (3, 2))
```

**Output:**

After running the above code we get the following output in which we can see that the PyTorch reshape tensor value is printed on the screen.

So, with this, we have learned about the PyTorch reshape tensor with the help of an example.

Read: PyTorch Numpy to Tensor

## PyTorch reshape tensor view

In this section, we will learn about python’s PyTorch reshape tensor view.

The PyTorch reshape tensor view is defined as a process that returns a new tensor with the same data as the self tensor but of a non-identical shape.

It returns the tensor and allocates similar data and should have the same number of elements but may have a dissimilar size.

**Code:**

In the following code, we will import all the necessary such as import torch and import torch.nn.

**s = torch.tensor([[1, 2], [3, 4]]):**Here we are describing the s variable by using torch.tensor() function.**torch.reshape(s, (-1,)):**Here we are using the torch.reshape() function and within this function, we are using some parameters such as input and shape.**a = s.view(4)**Here we are using the view() function that returns a new tensor with the same data as the self tensor but of a non-identical shape.**print(a)**is used to print the variable by using the print() function.

```
# Importing Libraries
import torch
import torch. nn as nn
# Describe a variable
s = torch.tensor([[1, 2], [3, 4]])
# Using reshape() method
torch.reshape(s, (-1,))
# Using View() function
a = s.view(4)
print(a)
```

**Output:**

After running the above code, we get the following output in which we can see that the PyTorch reshape tensor view is printed on the screen.

This is how we can use the PyTorch reshape view is used.

Read: PyTorch Activation Function

## PyTorch reshape one-dimension tensor into two dimension tensor

In this section, we will learn about the **PyTorch reshape tensor one-dimension tensor to two dimension tensor** in python.

The reshape method is used to reshape the tensor into the given shape. Here we are reshaping the one dimension tensor into two dimension tensor.

Or we can say that the one dimension tensor can be converted into two dimensions tensor with the help of reshape method.

**Code:**

In the following code, firstly we import the torch library such as import torch.

**r = torch.tensor([2, 4, 6, 8, 10, 12]):**Here we are creating an one dimensional tensor with 6 elements.**print(r.shape):**Here we are displaying the tensor shape with the help of the print() function.**print(r):**Here we are displaying the actual tensor with the help of the print() function.**print(r.reshape([3, 2])):**Here we are reshaping the tensor into three rows and two columns.**print(r.shape):**Here we are displaying the shape of a reshaped tensor by using the print() function.

```
# Import linrary
import torch
# Create an 1 D tensor with 6 elements
r = torch.tensor([2, 4, 6, 8, 10, 12])
# Display tensor shape
print(r.shape)
# Display actual tensor
print(r)
# Reshape tensor into 3 rows and 2 columns
print(r.reshape([3, 2]))
# Display shape of reshaped tensor
print(r.shape)
```

**Output:**

After running the above code, we get the following output in which we can see that the PyTorch reshape one dimension tensor to two dimension tensor printed on the screen.

So, with this, we understood about the PyTorch reshape one dimension tensor into two dimensions tensor.

Read: PyTorch MNIST Tutorial

## PyTorch reshape a tensor into 4 rows and 2

## columns

In this section, we will learn about the** PyTorch reshaping a tensor into 4 rows and 2 columns** in python.

The reshape method is used to reshape the tensor into the given shape. Here we are reshaping the tensor into four rows and two columns.

**Code:**

In the following code, firstly we will import the torch library such as import torch.

**r = torch.tensor([2, 4, 6, 8, 10, 12, 14, 16]):**Here we are creating an one dimension tensor with eight elements.**print(r.shape):**Here we are displaying a tensor shape by using the print() function.**print(r.reshape([4, 2])):**Here we are reshaping the tensor into four rows into two columns.**print(r.shape):**Here we are displaying the shape with the help of the print() function.

```
# import torch module
import torch
# Creating an one Dimensional tensor with Eight elements
r = torch.tensor([2, 4, 6, 8, 10, 12, 14, 16])
# Showing tensor shape
print(r.shape)
# Showing actual tensor
print(r)
# Reshape tensor into Four rows and Two columns
print(r.reshape([4, 2]))
# Showing the shape
print(r.shape)
```

**Output:**

After running the above code, we get the following output in which we can see that the PyTorch reshape the tensor into 4 rows and 2 columns.

So, with this, we understood about the PyTorch reshape tensor into 4 rows and 2 columns.

Read: PyTorch Model Summary

## PyTorch reshape a tensor into 8 rows and 1 column

In this section, we will learn about the **PyTorch reshape a tensor into 8 rows** **and 1 column** in python.

The reshape method is used to reshape the tensor into the given shape. Here we are reshaping the tensor into eight rows and one column.

**Code:**

In the following code, firstly we will import the torch library such as import torch.

**r = torch.tensor([2, 4, 6, 8, 10, 12, 14, 16]):**Here we are creating a one dimension tensor with eight elements.**print(r.shape):**Here we are displaying a tensor shape by using the print() function.**print(r):**Here we are displaying the actual tensor with the help of the print() function.**print(r.reshape([8, 1])):**Here we are reshaping the tensor into eight rows and one column.**print(r.shape):**Here we are displaying the shape with the help of the print() function.

```
# import torch module
import torch
# Creating an one Dimensional tensor with Eight elements
r = torch.tensor([2, 4, 6, 8, 10, 12, 14, 16])
# Showing tensor shape
print(r.shape)
# Showing the actual tensor
print(r)
# Reshapeing teh tensor into eight rows and one column
print(r.reshape([8, 1]))
# Showing the shape
print(r.shape)
```

**Output:**

After running the above code we get the following output in which we can see that the PyTorch reshape a tensor into 8 rows and 1 column is printed on the screen.

So, with this we understood about the PyTorch reshape a tensor into 8 columns and 1 row.

Also, take a look at some more PyTorch tutorials.

- PyTorch Dataloader + Examples
- Create PyTorch Empty Tensor
- PyTorch Early Stopping
- PyTorch Add Dimension
- PyTorch Pretrained Model
- PyTorch Tensor to Numpy

So, in this tutorial, we discussed **PyTorch Reshape Tensor** and we have also covered different examples related to its implementation. Here is the list of examples that we have covered.

- PyTorch reshape tensor
- PyTorch reshape tensor example
- PyTorch reshape tensor view
- PyTorch reshape one-dimension tensor into two dimension tensor
- PyTorch reshape tensor into 4 rows and 2 columns
- PyTorch reshape tensor into 8 rows and 1 column

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