# How does the "view" method work in PyTorch?

I am confused about the method `view()`

in the following code snippet.

```
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2,2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16*5*5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
```

My confusion is regarding the following line.

```
x = x.view(-1, 16*5*5)
```

What does `tensor.view()`

function do? I have seen its usage in many places, but I can't understand how it interprets its parameters.

What happens if I give negative values as parameters to the `view()`

function? For example, what happens if I call, `tensor_variable.view(1, 1, -1)`

?

Can anyone explain the main principle of `view()`

function with some examples?

The view function is meant to reshape the tensor.

Say you have a tensor

```
import torch
a = torch.range(1, 16)
```

`a`

is a tensor that has 16 elements from 1 to 16(included). If you want to reshape this tensor to make it a `4 x 4`

tensor then you can use

```
a = a.view(4, 4)
```

Now `a`

will be a `4 x 4`

tensor. *Note that after the reshape the total number of elements need to remain the same. Reshaping the tensor a to a 3 x 5 tensor would not be appropriate.*

### What is the meaning of parameter -1?

If there is any situation that you don't know how many rows you want but are sure of the number of columns, then you can specify this with a -1. ( *Note that you can extend this to tensors with more dimensions. Only one of the axis value can be -1* ). This is a way of telling the library: "give me a tensor that has these many columns and you compute the appropriate number of rows that is necessary to make this happen".

This can be seen in the neural network code that you have given above. After the line `x = self.pool(F.relu(self.conv2(x)))`

in the forward function, you will have a 16 depth feature map. You have to flatten this to give it to the fully connected layer. So you tell pytorch to reshape the tensor you obtained to have specific number of columns and tell it to decide the number of rows by itself.

Drawing a similarity between numpy and pytorch, `view`

is similar to numpy's reshape function.

From: stackoverflow.com/q/42479902

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