Input data is not added 'requires_grad=True',Through a neural network operation, the operation of backward () can be performed.Why?

1

This is an official tutorial code：

import torch
import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):

def __init__(self):
    super(Net, self).__init__()
    # 1 input image channel, 6 output channels, 3x3 square convolution
    # kernel
    self.conv1 = nn.Conv2d(1, 6, 3)
    self.conv2 = nn.Conv2d(6, 16, 3)
    # an affine operation: y = Wx + b
    self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension
    self.fc2 = nn.Linear(120, 84)
    self.fc3 = nn.Linear(84, 10)

def forward(self, x):
    # Max pooling over a (2, 2) window
    x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
    # If the size is a square you can only specify a single number
    x = F.max_pool2d(F.relu(self.conv2(x)), 2)
    x = x.view(-1, self.num_flat_features(x))
    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    x = self.fc3(x)
    return x

def num_flat_features(self, x):
    size = x.size()[1:]  # all dimensions except the batch dimension
    num_features = 1
    for s in size:
        num_features *= s
    return num_features

input = torch.randn(1, 1, 32, 32)
out = net(input)
print(out)

out:tensor([[ 0.0182, 0.1101, -0.0959, 0.0026, -0.0224, 0.1947, 0.1172, 0.0107,
0.0149, -0.1188]], grad_fn=)

I don’t use ‘requires_grad’, Why does the output appear ‘grad_fn=’？

2

Parameters of newly constructed modules have requires_grad=True by default. Please read this doc.

3

Thanks for your help.