human
I am working on the file "PyTorch Tutorials/Deep Learning with PyTorch A 60 Minute Blitz.ipynb".
The first task is:
Neural Networks
Neural networks can be constructed using the torch.nn package.
Now that you had a glimpse of autograd, nn depends on autograd to define models and
differentiate them. An nn.Module contains layers, and a method forward(input)that returns the output.
Can you write Python code for it?
 ... Write me a the Python code you requested?
assistant
#Here is the Python code you requested.

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, 5x5 square convolution kernel
        self.conv1 = nn.Conv2d(1, 6, 5)
        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 = F.max_pool2d(F.relu(self.conv1(x)), (2,2))
        x = F.max_pool2d(F.relu(self.conv2(x)),(2))# If the size is a square you can only specify a single number
        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

net = Net()
print(net)

params = list(net.parameters())
print(len(params))
print(params[0].size())# conv1's .weight

for i in range(len(params)):
    print(i,params[i].size())

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

net.zero_grad()
out.backward(torch.randn(1,10))