Apologize for the naiveness of the question but I’ve been struggling for days trying to understand the Pytorch tutorial examples.
Specially the following codes are from this link: http://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html#sphx-glr-beginner-blitz-neural-networks-tutorial-py 139
My quesion is :

What does “def forward” do? When and how is the function called?
In the feedforward function, what does “x = x.view(-1, self.num_flat_features(x))” do?

Thanks! Also, I might be missing some pieces in programming so I have difficulties understanding the codes, and it would be helpful if you could kindly point it out!
[codes start from here]
import torch
from torch.autograd import Variable  
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)
    # an affine operation: y = Wx + b
    self.fc1 = nn.Linear(16 * 5 * 5, 120)
    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


net = Net()
print(net)