Hi, I started to learn PyTorch after doing Machine learning course in Coursera.
Reading the docs I find that Im unfamiliar with the terms and the logic of the nn creation.
For example this code:
    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

What is relu? why we need to call it twice? why we need to use x.view after this? why we need to call fc1/2/3? I dont get the intuition of this forward function.
I fond example of a “simple” nn design with a forward function like this which is how I was thought nn is built:
 #activation function ==> S(x) = 1/1+e^(-x)
    def sigmoid(self, x, deriv=False):
        if deriv == True:
            return x * (1 - x)
        return 1 / (1 + np.exp(-x))

    # data will flow through the neural network.
    def feed_forward(self):
        self.hidden = self.sigmoid(np.dot(self.inputs, self.weights))

I also dont understand the init function:
    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)

I tried to go for the docs but Im still clueless about the logic flow of the init function. Why we need t occall Conv2d and then MaxPool2d and then Linear.
What channels means? why do we need channels? I know the simple design on input layer, hidden layer and output layer.

The simple code for nn as I learned it is found here:

  
      
      Medium – 24 Oct 19
  
  
    

Inroduction to Neural Networks in Python 38

A detail-oriented introduction to neural networks (with lots of math).

  Reading time: 11 min read
    

  
  
    
    
  
  


Does PyTorch have a detailed explanation for each line of the nn design, explaining to a total newbie in PyTorch.
Thanks.