How to build a neural network with PyTorch

There are three layers in a neural network:

Input layer: The input is fed into the input layer, and each node in the layer provides an output. These outputs serve as the input of the hidden layer.
Hidden layer: This layer performs computation and provides weights to the input. The output of this layer is given as input to the output layer.
Output layer: This layer is the final layer of the neural network where desired predictions are obtained. The output of this layer is provided to the outside world.

from pyexpat import model #optional 
from torch import nn
class Network(nn.Module):
    def __init__(self):
        super().__init__()
        
        # Inputs to hidden layer
        self.hidden = nn.Linear(600, 60)
        # Output layer
        self.output = nn.Linear(60, 6)
        
        # Defining sigmoid activation and softmax output 
        self.sigmoid = nn.Sigmoid()
        self.softmax = nn.Softmax(dim=1)
        
    def forward(self, tensor):
        # Pass the input tensor through each operation
        tensor = self.hidden(tensor)
        tensor = self.sigmoid(tensor)
        tensor = self.output(tensor)
        tensor = self.softmax(tensor)
        
        return tensor
        model= Network()

Explanation

Line 2: We import the nn module of PyTorch that makes building neural networks easier.
Line 4–6: We'll inherit from the nn.Module in the class Network declaration. When it is combined with super().__init__() a class is created that provides a lot of useful methods.
Line 9: We create a module for the linear transformation $xW+bxW+b$ . It takes 600 inputs and gives 60 outputs, and assigns it to self.hidden.

Note: This module automatically creates weights and bias tensors. We can access them using net.hidden.weight and net.hidden.bias.

Line 11: We create another linear transformation with 60 inputs and 6 outputs (10 units - one for each digit).
Line 14: This line contains the operation for sigmoid activation.
Line 15: This line contains the operation for softmax output. Set the dim=1 to calculate softmax across the columns.
Line 17: Networks created nn.Module must have a forward method. It takes in a tensor as a parameter.
Line 19–22: We pass the tensor through these operations, and its value is reassigned.
Line 25: A network is being created.

In this answer, we've learned how to build a neural network with PyTorch. In order to use this model, we'll have to train the model to learn the data set before using it to derive predictions.

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How to build a neural network with PyTorch

PyTorch to build neural networks

Build a neural network

Explanation