In this blog post, we’ll show you how to build a fully connected network in Pytorch. We’ll go over the necessary steps to get your network up and running, and we’ll also provide some tips on how to optimize your network for better performance.
For more information check out this video:
Fully connected neural networks are the standard neural network architecture for many tasks, such as image classification and regression. In this tutorial, we will learn how to build a fully connected network in Pytorch, and how to train it on data. We will also learn how to evaluate the performance of our model.
Fully connected neural networks are the standard neural networks that you have probably seen before. They are called “fully” connected because each neuron in the hidden layer is connected to every neuron in the input and output layers. In Pytorch, these networks are implemented using the torch.nn module.
The most basic form of a fully connected network is shown in the diagram below. It has an input layer (in green) with three neurons, a hidden layer (in blue) with two neurons, and an output layer (in red) with two neurons. The hidden and output layers are fully connected to the input layer.
The first step in building a fully connected network is to define the network architecture. This is done using the torch.nn.Module class. The code below defines a simple fully connected network with one hidden layer:
import torch.nn as nn
self.fc1 = nn
Building the Network
A fully connected neural network is a neural network where each neuron in the hidden layers is connected to every other neuron in adjacent layers. In other word, the input layer is fully connected to the first hidden layer, the first hidden layer is fully connected to the second hidden layer, and so on. The last hidden layer is then fully connected to the output layer.
Connecting the Network
Fully connected networks are those where each node in the network is connected to every other node in the network. In Pytorch, we can create fully connected networks using the nn.Linear module.
To create a fully connected network, we first need to create a class that inherits from the nn.Module class. In this class, we will define the __init__() and forward() methods.
In the __init__() method, we will create an nn.Linear module and assign it to the self.fc1 attribute. This module will take in an input of size 784 (the size of our MNIST images) and output a size 10 (the number of classes in our dataset).
In the forward() method, we will take in an input image and pass it through the self.fc1 layer. We will then return the output of this layer.
Now that we have our class defined, we can instantiate it and pass in our MNIST images as inputs:
Testing the Network
Now that you have built your network, it’s time to test it out! In this section, you’ll learn how to evaluate your network and make predictions on new data.
First, you’ll need to load your data. If you have not already downloaded the dataset, please do so now. You can find the dataset [here](https://drive.google.com/file/d/1m7lzrL8cv9mXIo5f5Uqy4V4D_sHc-gK0/view?usp=sharing).
Once the dataset is downloaded, unzip it and move it to the `data` directory. Then, open a Jupyter notebook and import the following libraries:
import matplotlib.pyplot as plt
In closing, we have built a fully connected neural network in Pytorch. We have looked at the important aspects of building such a network, including Forward and Backward Propagation, Loss Functions, and Optimizers. We have also seen how to implement these concepts in code.
Keyword: Building a Fully Connected Network in Pytorch