FNN pytorch

FNN (Feedforward Neural Network) is a type of neural network model that consists of multiple layers of interconnected nodes, also known as neurons. It is widely used for various tasks such as classification and regression. PyTorch is a popular deep learning framework that provides tools and libraries for building and training neural network models, including FNNs.

To implement an FNN in PyTorch, you would typically define a class that inherits from `torch.nn.Module` and specify the architecture of the network in the `__init__` method. You can use `torch.nn.Linear` to define fully connected layers in the network. Then, you would define the forward pass by overriding the `forward` method.

Here's a simple example of an FNN implemented in PyTorch:

import torch
import torch.nn as nn

class FNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(FNN, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

In this example, `input_size` represents the size of the input feature, `hidden_size` represents the number of neurons in the hidden layer, and `output_size` represents the size of the output.

You can then instantiate an instance of this FNN class and use it to train and make predictions on your dataset.