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How to define a simple artificial neural network in PyTorch?
To define a simple artificial neural network (ANN), we could use the following steps −
Steps
First we import the important libraries and packages. We try to implement a simple ANN in PyTorch. In all the following examples, the required Python library is torch. Make sure you have already installed it.
import torch import torch.nn as nn
Our next step is to build a simple ANN model. Here, we use the nn package to implement our model. For this, we define a class MyNetwork and pass nn.Module as the parameter.
class MyNetwork(nn.Module):
We need to create two functions inside the class to get our model ready. First is the init() and the second is the forward(). Within the init() function, we call a super() function and define different layers.
We need to instantiate the class to use for training on the dataset. When we instantiate the class, the forward() function is executed.
model = MyNetwork()
Print the model to see the different layers.
print(model)
Example 1
In the following example, we create a simple Artificial Neural Network with four layers without forward function.
# Import the required libraries import torch from torch import nn # define a simple sequential model model = nn.Sequential( nn.Linear(32, 128), nn.ReLU(), nn.Linear(128, 10), nn.Sigmoid() ) # print the model print(model)
Output
Sequential( (0): Linear(in_features=32, out_features=128, bias=True) (1): ReLU() (2): Linear(in_features=128, out_features=10, bias=True) (3): Sigmoid() )
Example 2
The following Python program shows a different way to build a simple Neural network.
import torch import torch.nn as nn import torch.nn.functional as F class MyNet(nn.Module): def __init__(self): super(MyNet, self).__init__() self.fc1 = nn.Linear(4, 8) self.fc2 = nn.Linear(8, 16) self.fc3 = nn.Linear(16, 4) self.fc4 = nn.Linear(4,1) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) return torch.sigmoid(self.fc4(x)) model = MyNet() print(model)
Output
MyNet( (fc1): Linear(in_features=4, out_features=8, bias=True) (fc2): Linear(in_features=8, out_features=16, bias=True) (fc3): Linear(in_features=16, out_features=4, bias=True) (fc4): Linear(in_features=4, out_features=1, bias=True) )
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