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PyTorch includes a special feature of creating and implementing neural networks. In this chapter, we will create a simple neural network with one hidden layer developing a single output unit.
We shall use following steps to implement the first neural network using PyTorch −
Step 1
First, we need to import the PyTorch library using the below command −
import torch import torch.nn as nn
Step 2
Define all the layers and the batch size to start executing the neural network as shown below −
# Defining input size, hidden layer size, output size and batch size respectively n_in, n_h, n_out, batch_size = 10, 5, 1, 10
Step 3
As neural network includes a combination of input data to get the respective output data, we will be following the same procedure as given below −
# Create dummy input and target tensors (data) x = torch.randn(batch_size, n_in) y = torch.tensor([[1.0], [0.0], [0.0], [1.0], [1.0], [1.0], [0.0], [0.0], [1.0], [1.0]])
Step 4
Create a sequential model with the help of in-built functions. Using the below lines of code, create a sequential model −
# Create a model model = nn.Sequential(nn.Linear(n_in, n_h), nn.ReLU(), nn.Linear(n_h, n_out), nn.Sigmoid())
Step 5
Construct the loss function with the help of Gradient Descent optimizer as shown below −
Construct the loss function criterion = torch.nn.MSELoss() # Construct the optimizer (Stochastic Gradient Descent in this case) optimizer = torch.optim.SGD(model.parameters(), lr = 0.01)
Step 6
Implement the gradient descent model with the iterating loop with the given lines of code −
# Gradient Descent for epoch in range(50): # Forward pass: Compute predicted y by passing x to the model y_pred = model(x) # Compute and print loss loss = criterion(y_pred, y) print(''epoch: '', epoch,'' loss: '', loss.item()) # Zero gradients, perform a backward pass, and update the weights. optimizer.zero_grad() # perform a backward pass (backpropagation) loss.backward() # Update the parameters optimizer.step()
Step 7
The output generated is as follows −
epoch: 0 loss: 0.2545787990093231 epoch: 1 loss: 0.2545052170753479 epoch: 2 loss: 0.254431813955307 epoch: 3 loss: 0.25435858964920044 epoch: 4 loss: 0.2542854845523834 epoch: 5 loss: 0.25421255826950073 epoch: 6 loss: 0.25413978099823 epoch: 7 loss: 0.25406715273857117 epoch: 8 loss: 0.2539947032928467 epoch: 9 loss: 0.25392240285873413 epoch: 10 loss: 0.25385022163391113 epoch: 11 loss: 0.25377824902534485 epoch: 12 loss: 0.2537063956260681 epoch: 13 loss: 0.2536346912384033 epoch: 14 loss: 0.25356316566467285 epoch: 15 loss: 0.25349172949790955 epoch: 16 loss: 0.25342053174972534 epoch: 17 loss: 0.2533493936061859 epoch: 18 loss: 0.2532784342765808 epoch: 19 loss: 0.25320762395858765 epoch: 20 loss: 0.2531369626522064 epoch: 21 loss: 0.25306645035743713 epoch: 22 loss: 0.252996027469635 epoch: 23 loss: 0.2529257833957672 epoch: 24 loss: 0.25285571813583374 epoch: 25 loss: 0.25278574228286743 epoch: 26 loss: 0.25271597504615784 epoch: 27 loss: 0.25264623761177063 epoch: 28 loss: 0.25257670879364014 epoch: 29 loss: 0.2525072991847992 epoch: 30 loss: 0.2524380087852478 epoch: 31 loss: 0.2523689270019531 epoch: 32 loss: 0.25229987502098083 epoch: 33 loss: 0.25223103165626526 epoch: 34 loss: 0.25216227769851685 epoch: 35 loss: 0.252093642950058 epoch: 36 loss: 0.25202515721321106 epoch: 37 loss: 0.2519568204879761 epoch: 38 loss: 0.251888632774353 epoch: 39 loss: 0.25182053446769714 epoch: 40 loss: 0.2517525553703308 epoch: 41 loss: 0.2516847252845764 epoch: 42 loss: 0.2516169846057892 epoch: 43 loss: 0.2515493929386139 epoch: 44 loss: 0.25148195028305054 epoch: 45 loss: 0.25141456723213196 epoch: 46 loss: 0.2513473629951477 epoch: 47 loss: 0.2512802183628082 epoch: 48 loss: 0.2512132525444031 epoch: 49 loss: 0.2511464059352875
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