PyTorch - Implementing First Neural Network

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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