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Feed Forward Neural Network - PyTorch Beginner 13

Learn all the basics you need to get started with this deep learning framework! In this part we will implement our first multilayer neural network that can do digit classification based on the famous MNIST dataset.

We put all the things from the last tutorials together:

All code from this course can be found on GitHub.

Neural Network in PyTorch

import torch import torch.nn as nn import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt # Device configuration device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # Hyper-parameters input_size = 784 # 28x28 hidden_size = 500 num_classes = 10 num_epochs = 2 batch_size = 100 learning_rate = 0.001 # MNIST dataset train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True) test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor()) # Data loader train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False) examples = iter(test_loader) example_data, example_targets = examples.next() for i in range(6): plt.subplot(2,3,i+1) plt.imshow(example_data[i][0], cmap='gray') plt.show() # Fully connected neural network with one hidden layer class NeuralNet(nn.Module): def __init__(self, input_size, hidden_size, num_classes): super(NeuralNet, self).__init__() self.input_size = input_size self.l1 = nn.Linear(input_size, hidden_size) self.relu = nn.ReLU() self.l2 = nn.Linear(hidden_size, num_classes) def forward(self, x): out = self.l1(x) out = self.relu(out) out = self.l2(out) # no activation and no softmax at the end return out model = NeuralNet(input_size, hidden_size, num_classes).to(device) # Loss and optimizer criterion = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # Train the model n_total_steps = len(train_loader) for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): # origin shape: [100, 1, 28, 28] # resized: [100, 784] images = images.reshape(-1, 28*28).to(device) labels = labels.to(device) # Forward pass outputs = model(images) loss = criterion(outputs, labels) # Backward and optimize optimizer.zero_grad() loss.backward() optimizer.step() if (i+1) % 100 == 0: print (f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}') # Test the model # In test phase, we don't need to compute gradients (for memory efficiency) with torch.no_grad(): n_correct = 0 n_samples = 0 for images, labels in test_loader: images = images.reshape(-1, 28*28).to(device) labels = labels.to(device) outputs = model(images) # max returns (value ,index) _, predicted = torch.max(outputs.data, 1) n_samples += labels.size(0) n_correct += (predicted == labels).sum().item() acc = 100.0 * n_correct / n_samples print(f'Accuracy of the network on the 10000 test images: {acc} %')

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