123 lines
3.9 KiB
Python
123 lines
3.9 KiB
Python
import numpy as np
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import torch
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import torch.nn.functional as F
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from matplotlib import pyplot as plt
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from torch.utils.data import DataLoader
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from torchvision import datasets, transforms
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# Super parameters ------------------------------------------------------------------------------------
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batch_size = 64
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learning_rate = 0.01
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momentum = 0.5
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EPOCH = 10
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# Prepare dataset ------------------------------------------------------------------------------------
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transform = transforms.Compose(
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[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]
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)
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train_dataset = datasets.MNIST(
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root="./data/mnist", train=True, download=True, transform=transform
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)
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test_dataset = datasets.MNIST(
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root="./data/mnist", train=False, download=True, transform=transform
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)
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
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# Design model using class ------------------------------------------------------------------------------
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class Net(torch.nn.Module):
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def __init__(self):
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super(Net, self).__init__()
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self.fc1 = torch.nn.Linear(
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28 * 28, 512
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) # Flatten 28x28 input to a vector of 512 units
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self.fc2 = torch.nn.Linear(512, 128) # First hidden layer
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self.fc3 = torch.nn.Linear(
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128, 10
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) # Output layer with 10 units (one for each digit)
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def forward(self, x):
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x = x.view(-1, 28 * 28) # Flatten the image
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x = self.fc1(x) # the first linear layer
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x = F.relu(x) # Activation
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x = self.fc2(x) # the second linear layer
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x = F.relu(x) # Activation
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x = self.fc3(x) # No activation on the final output layer (for classification)
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return x
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model = Net()
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# Construct loss and optimizer ----------------------------------------------------------------------
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criterion = (
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torch.nn.CrossEntropyLoss()
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) # CrossEntropy loss for multi-class classification
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optimizer = torch.optim.SGD(
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model.parameters(), lr=learning_rate, momentum=momentum
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) # SGD optimizer
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# Train and Test CLASS -----------------------------------------------------------------------------------
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def train(epoch):
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running_loss = 0.0
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running_total = 0
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running_correct = 0
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for batch_idx, data in enumerate(train_loader):
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inputs, target = data
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optimizer.zero_grad()
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# forward + backward + update
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outputs = model(inputs)
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loss = criterion(outputs, target)
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loss.backward()
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optimizer.step()
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# Accumulate loss and accuracy
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running_loss += loss.item()
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_, predicted = torch.max(outputs.data, dim=1)
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running_total += target.shape[0]
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running_correct += (predicted == target).sum().item()
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# Print after each epoch
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avg_loss = running_loss / len(train_loader)
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avg_acc = 100 * running_correct / running_total
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print(
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"[%d / %d]: Training Loss: %.3f, Training Accuracy: %.2f %%"
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% (epoch + 1, EPOCH, avg_loss, avg_acc)
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)
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def test(epoch):
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correct = 0
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total = 0
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with torch.no_grad():
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for data in test_loader:
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images, labels = data
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outputs = model(images)
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_, predicted = torch.max(outputs.data, dim=1)
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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acc = correct / total
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print(
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"[%d / %d]: Accuracy on test set after epoch %d: %.1f %%"
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% (epoch + 1, EPOCH, epoch + 1, 100 * acc)
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)
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return 100 * acc
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# Start train and Test -----------------------------------------------------------------------------------
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if __name__ == "__main__":
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acc_list_test = []
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for epoch in range(EPOCH):
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train(epoch)
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acc_test = test(epoch) # Test after each epoch
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acc_list_test.append(acc_test)
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plt.plot(range(1, EPOCH + 1), acc_list_test)
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plt.xlabel("Epoch")
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plt.ylabel("Accuracy On TestSet")
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plt.show()
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