131 lines
3.3 KiB
Python
131 lines
3.3 KiB
Python
import torch
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from torch import nn
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from torch.autograd import Variable
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from torch.utils.data import DataLoader
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from torchvision import datasets
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from torchvision.transforms import ToTensor, Lambda, Compose
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import matplotlib.pyplot as plt
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training_data = datasets.MNIST(
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root=".data",
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train=True,
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download=True,
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transform=ToTensor(),
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)
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test_data = datasets.MNIST(
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root=".data",
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train=False,
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download=True,
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transform=ToTensor(),
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)
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batch_size = 100
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train_loader = DataLoader(training_data, batch_size=batch_size)
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test_loader = DataLoader(test_data, batch_size=batch_size)
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class CNN(nn.Module):
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def __init__(self):
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super(CNN, self).__init__()
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self.layer1 = nn.Sequential(
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nn.Conv2d(in_channels=1, out_channels=32, kernel_size=3, padding=1),
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nn.BatchNorm2d(32),
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=2)
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)
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self.layer2 = nn.Sequential(
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nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3),
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nn.BatchNorm2d(64),
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nn.ReLU(),
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nn.MaxPool2d(2)
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)
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self.fc1 = nn.Linear(in_features=64*6*6, out_features=600)
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self.drop = nn.Dropout2d(0.25)
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self.fc2 = nn.Linear(in_features=600, out_features=120)
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self.fc3 = nn.Linear(in_features=120, out_features=10)
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def forward(self, x):
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out = self.layer1(x)
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out = self.layer2(out)
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out = out.view(out.size(0), -1)
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out = self.fc1(out)
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out = self.drop(out)
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out = self.fc2(out)
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out = self.fc3(out)
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return out
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model = CNN()
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error = nn.CrossEntropyLoss()
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learning_rate = 0.001
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optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
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num_epochs = 5
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count = 0
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loss_list = []
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iteration_list = []
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accuracy_list = []
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predictions_list = []
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labels_list = []
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for epoch in range(num_epochs):
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for images, labels in train_loader:
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train = Variable(images.view(batch_size, 1, 28, 28))
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labels = Variable(labels)
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outputs = model(train)
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loss = error(outputs, labels)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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count += 1
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if count % 50 == 0:
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total = 0
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correct = 0
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for images, labels in test_loader:
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labels_list.append(labels)
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test = Variable(images.view(batch_size, 1, 28, 28))
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outputs = model(test)
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predictions = torch.max(outputs, 1)[1]
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predictions_list.append(predictions)
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correct += (predictions == labels).sum()
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total += len(labels)
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accuracy = correct * batch_size / total
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loss_list.append(loss.data)
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iteration_list.append(count)
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accuracy_list.append(accuracy)
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print("Iteration: {}, Loss: {}, Accuracy: {}%".format(count, loss.data, accuracy))
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torch.save(model.state_dict(), "model.pth")
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print("Saved PyTorch Model State to model.pth")
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plt.plot(iteration_list, loss_list)
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plt.xlabel("No. of Iteration")
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plt.ylabel("Loss")
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plt.title("Iterations vs Loss")
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plt.savefig("loss.png")
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plt.plot(iteration_list, accuracy_list)
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plt.xlabel("No. of Iteration")
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plt.ylabel("Accuracy")
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plt.title("Iterations vs Accuracy")
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plt.savefig("accuracy.png")
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