cleanup
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#imports
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import torch.nn.functional as F
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from torch.utils.data import DataLoader
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import torchvision.datasets as datasets
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import torchvision.transforms as transforms
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#create fully connected network
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class NN(nn.Module):
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def __init__(self, input_size, num_classes): #1 layer (28x28 = 784 nodes)
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super(NN,self)._init_()
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self.fc1 = nn.Linear(input_size, 50)
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self.fc2 = nn.Linear(50,num_classes)
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def forward(self, x):
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x = F.relu(self.fc1(x))
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x = self.fc2(x)
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return x
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# model = NN(784, 10)
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# x = torch.rand(64, 784)
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# print(model(x).shape)
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#set device
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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#hyperparameters
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input_size = 784
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num_classes = 10
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learning_rate = 0.001
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batch_size = 64
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num_epochs = 1
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#load data
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train_dataset = datasets.MNIST(root='dataset/', train = True, transform = transforms.toTensor(), download = True)
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train_loader = DataLoader(dataset= train_dataset, batch_size = batch_size, shuffle = True)
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test_dataset = datasets.MNIST(root='dataset/', train = False, transform = transforms.toTensor(), download = True)
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test_loader = DataLoader(dataset= test_dataset, batch_size = batch_size, shuffle = True)
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#initialize network
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model = NN(input_size=input_size, num_classes=num_classes).to(device)
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#loss and optimizer
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=learning_rate)
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#train network
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for epoch in range(num_epochs):
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for batch_idx, (data, targets) in enumerate(train_loader):
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#get data to cuda if possible
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data = data.to(device = device)
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targets = targets.to(device = device)
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#get to correct shape
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data = data.reshape(data.shape[0], -1)
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# forward
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scores = model(data)
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loss = criterion(scores, targets)
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#backward
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optimizer.zero_grad()
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loss.backward()
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#gradient descent or adam step
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optimizer.step()
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#check accuracy on training and test to see how good our model
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def check_accuracy(loader, model):
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if loader.dataset.train:
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print("Checking accuracy on training data")
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else:
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print("Checking accuracy on test data")
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num_correct = 0
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num_samples = 0
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model.eval()
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with torch.no_grad():
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for x,y in loader:
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x = x.to(device = device)
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y = y.to(device = device)
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x = x.reshape(x.shape[0], -1)
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scores = model(x)
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_, predictions = scores.max(1)
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num_correct += (predictions == y).sum()
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num_samples += predictions.size(0)
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print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}')
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model.train()
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return acc
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check_accuracy(train_loader, model)
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check_accuracy(test_loader, model)
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