mirror of
https://github.com/aladdinpersson/Machine-Learning-Collection.git
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111 lines
3.2 KiB
Python
111 lines
3.2 KiB
Python
import torch
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import torch.nn.functional as F
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import torchvision.datasets as datasets
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import torchvision.transforms as transforms
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from torch import nn, optim
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from torch.utils.data import random_split
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class NN(nn.Module):
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def __init__(self, input_size, num_classes):
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super().__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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# Set device cuda for GPU if it's available otherwise run on the CPU
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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 = 3
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# Load Data
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entire_dataset = datasets.MNIST(
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root="dataset/", train=True, transform=transforms.ToTensor(), download=True
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)
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train_ds, val_ds = random_split(entire_dataset, [50000, 10000])
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test_ds = datasets.MNIST(
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root="dataset/", train=False, transform=transforms.ToTensor(), download=True
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)
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train_loader = DataLoader(dataset=train_ds, batch_size=batch_size, shuffle=True)
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val_loader = DataLoader(dataset=train_ds, batch_size=batch_size, shuffle=True)
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test_loader = DataLoader(dataset=test_ds, batch_size=batch_size, shuffle=False)
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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(tqdm(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 & test to see how good our model
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def check_accuracy(loader, model):
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num_correct = 0
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num_samples = 0
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model.eval()
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# We don't need to keep track of gradients here so we wrap it in torch.no_grad()
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with torch.no_grad():
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# Loop through the data
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for x, y in loader:
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# Move data to device
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x = x.to(device=device)
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y = y.to(device=device)
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# Get to correct shape
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x = x.reshape(x.shape[0], -1)
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# Forward pass
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scores = model(x)
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_, predictions = scores.max(1)
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# Check how many we got correct
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num_correct += (predictions == y).sum()
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# Keep track of number of samples
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num_samples += predictions.size(0)
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model.train()
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return num_correct / num_samples
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# Check accuracy on training & test to see how good our model
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model.to(device)
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print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:.2f}")
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print(f"Accuracy on validation set: {check_accuracy(val_loader, model)*100:.2f}")
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print(f"Accuracy on test set: {check_accuracy(test_loader, model)*100:.2f}")
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