image_recognition #5

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s473558 merged 23 commits from image_recognition into master 2023-06-12 16:25:59 +02:00
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#imports
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
import torchvision.transforms as transforms
#create fully connected network
class NN(nn.Module):
def __init__(self, input_size, num_classes): #1 layer (28x28 = 784 nodes)
super(NN,self)._init_()
self.fc1 = nn.Linear(input_size, 50)
self.fc2 = nn.Linear(50,num_classes)
def forward(self, x):
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# model = NN(784, 10)
# x = torch.rand(64, 784)
# print(model(x).shape)
#set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#hyperparameters
input_size = 784
num_classes = 10
learning_rate = 0.001
batch_size = 64
num_epochs = 1
#load data
train_dataset = datasets.MNIST(root='dataset/', train = True, transform = transforms.toTensor(), download = True)
train_loader = DataLoader(dataset= train_dataset, batch_size = batch_size, shuffle = True)
test_dataset = datasets.MNIST(root='dataset/', train = False, transform = transforms.toTensor(), download = True)
test_loader = DataLoader(dataset= test_dataset, batch_size = batch_size, shuffle = True)
#initialize network
model = NN(input_size=input_size, num_classes=num_classes).to(device)
#loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
#train network
for epoch in range(num_epochs):
for batch_idx, (data, targets) in enumerate(train_loader):
#get data to cuda if possible
data = data.to(device = device)
targets = targets.to(device = device)
#get to correct shape
data = data.reshape(data.shape[0], -1)
# forward
scores = model(data)
loss = criterion(scores, targets)
#backward
optimizer.zero_grad()
loss.backward()
#gradient descent or adam step
optimizer.step()
#check accuracy on training and test to see how good our model
def check_accuracy(loader, model):
if loader.dataset.train:
print("Checking accuracy on training data")
else:
print("Checking accuracy on test data")
num_correct = 0
num_samples = 0
model.eval()
with torch.no_grad():
for x,y in loader:
x = x.to(device = device)
y = y.to(device = device)
x = x.reshape(x.shape[0], -1)
scores = model(x)
_, predictions = scores.max(1)
num_correct += (predictions == y).sum()
num_samples += predictions.size(0)
print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}')
model.train()
return acc
check_accuracy(train_loader, model)
check_accuracy(test_loader, model)