| import torch
|
| import torch.nn as nn
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| import torch.optim as optim
|
| import torchvision
|
| import torchvision.transforms as transforms
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| from torch.utils.data import DataLoader
|
| import pygame
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| import numpy as np
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| import cv2
|
|
|
| class ANN(nn.Module):
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| def __init__(self):
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| super(ANN,self).__init__()
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| self.fc1 = nn.Linear(28*28,128)
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| self.fc2 = nn.Linear(128,128)
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| self.fc3 = nn.Linear(128,128)
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| self.fc4 = nn.Linear(128,10)
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| self.activation = nn.ReLU()
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| def forward(self,x):
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| x = x.view(-1,28*28)
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| x = self.activation(self.fc1(x))
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| x = self.activation(self.fc2(x))
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| x = self.activation(self.fc3(x))
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| x = self.fc4(x)
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| return x
|
|
|
| transform = transforms.Compose([
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| transforms.ToTensor(),
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| transforms.Normalize((0.5,),(0.5,))]
|
| )
|
| trainset = torchvision.datasets.MNIST(root='./data',train=True,download=True,transform=transform)
|
| testset = torchvision.datasets.MNIST(root='./data',train=False,download=True,transform=transform)
|
|
|
| trainloader = DataLoader(trainset,batch_size=64,shuffle=True)
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| testloader = DataLoader(testset,batch_size=64,shuffle=False)
|
|
|
| model = ANN()
|
| los_fn = nn.CrossEntropyLoss()
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| optimizer = optim.Adam(model.parameters(),lr=0.001)
|
|
|
|
|
| episodes = 10
|
|
|
|
|
| for epoch in range(episodes):
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| running_loss = 0.0
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| for images, labels in trainloader:
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| optimizer.zero_grad()
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| outputs = model(images)
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| loss = los_fn(outputs,labels)
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| loss.backward()
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| optimizer.step()
|
| running_loss += loss.item()
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| print(f"Epoch {epoch+1}, Loss: {running_loss/len(trainloader)}")
|
|
|
|
|
| correct = 0
|
| total = 0
|
| model.eval()
|
| with torch.no_grad():
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| for images, labels in testloader:
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| outputs = model(images)
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| _, predicted = torch.max(outputs.data,1)
|
| total += labels.size(0)
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| correct += (predicted == labels).sum().item()
|
| print(f"Test Accuracy: {100 * correct / total}%")
|
|
|
| def draw_digi():
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| pygame.init()
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| window_size = 280
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| display_height = window_size+50
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| screen = pygame.display.set_mode((window_size,display_height))
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| pygame.display.set_caption("Draw a digit")
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| clock = pygame.time.Clock()
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| screen.fill((0,0,0))
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| drawing = False
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| prediction = None
|
|
|
| font = pygame.font.Font(None,36)
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| while True:
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|
|
| for event in pygame.event.get():
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| if event.type == pygame.QUIT:
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| pygame.quit()
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| return
|
| if event.type == pygame.MOUSEBUTTONDOWN:
|
| drawing = True
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| if event.type == pygame.MOUSEBUTTONUP:
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| drawing = False
|
| prediction = predict_digit(screen)
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| if event.type == pygame.KEYDOWN:
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| if event.key == pygame.K_c:
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| screen.fill((0,0,0))
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| prediction = None
|
|
|
| if event.type == pygame.MOUSEMOTION and drawing:
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| pygame.draw.circle(screen,(255,255,255),event.pos,8)
|
|
|
| if prediction is not None:
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| text = font.render(f"Prediction: {prediction}",True,(0,255,0))
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| screen.blit(text,(10,window_size+10))
|
|
|
| pygame.display.flip()
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| clock.tick(60)
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|
|
| def process_drawing(screen):
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| surface = pygame.surfarray.array3d(screen)
|
| gray = np.dot(surface[...,:3],[0.2989,0.5870,0.1140])
|
| gray = np.transpose(gray,(1,0))
|
| gray = cv2.resize(gray,(28,28),interpolation=cv2.INTER_AREA)
|
| gray = gray.astype(np.float32)/255.0
|
| gray = (gray - 0.5)/0.5
|
| tensor = torch.tensor(gray,dtype=torch.float32).unsqueeze(0).unsqueeze(0)
|
| return tensor
|
| def predict_digit(screen):
|
| image = process_drawing(screen)
|
| if image is None:
|
| return None
|
| model.eval()
|
| with torch.no_grad():
|
| output = model(image)
|
| _, predicted = torch.max(output.data,1)
|
| return predicted.item()
|
|
|
| draw_digi() |
