66 lines
1.8 KiB
Python
66 lines
1.8 KiB
Python
import torch
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import torchvision
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.transforms as transforms
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import matplotlib.pyplot as plt
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import numpy as np
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transform = transforms.Compose(
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[transforms.ToTensor(),
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transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
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trainset = torchvision.datasets.ImageFolder(
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root='./resources/zbior_uczacy', transform=transform)
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trainloader = torch.utils.data.DataLoader(
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trainset, batch_size=4, shuffle=True, num_workers=2)
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testset = torchvision.datasets.ImageFolder(
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root='./resources/smieci', transform=transform)
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testloader = torch.utils.data.DataLoader(
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testset, batch_size=4, shuffle=False, num_workers=2)
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classes = ('glass', 'metal', 'paper', 'plastic')
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def imshow(img):
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img = img / 2 + 0.5
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npimg = img.numpy()
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plt.imshow(np.transpose(npimg, (1, 2, 0)))
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plt.show()
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dataiter = iter(trainloader)
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images, labels = dataiter.next()
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# show images
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imshow(torchvision.utils.make_grid(images))
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# print labels
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print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
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class Siec(nn.Module):
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def __init__(self):
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super(Siec, self).__init__()
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self.conv1 = nn.Conv2d(1, 6, 3)
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self.conv2 = nn.Conv2d(6, 16, 3)
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self.fc1 = nn.Linear(16*6*6, 120)
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self.fc2 = nn.Linear(120, 84)
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self.fc3 = nn.Linear(84, 10)
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def forward(self, x):
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x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
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x = F.max_pool2d(F.relu(self.conv2(x)), 2)
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x = x.view(-1, self.num_flat_features(x))
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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def num_flat_features(self, x):
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size = x.size()[1:] # all dimensions except the batch dimension
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num_features = 1
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for s in size:
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num_features *= s
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return num_features
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