83 lines
2.4 KiB
Python
83 lines
2.4 KiB
Python
import pandas as pd
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import numpy as np
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import zadanie1 as z
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import train as tr
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import torch
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import torchvision
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import torchvision.transforms as transforms
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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class Net(nn.Module):
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def __init__(self):
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super().__init__()
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#self.conv1 = nn.Conv2d(3, 6, 5)
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#self.pool = nn.MaxPool2d(2, 2)
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#self.conv2 = nn.Conv2d(6, 16, 5)
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#self.fc1 = nn.Linear(16 * 5 * 5, 120)
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#self.fc2 = nn.Linear(20, 6)
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self.fc3 = nn.Linear(6, 6)
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def forward(self, x):
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#x = self.pool(F.relu(self.conv1(x)))
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#x = self.pool(F.relu(self.conv2(x)))
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#x = torch.flatten(x, 1)
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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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testdata = []
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def testNet(testloader):
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PATH = './cifar_net.pth'
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net = Net()
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net.load_state_dict(torch.load(PATH))
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correct = 0
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total = 0
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with torch.no_grad():
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for data in testloader:
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input, labels = data
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labelsX = torch.Tensor([x for x in labels])
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labels = labelsX.type(torch.LongTensor)
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outputs = net(input)
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_, predicted = torch.max(outputs.data, 1)
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testdata.append([input, labels, predicted])
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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print(f'Accuracy of the network: {100 * correct // total} %')
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if __name__ == '__main__':
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train, dev, test = z.prepareData()
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batch_size = 4
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trainlist = train.values.tolist()
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testlist = test.values.tolist()
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trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist]
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trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
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shuffle=True, num_workers=2)
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testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist]
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testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
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shuffle=False, num_workers=2)
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classes = ('male', 'female')
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testNet(testloader)
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with open('testresults.txt', 'w') as the_file:
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for item in testdata:
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for i in range(len(item)):
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the_file.write(f'data: {item[0][i]} \n true value: {item[1][i]} \n prediction: {item[2][i]}\n')
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