96 lines
2.5 KiB
Python
96 lines
2.5 KiB
Python
#!/usr/bin/python
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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 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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from sacred import Experiment
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from sacred.observers import FileStorageObserver
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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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def trainNet(trainloader, criterion, optimizer, epochs=20):
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for epoch in range(epochs):
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for i, data in enumerate(trainloader, 0):
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inputs, 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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optimizer.zero_grad()
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outputs = net(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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print('Finished Training')
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ex = Experiment("sacred_scopes", interactive=True)
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ex.observers.append(FileStorageObserver('my_runs'))
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@ex.config
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def my_config():
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epochs = 10
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learning_rate = 0.001
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@ex.main
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def my_main(epochs, learning_rate):
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print(f'{epochs} {learning_rate}')
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if __name__ == '__main__':
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ex.run()
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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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net = Net()
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
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#trainNet(trainloader, criterion, optimizer, int(float(epochs)))
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#PATH = './cifar_net.pth'
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#torch.save(net.state_dict(), PATH)
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