ium_452627/my_runs/_sources/sacred_train_69457f4c158c0d7ad12c17d05b383cdb.py
2023-05-11 20:25:07 +02:00

96 lines
2.5 KiB
Python

#!/usr/bin/python
import pandas as pd
import numpy as np
import zadanie1 as z
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from sacred import Experiment
from sacred.observers import FileStorageObserver
class Net(nn.Module):
def __init__(self):
super().__init__()
#self.conv1 = nn.Conv2d(3, 6, 5)
#self.pool = nn.MaxPool2d(2, 2)
#self.conv2 = nn.Conv2d(6, 16, 5)
#self.fc1 = nn.Linear(16 * 5 * 5, 120)
#self.fc2 = nn.Linear(20, 6)
self.fc3 = nn.Linear(6, 6)
def forward(self, x):
#x = self.pool(F.relu(self.conv1(x)))
#x = self.pool(F.relu(self.conv2(x)))
#x = torch.flatten(x, 1)
#x = F.relu(self.fc1(x))
#x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
def trainNet(trainloader, criterion, optimizer, epochs=20):
for epoch in range(epochs):
for i, data in enumerate(trainloader, 0):
inputs, labels = data
labelsX = torch.Tensor([x for x in labels])
labels = labelsX.type(torch.LongTensor)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
print('Finished Training')
ex = Experiment("sacred_scopes", interactive=True)
ex.observers.append(FileStorageObserver('my_runs'))
@ex.config
def my_config():
epochs = 10
learning_rate = 0.001
@ex.main
def my_main(epochs, learning_rate):
print(f'{epochs} {learning_rate}')
if __name__ == '__main__':
ex.run()
train, dev, test = z.prepareData()
batch_size = 4
trainlist = train.values.tolist()
testlist = test.values.tolist()
trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist]
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
shuffle=True, num_workers=2)
testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist]
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
shuffle=False, num_workers=2)
classes = ('male', 'female')
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
#trainNet(trainloader, criterion, optimizer, int(float(epochs)))
#PATH = './cifar_net.pth'
#torch.save(net.state_dict(), PATH)