ium_452627/sacred_train.py

#!/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
from sacred.observers import MongoObserver

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(_run, trainloader, criterion, optimizer, net, 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)

            _run.log_scalar("training.loss", loss)
            loss.backward()
            optimizer.step()

    print('Finished Training')

ex = Experiment("s452627", interactive=True) 
ex.observers.append(FileStorageObserver('my_runs'))  
ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred')) 

@ex.config
def my_config():
    epochs = 10
    learning_rate = 0.001

@ex.automain
def my_main(epochs, learning_rate, _run):

    ex.open_resource("Customers.csv", "r")

    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=learning_rate, momentum=0.9)

    trainNet(_run, trainloader, criterion, optimizer, net, int(float(epochs)))

    PATH = './cifar_net.pth'
    torch.save(net.state_dict(), PATH)

    ex.add_artifact("cifar_net.pth")

#if __name__ == '__main__':

    #ex.run()