101 lines
1.8 KiB
Python
101 lines
1.8 KiB
Python
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#!/usr/bin/env python
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# coding: utf-8
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# In[ ]:
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import numpy as np
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import pandas as pd
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import torch
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from torch import nn, optim
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import torch.nn.functional as F
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import sys
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# In[ ]:
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epochs = int(sys.argv[1])
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# In[ ]:
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X_train = pd.read_csv('X_train.csv')
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y_train = pd.read_csv('y_train.csv')
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# In[ ]:
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X_train = torch.from_numpy(np.array(X_train)).float()
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y_train = torch.squeeze(torch.from_numpy(y_train.values).float())
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# In[ ]:
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class Net(nn.Module):
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def __init__(self, n_features):
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super(Net, self).__init__()
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self.fc1 = nn.Linear(n_features, 5)
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self.fc2 = nn.Linear(5, 3)
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self.fc3 = nn.Linear(3, 1)
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def forward(self, 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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return torch.sigmoid(self.fc3(x))
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# In[ ]:
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net = Net(X_train.shape[1])
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criterion = nn.BCELoss()
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optimizer = optim.Adam(net.parameters(), lr=0.001)
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# In[ ]:
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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X_train = X_train.to(device)
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y_train = y_train.to(device)
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net = net.to(device)
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criterion = criterion.to(device)
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# In[ ]:
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def calculate_accuracy(y_true, y_pred):
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predicted = y_pred.ge(.5).view(-1)
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return (y_true == predicted).sum().float() / len(y_true)
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def round_tensor(t, decimal_places=3):
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return round(t.item(), decimal_places)
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for epoch in range(epochs):
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y_pred = net(X_train)
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y_pred = torch.squeeze(y_pred)
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train_loss = criterion(y_pred, y_train)
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if epoch % 100 == 0:
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train_acc = calculate_accuracy(y_train, y_pred)
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print(
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f'''epoch {epoch}
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Train set - loss: {round_tensor(train_loss)}, accuracy: {round_tensor(train_acc)}
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''')
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optimizer.zero_grad()
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train_loss.backward()
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optimizer.step()
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# In[ ]:
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torch.save(net, 'model.pth')
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