38 lines
1.1 KiB
Python
38 lines
1.1 KiB
Python
import torch
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import numpy as np
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n_features = 1000
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batch_size = 5
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class NeuralNetworkModel(torch.nn.Module):
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def __init__(self):
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super(NeuralNetworkModel, self).__init__()
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self.fc1 = torch.nn.Linear(n_features, 500)
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self.fc2 = torch.nn.Linear(500, 1)
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def forward(self, x):
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x = self.fc1(x)
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x = torch.relu(x)
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x = self.fc2(x)
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x = torch.sigmoid(x)
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return x
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def get_loss_acc(model, X_dataset, Y_dataset, criterion, optimizer,):
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loss_score = 0
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acc_score = 0
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items_total = 0
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model.eval()
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for i in range(0, Y_dataset.shape[0], batch_size):
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X = X_dataset[i:i+batch_size]
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X = torch.tensor(X.astype(np.float32))
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Y = Y_dataset[i:i+batch_size]
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Y = torch.tensor(Y.astype(np.float32)).reshape(-1,1)
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Y_predictions = model(X)
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acc_score += torch.sum((Y_predictions > 0.5) == Y).item()
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items_total += Y.shape[0]
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optimizer.zero_grad()
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loss = criterion(Y_predictions, Y)
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loss.backward()
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optimizer.step()
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loss_score += loss.item() * Y.shape[0]
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return (loss_score / items_total), (acc_score / items_total) |