mirror of
https://github.com/aladdinpersson/Machine-Learning-Collection.git
synced 2026-02-21 19:27:58 +00:00
56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
import torch
|
|
from sklearn import metrics
|
|
from tqdm import tqdm
|
|
import torch.nn as nn
|
|
import torch.optim as optim
|
|
from utils import get_predictions
|
|
from dataset import get_data
|
|
from torch.utils.data import DataLoader
|
|
import torch.nn.functional as F
|
|
|
|
class NN(nn.Module):
|
|
def __init__(self, input_size, hidden_dim):
|
|
super(NN, self).__init__()
|
|
self.bn = nn.BatchNorm1d(input_size)
|
|
self.fc1 = nn.Linear(2, hidden_dim)
|
|
self.fc2 = nn.Linear(input_size//2*hidden_dim, 1)
|
|
|
|
def forward(self, x):
|
|
N = x.shape[0]
|
|
x = self.bn(x)
|
|
orig_features = x[:, :200].unsqueeze(2) # (N, 200, 1)
|
|
new_features = x[:, 200:].unsqueeze(2) # (N, 200, 1)
|
|
x = torch.cat([orig_features, new_features], dim=2) # (N, 200, 2)
|
|
x = F.relu(self.fc1(x)).reshape(N, -1) # (N, 200*hidden_dim)
|
|
return torch.sigmoid(self.fc2(x)).view(-1)
|
|
|
|
|
|
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
|
|
model = NN(input_size=400, hidden_dim=100).to(DEVICE)
|
|
optimizer = optim.Adam(model.parameters(), lr=2e-3, weight_decay=1e-4)
|
|
loss_fn = nn.BCELoss()
|
|
train_ds, val_ds, test_ds, test_ids = get_data()
|
|
train_loader = DataLoader(train_ds, batch_size=1024, shuffle=True)
|
|
val_loader = DataLoader(val_ds, batch_size=1024)
|
|
test_loader = DataLoader(test_ds, batch_size=1024)
|
|
|
|
for epoch in range(20):
|
|
probabilities, true = get_predictions(val_loader, model, device=DEVICE)
|
|
print(f"VALIDATION ROC: {metrics.roc_auc_score(true, probabilities)}")
|
|
|
|
for batch_idx, (data, targets) in enumerate(train_loader):
|
|
data = data.to(DEVICE)
|
|
targets = targets.to(DEVICE)
|
|
|
|
# forward
|
|
scores = model(data)
|
|
loss = loss_fn(scores, targets)
|
|
optimizer.zero_grad()
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
from utils import get_submission
|
|
get_submission(model, test_loader, test_ids, DEVICE)
|
|
|
|
|