126 lines
4.3 KiB
Python
126 lines
4.3 KiB
Python
import torch
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import DataLoader, Dataset
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import LabelEncoder
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import torch.nn.functional as F
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from sacred import Experiment
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from sacred.observers import FileStorageObserver, MongoObserver
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device = (
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"cuda"
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if torch.cuda.is_available()
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else "cpu"
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)
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ex = Experiment("464914", interactive=True, save_git_info=False)
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ex.observers.append(FileStorageObserver('experiments'))
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ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017',
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db_name='sacred'))
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class Model(nn.Module):
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def __init__(self, input_features=54, hidden_layer1=25, hidden_layer2=30, output_features=8):
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super().__init__()
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self.fc1 = nn.Linear(input_features,output_features)
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self.bn1 = nn.BatchNorm1d(hidden_layer1) # Add batch normalization
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self.fc2 = nn.Linear(hidden_layer1, hidden_layer2)
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self.bn2 = nn.BatchNorm1d(hidden_layer2) # Add batch normalization
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self.out = nn.Linear(hidden_layer2, output_features)
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def forward(self, x):
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x = F.relu(self.fc1(x)) # Apply batch normalization after first linear layer
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#x = F.relu(self.bn2(self.fc2(x))) # Apply batch normalization after second linear layer
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#x = self.out(x)
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return x
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@ex.capture
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def capture_params(epochs):
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print(f"epochs: {epochs}")
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@ex.main
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def main(_run):
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forest_train_ex = ex.open_resource('forest_train.csv')
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forest_val_ex = ex.open_resource('forest_val.csv')
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forest_val = pd.read_csv('forest_val.csv')
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forest_train = pd.read_csv('forest_train.csv')
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X_train = forest_train.drop(columns=['Cover_Type']).values
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y_train = forest_train['Cover_Type'].values
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X_val = forest_val.drop(columns=['Cover_Type']).values
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y_val = forest_val['Cover_Type'].values
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# Initialize model, loss function, and optimizer
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model = Model().to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.Adam(model.parameters(), lr=0.001)
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# Convert to PyTorch tensors
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X_train = torch.tensor(X_train, dtype=torch.float32).to(device)
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y_train = torch.tensor(y_train, dtype=torch.long).to(device)
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X_val = torch.tensor(X_val, dtype=torch.float32).to(device)
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y_val = torch.tensor(y_val, dtype=torch.long).to(device)
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# Create DataLoader
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train_loader = DataLoader(list(zip(X_train, y_train)), batch_size=64, shuffle=True)
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val_loader = DataLoader(list(zip(X_val, y_val)), batch_size=64)
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# Training loop
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epochs = 10
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for epoch in range(epochs):
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model.train() # Set model to training mode
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running_loss = 0.0
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for inputs, labels in train_loader:
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inputs, labels = inputs.to(device), labels.to(device)
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optimizer.zero_grad()
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outputs = model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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running_loss += loss.item() * inputs.size(0)
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# Calculate training loss
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epoch_loss = running_loss / len(train_loader.dataset)
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# Validation
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model.eval() # Set model to evaluation mode
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val_running_loss = 0.0
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correct = 0
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total = 0
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with torch.no_grad():
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for inputs, labels in val_loader:
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inputs, labels = inputs.to(device), labels.to(device)
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outputs = model(inputs)
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val_loss = criterion(outputs, labels)
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val_running_loss += val_loss.item() * inputs.size(0)
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_, predicted = torch.max(outputs, 1)
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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# Calculate validation loss and accuracy
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val_epoch_loss = val_running_loss / len(val_loader.dataset)
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val_accuracy = correct / total
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print(f"Epoch {epoch+1}/{epochs}, "
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f"Train Loss: {epoch_loss:.4f}, "
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f"Val Loss: {val_epoch_loss:.4f}, "
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f"Val Accuracy: {val_accuracy:.4f}")
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_run.log_scalar("train loss", epoch_loss)
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_run.log_scalar("val loss", val_epoch_loss)
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capture_params(epochs)
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torch.save(model.state_dict(), 'model.pth')
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ex.add_artifact("model.pth")
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ex.run() |