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_nn_model.py
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_nn_model.py
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import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset, random_split
class SimpleNN(nn.Module):
def __init__(self, input_dim, output_dim, latent_dim =128, dropout_rate=0.5):
super(SimpleNN, self).__init__()
self.fc1 = nn.Linear(input_dim, latent_dim)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(dropout_rate)
self.fc2 = nn.Linear(latent_dim, output_dim)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
x = self.relu(self.fc1(x))
x = self.dropout(x)
x = self.softmax(self.fc2(x))
return x
class EarlyStopping:
def __init__(self, patience=7, verbose=False, delta=0):
self.patience = patience
self.verbose = verbose
self.delta = delta
self.counter = 0
self.best_score = None
self.early_stop = False
self.val_loss_min = np.Inf
def __call__(self, val_loss):
score = -val_loss
if self.best_score is None:
self.best_score = score
self.val_loss_min = val_loss
elif score < self.best_score + self.delta:
self.counter += 1
if self.verbose:
print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
if self.counter >= self.patience:
self.early_stop = True
else:
self.best_score = score
self.val_loss_min = val_loss
self.counter = 0
def train_model(model, train_loader, patience=10, n_epochs=100):
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
early_stopping = EarlyStopping(patience=patience, verbose=True)
for epoch in range(n_epochs):
train_loss = 0.0
model.train() # Set model to training mode
for data, target in train_loader:
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss += loss.item() * data.size(0)
train_loss /= len(train_loader.dataset)
if epoch%10==0:
print(f'Epoch {epoch+1} \tTraining Loss: {train_loss:.6f}')
early_stopping(train_loss)
if early_stopping.early_stop:
print("Early stopping")
break
return model