ium_426206/evaluation.py

import torch
import numpy as np
from datetime import datetime
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, TensorDataset, DataLoader

class LayerLinearRegression(nn.Module):
    def __init__(self):
        super().__init__()
        # Instead of our custom parameters, we use a Linear layer with single input and single output
        self.linear = nn.Linear(1, 1)

    def forward(self, x):
        # Now it only takes a call to the layer to make predictions
        return self.linear(x)

checkpoint = torch.load('model.pt')

model = LayerLinearRegression()
#optimizer = optim.SGD(model.parameters(), lr=checkpoint['loss'])

model.load_state_dict(checkpoint['model_state_dict'])
#optimizer.load_state_dict(checkpoint['optimizer_state_dict'])

model.eval()


loss_fn = nn.MSELoss(reduction='mean')
val_dataset = torch.load('val_dataset.pt')
val_loader = DataLoader(dataset=val_dataset)

with torch.no_grad():
        val_losses = []
        # Uses loader to fetch one mini-batch for validation
        for x_val, y_val in val_loader:
            # Again, sends data to same device as model
            # x_val = x_val.to(device)
            # y_val = y_val.to(device)
            
            model.eval()
            # Makes predictions
            yhat = model(x_val)
            # Computes validation loss
            val_loss = loss_fn(y_val, yhat)
            val_losses.append(val_loss.item())
        validation_loss = np.mean(val_losses)

#now = datetime.now()
#print("\n-----------{}-----------".format(now.strftime("%d/%m/%Y, %H:%M:%S")))
#print(f"Mean Squared Error: {validation_loss:.4f}")
#print("------------------------------------------\n")
print(f"{validation_loss:.4f}")
# # Checks model's parameters
# print("Model's state_dict:")
# for param_tensor in model.state_dict():
#     print(param_tensor, "\t", model.state_dict()[param_tensor])

# # Print optimizer's state_dict
# print("Optimizer's state_dict:")
# for var_name in optimizer.state_dict():
#     print(var_name, "\t", optimizer.state_dict()[var_name])
# print("----------------------\n")