Student_result_predictor.py

import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader
import torch.nn.functional as F


inputs = np.array([[73, 67, 43], [91, 88, 64], [87, 34, 58], 
                   [20, 43, 37], [69, 96, 70], [73, 67, 43], 
                   [91, 88, 64], [87, 34, 58], [12, 43, 37], 
                   [69, 96, 70], [73, 67, 43], [91, 88, 64], 
                   [87, 14, 58], [12, 43, 37], [69, 96, 70]], 
                  dtype='float32')
targets = np.array([[0.0], [1.0], [0.0], 
                    [0.0], [1.0], [0.0], 
                    [1.0], [0.0], [1.0], 
                    [1.0], [0.0], [1.0], 
                    [0.0], [0.0], [1.0]], 
                   dtype='float32')


targets = torch.from_numpy(targets)
inputs = torch.from_numpy(inputs)

model = nn.Linear(3, 1)

preds = model(inputs)


loss_fn = F.mse_loss
loss = loss_fn(model(inputs), targets)


opt = torch.optim.SGD(model.parameters(), lr=0.00001)

def fit(num_epochs, model, loss_fn, opt):
    
    # Repeat for given number of epochs
    for epoch in range(num_epochs):
        # 1. Generate predictions
        pred = model(inputs)
            
            # 2. Calculate loss
        loss = loss_fn(pred, targets)
            
            # 3. Compute gradients
        loss.backward()
            
            # 4. Update parameters using gradients
        opt.step()
            
            # 5. Reset the gradients to zero
        opt.zero_grad()
        
        # Print the progress
        if (epoch+1) % 10 == 0:
            
            print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
            
            
fit(900000, model, loss_fn, opt)

preds=model(inputs)

print(targets)#to be predicted
print(preds) #predicted