import numpy as np
import torch
Input (temp, rainfall, humidity)
inputs = np.array([[73, 67, 43],
[91, 88, 64],
[87, 134, 58],
[102, 43, 37],
[69, 96, 70]], dtype=‘float32’)
Targets (apples, oranges)
targets = np.array([[56, 70],
[81, 101],
[119, 133],
[22, 37],
[103, 119]], dtype=‘float32’)
Convert inputs and targets to tensors
inputs = torch.from_numpy(inputs)
targets = torch.from_numpy(targets)
print(inputs)
print(targets)
Weights and biases
w = torch.randn(2, 3, requires_grad=True)
b = torch.randn(2, requires_grad=True)
print(w)
print(b)
def model(x):
return x @ w.t() + b
Generate predictions
preds = model(inputs)
print(preds)
MSE(mean squared error) loss
def mse(t1, t2):
diff = t1 - t2
return torch.sum(diff * diff) / diff.numel()
Compute loss
loss = mse(preds, targets)
print(loss)
Compute gradients
loss.backward()
Gradients for weights
print(w)
print(w.grad)
Help me how is how w.grad is being calculated here