Softmax cross-entropy forward and backward, then a training loop

Implement from scratch, with NumPy but no autograd, the forward and backward pass of a softmax layer followed by cross-entropy loss. Keep it numerically stable. Then use it in a small training loop (for example a linear classifier trained with gradient descent) and show the loss going down.