Pytorch, Machine Learning, Gradient Descent, Python, Hands On Tutorials.
plt.xlabel('Bias') plt.ylabel('Weight') i = ax.imshow(losses, cmap=cm, interpolation='nearest', extent=[-5, 8, -5, 8]) fig.colorbar(i) plt.show() Figure 3. L2 loss function on toy problem. Now we can capture the model parameters while running gradient descent to show us how the optimizer is