f0a81e2d218aa41b538de5e5cfebcc149f758dc5,examples/rbm_tutorial.py,BinomialRBM,fit,#BinomialRBM#Any#Any#Any#Any#Any#Any#Any#,239

Before Change

                epoch_list.append(ep)
           
            // Save fidelities at the end of training.
            if ep == epochs:
                print ("Finished training." )               

                ax = plt.axes()
                ax.plot(epoch_list, fidelity_list, color="blue")
                ax.grid()
                ax.set_xlim(0,epochs)
                ax.set_xlabel("Epochs")
                ax.set_ylabel("Fidelity")

                self.save_params()
                print ("Saved weights and biases.")
                break

            for batch in batches:
                all_grads = self.compute_batch_gradients(k, batch)

                optimizer.zero_grad()  // clear any cached gradients

After Change

                //plt.ylabel("F",fontsize=20)
                plt.xticks(fontsize=20)
                plt.yticks(fontsize=20)
                plt.gca().text(0.95, 0.2, "F = "+"{:.3}".format(fidelity_.item()),
                        verticalalignment="bottom", horizontalalignment="right",
                        color="k", fontsize=20,transform=plt.gca().transAxes)
                //plt.pause(0.05)
                display.clear_output(wait=True)
                display.display(plt.gcf())
            

Instances