summaries_axes[0].set_xlabel('Iterations')
summaries_axes[0].set_xlim((0, max_iter))
summaries_axes[0].set_ylabel('Loss')
summaries_axes[0].set_title('Learning curves')
# Display the error rate across folds
summaries_axes[1].bar(np.arange(1, K+1), np.squeeze(np.asarray(errors)), color=color_list)
summaries_axes[1].set_xlabel('Fold');
summaries_axes[1].set_xticks(np.arange(1, K+1))
summaries_axes[1].set(xlim=(1/2, K+1/2), ylim=(0, 1))
summaries_axes[1].set_ylabel('Error rate');
summaries_axes[1].set_title('Test misclassification rates ANN')
# Display the error rate across folds
summaries_axes[2].bar(np.arange(1, K+1), np.squeeze(np.asarray(errors_base)), color=color_list)
summaries_axes[2].set_xlabel('Fold');
summaries_axes[2].set_xticks(np.arange(1, K+1))
summaries_axes[2].set(xlim=(1/2, K+1/2), ylim=(0, 1))
summaries_axes[2].set_ylabel('Error rate');
summaries_axes[2].set_title('Test misclassification rates baseline')
print('Diagram of best neural net in last fold:')
weights = [net[i].weight.data.numpy().T for i in [0,2]]
biases = [net[i].bias.data.numpy() for i in [0,2]]
tf = [str(net[i]) for i in [1,3]]
draw_neural_net(weights, biases, tf, attributeNames,classNames)
# Print the average classification error rate
print('\nGeneralization error/average error rate: {0}%'.format(round(100*np.mean(errors),4)))
print('Ran Exercise Magnus')
error_rate = (sum(e).type(torch.float) / len(y_test)).data.numpy()
errors.append(error_rate) # store error rate for current CV fold
# Display the learning curve for the best net in the current fold
h, = summaries_axes[0].plot(learning_curve, color=color_list[k])
h.set_label('CV fold {0}'.format(k + 1))
summaries_axes[0].set_xlabel('Iterations')
summaries_axes[0].set_xlim((0, max_iter))
summaries_axes[0].set_ylabel('Loss')
summaries_axes[0].set_title('Learning curves')
# Display the error rate across folds
summaries_axes[1].bar(np.arange(1, K + 1),
np.squeeze(np.asarray(errors)),
color=color_list)
summaries_axes[1].set_xlabel('Fold')
summaries_axes[1].set_xticks(np.arange(1, K + 1))
summaries_axes[1].set_ylabel('Error rate')
summaries_axes[1].set_title('Test misclassification rates')
print('Diagram of best neural net in last fold:')
weights = [net[i].weight.data.numpy().T for i in [0, 2]]
biases = [net[i].bias.data.numpy() for i in [0, 2]]
tf = [str(net[i]) for i in [1, 3]]
draw_neural_net(weights, biases, tf, attribute_names=attributeNames)
# Print the average classification error rate
print('\nGeneralization error/average error rate: {0}%'.format(
round(100 * np.mean(errors), 4)))
print('Ran Exercise 8.2.5')
summaries_axes[0].set_xlabel('Iterations')
summaries_axes[0].set_xlim((0, max_iter))
summaries_axes[0].set_ylabel('Loss')
summaries_axes[0].set_title('Learning curves')
# Display the error rate across folds
summaries_axes[1].bar(np.arange(1, K + 1),
np.squeeze(np.asarray(errors)),
color=color_list)
summaries_axes[1].set_xlabel('Fold')
summaries_axes[1].set_xticks(np.arange(1, K + 1))
summaries_axes[1].set_ylabel('Error rate')
summaries_axes[1].set_title('Test misclassification rates')
# Show the plots
# plt.show(decision_boundaries.number) # try these lines if the following code fails (depends on package versions)
# plt.show(summaries.number)
plt.show()
# Display a diagram of the best network in last fold
print('Diagram of best neural net in last fold:')
weights = [net[i].weight.data.numpy().T for i in [0, 2]]
biases = [net[i].bias.data.numpy() for i in [0, 2]]
tf = [str(net[i]) for i in [1, 3]]
draw_neural_net(weights, biases, tf)
# Print the average classification error rate
print('\nGeneralization error/average error rate: {0}%'.format(
round(100 * np.mean(errors), 4)))
print('Ran exercise 8.2.2.')
