learnnn py -(continued from learnlinear import plot_steps from learnproblem import evaluate to show plotsplot_steps(learner nn data datacriterion=evaluate log_lossnum_steps= log_scale=falselegend_label="nn "plot_steps(learner nn data datacriterion=evaluate log_lossnum_steps= log_scale=falselegend_label="nn "plot_steps(learner nn data datacriterion=evaluate log_lossnum_steps= log_scale=falselegend_label="nn "plot_steps(learner nn data datacriterion=evaluate log_lossnum_steps= log_scale=falselegend_label="nn " plot_steps(learner nn data datacriterion=evaluate accuracynum_steps= log_scale=falselegend_label="nn "plot_steps(learner nn data datacriterion=evaluate accuracynum_steps= log_scale=falselegend_label="nn "plot_steps(learner nn data datacriterion=evaluate accuracynum_steps= log_scale=falselegend_label="nn "plot_steps(learner nn data datacriterion=evaluate accuracynum_steps= log_scale=falselegend_label="nn " print some training examples #for eg in random sample(data train, )print(eg,nn predictor(eg) print some test examples #for eg in random sample(data test, )print(eg,nn predictor(eg) to see the weights learned in linear layers nn layers[ weights nn layers[ weights print testfor in data trainprint( ,nn predictor( ) def test(datahidden_widths [ ]epochs= optimizers [linear_complete_layerlinear_complete_layer_momentumlinear_complete_layer_rms_prop])data display( ,"batch\ ","\tjoin(criterion __doc__ for criterion in evaluate all_criteria)for optimizer in optimizersnn nn(datafor width in hidden_widthsnn add_layer(optimizer(nn,width)nn add_layer(relu_layer(nn)if data target ftype ="boolean"nn add_layer(optimizer(nn, )version june