def plot(predictions, label): xs = np.arange(len(predictions)) * test_every ys = [[100.0 - preds[i, i] / preds[i].sum() * 100 for preds in predictions] for i in range(model.output_size)] mpl.rc('savefig', format='svg') mpl.rc('lines', linewidth=0.5) mpl.style.use('seaborn') for i in range(model.output_size): plt.plot(xs, ys[i], label=str(i)) plt.legend() plt.xlabel('Epoch') plt.ylabel('Error (%)') plt.savefig(f"predictions_{label}_error") plt.close()
np.save(fname + '_hist', history) np.save(fname + '_model', model) np.save(fname + '_ngram', ngram) with open(fname + '_doc', "w+") as doc: doc.write("primal_lr: {}\ndual_lr: {}\nn: {}\n{}".format( primal_lr, dual_lr, ngram.n, comment)) # %% PLOTTING TEST xs = np.arange(len(history['predictions'])) * test_every ys = [[ 100.0 - preds[i, i] / preds[i].sum() * 100 for preds in history['predictions'] ] for i in range(model.output_size)] mpl.rc('savefig', format='svg') mpl.rc('lines', linewidth=0.5) mpl.style.use('seaborn') for i in range(model.output_size): plt.plot(xs, ys[i], label=str(i)) plt.legend() plt.xlabel('Epoch') plt.ylabel('Error (%)') plt.savefig("predictions_test_error") plt.close() # %% PLOTTING DATA ys = [[ 100.0 - preds[i, i] / preds[i].sum() * 100 for preds in history['predictions_data']