def callback(epoch, paramvec, vals): print 'epoch {}: {}'.format( epoch, prediction_error(paramvec, test_im, test_angle)) update_training_progress(fig, ax, line, vals) plot_images_and_angles(test_im, predict(test_im, paramvec), prediction_fig) if save_figs: prediction_fig.figure.savefig('orientations.png')
def callback(epoch, paramvec, vals): print 'epoch {}: {}'.format(epoch, prediction_error(paramvec, test_im, test_angle)) update_training_progress(fig, ax, line, vals) plot_images_and_angles(test_im, predict(test_im, paramvec), prediction_fig) if save_figs: prediction_fig.figure.savefig('orientations.png')
fig.draw_artist(line) if save_figs: fig.savefig('training.png') plt.pause(1e-6) if __name__ == "__main__": npr.seed(0) plt.ion() # load training data and plot some examples (images, angles), (test_im, test_angle) = load_training_data('data/labeled_images.pkl', augmentation=19, hold_out=80) data_fig = plot_images_and_angles(images[:80], angles[:80]) if save_figs: data_fig.figure.savefig('data.png') imsize = images.shape[1] hdims = [50, 50] # l2_reg = empirical_l2_reg(images, hdims) l2_reg = 0. paramvec, unflatten = flatten(init_gmlp(hdims, imsize, 1)) predict, loss, prediction_error = make_regression(l2_reg, unflatten) # make a figure for training progress fig, ax = plt.subplots() line, = ax.plot([]) plt.draw()
def update_training_progress(fig, ax, line, vals): line.set_data(range(1, len(vals)+1), vals) ax.set_ylim(min(vals), np.percentile(vals, 99)) ax.set_xlim(1, len(vals)+1) fig.draw_artist(line) if save_figs: fig.savefig('training.png') plt.pause(1e-6) if __name__ == "__main__": npr.seed(0) plt.ion() # load training data and plot some examples (images, angles), (test_im, test_angle) = load_training_data( 'data/labeled_images.pkl', augmentation=19, hold_out=80) data_fig = plot_images_and_angles(images[:80], angles[:80]) if save_figs: data_fig.figure.savefig('data.png') imsize = images.shape[1] hdims = [50, 50] # l2_reg = empirical_l2_reg(images, hdims) l2_reg = 0. paramvec, unflatten = flatten(init_gmlp(hdims, imsize, 1)) predict, loss, prediction_error = make_regression(l2_reg, unflatten) # make a figure for training progress fig, ax = plt.subplots() line, = ax.plot([]) plt.draw()