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| import matplotlib | |
| #matplotlib.use("TkAgg") | |
| matplotlib.use("Agg") | |
| import scipy.io as sio | |
| import matplotlib.pyplot as plt | |
| import os | |
| import numpy as np | |
| import sys | |
| if sys.version_info >= (3, 0): | |
| import _pickle as cPickle | |
| else: | |
| import cPickle | |
| def update_axes(axes, f, xlabel, ylabel, xlim, ylim, title='', xscale=None, yscale=None, x_ticks=None, y_ticks=None, | |
| p_0=None, p_1=None, p_3=None, p_4=None, | |
| p_5=None, title_size=22): | |
| """adjust the axes to the ight scale/ticks and labels""" | |
| font_size = 30 | |
| axis_font = 25 | |
| legend_font = 16 | |
| categories = 6 * [''] | |
| labels = ['$10^{-4}$', '$10^{-3}$', '$10^{-2}$', '$10^{-1}$', '$10^0$', '$10^1$'] | |
| # If we want grey line in the midle | |
| # axes.axvline(x=370, color='grey', linestyle=':', linewidth = 4) | |
| # The legents of the mean and the std | |
| """ | |
| if p_0: | |
| leg1 = f.legend([p_0[0],p_0[1],p_0[2],p_0[3],p_0[4], p_0[5]], categories, title=r'$\|Mean\left(\nabla{W_i}\right)\|$',bbox_to_anchor=(0.09, 0.95), loc=2,fontsize = legend_font,markerfirst = False, handlelength = 5) | |
| leg1.get_title().set_fontsize('21') # legend 'Title' fontsize | |
| axes.add_artist(leg1) | |
| if p_1: | |
| leg2 = f.legend([p_1[0],p_1[1],p_1[2],p_1[3],p_1[4], p_1[5]], categories, title=r'$Variance\left(\nabla{W_i}\right)$', loc=2,bbox_to_anchor=(0.25, 0.95), fontsize = legend_font ,markerfirst = False,handlelength = 5) | |
| leg2.get_title().set_fontsize('21') # legend 'Title' fontsize | |
| axes.add_artist(leg2) | |
| if p_3: | |
| leg2 = f.legend([p_3[0], p_3[1], p_3[2], p_3[3], p_3[4], p_3[5]], categories, | |
| title=r'$SNR\left(\nabla{W_i}\right)$', loc=3, fontsize=legend_font, | |
| markerfirst=False, handlelength=5,bbox_to_anchor=(0.15, 0.1)) | |
| leg2.get_title().set_fontsize('21') # legend 'Title' | |
| if p_4: | |
| leg2 = f.legend([p_4[0], p_4[1], p_4[2], p_4[3], p_4[4], p_4[5]], categories, | |
| title=r'$\log\left(1+ SNR\left(\nabla{W_i}\right)\right)$', loc=3, fontsize=legend_font, | |
| markerfirst=False, handlelength=5, bbox_to_anchor=(0.15, 0.1)) | |
| leg2.get_title().set_fontsize('21') # legend 'Title' | |
| if p_5: | |
| pass | |
| #leg2 = axes.legend(handles=[r'$\frac{|d Error|}{STD\left(Error)\right)}$'], loc=3, fontsize=legend_font, | |
| # bbox_to_anchor=(0.15, 0.1)) | |
| #leg2.get_title().set_fontsize('21') | |
| """ | |
| # plt.gca().add_artist(leg2) | |
| axes.set_xscale(xscale) | |
| axes.set_yscale(yscale) | |
| axes.set_xlabel(xlabel, fontsize=font_size) | |
| axes.set_ylabel(ylabel, fontsize=font_size) | |
| axes.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter()) | |
| axes.yaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter()) | |
| if y_ticks: | |
| axes.set_xticks(x_ticks) | |
| axes.set_yticks(y_ticks) | |
| axes.tick_params(axis='x', labelsize=axis_font) | |
| axes.tick_params(axis='y', labelsize=axis_font) | |
| axes.xaxis.major.formatter._useMathText = True | |
| axes.set_yticklabels(labels, fontsize=font_size) | |
| axes.set_title(title, fontsize=title_size) | |
| axes.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter(useMathText=True)) | |
| axes.set_xlim(xlim) | |
| if ylim: | |
| axes.set_ylim(ylim) | |
| def update_axes_norms(axes, xlabel, ylabel): | |
| """Adjust the axes of the norms figure with labels/ticks""" | |
| font_size = 30 | |
| axis_font = 25 | |
| legend_font = 16 | |
| # the legends | |
| categories = [r'$\|W_1\|$', r'$\|W_2\|$', r'$\|W_3\|$', r'$\|W_4\|$', r'$\|W_5\|$', r'$\|W_6\|$'] | |
| # Grey line in the middle | |
| axes.axvline(x=370, color='grey', linestyle=':', linewidth=4) | |
| axes.legend(categories, loc='best', fontsize=legend_font) | |
| axes.set_xlabel(xlabel, fontsize=font_size) | |
| axes.set_ylabel(ylabel, fontsize=font_size) | |
| axes.tick_params(axis='x', labelsize=axis_font) | |
| axes.tick_params(axis='y', labelsize=axis_font) | |
| def update_axes_snr(axes, xlabel, ylabel): | |
| """Adjust the axes of the norms figure with labels/ticks""" | |
| font_size = 30 | |
| axis_font = 25 | |
| legend_font = 16 | |
| # the legends | |
| categories = [r'$W_1$', r'$W_2$', r'$W_3$', r'$W_4$', r'$W_5$', r'$W_6$'] | |
| # Grey line in the middle | |
| axes.set_title('The SNR ($norm^2/variance$)') | |
| # axes.axvline(x=370, color='grey', linestyle=':', linewidth=4) | |
| axes.legend(categories, loc='best', fontsize=legend_font) | |
| axes.set_xlabel(xlabel, fontsize=font_size) | |
| axes.set_ylabel(ylabel, fontsize=font_size) | |
| axes.tick_params(axis='x', labelsize=axis_font) | |
| axes.tick_params(axis='y', labelsize=axis_font) | |
| def adjust_axes(axes_log, axes_norms, p_0, p_1, f_log, f_norms, axes_snr=None, f_snr=None, p_3=None, axes_gaus=None, | |
| f_gau=None, p_4=None, directory_name=''): | |
| # adejust the figure according the specipic labels, scaling and legends | |
| # Change the log and log to linear if you want linear scaling | |
| # update_axes(reg_axes, '# Epochs', 'Normalized Mean and STD', [0, 10000], [0.000001, 10], '', 'log', 'log', [1, 10, 100, 1000, 10000], [0.00001, 0.0001, 0.001, 0.01, 0.1, 1, 10], p_0, p_1) | |
| title = 'The Mean and std of the gradients of each layer' | |
| update_axes(axes_log, f_log, '# Epochs', 'Mean and STD', [0, 7000], [0.001, 10], title, 'log', 'log', | |
| [1, 10, 100, 1000, 7000], [0.001, 0.01, 0.1, 1, 10], p_0, p_1) | |
| update_axes_norms(axes_norms, '# Epochs', '$L_2$') | |
| if p_3: | |
| title = r'SNR of the gradients ($\frac{norm^2}{variance}$)' | |
| update_axes(axes_snr, f_snr, '# Epochs', 'SNR', [0, 7000], [0.0001, 10], title, 'log', 'log', | |
| [1, 10, 100, 1000, 7000], [0.0001, 0.001, 0.01, 0.1, 1, 10], p_3=p_3) | |
| if p_4: | |
| title = r'Gaussian Channel bounds of the gradients ($\log\left(1+SNR\right)$)' | |
| update_axes(axes_gaus, f_gau, '# Epochs', 'log(SNR+1)', [0, 7000], [0.0001, 10], title, 'log', 'log', | |
| [1, 10, 100, 1000, 7000], [0.0001, 0.001, 0.01, 0.1, 1, 10], p_4=p_4) | |
| # axes_log.plot(epochsInds[1:], np.abs(np.diff(np.squeeze(data_array['loss_train']))) / np.diff(epochsInds[:]), color='black', linewidth = 3) | |
| # axes_log.plot(epochsInds[0:], np.sum(np.array(sum_y), axis=0), color='c', linewidth = 3) | |
| # axes_log.plot(epochsInds[1:], diff_mean_loss, color='red', linewidth = 3) | |
| # f_log1, (axes_log1) = plt.subplots(1, 1, figsize=fig_size) | |
| # axes_log1.plot(epochsInds[1:], np.sum(np.array(sum_y), axis=0)[1:] / diff_mean_loss, color='c', linewidth=3) | |
| # axes_log.set_xscale('log') | |
| f_log.savefig(directory_name + 'log_gradient.svg', dpi=200, format='svg') | |
| f_norms.savefig(directory_name + 'norms.jpg', dpi=200, format='jpg') | |
| def adjustAxes(axes, axis_font=20, title_str='', x_ticks=[], y_ticks=[], x_lim=None, y_lim=None, | |
| set_xlabel=True, set_ylabel=True, x_label='', y_label='', set_xlim=True, set_ylim=True, set_ticks=True, | |
| label_size=20, set_yscale=False, | |
| set_xscale=False, yscale=None, xscale=None, ytick_labels='', genreal_scaling=False): | |
| """Organize the axes of the given figure""" | |
| if set_xscale: | |
| axes.set_xscale(xscale) | |
| if set_yscale: | |
| axes.set_yscale(yscale) | |
| if genreal_scaling: | |
| axes.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter()) | |
| axes.yaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter()) | |
| axes.xaxis.major.formatter._useMathText = True | |
| axes.set_yticklabels(ytick_labels) | |
| axes.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter(useMathText=True)) | |
| if set_xlim: | |
| axes.set_xlim(x_lim) | |
| if set_ylim: | |
| axes.set_ylim(y_lim) | |
| axes.set_title(title_str, fontsize=axis_font + 2) | |
| axes.tick_params(axis='y', labelsize=axis_font) | |
| axes.tick_params(axis='x', labelsize=axis_font) | |
| if set_ticks: | |
| axes.set_xticks(x_ticks) | |
| axes.set_yticks(y_ticks) | |
| if set_xlabel: | |
| axes.set_xlabel(x_label, fontsize=label_size) | |
| if set_ylabel: | |
| axes.set_ylabel(y_label, fontsize=label_size) | |
| def create_color_bar(f, cmap, colorbar_axis, bar_font, epochsInds, title): | |
| sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=0, vmax=1)) | |
| sm._A = [] | |
| cbar_ax = f.add_axes(colorbar_axis) | |
| cbar = f.colorbar(sm, ticks=[], cax=cbar_ax) | |
| cbar.ax.tick_params(labelsize=bar_font) | |
| cbar.set_label(title, size=bar_font) | |
| cbar.ax.text(0.5, -0.01, epochsInds[0], transform=cbar.ax.transAxes, | |
| va='top', ha='center', size=bar_font) | |
| cbar.ax.text(0.5, 1.0, str(epochsInds[-1]), transform=cbar.ax.transAxes, | |
| va='bottom', ha='center', size=bar_font) | |
| def get_data(name): | |
| """Load data from the given name""" | |
| gen_data = {} | |
| # new version | |
| if os.path.isfile(name + 'data.pickle'): | |
| curent_f = open(name + 'data.pickle', 'rb') | |
| d2 = cPickle.load(curent_f) | |
| # Old version | |
| else: | |
| curent_f = open(name, 'rb') | |
| d1 = cPickle.load(curent_f) | |
| data1 = d1[0] | |
| data = np.array([data1[:, :, :, :, :, 0], data1[:, :, :, :, :, 1]]) | |
| # Convert log e to log2 | |
| normalization_factor = 1 / np.log2(2.718281) | |
| epochsInds = np.arange(0, data.shape[4]) | |
| d2 = {} | |
| d2['epochsInds'] = epochsInds | |
| d2['information'] = data / normalization_factor | |
| return d2 | |
| def load_reverese_annealing_data(name, max_beta=300, min_beta=0.8, dt=0.1): | |
| """Load mat file of the reverse annealing data with the give params""" | |
| with open(name + '.mat', 'rb') as handle: | |
| d = sio.loadmat(name + '.mat') | |
| F = d['F'] | |
| ys = d['y'] | |
| PXs = np.ones(len(F)) / len(F) | |
| f_PYs = np.mean(ys) | |
| PYs = np.array([f_PYs, 1 - f_PYs]) | |
| PYX = np.concatenate((np.array(ys)[None, :], 1 - np.array(ys)[None, :])) | |
| mybetaS = 2 ** np.arange(np.log2(min_beta), np.log2(max_beta), dt) | |
| mybetaS = mybetaS[::-1] | |
| PTX0 = np.eye(PXs.shape[0]) | |
| return mybetaS, np.squeeze(PTX0), np.squeeze(PXs), np.squeeze(PYX), np.squeeze(PYs) | |
| def get_data(name): | |
| """Load data from the given name""" | |
| gen_data = {} | |
| # new version | |
| if os.path.isfile(name + 'data.pickle'): | |
| curent_f = open(name + 'data.pickle', 'rb') | |
| d2 = cPickle.load(curent_f) | |
| # Old version | |
| else: | |
| curent_f = open(name, 'rb') | |
| d1 = cPickle.load(curent_f) | |
| data1 = d1[0] | |
| data = np.array([data1[:, :, :, :, :, 0], data1[:, :, :, :, :, 1]]) | |
| # Convert log e to log2 | |
| normalization_factor = 1 / np.log2(2.718281) | |
| epochsInds = np.arange(0, data.shape[4]) | |
| d2 = {} | |
| d2['epochsInds'] = epochsInds | |
| d2['information'] = data / normalization_factor | |
| return d2 | |