def update_line_specipic_points(nums, data, axes, to_do, font_size, axis_font):
"""Update the lines in the axes for snapshot of the whole process"""
colors = LAYERS_COLORS
x_ticks = [0, 2, 4, 6, 8, 10]
#Go over all the snapshot
for i in range(len(nums)):
num = nums[i]
#Plot the right layer
for layer_num in range(data.shape[3]):
axes[i].scatter(data[0, :, num, layer_num],
data[1, :, num, layer_num],
color=colors[layer_num],
s=105,
edgecolors='black',
alpha=0.85)
utils.adjustAxes(axes[i],
axis_font=axis_font,
title_str='',
x_ticks=x_ticks,
y_ticks=[],
x_lim=None,
y_lim=None,
set_xlabel=to_do[i][0],
set_ylabel=to_do[i][1],
x_label='$I(X;T)$',
y_label='$I(T;Y)$',
set_xlim=True,
set_ylim=True,
set_ticks=True,
label_size=font_size)
def update_line_each_neuron(num,
print_loss,
Ix,
axes,
Iy,
train_data,
accuracy_test,
epochs_bins,
loss_train_data,
loss_test_data,
colors,
epochsInds,
font_size=18,
axis_font=16,
x_lim=[0, 12.2],
y_lim=[0, 1.08],
x_ticks=[],
y_ticks=[]):
"""Update the figure of the infomration plane for the movie"""
#Print the line between the points
axes[0].clear()
if len(axes) > 1:
axes[1].clear()
#Print the points
for layer_num in range(Ix.shape[2]):
for net_ind in range(Ix.shape[0]):
axes[0].scatter(Ix[net_ind, num, layer_num],
Iy[net_ind, num, layer_num],
color=colors[layer_num],
s=35,
edgecolors='black',
alpha=0.85)
title_str = 'Information Plane - Epoch number - ' + str(epochsInds[num])
utils.adjustAxes(axes[0],
axis_font,
title_str,
x_ticks,
y_ticks,
x_lim,
y_lim,
set_xlabel=True,
set_ylabel=True,
x_label='$I(X;T)$',
y_label='$I(T;Y)$')
#Print the loss function and the error
if len(axes) > 1:
axes[1].plot(epochsInds[:num],
1 - np.mean(accuracy_test[:, :num], axis=0),
color='g')
if print_loss:
axes[1].plot(epochsInds[:num],
np.mean(loss_test_data[:, :num], axis=0),
color='y')
nereast_val = np.searchsorted(epochs_bins,
epochsInds[num],
side='right')
axes[1].set_xlim([0, epochs_bins[nereast_val]])
axes[1].legend(('Accuracy', 'Loss Function'), loc='best')
def plot_by_training_samples(I_XT_array, I_TY_array, axes, epochsInds, f,
index_i, index_j, size_ind, font_size, y_ticks,
x_ticks, colorbar_axis, title_str, axis_font,
bar_font, save_name, samples_labels):
"""Print the final epoch of all the diffrenet training samples size """
max_index = size_ind if size_ind != -1 else I_XT_array.shape[2] - 1
cmap = plt.get_cmap('gnuplot')
colors = [cmap(i) for i in np.linspace(0, 1, max_index + 1)]
#Print the final epoch
nums_epoch = -1
#Go over all the samples size and plot them with the right color
for index_in_range in range(0, max_index):
XT, TY = [], []
for layer_index in range(0, I_XT_array.shape[4]):
XT.append(
np.mean(I_XT_array[:, -1, index_in_range, nums_epoch,
layer_index],
axis=0))
TY.append(
np.mean(I_TY_array[:, -1, index_in_range, nums_epoch,
layer_index],
axis=0))
axes[index_i, index_j].plot(XT,
TY,
marker='o',
linestyle='-',
markersize=12,
markeredgewidth=0.2,
linewidth=0.5,
color=colors[index_in_range])
utils.adjustAxes(axes[index_i, index_j],
axis_font=axis_font,
title_str=title_str,
x_ticks=x_ticks,
y_ticks=y_ticks,
x_lim=None,
y_lim=None,
set_xlabel=index_i == axes.shape[0] - 1,
set_ylabel=index_j == 0,
x_label='$I(X;T)$',
y_label='$I(T;Y)$',
set_xlim=True,
set_ylim=True,
set_ticks=True,
label_size=font_size)
#Create color bar and save it
if index_i == axes.shape[0] - 1 and index_j == axes.shape[1] - 1:
utils.create_color_bar(f,
cmap,
colorbar_axis,
bar_font,
epochsInds,
title='Training Data')
f.savefig(save_name + '.png', dpi=150, format='png')
def plot_alphas(str_name, save_name='dist'):
data_array = utils.get_data(str_name)
params = np.squeeze(np.array(data_array['information']))
I_XT_array = np.squeeze(np.array(extract_array(params, 'local_IXT')))
""""
for i in range(I_XT_array.shape[2]):
f1, axes1 = plt.subplots(1, 1)
axes1.plot(I_XT_array[:,:,i])
plt.show()
return
"""
I_XT_array_var = np.squeeze(
np.array(extract_array(params, 'IXT_vartional')))
I_TY_array_var = np.squeeze(
np.array(extract_array(params, 'ITY_vartional')))
I_TY_array = np.squeeze(np.array(extract_array(params, 'local_ITY')))
"""
f1, axes1 = plt.subplots(1, 1)
#axes1.plot(I_XT_array,I_TY_array)
f1, axes2 = plt.subplots(1, 1)
axes1.plot(I_XT_array ,I_TY_array_var)
axes2.plot(I_XT_array ,I_TY_array)
f1, axes1 = plt.subplots(1, 1)
axes1.plot(I_TY_array, I_TY_array_var)
axes1.plot([0, 1.1], [0, 1.1], transform=axes1.transAxes)
#axes1.set_title('Sigmma=' + str(sigmas[i]))
axes1.set_ylim([0, 1.1])
axes1.set_xlim([0, 1.1])
plt.show()
return
"""
#for i in range()
sigmas = np.linspace(0, 0.3, 20)
for i in range(0, 20):
print(i, sigmas[i])
f1, axes1 = plt.subplots(1, 1)
axes1.plot(I_XT_array, I_XT_array_var[:, :, i], linewidth=5)
axes1.plot([0, 15.1], [0, 15.1], transform=axes1.transAxes)
axes1.set_title('Sigmma=' + str(sigmas[i]))
axes1.set_ylim([0, 15.1])
axes1.set_xlim([0, 15.1])
plt.show()
return
epochs_s = data_array['params']['epochsInds']
f, axes = plt.subplots(1, 1)
#epochs_s = []
colors = LAYERS_COLORS
linestyles = ['--', '-.', '-', '', ' ', ':', '']
epochs_s = [0, -1]
for j in epochs_s:
for i in range(0, I_XT_array.shape[1]):
axes.plot(sigmas,
I_XT_array_var[j, i, :],
color=colors[i],
linestyle=linestyles[j],
label='Layer-' + str(i) + ' Epoch - ' + str(epochs_s[j]))
title_str = 'I(X;T) for different layers as function of $\sigma$ (The width of the gaussian)'
x_label = '$\sigma$'
y_label = '$I(X;T)$'
x_lim = [0, 3]
utils.adjustAxes(axes,
axis_font=20,
title_str=title_str,
x_ticks=[],
y_ticks=[],
x_lim=x_lim,
y_lim=None,
set_xlabel=True,
set_ylabel=True,
x_label=x_label,
y_label=y_label,
set_xlim=True,
set_ylim=False,
set_ticks=False,
label_size=20,
set_yscale=False,
set_xscale=False,
yscale=None,
xscale=None,
ytick_labels='',
genreal_scaling=False)
axes.legend()
plt.show()
def update_axes(axes,
xlabel,
ylabel,
xlim,
ylim,
title,
xscale,
yscale,
x_ticks,
y_ticks,
p_0,
p_1,
font_size=30,
axis_font=25,
legend_font=16):
"""adjust the axes to the ight scale/ticks and labels"""
categories = 6 * ['']
labels = [
'$10^{-5}$', '$10^{-4}$', '$10^{-3}$', '$10^{-2}$', '$10^{-1}$',
'$10^0$', '$10^1$'
]
#The legents of the mean and the std
leg1 = plt.legend(p_0,
categories,
title=r'$\|Mean\left(\nabla{W_i}\right)\|$',
loc='best',
fontsize=legend_font,
markerfirst=False,
handlelength=5)
leg2 = plt.legend(p_1,
categories,
title=r'$STD\left(\nabla{W_i}\right)$',
loc='best',
fontsize=legend_font,
markerfirst=False,
handlelength=5)
leg1.get_title().set_fontsize('21') # legend 'Title' fontsize
leg2.get_title().set_fontsize('21') # legend 'Title' fontsize
plt.gca().add_artist(leg1)
plt.gca().add_artist(leg2)
utils.adjustAxes(axes,
axis_font=20,
title_str='',
x_ticks=x_ticks,
y_ticks=y_ticks,
x_lim=xlim,
y_lim=ylim,
set_xlabel=True,
set_ylabel=True,
x_label=xlabel,
y_label=ylabel,
set_xlim=True,
set_ylim=True,
set_ticks=True,
label_size=font_size,
set_yscale=True,
set_xscale=True,
yscale=yscale,
xscale=xscale,
ytick_labels=labels,
genreal_scaling=True)
def update_line(num,
print_loss,
data,
axes,
epochsInds,
test_error,
test_data,
epochs_bins,
loss_train_data,
loss_test_data,
colors,
font_size=18,
axis_font=16,
x_lim=[0, 12.2],
y_lim=[0, 1.08],
x_ticks=[],
y_ticks=[]):
"""Update the figure of the infomration plane for the movie"""
#Print the line between the points
cmap = ListedColormap(LAYERS_COLORS)
segs = []
for i in range(0, data.shape[1]):
x = data[0, i, num, :]
y = data[1, i, num, :]
points = np.array([x, y]).T.reshape(-1, 1, 2)
segs.append(np.concatenate([points[:-1], points[1:]], axis=1))
segs = np.array(segs).reshape(-1, 2, 2)
axes[0].clear()
if len(axes) > 1:
axes[1].clear()
lc = LineCollection(segs,
cmap=cmap,
linestyles='solid',
linewidths=0.3,
alpha=0.6)
lc.set_array(np.arange(0, 5))
#Print the points
for layer_num in range(data.shape[3]):
axes[0].scatter(data[0, :, num, layer_num],
data[1, :, num, layer_num],
color=colors[layer_num],
s=35,
edgecolors='black',
alpha=0.85)
axes[1].plot(epochsInds[:num],
1 - np.mean(test_error[:, :num], axis=0),
color='r')
title_str = 'Information Plane - Epoch number - ' + str(epochsInds[num])
utils.adjustAxes(axes[0],
axis_font,
title_str,
x_ticks,
y_ticks,
x_lim,
y_lim,
set_xlabel=True,
set_ylabel=True,
x_label='$I(X;T)$',
y_label='$I(T;Y)$')
title_str = 'Precision as function of the epochs'
utils.adjustAxes(axes[1],
axis_font,
title_str,
x_ticks,
y_ticks,
x_lim,
y_lim,
set_xlabel=True,
set_ylabel=True,
x_label='# Epochs',
y_label='Precision')
def plot_all_epochs(I_XT_array,
I_TY_array,
axes,
epochsInds,
f,
index_i,
index_j,
size_ind,
font_size,
y_ticks,
x_ticks,
colorbar_axis,
title_str,
axis_font,
bar_font,
save_name,
epochFlag,
plot_error=True,
index_to_emphasis=1000):
"""Plot the infomration plane with the epochs in diffrnet colors """
#If we want to plot the train and test error
# if plot_error:
# fig_strs = ['train_error','test_error','loss_train','loss_test' ]
# fig_data = [np.squeeze(gen_data[fig_str]) for fig_str in fig_strs]
# f1 = plt.figure(figsize=(12, 8))
# ax1 = f1.add_subplot(111)
# mean_sample = False if len(fig_data[0].shape)==1 else True
# if mean_sample:
# fig_data = [ np.mean(fig_data_s, axis=0) for fig_data_s in fig_data]
# for i in range(len(fig_data)):
# ax1.plot(epochsInds, fig_data[i],':', linewidth = 3 , label = fig_strs[i])
# ax1.legend(loc='best')
f = plt.figure(figsize=(12, 8))
axes = f.add_subplot(111)
axes = np.array([[axes]])
I_XT_array = np.squeeze(I_XT_array)
I_TY_array = np.squeeze(I_TY_array)
if len(I_TY_array[0].shape) > 1:
I_XT_array = np.mean(I_XT_array, axis=0)
I_TY_array = np.mean(I_TY_array, axis=0)
max_index = size_ind if size_ind != -1 else (I_XT_array.shape[0] - 2)
cmap = plt.get_cmap('gnuplot')
#For each epoch we have diffrenet color
if epochFlag:
colors = [cmap(i) for i in np.linspace(0, 1, np.max(epochsInds) + 1)]
else:
colors = [cmap(i) for i in np.linspace(0, 1,
np.max(epochsInds) + 1)][::-1]
#Change this if we have more then one network arch
nums_arc = -1
#Go over all the epochs and plot then with the right color
for index_in_range in range(0, max_index):
XT = I_XT_array[index_in_range, :]
TY = I_TY_array[index_in_range, :]
#If this is the index that we want to emphsis
# if epochsInds[index_in_range] ==index_to_emphasis:
# axes[index_i, index_j].plot(XT, TY, marker='o', linestyle=None, markersize=19, markeredgewidth=0.04,
# linewidth=2.1,
# color='g',zorder=10)
# else:
axes[index_i,
index_j].plot(XT[:],
TY[:],
marker='o',
linestyle='-',
markersize=12,
markeredgewidth=0.01,
linewidth=0.2,
color=colors[int(epochsInds[index_in_range])])
utils.adjustAxes(axes[index_i, index_j],
axis_font=axis_font,
title_str=title_str,
x_ticks=x_ticks,
y_ticks=y_ticks,
x_lim=[0, 25.1],
y_lim=None,
set_xlabel=index_i == axes.shape[0] - 1,
set_ylabel=index_j == 0,
x_label='$I(X;T)$',
y_label='$I(T;Y)$',
set_xlim=False,
set_ylim=False,
set_ticks=True,
label_size=font_size)
#Save the figure and add color bar
if index_i == axes.shape[0] - 1 and index_j == axes.shape[1] - 1:
if epochFlag:
utils.create_color_bar(f,
cmap,
colorbar_axis,
bar_font,
np.sort(epochsInds),
title='Epochs')
else:
utils.create_color_bar(f,
cmap,
colorbar_axis,
bar_font,
np.sort(epochsInds)[::-1],
title='Traces')
f.savefig(save_name + '.png', dpi=500, format='png')