def plot_animation(name_s, save_name):
"""Plot the movie for all the networks in the information plane"""
# If we want to print the loss function also
print_loss = False
#The bins that we extened the x axis of the accuracy each time
epochs_bins = [0, 500, 1500, 3000, 6000, 10000, 20000]
data_array = utils.get_data(name_s[0][0])
data = data_array['infomration']
epochsInds = data_array['epochsInds']
loss_train_data = data_array['loss_train']
loss_test_data = data_array['loss_test_data']
f, (axes) = plt.subplots(2, 1)
f.subplots_adjust(left=0.14, bottom=0.1, right=.928, top=0.94, wspace=0.13, hspace=0.55)
colors = LAYERS_COLORS
#new/old version
if False:
Ix = np.squeeze(data[0,:,-1,-1, :, :])
Iy = np.squeeze(data[1,:,-1,-1, :, :])
else:
Ix = np.squeeze(data[0, :, -1, -1, :, :])[np.newaxis,:,:]
Iy = np.squeeze(data[1, :, -1, -1, :, :])[np.newaxis,:,:]
#Interploation of the samplings (because we don't cauclaute the infomration in each epoch)
interp_data_x = interp1d(epochsInds, Ix, axis=1)
interp_data_y = interp1d(epochsInds, Iy, axis=1)
new_x = np.arange(0,epochsInds[-1])
new_data = np.array([interp_data_x(new_x), interp_data_y(new_x)])
""""
train_data = interp1d(epochsInds, np.squeeze(train_data), axis=1)(new_x)
test_data = interp1d(epochsInds, np.squeeze(test_data), axis=1)(new_x)
"""
if print_loss:
loss_train_data = interp1d(epochsInds, np.squeeze(loss_train_data), axis=1)(new_x)
loss_test_data=interp1d(epochsInds, np.squeeze(loss_test_data), axis=1)(new_x)
line_ani = animation.FuncAnimation(f, update_line, len(new_x), repeat=False,
interval=1, blit=False, fargs=(print_loss, new_data, axes,new_x,train_data,test_data,epochs_bins, loss_train_data,loss_test_data, colors))
Writer = animation.writers['ffmpeg']
writer = Writer(fps=100)
#Save the movie
line_ani.save(save_name+'_movie2.mp4',writer=writer,dpi=250)
plt.show()
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