import matplotlib.pyplot as plt
import numpy as np
from figure_creation.figure_helpers import load_object, get_plot_data, get_smoothed_data
error_raw_iters, error_raw_vals = load_object(
'./raw_data/sum_squares_error.pkl')
num_vals = len(error_raw_iters) - 100000
smoothed_train_error_values = get_smoothed_data(error_raw_vals[:num_vals],
35000)
plt.figure()
plt.ylim((0, max(smoothed_train_error_values) + 0.01))
plt.plot(error_raw_iters[:num_vals], smoothed_train_error_values, c='b')
plt.xlabel('Iterations')
plt.ylabel('Training Error')
plt.savefig('figure2.pdf', bbox_inches='tight')
plt.show()
'./raw_data/fb_plas_0-001/sum_squares_error.pkl')
#error_fb_plas_0_00075_vals_iters, error_fb_plas_0_00075_vals = load_object('./raw_data/fb_plas_0-00075/sum_squares_error.pkl')
error_fb_plas_0_0005_vals_iters, error_fb_plas_0_0005_vals = load_object(
'./raw_data/fb_plas_0-0005/sum_squares_error.pkl')
error_fb_plas_0_00025_vals_iters, error_fb_plas_0_00025_vals = load_object(
'./raw_data/fb_plas_0-00025/sum_squares_error.pkl')
error_fb_plas_0_000125_vals_iters, error_fb_plas_0_000125_vals = load_object(
'./raw_data/fb_plas_0-000125/sum_squares_error.pkl')
error_fb_plas_0_0000625_vals_iters, error_fb_plas_0_0000625_vals = load_object(
'./raw_data/fb_plas_0-0000625/sum_squares_error.pkl')
error_fb_plas_0_00003125_vals_iters, error_fb_plas_0_00003125_vals = load_object(
'./raw_data/fb_plas_0-00003125/sum_squares_error.pkl')
error_fb_align_vals_iters, error_fb_align_feedback_vals = load_object(
'./raw_data/fb_align/sum_squares_error.pkl')
error_fb_plas_0_001_vals = get_smoothed_data(error_fb_plas_0_001_vals, 2500)
error_fb_plas_0_0005_vals = get_smoothed_data(error_fb_plas_0_0005_vals, 2500)
error_fb_plas_0_00025_vals = get_smoothed_data(error_fb_plas_0_00025_vals,
2500)
error_fb_plas_0_000125_vals = get_smoothed_data(error_fb_plas_0_000125_vals,
2500)
error_fb_plas_0_0000625_vals = get_smoothed_data(error_fb_plas_0_0000625_vals,
2500)
error_fb_plas_0_00003125_vals = get_smoothed_data(
error_fb_plas_0_00003125_vals, 2500)
error_fb_align_feedback_vals = get_smoothed_data(error_fb_align_feedback_vals,
2500)
num_vals = len(error_fb_align_vals_iters)
plt.figure()
noise_0_45_layer_1_feedforward_feedback_weight_angle_iters, noise_0_45_layer_1_feedforward_feedback_weight_angle_vals = load_append_data(
'./raw_data/noise0-45/layer_1_feedforward_feedback_weight_angle', 3)
noise_0_60_layer_1_feedforward_feedback_weight_angle_iters, noise_0_60_layer_1_feedforward_feedback_weight_angle_vals = load_append_data(
'./raw_data/noise0-60/layer_1_feedforward_feedback_weight_angle', 3)
noise_0_90_layer_1_feedforward_feedback_weight_angle_iters, noise_0_90_layer_1_feedforward_feedback_weight_angle_vals = load_append_data(
'./raw_data/noise0-90/layer_1_feedforward_feedback_weight_angle', 3)
noise_1_20_layer_1_feedforward_feedback_weight_angle_iters, noise_1_20_layer_1_feedforward_feedback_weight_angle_vals = load_append_data(
'./raw_data/noise1-20/layer_1_feedforward_feedback_weight_angle', 3)
num_vals = len(
noiseless_layer_1_feedforward_feedback_weight_angle_iters) - 15000
#smoothed_target_values = get_smoothed_data(target_potential_vals[:num_vals:5], 35000)
smoothed_noiseless_layer_1_feedforward_feedback_weight_angle_values = get_smoothed_data(
noiseless_layer_1_feedforward_feedback_weight_angle_vals[:num_vals:5],
35000)
print("1")
smoothed_noise_0_15_layer_1_feedforward_feedback_weight_angle_values = get_smoothed_data(
noise_0_15_layer_1_feedforward_feedback_weight_angle_vals[:num_vals:5],
35000)
print("1")
smoothed_noise_0_30_layer_1_feedforward_feedback_weight_angle_values = get_smoothed_data(
noise_0_30_layer_1_feedforward_feedback_weight_angle_vals[:num_vals:5],
35000)
print("1")
smoothed_noise_0_45_layer_1_feedforward_feedback_weight_angle_values = get_smoothed_data(
noise_0_45_layer_1_feedforward_feedback_weight_angle_vals[:num_vals:5],
35000)
print("1")
smoothed_noise_0_60_layer_1_feedforward_feedback_weight_angle_values = get_smoothed_data(
file_base = '/monitors/'
save_location_base = 'C:/Users/Will/PycharmProjects/MScProject/figure_creation/target_network_noise/raw_data/'
train_errors = []
test_errors = []
for folder in folders:
path_base = folder_base + folder
best_test_error = np.inf
best_train_error = np.inf
for i, sub_folder in enumerate(sub_folders):
src_path = path_base + sub_folder + file_base + 'sum_squares_error.pkl'
data = load_object(src_path)[1]
test_error = np.mean(data[-1000:])
smoothed_error = get_smoothed_data(data, 35000)
min_training_error = np.min(smoothed_error)
best_train_error = min(min_training_error, best_train_error)
best_test_error = min(best_test_error, test_error)
print(test_error, min_training_error, folder, sub_folder)
print(best_test_error, folder, 'best')
print()
train_errors += [best_train_error]
test_errors += [best_test_error]
import matplotlib.pyplot as plt
#plt.plot([0, 0.15, 0.30, 0.45, 0.6, 0.9, 1.2], [0.09022911465989959, 0.08967617669387362, 0.08861968903256999, 0.08699136676083595, 0.08551288832369616, 0.13205351973933888, 0.12959720375218972])
plt.figure()
noise_0_15_layer_1_weight_magnitudes_iters, noise_0_15_layer_1_weight_magnitudes_vals = load_append_data(
'./raw_data/noise0-15/layer_1_feedforward_predict_weight_angle', 3)
noise_0_30_layer_1_weight_magnitudes_iters, noise_0_30_layer_1_weight_magnitudes_vals = load_append_data(
'./raw_data/noise0-30/layer_1_feedforward_predict_weight_angle', 3)
noise_0_45_layer_1_weight_magnitudes_iters, noise_0_45_layer_1_weight_magnitudes_vals = load_append_data(
'./raw_data/noise0-45/layer_1_feedforward_predict_weight_angle', 3)
noise_0_60_layer_1_weight_magnitudes_iters, noise_0_60_layer_1_weight_magnitudes_vals = load_append_data(
'./raw_data/noise0-60/layer_1_feedforward_predict_weight_angle', 3)
noise_0_90_layer_1_weight_magnitudes_iters, noise_0_90_layer_1_weight_magnitudes_vals = load_append_data(
'./raw_data/noise0-90/layer_1_feedforward_predict_weight_angle', 3)
noise_1_20_layer_1_weight_magnitudes_iters, noise_1_20_layer_1_weight_magnitudes_vals = load_append_data(
'./raw_data/noise1-20/layer_1_feedforward_predict_weight_angle', 3)
num_vals = len(noiseless_layer_1_weight_magnitudes_iters) - 15000
smoothed_noiseless_layer_1_weight_magnitudes_values = get_smoothed_data(
noiseless_layer_1_weight_magnitudes_vals[:num_vals:5], 35000)
print("1")
smoothed_noise_0_15_layer_1_weight_magnitudes_values = get_smoothed_data(
noise_0_15_layer_1_weight_magnitudes_vals[:num_vals:5], 35000)
print("1")
smoothed_noise_0_30_layer_1_weight_magnitudes_values = get_smoothed_data(
noise_0_30_layer_1_weight_magnitudes_vals[:num_vals:5], 35000)
print("1")
smoothed_noise_0_45_layer_1_weight_magnitudes_values = get_smoothed_data(
noise_0_45_layer_1_weight_magnitudes_vals[:num_vals:5], 35000)
print("1")
smoothed_noise_0_60_layer_1_weight_magnitudes_values = get_smoothed_data(
noise_0_60_layer_1_weight_magnitudes_vals[:num_vals:5], 35000)
print("1")
smoothed_noise_0_90_layer_1_weight_magnitudes_values = get_smoothed_data(
noise_0_90_layer_1_weight_magnitudes_vals[:num_vals:5], 35000)
noise_0_15_error_raw_vals_iters, noise_0_15_error_raw_vals_vals = load_append_data(
'./raw_data/noise0-15/sum_squares_error', 3)
noise_0_30_error_raw_vals_iters, noise_0_30_error_raw_vals_vals = load_append_data(
'./raw_data/noise0-30/sum_squares_error', 3)
noise_0_45_error_raw_vals_iters, noise_0_45_error_raw_vals_vals = load_append_data(
'./raw_data/noise0-45/sum_squares_error', 3)
noise_0_60_error_raw_vals_iters, noise_0_60_error_raw_vals_vals = load_append_data(
'./raw_data/noise0-60/sum_squares_error', 3)
noise_0_90_error_raw_vals_iters, noise_0_90_error_raw_vals_vals = load_append_data(
'./raw_data/noise0-90/sum_squares_error', 3)
noise_1_20_error_raw_vals_iters, noise_1_20_error_raw_vals_vals = load_append_data(
'./raw_data/noise1-20/sum_squares_error', 3)
num_vals = len(noiseless_error_raw_vals_iters) - 15000
smoothed_noiseless_train_error_values = get_smoothed_data(
noiseless_error_raw_vals_vals[:num_vals:5], 35000)
print("1")
smoothed_noise_0_15_train_error_values = get_smoothed_data(
noise_0_15_error_raw_vals_vals[:num_vals:5], 35000)
print("1")
smoothed_noise_0_30_train_error_values = get_smoothed_data(
noise_0_30_error_raw_vals_vals[:num_vals:5], 35000)
print("1")
smoothed_noise_0_45_train_error_values = get_smoothed_data(
noise_0_45_error_raw_vals_vals[:num_vals:5], 35000)
print("1")
smoothed_noise_0_60_train_error_values = get_smoothed_data(
noise_0_60_error_raw_vals_vals[:num_vals:5], 35000)
print("1")
smoothed_noise_0_90_train_error_values = get_smoothed_data(
noise_0_90_error_raw_vals_vals[:num_vals:5], 35000)
import matplotlib.pyplot as plt
import numpy as np
from figure_creation.figure_helpers import load_object, get_smoothed_data
layer_1_backprop_angle_iters, layer_1_backprop_angle_vals = load_object('./raw_data/layer_1_backprop_update_angle.pkl')
smoothed_layer_1_backprop_angle_vals = get_smoothed_data(layer_1_backprop_angle_vals, 200)
layer_2_backprop_angle_iters, layer_2_backprop_angle_vals = load_object('./raw_data/layer_2_backprop_update_angle.pkl')
smoothed_layer_2_backprop_angle_vals = get_smoothed_data(layer_2_backprop_angle_vals, 200)
plt.figure()
plt.xlim((0, layer_1_backprop_angle_iters[-1]))
plt.ylim((40, 140))
plt.plot(layer_1_backprop_angle_iters, layer_1_backprop_angle_vals, c='b')
plt.plot(layer_1_backprop_angle_iters, smoothed_layer_1_backprop_angle_vals, c='r')
plt.plot(layer_1_backprop_angle_iters, [90 for a in layer_2_backprop_angle_iters], c='#bf5c00', linestyle='--')
plt.xlabel('Iterations')
plt.ylabel('$\\angle (\delta_{BP}, \delta_{DEN})$', size=12)
plt.savefig('figure20-l.pdf', bbox_inches='tight')
plt.figure()
plt.xlim((0, layer_2_backprop_angle_iters[-1]))
plt.ylim((0, 100))
plt.plot(layer_2_backprop_angle_iters, layer_2_backprop_angle_vals, c='b')
plt.plot(layer_2_backprop_angle_iters, smoothed_layer_2_backprop_angle_vals, c='r')
plt.xlabel('Iterations')
plt.ylabel('$\\angle (\delta_{BP}, \delta_{DEN})$', size=12)
plt.savefig('figure20-r.pdf', bbox_inches='tight')
plt.show()