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02c_deep_fc_network_dfa_vs_bp.py
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02c_deep_fc_network_dfa_vs_bp.py
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from multiprocessing import freeze_support
import matplotlib.pyplot as plt
import numpy as np
import scipy.ndimage.filters
import scipy.interpolate
import dataset.mnist_dataset
from network import activation
from network.layers.conv_to_fully_connected import ConvToFullyConnected
from network.layers.fully_connected import FullyConnected
from network.model import Model
from network.optimizer import GDMomentumOptimizer
if __name__ == '__main__':
freeze_support()
# colors = [('deepskyblue', 'darkblue'), ('red', 'maroon'), ('goldenrod', 'sienna'), ('limegreen', 'darkgreen'),
# ('purple', 'magenta'), ('gray', 'black')]
# depths = [10, 15, 20, 30, 50, 100]
# iterations = ([2] * 5) + [4]
colors = [('blue', 'blue'), ('red', 'red'), ('green', 'green'), ('orange', 'orange'), ('black', 'black')]
depths = [10, 15, 20, 50, 100]
iterations = ([4] * 5)
# colors = [('blue', 'blue')]
# depths = [20]
# iterations = [20]
data = dataset.mnist_dataset.load('dataset/mnist')
statistics = []
for depth, num_passes in zip(depths, iterations):
layers = [ConvToFullyConnected()] + \
[FullyConnected(size=240, activation=activation.tanh) for _ in range(depth)] + \
[FullyConnected(size=10, activation=None, last_layer=True)]
""" DFA """
model = Model(
layers=layers,
num_classes=10,
optimizer=GDMomentumOptimizer(lr=1e-3, mu=0.9),
)
print("\nRun training:\n------------------------------------")
stats_dfa = model.train(data_set=data, method='dfa', num_passes=num_passes, batch_size=64)
loss, accuracy = model.cost(*data.test_set())
print("\nResult:\n------------------------------------")
print('loss on test set: {}'.format(loss))
print('accuracy on test set: {}'.format(accuracy))
print("\nTrain statisistics:\n------------------------------------")
print("time spend during forward pass: {}".format(stats_dfa['forward_time']))
print("time spend during backward pass: {}".format(stats_dfa['backward_time']))
print("time spend during update pass: {}".format(stats_dfa['update_time']))
print("time spend in total: {}".format(stats_dfa['total_time']))
""" BP """
model = Model(
layers=layers,
num_classes=10,
optimizer=GDMomentumOptimizer(lr=1e-2, mu=0.9)
)
print("\nRun training:\n------------------------------------")
stats_bp = model.train(data_set=data, method='bp', num_passes=num_passes, batch_size=64)
loss, accuracy = model.cost(*data.test_set())
print("\nResult:\n------------------------------------")
print('loss on test set: {}'.format(loss))
print('accuracy on test set: {}'.format(accuracy))
print("\nTrain statisistics:\n------------------------------------")
print("time spend during forward pass: {}".format(stats_bp['forward_time']))
print("time spend during backward pass: {}".format(stats_bp['backward_time']))
print("time spend during update pass: {}".format(stats_bp['update_time']))
print("time spend in total: {}".format(stats_bp['total_time']))
statistics.append((stats_dfa, stats_bp))
plt.title('Loss function')
plt.xlabel('epoch')
plt.ylabel('loss')
for color, stats in zip(colors, statistics):
color_dfa = color[0]
stats_dfa = stats[0]
color_bp = color[1]
stats_bp = stats[1]
dfa_train_loss = scipy.ndimage.filters.gaussian_filter1d(stats_dfa['train_loss'], sigma=10)
bp_train_loss = scipy.ndimage.filters.gaussian_filter1d(stats_bp['train_loss'], sigma=10)
plt.plot(np.arange(len(stats_dfa['train_loss'])), dfa_train_loss, color=color_dfa)
plt.plot(np.arange(len(stats_bp['train_loss'])), bp_train_loss, linestyle='--', color=color_bp)
legends = []
for depth in depths:
legends.append('{}xfc tanh, train loss dfa'.format(depth))
legends.append('{}xfc tanh, train loss bp'.format(depth))
plt.legend(legends, loc='upper right')
plt.grid(True)
plt.show()
plt.title('Accuracy')
plt.xlabel('epoch')
plt.ylabel('accuracy')
for color, stats in zip(colors, statistics):
color_dfa = color[0]
stats_dfa = stats[0]
color_bp = color[1]
stats_bp = stats[1]
dfa_train_accuracy = scipy.ndimage.filters.gaussian_filter1d(stats_dfa['train_accuracy'], sigma=10)
bp_train_accuracy = scipy.ndimage.filters.gaussian_filter1d(stats_bp['train_accuracy'], sigma=10)
plt.plot(np.arange(len(stats_dfa['train_accuracy'])), dfa_train_accuracy, color=color_dfa)
plt.plot(np.arange(len(stats_bp['train_accuracy'])), bp_train_accuracy, linestyle='--', color=color_bp)
legends = []
for depth in depths:
legends.append('{}xfc tanh, train accuracy dfa'.format(depth))
legends.append('{}xfc tanh, train accuracy bp'.format(depth))
plt.legend(legends, loc='lower right')
plt.grid(True)
plt.show()