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00b_fc_newtork_train_performance.py
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00b_fc_newtork_train_performance.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.cifar10_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__':
"""
Goal: Compare DFA and BP training performances with respect to train loss, train accuracy and
training time on a fully connected NN
Initial learning rate and learning rate decay parameters were evaluated by hand by comparing the training
performance on the training set for various
parameter combinations
"""
freeze_support()
num_iteration = 35
data = dataset.cifar10_dataset.load()
layers = [
ConvToFullyConnected(),
FullyConnected(size=500, activation=activation.tanh),
FullyConnected(size=500, activation=activation.tanh),
FullyConnected(size=500, activation=activation.tanh),
FullyConnected(size=500, activation=activation.tanh),
FullyConnected(size=500, activation=activation.tanh),
FullyConnected(size=10, activation=None, last_layer=True)
]
# -------------------------------------------------------
# Train with BP
# -------------------------------------------------------
model = Model(
layers=layers,
num_classes=10,
optimizer=GDMomentumOptimizer(lr=7*1e-3, mu=0.9),
)
print("\nRun training:\n------------------------------------")
stats_bp = model.train(data_set=data, method='bp', num_passes=num_iteration, 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']))
# plt.title('Loss function')
# plt.xlabel('epoch')
# plt.ylabel('loss')
# plt.plot(np.arange(len(stats_bp['train_loss'])), stats_bp['train_loss'])
# plt.legend(['train loss bp'], loc='best')
# plt.grid(True)
# plt.show()
# plt.title('Accuracy')
# plt.xlabel('epoch')
# plt.ylabel('accuracy')
# plt.plot(np.arange(len(stats_bp['train_accuracy'])), stats_bp['train_accuracy'])
# plt.legend(['train accuracy bp'], loc='best')
# plt.grid(True)
# plt.show()
# exit()
# -------------------------------------------------------
# Train with 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_iteration, 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']))
# plt.title('Loss function')
# plt.xlabel('epoch')
# plt.ylabel('loss')
# plt.plot(np.arange(len(stats_dfa['train_loss'])), stats_dfa['train_loss'])
# plt.legend(['train loss dfa'], loc='best')
# plt.grid(True)
# plt.show()
# plt.title('Accuracy')
# plt.xlabel('epoch')
# plt.ylabel('accuracy')
# plt.plot(np.arange(len(stats_dfa['train_accuracy'])), stats_dfa['train_accuracy'])
# plt.legend(['train accuracy dfa'], loc='best')
# plt.grid(True)
# plt.show()
# exit()
# train only
plt.title('Loss vs epoch')
plt.xlabel('epoch')
plt.ylabel('loss')
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)
plt.plot(np.arange(len(stats_bp['train_loss'])), bp_train_loss)
plt.legend(['train loss dfa', 'train loss bp'], loc='best')
plt.grid(True)
plt.show()
plt.title('Accuracy vs epoch')
plt.xlabel('epoch')
plt.ylabel('accuracy')
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)
plt.plot(np.arange(len(stats_bp['train_accuracy'])), bp_train_accuracy)
plt.legend(['train accuracy dfa', 'train accuracy bp'], loc='lower right')
plt.grid(True)
plt.show()
# train & valid
plt.title('Loss vs epoch')
plt.xlabel('epoch')
plt.ylabel('loss')
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)
plt.plot(stats_dfa['valid_step'], stats_dfa['valid_loss'])
plt.plot(np.arange(len(stats_bp['train_loss'])), bp_train_loss)
plt.plot(stats_bp['valid_step'], stats_bp['valid_loss'])
plt.legend(['train loss dfa', 'validation loss dfa', 'train loss bp', 'validation loss bp'], loc='best')
plt.grid(True)
plt.show()
plt.title('Accuracy vs epoch')
plt.xlabel('epoch')
plt.ylabel('accuracy')
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)
plt.plot(stats_dfa['valid_step'], stats_dfa['valid_accuracy'])
plt.plot(np.arange(len(stats_bp['train_accuracy'])), bp_train_accuracy)
plt.plot(stats_bp['valid_step'], stats_bp['valid_accuracy'])
plt.legend(['train accuracy dfa', 'validation accuracy dfa', 'train accuracy bp', 'validation accuracy bp'], loc='best')
plt.grid(True)
plt.show()
# Forward, regularization, update and validation passes are excactly the same operations for dfa and bp. Therefore
# they should take euqally long. To ensure that inequalities don't affect the result, we normalize the time here.
# The reference time is the one measured for bp.
total_time_bp = stats_bp['total_time']
total_time_dfa = total_time_bp - stats_bp['backward_time'] + stats_dfa['backward_time']
step_to_time_bp = total_time_bp / len(stats_bp['train_loss'])
step_to_time_dfa = step_to_time_bp * total_time_dfa / stats_bp['total_time']
plt.title('Loss vs time')
plt.xlabel('time')
plt.ylabel('loss')
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'])) * step_to_time_dfa, dfa_train_loss)
plt.plot(np.arange(len(stats_bp['train_loss'])) * step_to_time_bp, bp_train_loss)
plt.legend(['train loss dfa', 'train loss bp'], loc='best')
plt.grid(True)
plt.show()
plt.title('Accuracy vs time')
plt.xlabel('time')
plt.ylabel('accuracy')
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'])) * step_to_time_dfa, dfa_train_accuracy)
plt.plot(np.arange(len(stats_bp['train_accuracy'])) * step_to_time_bp, bp_train_accuracy)
plt.legend(['train accuracy dfa', 'train accuracy bp'], loc='lower right')
plt.grid(True)
plt.show()
plt.title('Loss vs time')
plt.xlabel('time')
plt.ylabel('loss')
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'])) * step_to_time_dfa, dfa_train_loss)
plt.plot(np.asarray(stats_dfa['valid_step']) * step_to_time_dfa, stats_dfa['valid_loss'])
plt.plot(np.arange(len(stats_bp['train_loss'])) * step_to_time_bp, bp_train_loss)
plt.plot(np.asarray(stats_bp['valid_step']) * step_to_time_bp, stats_bp['valid_loss'])
plt.legend(['train loss dfa', 'validation loss dfa', 'train loss bp', 'validation loss bp'], loc='best')
plt.grid(True)
plt.show()
plt.title('Accuracy vs time')
plt.xlabel('time')
plt.ylabel('accuracy')
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'])) * step_to_time_dfa, dfa_train_accuracy)
plt.plot(np.asarray(stats_dfa['valid_step']) * step_to_time_dfa, stats_dfa['valid_accuracy'])
plt.plot(np.arange(len(stats_bp['train_accuracy'])) * step_to_time_bp, bp_train_accuracy)
plt.plot(np.asarray(stats_bp['valid_step']) * step_to_time_bp, stats_bp['valid_accuracy'])
plt.legend(['train accuracy dfa', 'validation accuracy dfa', 'train accuracy bp', 'validation accuracy bp'], loc='lower right')
plt.grid(True)
plt.show()