]
model = Model(
layers=layers,
num_classes=10,
optimizer=GDMomentumOptimizer(lr=1e-3, mu=0.9),
)
print("\n\n------------------------------------")
print("Initialize: {}".format(initializer))
print("\nRun training:\n------------------------------------")
stats = model.train(data_set=data, method='dfa', num_passes=num_passes, batch_size=50)
loss, accuracy = model.cost(*data.test_set())
print("\nResult:\n------------------------------------")
print('loss on test set: {}'.format(loss))
print('accuracy on test set: {}'.format(accuracy))
statistics.append(stats)
plt.title('Loss')
plt.xlabel('epoch')
plt.ylabel('loss')
for stats in statistics:
train_loss = scipy.ndimage.filters.gaussian_filter1d(stats['train_loss'], sigma=10)
plt.plot(np.arange(len(stats['train_loss'])), train_loss)
plt.legend(labels, loc='upper right')
plt.grid(True)
def animate(i):
ax1.clear()
ax1.plot(np.arange(len(model_dfa.statistics['train_loss'])),
model_dfa.statistics['train_loss'])
ax1.plot(np.arange(len(model_bp.statistics['train_loss'])),
model_bp.statistics['train_loss'])
dfa_train_thread = threading.Thread(target=Trainer(model_dfa, 'dfa'))
bp_train_thread = threading.Thread(target=Trainer(model_bp, 'bp'))
dfa_train_thread.start()
bp_train_thread.start()
ani = animation.FuncAnimation(fig, animate, interval=1000)
plt.show()
dfa_train_thread.join()
bp_train_thread.join()
loss, accuracy = model_dfa.cost(*data.test_set())
print("\nResult DFA:\n------------------------------------")
print('loss on test set: {}'.format(loss))
print('accuracy on test set: {}'.format(accuracy))
loss, accuracy = model_bp.cost(*data.test_set())
print("\nResult BP:\n------------------------------------")
print('loss on test set: {}'.format(loss))
print('accuracy on test set: {}'.format(accuracy))
