# Compute quantities required for featurewise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(X_train)
# Fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(X_train,
Y_train,
batch_size=batch_size,
shuffle=True),
steps_per_epoch=X_train.shape[0] // batch_size,
validation_data=(X_test, Y_test),
epochs=nb_epoch,
verbose=1,
callbacks=[lr_finder])
# from plot we see, the model isnt impacted by the weight_decay very much at all
# so we can use any of them.
for momentum in MOMENTUMS:
directory = 'weights/momentum/momentum-%s/' % str(momentum)
losses, lrs = lr_finder.restore_schedule_from_dir(directory, 10, 5)
plt.plot(lrs, losses, label='momentum=%0.2f' % momentum)
plt.title("Momentum")
plt.xlabel("Learning rate")
plt.ylabel("Validation Loss")
plt.legend()
plt.show()
def main(data_path=args['data_path'], train_from=train_from):
train_gen, test_gen = create_data_generator(data_path)
valid_x, valid_y = create_valid_data()
MOMENTUMS = [0.9, 0.95, 0.99]
for momentum in MOMENTUMS:
K.clear_session()
# Learning rate range obtained from `find_lr_schedule.py`
# NOTE : Minimum is 10x smaller than the max found above !
# NOTE : It is preferable to use the validation data here to get a correct value
lr_finder = LRFinder(NUM_SAMPLES,
BS,
minimum_lr=0.0001,
maximum_lr=0.001,
validation_data=(valid_x, valid_y),
validation_sample_rate=5,
lr_scale='linear',
save_dir='weights/momentum/momentum-%s/' %
str(momentum),
verbose=True)
if train_from == 'trained_weights':
model = load_model_from_trained_weights(
imagedims=IMAGE_DIMS,
nb_classes=len(train_gen.class_indices),
weights=args['weight_path'],
freeze_until=freeze_until)
elif train_from == 'trained_model':
model = load_model_from_trained_model()
else:
model = load_models(imagedims=IMAGE_DIMS,
nb_classes=len(train_gen.class_indices))
print('[INFO] compiling model...')
model.compile(loss="categorical_crossentropy",
optimizer=OPT,
metrics=["accuracy"])
# set the weight_decay here !
# lr doesnt matter as it will be over written by the callback
optimizer = SGD(lr=0.001, momentum=momentum, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=optimizer,
metrics=['accuracy'])
callbacks = [lr_finder]
H = model.fit_generator(
train_gen,
validation_data=(valid_x, valid_y),
epochs=EPOCHS,
#steps_per_epoch=209,
callbacks=callbacks,
verbose=1)
for momentum in MOMENTUMS:
directory = 'weights/momentum/momentum-%s/' % str(momentum)
losses, lrs = LRFinder.restore_schedule_from_dir(directory, 10, 5)
plt.plot(lrs, losses, label='momentum=%0.2f' % momentum)
plt.title("Momentum")
plt.xlabel("Learning rate")
plt.ylabel("Validation Loss")
plt.legend()
plt.show()