def test_value_manipulation(self):
val = np.random.random((4, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
# get_value
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# set_value
val = np.random.random((4, 2))
KTH.set_value(xth, val)
KTF.set_value(xtf, val)
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# count_params
assert KTH.count_params(xth) == KTF.count_params(xtf)
# print_tensor
check_single_tensor_operation('print_tensor', ())
check_single_tensor_operation('print_tensor', (2,))
check_single_tensor_operation('print_tensor', (4, 3))
check_single_tensor_operation('print_tensor', (1, 2, 3))
val = np.random.random((3, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
assert KTH.get_variable_shape(xth) == KTF.get_variable_shape(xtf)
def test_value_manipulation(self):
val = np.random.random((4, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
# get_value
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# set_value
val = np.random.random((4, 2))
KTH.set_value(xth, val)
KTF.set_value(xtf, val)
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# count_params
assert KTH.count_params(xth) == KTF.count_params(xtf)
# print_tensor
check_single_tensor_operation('print_tensor', ())
check_single_tensor_operation('print_tensor', (2, ))
check_single_tensor_operation('print_tensor', (4, 3))
check_single_tensor_operation('print_tensor', (1, 2, 3))
val = np.random.random((3, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
assert KTH.get_variable_shape(xth) == KTF.get_variable_shape(xtf)
def test_value_manipulation(self):
val = np.random.random((4, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
# get_value
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# set_value
val = np.random.random((4, 2))
KTH.set_value(xth, val)
KTF.set_value(xtf, val)
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# count_params
assert KTH.count_params(xth) == KTF.count_params(xtf)
def test_value_manipulation(self):
val = np.random.random((4, 2))
xth = KTH.variable(val)
xtf = KTF.variable(val)
# get_value
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# set_value
val = np.random.random((4, 2))
KTH.set_value(xth, val)
KTF.set_value(xtf, val)
valth = KTH.get_value(xth)
valtf = KTF.get_value(xtf)
assert valtf.shape == valth.shape
assert_allclose(valth, valtf, atol=1e-05)
# count_params
assert KTH.count_params(xth) == KTF.count_params(xtf)
def train(batch_size, input_shape,
x_train, y_train,
x_valid, y_valid,
model_name, num_workers,
resume):
print('Found {} images belonging to {} classes'.format(len(x_train), 128))
print('Found {} images belonging to {} classes'.format(len(x_valid), 128))
train_generator = AugmentedDataset(
x_train, y_train,
batch_size=batch_size, input_shape=input_shape)
valid_generator = FurnituresDatasetNoAugmentation(
x_valid, y_valid,
batch_size=batch_size, input_shape=input_shape)
class_weight = compute_class_weight(
'balanced', np.unique(y_train), y_train)
class_weight_dict = dict.fromkeys(np.unique(y_train))
for key in class_weight_dict.keys():
class_weight_dict.update({key: class_weight[key]})
filepath = 'checkpoint/{}/iter1.hdf5'.format(model_name)
save_best = ModelCheckpoint(filepath=filepath,
verbose=1,
monitor='val_acc',
save_best_only=True,
mode='max')
save_on_train_end = ModelCheckpoint(filepath=filepath,
verbose=1,
monitor='val_acc',
period=args.epochs)
reduce_lr = ReduceLROnPlateau(monitor='val_acc',
factor=0.2,
patience=2,
verbose=1)
callbacks = [save_best, save_on_train_end, reduce_lr]
if resume == 'True':
print('\nResume training from the last checkpoint')
model = load_model(filepath)
trainable_count = int(
np.sum([K.count_params(p) for p in set(model.trainable_weights)]))
print('Trainable params: {:,}'.format(trainable_count))
model.fit_generator(generator=train_generator,
epochs=args.epochs,
callbacks=callbacks,
validation_data=valid_generator,
class_weight=class_weight_dict,
workers=num_workers)
else:
print('\nTrain the last Dense layer')
if model_name == 'densenet_201':
model = build_densenet_201()
elif model_name == 'inception_v3':
model = build_inception_v3()
elif model_name == 'inception_resnet_v2':
model = build_inception_resnet_v2()
elif model_name == 'xception':
model = build_xception()
for layer in model.layers[:-1]:
layer.trainable = False
model.compile(optimizer=Adam(lr=0.001), loss='categorical_crossentropy',
metrics=['acc'])
model.fit_generator(generator=train_generator,
epochs=5,
callbacks=callbacks,
validation_data=valid_generator,
class_weight=class_weight_dict,
workers=num_workers)
K.clear_session()
print("\nFine-tune the network")
model = load_model(filepath)
for layer in model.layers:
layer.trainable = True
if hasattr(layer, 'kernel_regularizer'):
layer.kernel_regularizer = regularizers.l2(0.0001)
trainable_count = int(
np.sum([K.count_params(p) for p in set(model.trainable_weights)]))
print('Trainable params: {:,}'.format(trainable_count))
model.compile(optimizer=Adam(lr=3e-5),
loss='categorical_crossentropy',
metrics=['acc'])
model.fit_generator(generator=train_generator,
epochs=30,
callbacks=callbacks,
validation_data=valid_generator,
class_weight=class_weight_dict,
workers=num_workers)
K.clear_session()
def train_with_sift_features(batch_size, input_shape, x_train, y_train,
x_valid, y_valid, sift_features_train,
sift_features_valid, model_name, num_workers,
resume):
print('Found {} images belonging to {} classes'.format(len(x_train), 128))
print('Found {} images belonging to {} classes'.format(len(x_valid), 128))
train_generator = AugmentedDatasetWithSiftFeatures(x_train,
y_train,
sift_features_train,
batch_size=batch_size,
input_shape=input_shape)
valid_generator = DatasetWithSiftFeatures(x_valid,
y_valid,
sift_features_valid,
batch_size=batch_size,
input_shape=input_shape)
class_weight = compute_class_weight('balanced', np.unique(y_train),
y_train)
class_weight_dict = dict.fromkeys(np.unique(y_train))
for key in class_weight_dict.keys():
class_weight_dict.update({key: class_weight[key]})
filepath = 'checkpoint/{}/sift_iter1.hdf5'.format(model_name)
save_best = ModelCheckpoint(filepath=filepath,
verbose=1,
monitor='val_acc',
save_best_only=True,
mode='max')
save_on_train_end = ModelCheckpoint(filepath=filepath,
verbose=1,
monitor='val_acc',
period=args.epochs)
reduce_lr = ReduceLROnPlateau(monitor='val_acc',
factor=0.2,
patience=2,
verbose=1)
callbacks = [save_best, save_on_train_end, reduce_lr]
if resume == 'True':
print('\nResume training from the last checkpoint')
model = load_model(filepath)
trainable_count = int(
np.sum([K.count_params(p) for p in set(model.trainable_weights)]))
print('Trainable params: {:,}'.format(trainable_count))
model.fit_generator(generator=train_generator,
epochs=args.epochs,
callbacks=callbacks,
validation_data=valid_generator,
class_weight=class_weight_dict,
workers=num_workers)
else:
model = Xception(include_top=False, pooling='max')
sift_features = Input(shape=(512, ))
x = Concatenate()([model.layers[-1].output, sift_features])
x = Dense(units=128,
activation='linear',
name='predictions',
kernel_regularizer=regularizers.l2(0.0001))(x)
model = Model([model.layers[0].input, sift_features], x)
for layer in model.layers[:-1]:
layer.trainable = False
model.compile(optimizer=Adam(lr=0.001),
loss='categorical_hinge',
metrics=['acc'])
model.fit_generator(generator=train_generator,
epochs=5,
callbacks=callbacks,
validation_data=valid_generator,
class_weight=class_weight_dict,
workers=num_workers)
K.clear_session()
print("\nFine-tune the network")
model = load_model(filepath)
for layer in model.layers:
layer.trainable = True
trainable_count = int(
np.sum([K.count_params(p) for p in set(model.trainable_weights)]))
print('Trainable params: {:,}'.format(trainable_count))
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
loss='categorical_hinge',
metrics=['acc'])
model.fit_generator(generator=train_generator,
epochs=30,
callbacks=callbacks,
validation_data=valid_generator,
class_weight=class_weight_dict,
workers=num_workers)
K.clear_session()
