else:
print("[INFO] loading {}...".format(args["model"]))
model = load_model(args["model"])
# update the learning rate
print("[INFO] old learning rate: {}".format(K.get_value(
model.optimizer.lr)))
K.set_value(model.optimizer.lr, 1e-5)
print("[INFO] new learning rate: {}".format(K.get_value(
model.optimizer.lr)))
# construct the set of callbacks
callbacks = [
EpochCheckpoint(args["checkpoints"], every=5, startAt=args["start_epoch"]),
TrainingMonitor(config.FIG_PATH,
jsonPath=config.JSON_PATH,
startAt=args["start_epoch"])
]
# train the network
model.fit_generator(trainGen.generator(),
steps_per_epoch=trainGen.numImages // 64,
validation_data=valGen.generator(),
validation_steps=valGen.numImages // 64,
epochs=10,
max_queue_size=10,
callbacks=callbacks,
verbose=1)
# close the databases
trainGen.close()
# otherwise, load the checkpoint from disk
else:
print("[INFO] loding {}...".format(args["model"]))
model = load_model(args["model"])
# update the learning rate
print("[INFO] old learning rate: {}".format(K.get_value(model.optimizer.lr)))
K.set_value(model.optimizer.lr, 1e-5)
print("[INFO] new learning rate: {}".format(K.get_value(model.optimizer.lr)))
# construct the set of callbacks
figPath = os.path.sep.join([config.OUTPUT_PATH, "vggnet_emotion_4.png"])
jsonPath = os.path.sep.join([config.OUTPUT_PATH, "vggnet_emotion_4.json"])
callbacks = [
EpochCheckpoint(args["checkpoints"], every = 5, startAt = args["start_epoch"]),
TrainingMonitor(figPath, jsonPath = jsonPath, startAt = args["start_epoch"])
]
# train network
model.fit_generator(
trainGen.generator(),
steps_per_epoch = trainGen.numImages // config.BATCH_SIZE,
validation_data = valGen.generator(),
validation_steps = valGen.numImages / / config.BATCH_SIZE,
epochs = 15,
max_queue_size = config.BATCH_SIZE * 2,
callbacks = callbacks,
verbose = 1
)
# close the dataset
iap = ImageToArrayPreprocessor()
# initialize the training and validation dataset generators
trainGen = HDF5DatasetGenerator(config.TRAIN_HDF5,
64,
aug=aug,
preprocessors=[sp, mp, iap],
classes=config.NUM_CLASSES)
valGen = HDF5DatasetGenerator(config.VAL_HDF5,
64,
preprocessors=[sp, mp, iap],
classes=config.NUM_CLASSES)
# initialize the set of callbacks
callbacks = [
TrainingMonitor(config.FIG_PATH, jsonPath=config.JSON_PATH),
LearningRateScheduler(poly_decay)
]
# initialize the optimizer and model (ResNet56)
print("[INFO] compiling model...")
model = ResNet.build(64,
64,
3,
config.NUM_CLASSES, (3, 4, 6), (64, 128, 256, 512),
reg=0.0005,
dataset="tiny_imagenet")
opt = SGD(lr=INIT_LR, momentum=0.9, nesterov=True)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# initialize the training and validation dataset generators
trainGen = HDF5DatasetGenerator(config.TRAIN_HDF5, 128, aug = aug,
preprocessors = [pp, mp, iap], classes = 2)
valGen = HDF5DatasetGenerator(config.VAL_HDF5, 128,
preprocessors = [sp, mp, iap], classes = 2)
# initialize the optimizer
print("[INFO] compiling model...")
opt = Adam(lr = 1e-3)
model = AlexNet.build(width = 227, height = 227, depth = 3, classes = 2, reg = 0.0002)
model.compile(loss = "binary_crossentropy", optimizer = opt, metrics = ["accuracy"])
# construct the set of callbacks
path = os.path.sep.join([config.OUTPUT_PATH, "{}.png".format(os.getpid())])
callbacks = [TrainingMonitor(path)]
# train the network
model.fit_generator(
trainGen.generator(),
steps_per_epoch = trainGen.numImages // 128,
validation_data = valGen.generator(),
validation_steps = valGen.numImages // 128,
epochs = 75,
max_queue_size = 10,
callbacks = callbacks, verbose = 1
)
# save the model to file
print("[INFO] serializing model...")
model.save(config.MODEL_PATH, overwrite = True)
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)
# initialize the label name for CIFAR-10 dataset
labelNames = ["airplane", "automobile", "bird", "cat", "deer",
"dog", "frog", "horse", "ship", "truck"]
# construct the image generator for data augmentation
aug = ImageDataGenerator(width_shift_range = 0.1, height_shift_range = 0.1,
horizontal_flip = True, fill_mode = "nearest")
# construct the set of callbacks
figPath = os.path.sep.join([args["output"], "{}.png".format(os.getpid())])
jsonPath = os.path.sep.join([args["output"], "{}.json".format(os.getpid())])
callbacks = [TrainingMonitor(figPath, jsonPath = jsonPath),
LearningRateScheduler(poly_decay)]
# initialize the optimizer and model
print("[INFO] compiling model...")
# opt = SGD(lr = INIT_LR, momentum = 0.9, nesterov = True)
opt = Adam(lr = INIT_LR)
model = MiniVGGNet.build(width = 32, height = 32, depth = 3, classes = 10)
model.compile(loss = "categorical_crossentropy", optimizer = opt, metrics = ["accuracy"])
# train the network
print("[INFO] training network...")
model.fit_generator(aug.flow(trainX, trainY, batch_size = 64),
validation_data = (testX, testY), steps_per_epoch = len(trainX) // 64,
epochs = NUM_EPOCHS, callbacks = callbacks, verbose = 1)
else:
print("[INFO] loading {}...".format(args["model"]))
model = load_model(args["model"])
# update the learning rate
print("[INFO] old learning rate: {}".format(K.get_value(
model.optimizer.lr)))
K.set_value(model.optimizer.lr, 1e-3)
print("[INFO] new learning rate: {}".format(K.get_value(
model.optimizer.lr)))
# construct the set of callbacks
callbacks = [
EpochCheckpoint(args["checkpoints"], every=5, startAt=args["start_epoch"]),
TrainingMonitor("output/resnet56_cifar10_3.png",
jsonPath="output/resnet56_cifar10_3.json",
startAt=args["start_epoch"])
]
# train the network
print("[INFO] training network...")
model.fit_generator(aug.flow(trainX, trainY, batch_size=64),
validation_data=(testX, testY),
steps_per_epoch=len(trainX) // 64,
epochs=10,
callbacks=callbacks,
verbose=1)
# evaluate network
print("[INFO] evaluating network...")
predictions = model.predict(testX, batch_size=64)
labelNames = [
"airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse",
"ship", "truck"
]
# construct the image generator for data augmentation
aug = ImageDataGenerator(width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=True,
fill_mode="nearest")
# construct the set of callbacks
figPath = os.path.sep.join([args["output"], "{}.png".format(os.getpid())])
jsonPath = os.path.sep.join([args["output"], "{}.json".format(os.getpid())])
callbacks = [
TrainingMonitor(figPath, jsonPath=jsonPath),
LearningRateScheduler(poly_decay)
]
# initialize the optimizer and model
print("[INFO] compiling model...")
opt = SGD(lr=INIT_LR, momentum=0.9)
model = MiniGoogLeNet.build(width=32, height=32, depth=3, classes=10)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# train the network
print("[INFO] training network...")
model.fit_generator(aug.flow(trainX, trainY, batch_size=64),
validation_data=(testX, testY),