fill_mode="nearest",
horizontal_flip=True,
preprocessing_function=train_preprocessors.preprocess)
val_aug = ImageDataGenerator(
preprocessing_function=val_preprocessors.preprocess)
train_generator = train_aug.flow_from_directory(directory=configs.TRAIN_IMAGE,
batch_size=configs.BATCH_SIZE)
val_generator = val_aug.flow_from_directory(directory=configs.TEST_IMAGE,
batch_size=configs.BATCH_SIZE)
# callback
path = os.path.sep.join([configs.OUTPUT_PATH, f"{os.getpid()}.png"])
callbacks = [
TrainingMonitor(plot_path=path),
]
# initialize optimizer and model
opt = Adam(lr=1e-3)
model = AlexNet.build(width=227, height=227, classes=configs.NUM_CLASSES)
# compile and train model
model.compile(opt, loss="binary_crossentropy", metrics=["accuracy"])
model.fit(
train_generator,
epochs=EPOCH,
steps_per_epoch=train_generator.samples // configs.BATCH_SIZE,
validation_data=val_generator,
validation_steps=val_generator.samples // configs.BATCH_SIZE,
max_queue_size=configs.BATCH_SIZE * 2,
preprocessors=[pp, mp, iap],
classes=2)
valGen = HDF5DatasetGenerator(config.VAL_HDF5,
32,
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 // 32,
validation_data=valGen.generator(),
validation_steps=valGen.numImages // 32,
epochs=75,
max_queue_size=128 * 2,
callbacks=callbacks,
verbose=1)
# save the model to file
print("[INFO] serializing model...")
model.save(config.MODEL_PATH, overwrite=True)
'airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse',
'ship', 'truck'
]
# initialize the SGD optimizer, but without any learning rate decay
print("[INFO] compiling model...")
opt = SGD(lr=0.01, momentum=0.9, nesterov=True)
model = MiniVGGNet.build(width=32,
height=32,
depth=3,
classes=10,
useBatchNormalization=useBatchNormalization)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# 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)]
# train the network
print('[INFO] training network...')
model.fit(trainX,
trainY,
validation_data=(testX, testY),
batch_size=64,
epochs=100,
callbacks=callbacks,
verbose=2)
else:
# load the checkpoint from disk
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-2)
print("[INFO] new learning rate: {}".format(K.get_value(
model.optimizer.lr)))
# build the path to the training plot and training history
plotPath = os.path.sep.join(["output", "resnet_fashion_mnist.png"])
jsonPath = os.path.sep.join(["output", "resnet_fashion_mnist.json"])
# construct the set of callbacks
callbacks = [
EpochCheckpoint(args["checkpoints"], every=2, startAt=args["start_epoch"]),
TrainingMonitor(plotPath, jsonPath=jsonPath, startAt=args["start_epoch"])
]
# train the network
print("[INFO] training network...")
model.fit_generator(aug.flow(trainX, trainY, batch_size=128),
validation_data=(testX, testY),
steps_per_epoch=len(trainX) // 128,
epochs=80,
callbacks=callbacks,
verbose=1)
checkpoint = ModelCheckpoint(filepath=frame,
monitor='val_loss',
mode='min',
save_best_only=True,
verbose=1)
figPath = os.path.sep.join([
'/home/pavel/PycharmProjects/nn/pyimagesearch/plot/',
"{}_.png".format(os.getpid())
])
jsonPath = os.path.sep.join([
'/home/pavel/PycharmProjects/nn/pyimagesearch/plot/',
"{}.json".format(os.getpid())
])
callbacks = [
checkpoint,
TrainingMonitor(jsonPath=jsonPath, figPath=figPath, val=False)
]
print("[INFO] training network...")
H = model.fit(x=trainX,
y=trainY,
validation_data=(testX, testY),
batch_size=64,
epochs=30,
callbacks=callbacks,
verbose=1)
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, 40), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, 40), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, 40), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, 40), H.history["val_acc"], label="val_acc")
"ship", "truck"
]
# initialize the optimizer and model
print("[INFO] compiling model...")
opt = SGD(lr=0.01, decay=0.01 / 50, momentum=0.9, nesterov=True)
model = MiniVGGNetwork.build(width=32, height=32, depth=3, classes=10)
model.compile(loss="categorical_crossentropy",
optimizer=opt,
metrics=["accuracy"])
# construct the set of callbacks
# initialize the training monitor
figPath = os.path.sep.join([args["output"], "{}.png".format(os.getpid())])
jsonPath = os.path.sep.join([args["output"], "{}.json".format(os.getpid())])
training_monitor = TrainingMonitor(figPath=figPath, jsonPath=jsonPath)
# initialize the checkpoint improvements
checkpoint_improvements = ModelCheckpoint(args["weight"],
monitor="val_loss",
mode="min",
save_best_only=True,
verbose=1)
# initialize the callbacks
callbacks = [training_monitor, checkpoint_improvements]
# train the network
print("[INFO] training network...")
model.fit(X_train,
y_train,