128,
aug=aug,
preprocessors=[pp, mp, iap],
classes=2)
valGen = HDF5DatasetGenerator(config.VAL_HDF5,
128,
preprocessors=[sp, mp, iap],
classes=2)
print("[INFO] compiling model...")
opt = Adam(lr=1e-3)
#pdb.set_trace()
model = AlexNet.build(width=227, height=227, depth=3, classes=2)
model.compile(loss="binary_crossentropy", optimizer=opt, metrics=["accuracy"])
path = os.path.sep.join([config.OUTPUT_PATH, "{}.png".format(os.getpid())])
callbacks = [TrainingMonitor(path)]
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=128 * 2,
callbacks=callbacks,
verbose=1)
from pyimagesearch.io import HDF5DatasetGenerator from pyimagesearch.preprocessing import SimplePreprocessor, MeanPreprocessor, PatchPreprocessor from config import plant_seedlings_config as config from pyimagesearch.nn.conv import AlexNet from keras.preprocessing.image import ImageDataGenerator import json import pickle mean = json.loads(open(config.DATASET_MEAN).read()) le = pickle.loads(open(config.LABEL_MAPPINGS, 'rb').read()) sp, mp, pp = SimplePreprocessor(224, 224), MeanPreprocessor(mean['R'], mean['G'], mean['B']), PatchPreprocessor(224, 224) aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1, height_shift_range=0.1, shear_range=0.15, zoom_range=0.1, horizontal_flip=True) train_gen = HDF5DatasetGenerator(config.TRAIN_HDF5, preprocessors=[pp, mp], aug=aug, batch_size=64, num_classes=len(le.classes_)) val_gen = HDF5DatasetGenerator(config.VAL_HDF5, preprocessors=[sp, mp], aug=aug, batch_size=64, num_classes=len(le.classes_)) model = AlexNet.build(224, 224, 3, len(le.classes_)) model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) model.fit(train_gen.generator(), steps_per_epoch=train_gen.num_images//64, epochs=100, verbose=2, )
# initialize image preprocessors
sp = SimplePreprocessor(227, 227)
pp = PatchPreprocessor(227, 227)
mp = MeanPreprocessor(means['R'], means['G'], means['B'])
iap = ImageToArrayPreprocessor()
# initialize training and validation dataset generators
print('[INFO] loading dataset...')
train_gen = HDF5DatasetGenerator(config.TRAIN_HDF5, batch_size=64*config.G, preprocessors=[pp, mp, iap], aug=aug, classes=2)
val_gen = HDF5DatasetGenerator(config.VAL_HDF5, batch_size=64*config.G, preprocessors=[sp, mp, iap], classes=2)
# initialize the optimizer
if config.G <= 1:
print(f'[INFO] compiling model with 1 GPU...')
model = AlexNet.build(width=227, height=227, depth=3, classes=2, reg=0.0002)
else:
print(f'[INFO] compiling model with {config.G} GPU...')
# opt = Adam(lr=1e-3)
with tf.device('/cpu:0'):
model = AlexNet.build(width=227, height=227, depth=3, classes=2, reg=0.0002)
model = multi_gpu_model(model, gpus=config.G)
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
# construct set of callbacks
path = os.path.sep.join([config.OUTPUT_PATH, f'{os.getpid()}.png'])
callbacks = [TrainingMonitor(path)]
# train the network
print('[INFO] training model...')
model.fit_generator(train_gen.generator(), steps_per_epoch=train_gen.num_images//(64*config.G), validation_data=val_gen.generator(), validation_steps=val_gen.num_images//(64*config.G), epochs=75, callbacks=callbacks, verbose=2)
def main():
"""Train AlexNet on Dogs vs Cats
"""
# construct the training image generator for data augmentation
augmentation = ImageDataGenerator(
rotation_range=20,
zoom_range=0.15,
width_shift_range=0.2,
height_shift_range=0.2,
shear_range=0.15,
horizontal_flip=True,
fill_mode="nearest",
)
# load the RGB means for the training set
means = json.loads(open(config.DATASET_MEAN).read())
# initialize the image preprocessors
simple_preprocessor = SimplePreprocessor(227, 227)
patch_preprocessor = PatchPreprocessor(227, 227)
mean_preprocessor = MeanPreprocessor(means["R"], means["G"], means["B"])
image_to_array_preprocessor = ImageToArrayPreprocessor()
# initialize the training and validation dataset generators
train_gen = HDF5DatasetGenerator(
config.TRAIN_HDF5,
128,
augmentation=augmentation,
preprocessors=[patch_preprocessor, mean_preprocessor, image_to_array_preprocessor],
classes=2,
)
val_gen = HDF5DatasetGenerator(
config.VAL_HDF5,
128,
preprocessors=[simple_preprocessor, mean_preprocessor, image_to_array_preprocessor],
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, regularization=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(
train_gen.generator(),
steps_per_epoch=train_gen.num_images // 128,
validation_data=val_gen.generator(),
validation_steps=val_gen.num_images // 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)
# close the HDF5 datasets
train_gen.close()
val_gen.close()