valid_batch = BatchGenerator(valid_img, generator_config, jitter=False)
# **Setup a few callbacks and start the training**
early_stop = EarlyStopping(monitor='loss',
min_delta=0.001,
patience=10,
mode='min',
verbose=1)
checkpoint = ModelCheckpoint('weights_yolo_adam_validdata_temp.h5',
monitor='loss',
verbose=1,
save_best_only=True,
mode='min',
period=1)
optimizer = Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
#optimizer = SGD(lr=1e-4, decay=0.0005, momentum=0.9)
#optimizer = RMSprop(lr=1e-5, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(loss=custom_loss, optimizer=optimizer)
model.fit_generator(generator=train_batch.get_generator(),
steps_per_epoch=train_batch.get_dateset_size(),
epochs=100,
verbose=1,
validation_data=valid_batch.get_generator(),
validation_steps=valid_batch.get_dateset_size(),
callbacks=[early_stop, checkpoint])
def train(self, train_imgs, # the list of images to train the model
valid_imgs, # the list of images used to validate the model
train_times, # the number of time to repeat the training set, often used for small datasets
valid_times, # the number of times to repeat the validation set, often used for small datasets
nb_epoch, # number of epoches
learning_rate, # the learning rate
batch_size, # the size of the batch
warmup_bs, # number of initial batches to let the model familiarize with the new dataset
object_scale,
no_object_scale,
coord_scale,
class_scale,
debug):
self.batch_size = batch_size
self.warmup_bs = warmup_bs
self.object_scale = object_scale
self.no_object_scale = no_object_scale
self.coord_scale = coord_scale
self.class_scale = class_scale
self.debug = debug
############################################
# Compile the model
############################################
optimizer = Adam(lr=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0)
self.model.compile(loss=self.custom_loss, optimizer=optimizer)
############################################
# Make train and validation generators
############################################
generator_config = {
'IMAGE_H' : self.input_size,
'IMAGE_W' : self.input_size,
'GRID_H' : self.grid_h,
'GRID_W' : self.grid_w,
'BOX' : self.nb_box,
'LABELS' : self.labels,
'CLASS' : len(self.labels),
'ANCHORS' : self.anchors,
'BATCH_SIZE' : self.batch_size,
'TRUE_BOX_BUFFER' : self.max_box_per_image,
}
train_batch = BatchGenerator(train_imgs, generator_config)
valid_batch = BatchGenerator(valid_imgs, generator_config, jitter=False)
############################################
# Make a few callbacks
############################################
early_stop = EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=3,
mode='min',
verbose=1)
checkpoint = ModelCheckpoint('best_weights.h5',
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min',
period=1)
tensorboard = TensorBoard(log_dir='~/logs/yolo/',
histogram_freq=0,
write_graph=True,
write_images=False)
############################################
# Start the training process
############################################
self.model.fit_generator(generator = train_batch.get_generator(),
steps_per_epoch = train_batch.get_dateset_size() * train_times,
epochs = nb_epoch,
verbose = 1,
validation_data = valid_batch.get_generator(),
validation_steps = valid_batch.get_dateset_size() * valid_times,
callbacks = [early_stop, checkpoint, tensorboard],
max_queue_size = 3)
