if not os.path.exists(PathOutput):
os.makedirs(PathOutput)
else:
for dirName, subdirList, fileList in os.walk(PathOutput):
for filename in fileList:
os.remove(PathOutput+filename)
logfile=PathOutput+'allnode_PIN.log'
csv_logger = CSVLogger(logfile)
filename="weights.{epoch:03d}-{val_loss:.2f}.hdf5"
checkpointer = ModelCheckpoint(monitor='val_loss', filepath=PathOutput+filename, verbose=1, save_best_only=True, save_weights_only=True)
#model.set_weights(init_weights)
checkpointer.epochs_since_last_save = 0
checkpointer.best = np.Inf
lrate = LearningRateScheduler(my_learning_rate,verbose=1)
model.fit(Data_0, Labels_0, epochs=epochs, batch_size=batch_size, validation_data=([Data_1,Labels_1]),
callbacks=[checkpointer,csv_logger,lrate], verbose=2, class_weight=weight)
##----------------------- Look for the best model to evaluate
for dirName, subdirList, fileList in os.walk(PathOutput):
fileList.sort()
tmp=fileList[len(fileList)-1]
print(tmp)
filename=PathOutput+tmp
model.load_weights(filename)
model.save(PathOutput+'best_model.hd5')
except:
print 'Loading weights'
model = create_network(args.model,
tile_size=args.tile_size,
lr=args.lr)
model.load_weights(checkpoint_path)
model_out_path = join(model_dir,
'checkpoint-{epoch:02d}-{val_loss:.3f}.hdf5')
checkpointer = ModelCheckpoint(model_out_path,
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='auto',
period=1)
checkpointer.epochs_since_last_save = initial_epoch
initial_epoch = int(initial_epoch)
print("Epochs since last save: %d." %
checkpointer.epochs_since_last_save)
datagen = ImageDataGenerator(rotation_range=180,
width_shift_range=0.2,
height_shift_range=0.2,
rescale=None,
shear_range=0.0,
zoom_range=args.zoom_range,
horizontal_flip=True,
fill_mode='reflect')
lr_scheduler = MyLearningRateScheduler(epoch_unfreeze=80,
start_lr=args.lr,
