# Hyper parameters
smooth = 1e-15
BATCH_SIZE = 32
LR = 1e-3
EPOCHS = 10
MOMENTUM = 0.9
NUM_CLASS = 1
# Data Loader
train_generator = DataLoader(META_DATA_PATH, batch_size=BATCH_SIZE,
abs_image_path=TRAINING_DATA_PATH, abs_mask_path=TRAINING_MASK_PATH,
phase='train', input_size=224, output_size=224)
test_generator = DataLoader(META_DATA_PATH, batch_size=BATCH_SIZE,
abs_image_path=TESTING_DATA_PATH, abs_mask_path=TESTING_MASK_PATH,
phase='test', input_size=224, output_size=224)
# Build model using Unet class
model = Unet(input_shape=(224, 224, 1)).build()
losses = tf.keras.losses.BinaryCrossentropy()
optimizer = tf.keras.optimizers.Adam(learning_rate=LR)
callback = tf.keras.callbacks.LearningRateScheduler(scheduler)
model.compile(optimizer=optimizer, loss=losses, metrics=iou)
# Training model with my custom generator
model.fit_generator(train_generator,
steps_per_epoch=len(train_generator),
epochs=EPOCHS,
callbacks=[callback],
validation_data=test_generator,
validation_steps=len(test_generator))
"*******************\nfailed to load checkpoint\n*******************")
train_options = {
"optimizer": get_config(optim),
"batchsize": BATCH_SIZE,
"loss_function": loss_func,
"input_shape": IMAGE_SHAPE,
"augmemtation": augm
}
print(json.dumps(train_options, indent=4, sort_keys=False))
with open(os.path.join(SEGMENT_RESULT_PATH, 'train_options.json'), 'w') as f:
f.write(json.dumps(train_options))
""" run train """
hist = unet.fit_generator(
generator=train_iterator,
steps_per_epoch=None,
epochs=100,
verbose=1,
callbacks=call_backs,
class_weight=None,
max_queue_size=30,
workers=1,
use_multiprocessing=False,
initial_epoch=init_epoch
#validation_data=test_iterator,
#validation_steps=None,
)
hist = pd.DataFrame(hist.history)
hist.to_csv(os.path.join(SEGMENT_RESULT_PATH, "history.csv"))
hist.head()
