def train(model, dataset, epochs):
# model_path = path_from_model_name(model)
model = create_model_masked(model)
history = {"loss": [], "accuracy": [], "val_loss": [], "val_accuracy": []}
train_model(model, history, dataset, epochs)
def train_wrapper(args: Namespace) -> None:
""" Function for training a network. """
model_name = args.model
if args.cont:
model = load_model(model_name)
history = model.__asf_model_history
weights = model.get_weights()
lr_schedule = ExponentialDecay(9.2e-4,
decay_steps=2000,
decay_rate=0.96,
staircase=True)
# optimizer = model.optimizer
model.compile(loss=jaccard_distance_loss,
optimizer=Adam(learning_rate=lr_schedule),
metrics=['accuracy', MeanIoU(num_classes=2)])
model.set_weights(weights)
# model.compile(
# loss='binary_crossentropy', optimizer='adam', metrics=["accuracy"]
# )
else:
model_path = path_from_model_name(model_name)
if not args.overwrite and os.path.isfile(model_path):
print(f"File {model_name} already exists!")
return
# model = create_model_masked(model_name)
model = create_cdl_model_masked(model_name)
history = {'loss': [], 'accuracy': [], "mean_io_u": []}
train_model(model, history, args.dataset, args.epochs)
def train_wrapper(args: Namespace) -> None:
""" Function for training a network. """
model_name = args.model
if args.cont:
model = load_model(model_name)
history = model.__asf_model_history
else:
model_path = path_from_model_name(model_name)
if not args.overwrite and os.path.isfile(model_path):
print(f"File {model_name} already exists!")
return
model = create_model_masked(model_name)
history = {"loss": [], "acc": [], "val_loss": [], "val_acc": []}
train_model(model, history, args.dataset, args.epochs)
def train_wrapper(args: Namespace) -> None:
"""Function for training a network"""
data_type = dataset_type(args.dataset)
model_name = args.model
if args.cont:
model = load_model(model_name)
history = model.__asf_model_history
else:
model_path = path_from_model_name(model_name)
if not args.overwrite and os.path.isfile(model_path):
print(f"File {model_name} already exists!")
return
model = create_model(model_name, data_type)
history = {"loss": [], "acc": [], "val_loss": [], "val_acc": []}
if model_type(model) != data_type:
print("ERROR: This dataset is not compatible with your model")
return
train_model(model, history, args.dataset, args.epochs)