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# Load model from file
if learning_rate is None:
full_model = load_model(
model_file,
custom_objects={
"softplus2": softplus2,
"Set2Set": Set2Set,
"mean_squared_error_with_scale": mean_squared_error_with_scale,
"MEGNetLayer": MEGNetLayer,
},
)
learning_rate = K.get_value(full_model.optimizer.lr)
# Set up model
model = MEGNetModel(
100,
2,
nblocks=args.n_blocks,
nvocal=95,
npass=args.n_pass,
lr=learning_rate,
loss=args.loss,
graph_convertor=cg,
is_classification=True if args.type == "classification" else False,
nfeat_node=None if embedding_file is None else 16,
)
model.load_weights(model_file)
initial_epoch = int(model_list[-1].split("_")[2])
print(
"warm start from : {}, \nlearning_rate is {}.".format(
model_file, learning_rate
y_scaler = StandardScaler()
train_targets = y_scaler.fit_transform(
np.array(train_targets).reshape(-1, 1)
).ravel()
val_targets = y_scaler.transform(
np.array(val_targets).reshape((-1, 1))
).ravel()
else:
y_scaler = None
# Initialize model
if warm_start is None:
# Set up model
if learning_rate is None:
learning_rate = 1e-3
model = MEGNetModel(
100,
2,
nblocks=args.n_blocks,
nvocal=95,
npass=args.n_pass,
lr=learning_rate,
loss=args.loss,
graph_convertor=cg,
is_classification=True if args.type == "classification" else False,
nfeat_node=None if embedding_file is None else 16,
)
initial_epoch = 0
else:
# Model file
model_list = [