def test(config_file,
meta_data_file,
id_map,
dataToken,
batch_data_dir,
max_doc_length=30,
model_name=None,
restore_path=None,
no_doc_index=False):
np.random.seed(RANDOM_SEED_NP)
data = DataDUELoader(meta_data_file=meta_data_file,
batch_data_dir=batch_data_dir,
id_map=id_map,
dataToken=dataToken,
max_doc_length=max_doc_length,
no_doc_index=no_doc_index)
model_spec = json_reader(config_file)
model = FM(feature_shape=(0 if no_doc_index else data.D) + data.U +
data.V + 1,
feature_dim=(0 if no_doc_index else 1) + 1 + max_doc_length,
label_dim=data.E,
model_spec=model_spec,
model_name=model_name)
model.initialization()
def performance(model_local, data_local):
preds = model_local.predict(data_generator=data_local)
labels = []
for data_batched in data_local.generate(
batch_size=model_spec["batch_size"], random_shuffle=False):
labels.append(data_batched["label"])
labels = np.concatenate(labels, axis=0)
# one-hot to index #
trues = np.argmax(labels, axis=-1)
perf = evaluate(preds=preds, trues=trues)
return perf
if restore_path is not None:
if not isinstance(restore_path, list):
restore_paths = [restore_path]
else:
restore_paths = restore_path
for restore_path in restore_paths:
model.restore(restore_path)
perf = performance(model_local=model, data_local=data)
print("ckpt_path: %s" % restore_path)
print("performance: %s" % str(perf))
else:
perf = performance(model_local=model, data_local=data)
print("random initialization")
print("performance: %s" % str(perf))
def train(config_file,
meta_data_file,
id_map,
dataToken,
batch_data_dir_train,
batch_data_dir_valid=None,
max_doc_length=30,
model_name=None,
restore_path=None,
no_doc_index=False):
np.random.seed(RANDOM_SEED_NP)
data_train = DataDUELoader(meta_data_file=meta_data_file,
batch_data_dir=batch_data_dir_train,
id_map=id_map,
dataToken=dataToken,
max_doc_length=max_doc_length,
no_doc_index=no_doc_index)
if batch_data_dir_valid is not None:
data_valid = DataDUELoader(meta_data_file=meta_data_file,
batch_data_dir=batch_data_dir_valid,
id_map=id_map,
dataToken=dataToken,
max_doc_length=max_doc_length,
no_doc_index=no_doc_index)
else:
data_valid = None
model_spec = json_reader(config_file)
model = FM(feature_shape=(0 if no_doc_index else data_train.D) +
data_train.U + data_train.V + 1,
feature_dim=(0 if no_doc_index else 1) + 1 + max_doc_length,
label_dim=data_train.E,
model_spec=model_spec,
model_name=model_name)
model.initialization()
if restore_path is not None:
model.restore(restore_path)
# train #
results = model.train(data_generator=data_train,
data_generator_valid=data_valid)
print("train_results: %s" % str(results))
best_epoch = read(directory="../summary/" + model.model_name,
main_indicator="epoch_losses_valid_00")[0]
print("best_epoch by validation loss: %d" % best_epoch)
