train_instances = index_instances(train_instances, vocab_token_to_id)
val_instances = index_instances(val_instances, vocab_token_to_id)
### TODO(Students) START
# make a config file here as expected by your MyAdvancedModel
config = {'vocab_size': vocab_size, 'embed_dim': args.embed_dim, 'training': True}
### TODO(Students END
model = MyAdvancedModel(**config)
config['type'] = 'advanced'
optimizer = optimizers.Adam()
embeddings = load_glove_embeddings(args.embed_file, args.embed_dim, vocab_id_to_token)
model.embeddings.assign(tf.convert_to_tensor(embeddings))
save_serialization_dir = os.path.join('serialization_dirs', 'advanced_4')
if not os.path.exists(save_serialization_dir):
os.makedirs(save_serialization_dir)
train_output = train(model, optimizer, train_instances, val_instances,
args.epochs, args.batch_size, save_serialization_dir)
config_path = os.path.join(save_serialization_dir, "config.json")
with open(config_path, 'w', encoding='UTF-8') as f:
json.dump(config, f)
vocab_path = os.path.join(save_serialization_dir, "vocab.txt")
save_vocabulary(vocab_id_to_token, vocab_path)
print(f"\nModel stored in directory: {save_serialization_dir}")
#!/usr/bin/env python
from train_lib import train
if __name__ == '__main__':
train()
