def testMLPModel(self):
m = models.build_mlp_model(config)
m.summary()
'trainable': True,
}
model_config = {
'vocab_size': 6000,
'embedding_size': 200,
'vec_dim': 200,
'weights': np.zeros((6000, 200))
}
runner_config = {'ckpt_period': 1, 'epochs': 2, 'model_dir': 'tmp/dssm'}
config.update(dataset_config)
config.update(model_config)
config.update(runner_config)
args, _ = parser.parse_known_args()
get_embedding_weight_from_pre_trained()
if 'mlp' == args.model:
model = models.build_mlp_model(config)
elif 'lstm' == args.model:
model = models.build_lstm_model(config)
else:
raise ValueError('Invalid model: %s' % args.model)
if 'train' == args.action:
train(model, config)
elif 'eval' == args.action:
evaluate(model, config)
elif 'predict' == args.action:
predict(model, config)
elif 'export' == args.action:
export(model, config)
else:
raise ValueError('Invalid action: %s' % args.action)