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)