test_recurrent_stress_tests.py 文件源码

def test_rnn_layer(self):
        i = 0
        numerical_err_models = []
        shape_err_models = []
        numerical_failiure = 0
        for base_params in self.base_layer_params:
            base_params = dict(zip(self.params_dict.keys(), base_params))
            for rnn_params in self.rnn_layer_params:
                rnn_params = dict(zip(self.simple_rnn_params_dict.keys(), rnn_params))
                model = Sequential()
                model.add(
                    SimpleRNN(
                        base_params['output_dim'],
                        input_length=base_params['input_dims'][1],
                        input_dim=base_params['input_dims'][2],
                        activation=base_params['activation'],
                        return_sequences=base_params['return_sequences'],
                        go_backwards=base_params['go_backwards'],
                        unroll=base_params['unroll'],
                    )
                )
                mlkitmodel = get_mlkit_model_from_path(model)
                input_data = generate_input(base_params['input_dims'][0], base_params['input_dims'][1],
                                            base_params['input_dims'][2])
                keras_preds = model.predict(input_data).flatten()
                if K.tensorflow_backend._SESSION:
                    import tensorflow as tf
                    tf.reset_default_graph()
                    K.tensorflow_backend._SESSION.close()
                    K.tensorflow_backend._SESSION = None
                input_data = np.transpose(input_data, [1, 0, 2])
                coreml_preds = mlkitmodel.predict({'data': input_data})['output'].flatten()
                try:
                    self.assertEquals(coreml_preds.shape, keras_preds.shape)
                except AssertionError:
                    print("Shape error:\nbase_params: {}\nkeras_preds.shape: {}\ncoreml_preds.shape: {}".format(
                        base_params, keras_preds.shape, coreml_preds.shape))
                    shape_err_models.append(base_params)
                    i += 1
                    continue
                try:
                    max_denominator = np.maximum(np.maximum(np.abs(coreml_preds), np.abs(keras_preds)), 1.0)
                    relative_error = coreml_preds / max_denominator - keras_preds / max_denominator
                    for i in range(len(relative_error)):
                        self.assertLessEqual(relative_error[i], 0.01)
                except AssertionError:
                    print("Assertion error:\nbase_params: {}\nkeras_preds: {}\ncoreml_preds: {}".format(base_params,
                                                                                                        keras_preds,
                                                                                                        coreml_preds))
                    numerical_failiure += 1
                    numerical_err_models.append(base_params)
                i += 1

        self.assertEquals(shape_err_models, [], msg='Shape error models {}'.format(shape_err_models))
        self.assertEquals(numerical_err_models, [], msg='Numerical error models {}\n'
                                                        'Total numerical failiures: {}/{}\n'.format(
            numerical_err_models,
            numerical_failiure, i)
                          )