我试图使用神经网络来预测3个类别中的一个。从错误信息来看，似乎我用来评估模型的变量是空的，但事实并非如此。

这是代码:

# 训练特征和标签
train_X = np.array(list(training[:, 0]))
train_y = np.array(list(training[:, 1]))
input_shape = (len(train_X[0]),)
output_shape = len(train_y[0])
epochs = 200
model = Sequential()
model.add(Dense(128, input_shape=input_shape, activation="relu"))
model.add(Dropout(0.6))
model.add(Dense(64, activation="relu"))
model.add(Dropout(0.6))
model.add(Dense(output_shape, activation = "softmax"))
model.compile(loss='categorical_crossentropy',              optimizer='adam',              metrics=["accuracy"])
model.summary()

然而，当我在训练数据上执行model.evaluate(train_X)方法时，它抛出了以下错误信息。

AttributeError                            Traceback (most recent call last)
<ipython-input-33-65f91bca3821> in <module>()
----> 1 model.evaluate(tf.convert_to_tensor(train_X, dtype=tf.int64))
9 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    975           except Exception as e:  # pylint:disable=broad-except
    976             if hasattr(e, "ag_error_metadata"):
--> 977               raise e.ag_error_metadata.to_exception(e)
    978             else:
    979               raise
AttributeError: in user code:
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1233 test_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1224 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1259 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2730 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3417 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1217 run_step  **
        outputs = model.test_step(data)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1188 test_step
        self.compiled_metrics.update_state(y, y_pred, sample_weight)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/compile_utils.py:387 update_state
        self.build(y_pred, y_true)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/compile_utils.py:318 build
        self._metrics, y_true, y_pred)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py:1163 map_structure_up_to
        **kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py:1258 map_structure_with_tuple_paths_up_to
        func(*args, **kwargs) for args in zip(flat_path_gen, *flat_value_gen)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py:1258 <listcomp>
        func(*args, **kwargs) for args in zip(flat_path_gen, *flat_value_gen)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/nest.py:1161 <lambda>
        lambda _, *values: func(*values),  # Discards the path arg.
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/compile_utils.py:418 _get_metric_objects
        return [self._get_metric_object(m, y_t, y_p) for m in metrics]
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/compile_utils.py:418 <listcomp>
        return [self._get_metric_object(m, y_t, y_p) for m in metrics]
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/compile_utils.py:439 _get_metric_object
        y_t_rank = len(y_t.shape.as_list())
    AttributeError: 'NoneType' object has no attribute 'shape'

回答：

model.evaluate()方法还需要标签。尝试去掉convert_to_tensor()部分，直接使用model.evaluate(train_X, train_Y)。

请确保在调用evaluate之前已经拟合了模型（你在这里没有展示这一步）。