在特征向量中应用 PCA 后，我们在数组中得到值错误我该如何解决这个问题？

如何解决在特征向量中应用 PCA 后，我们在数组中得到值错误我该如何解决这个问题？

我们正在尝试在 keras 中为图像标题构建模型。首先我们使用 inception resnet v2 提取图像特征，然后我们对训练、测试图像应用 PCA(n components = 5)。我们可以在训练后保存模型，但是当我们评估（bleu score）我们的模型时，我们得到了这个错误。我们如何解决这个错误？提前致谢。图片链接：1

值错误：在用户代码中：

/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1478 predict_function  *
    return step_function(self,iterator)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1468 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:1461 run_step  **
    outputs = model.predict_step(data)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:1434 predict_step
    return self(x,training=False)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:1012 _call_
    outputs = call_fn(inputs,*args,**kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py:425 call
    inputs,training=training,mask=mask)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/functional.py:560 _run_internal_graph
    outputs = node.layer(*args,**kwargs)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/base_layer.py:998 _call_
    input_spec.assert_input_compatibility(self.input_spec,inputs,self.name)
/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/input_spec.py:259 assert_input_compatibility
    ' but received input with shape ' + display_shape(x.shape))

ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 5 but received input with shape (None,1)