Question

我正在使用路透社示例数据集，它运行良好（我的模型已经过训练）。我阅读了有关如何保存模型的信息，因此以后可以加载它以再次使用。但是，如何使用此保存的模型预测新文本？我是否使用models.predict() ？

我是否需要以特殊方式准备此文本？

我尝试过

import keras.preprocessing.text

text = np.array(['this is just some random, stupid text'])
print(text.shape)

tk = keras.preprocessing.text.Tokenizer(
        nb_words=2000,
        filters=keras.preprocessing.text.base_filter(),
        lower=True,
        split=" ")

tk.fit_on_texts(text)
pred = tk.texts_to_sequences(text)
print(pred)

model.predict(pred)

但是我总是

(1L,)
[[2, 4, 1, 6, 5, 7, 3]]
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-83-42d744d811fb> in <module>()
      7 print(pred)
      8 
----> 9 model.predict(pred)

C:\Users\bkey\Anaconda2\lib\site-packages\keras\models.pyc in predict(self, x, batch_size, verbose)
    457         if self.model is None:
    458             self.build()
--> 459         return self.model.predict(x, batch_size=batch_size, verbose=verbose)
    460 
    461     def predict_on_batch(self, x):

C:\Users\bkey\Anaconda2\lib\site-packages\keras\engine\training.pyc in predict(self, x, batch_size, verbose)
   1132         x = standardize_input_data(x, self.input_names,
   1133                                    self.internal_input_shapes,
-> 1134                                    check_batch_dim=False)
   1135         if self.stateful:
   1136             if x[0].shape[0] > batch_size and x[0].shape[0] % batch_size != 0:

C:\Users\bkey\Anaconda2\lib\site-packages\keras\engine\training.pyc in standardize_input_data(data, names, shapes, check_batch_dim, exception_prefix)
     79     for i in range(len(names)):
     80         array = arrays[i]
---> 81         if len(array.shape) == 1:
     82             array = np.expand_dims(array, 1)
     83             arrays[i] = array

AttributeError: 'list' object has no attribute 'shape'

关于如何使用经过训练的模型进行预测，您有任何建议吗？