Keras, how do I predict after I trained a model?

Question

I'm playing with the reuters-example dataset and it runs fine (my model is trained). I read about how to save a model, so I could load it later to use again. But how do I use this saved model to predict a new text? Do I use models.predict()?

Do I have to prepare this text in a special way?

I tried it with

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) 

But I always get

(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' 

Do you have any recommendations as to how to make predictions with a trained model?

Answer 1

model.predict() expects the first parameter to be a numpy array. You supply a list, which does not have the shape attribute a numpy array has.

Otherwise your code looks fine, except that you are doing nothing with the prediction. Make sure you store it in a variable, for example like this:

prediction = model.predict(np.array(tk.texts_to_sequences(text))) print(prediction)