问题描述
我正在使用 reuters-example 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,以便稍后加载以再次使用.但是我如何使用这个保存的模型来预测新文本?我使用 models.predict()
吗?
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()
?
我必须以特殊的方式准备这篇文章吗?
Do I have to prepare this text in a special way?
我试过了
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)
但我总是得到
(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:UserskeyAnaconda2libsite-packageskerasmodels.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:UserskeyAnaconda2libsite-packageskerasengine raining.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:UserskeyAnaconda2libsite-packageskerasengine raining.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'
对于如何使用经过训练的模型进行预测,您有什么建议吗?
Do you have any recommendations as to how to make predictions with a trained model?
推荐答案
model.predict()
期望第一个参数是一个 numpy 数组.您提供一个列表,该列表不具有 numpy 数组所具有的 shape
属性.
否则你的代码看起来不错,只是你没有对预测做任何事情.确保将其存储在变量中,例如:
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)
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