Python Keras，我如何在训练模型后进行预测？
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Keras, how do I predict after I trained a model?
提问by ben
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()?
我正在使用 reuters-example 数据集，它运行良好（我的模型经过训练）。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？我用models.predict()吗？
Do I have to prepare this text in a special way?
我必须以特殊的方式准备这篇课文吗？
I tried it with
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)
But I always get
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:\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?
您对如何使用训练有素的模型进行预测有什么建议吗？
回答by nemo
model.predict()expects the first parameter to be a numpy array. You supply a list, which does not have the shapeattribute a numpy array has.
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shapenumpy 数组所具有的属性。
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:
否则您的代码看起来不错，只是您没有对预测做任何事情。确保将其存储在变量中，例如：
prediction = model.predict(np.array(tk.texts_to_sequences(text)))
print(prediction)
回答by Alex Punnen
model.predict_classes(<numpy_array>)
Sample https://gist.github.com/alexcpn/0683bb940cae510cf84d5976c1652abd
示例https://gist.github.com/alexcpn/0683bb940cae510cf84d5976c1652abd
回答by Thomas Decaux
You must use the same Tokenizer you used to build your model!
您必须使用用于构建模型的相同 Tokenizer！
Else this will give different vector to each word.
否则，这将为每个单词提供不同的向量。
Then, I am using:
然后，我正在使用：
phrase = "not good"
tokens = myTokenizer.texts_to_matrix([phrase])
model.predict(np.array(tokens))
回答by tauseef_CuriousGuy
I trained a neural network in Keras to perform non linear regression on some data. This is some part of my code for testing on new data using previously saved model configuration and weights.
我在 Keras 中训练了一个神经网络来对一些数据执行非线性回归。这是我使用以前保存的模型配置和权重测试新数据的代码的一部分。
fname = r"C:\Users\tauseef\Desktop\keras\tutorials\BestWeights.hdf5"
modelConfig = joblib.load('modelConfig.pkl')
recreatedModel = Sequential.from_config(modelConfig)
recreatedModel.load_weights(fname)
unseenTestData = np.genfromtxt(r"C:\Users\tauseef\Desktop\keras\arrayOf100Rows257Columns.txt",delimiter=" ")
X_test = unseenTestData
standard_scalerX = StandardScaler()
standard_scalerX.fit(X_test)
X_test_std = standard_scalerX.transform(X_test)
X_test_std = X_test_std.astype('float32')
unseenData_predictions = recreatedModel.predict(X_test_std)
