克拉斯，我训练了一个模特之后该怎么预测呢？

我正在使用路透社的示例数据集，它运行得很好(我的模型是经过训练的)。我了解了如何保存模型，以便以后可以加载它再次使用。但是我如何使用这个保存的模型来预测一个新的文本呢？我使用 models.predict()吗？

我需要以特殊的方式准备这篇课文吗？

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

(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'

关于如何用一个训练有素的模型做出预测，你有什么建议吗？

213928 次浏览
小开

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)
小开

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.

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)
小开 
model.predict_classes(<numpy_array>)

Sample https://gist.github.com/alexcpn/0683bb940cae510cf84d5976c1652abd

小开

You must use the same Tokenizer you used to build your model!

Else this will give different vector to each word.

Then, I am using:

phrase = "not good"
tokens = myTokenizer.texts_to_matrix([phrase])


model.predict(np.array(tokens))
小开

You can just "call" your model with an array of the correct shape:

model(np.array([[6.7, 3.3, 5.7, 2.5]]))

Full example:

from sklearn.datasets import load_iris
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
import numpy as np


X, y = load_iris(return_X_y=True)


model = Sequential([
Dense(16, activation='relu'),
Dense(32, activation='relu'),
Dense(1)])


model.compile(loss='mean_absolute_error', optimizer='adam')


history = model.fit(X, y, epochs=10, verbose=0)


print(model(np.array([[6.7, 3.3, 5.7, 2.5]])))

<tf.Tensor: shape=(1, 1), dtype=float64, numpy=array([[1.92517677]])>
小开

Your can use your tokenizer and pad sequencing for a new piece of text. This is followed by model prediction. This will return the prediction as a numpy array plus the label itself.

For example:

new_complaint = ['Your service is not good']
seq = tokenizer.texts_to_sequences(new_complaint)
padded = pad_sequences(seq, maxlen=maxlen)
pred = model.predict(padded)
print(pred, labels[np.argmax(pred)])

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