问题
I have trained a model to predict topic categories using word2vec and an lstm model using keras and got about 98% accuracy during training, I saved the model then loaded it into another file for trying on the test set, I used model.evaluate and model.predict and the results were very different.
I'm using keras with tensorflow as a backend, the model summary is:
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_1 (LSTM) (None, 22) 19624
_________________________________________________________________
dropout_1 (Dropout) (None, 22) 0
_________________________________________________________________
dense_1 (Dense) (None, 40) 920
_________________________________________________________________
activation_1 (Activation) (None, 40) 0
=================================================================
Total params: 20,544
Trainable params: 20,544
Non-trainable params: 0
_________________________________________________________________
None
The code:
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.load_weights(os.path.join('model', 'lstm_model_weights.hdf5'))
score, acc = model.evaluate(x_test, y_test, batch_size=batch_size)
print()
print('Score: %1.4f' % score)
print('Evaluation Accuracy: %1.2f%%' % (acc*100))
predicted = model.predict(x_test, batch_size=batch_size)
acc2 = np.count_nonzero(predicted.argmax(1) == y_test.argmax(1))/y_test.shape[0]
print('Prediction Accuracy: %1.2f%%' % (acc2*100))
The output of this code is
39680/40171 [============================>.] - ETA: 0s
Score: 0.1192
Evaluation Accuracy: 97.50%
Prediction Accuracy: 9.03%
Can anyone tell me what did I miss?
来源：https://stackoverflow.com/questions/57212021/getting-different-results-from-keras-model-evaluate-and-model-predict