model.compile(loss='mean_squared_error',
              optimizer='sgd',
              metrics=['mae', ignore_class_accuracy(0)])
model.evaluate(test_sentences_X, to_categorical(test_tags_y, len(tag2index)))


680/680 [==============================] - 10s 14ms/step
[0.0009350364973001621, 0.0011567071230862947, 0.9160830103105271]


# summarize history for accuracy
plt.plot(history.history['ignore_accuracy'])
plt.plot(history.history['val_ignore_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
plt.show()