Tensorflow Keras model: how to get the best score from a history object

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I'm trying to train multiple machine learning models using tensorflow keras, I was just wondering is there a way to obtain the best score achieved while training after training is complete. I found online that the .fit function returns a history object which can be accessed to get the best score, though from code i've tried it says "AttributeError: 'History' object has no attribute 'best_score'", I cannot find an attribute list online so this is why I am asking here.

Thanks in advance. 

History = model.fit(Xtrain, ytrain, epochs=1, validation_data=(Xtest, ytest), verbose=1)

print("Best: %f using %s" % (History.best_score, History.best_params_))

PS, I know training for 1 epoch will achieve nothing, I'm just trying to test the code

keras tensorflow python
2 Answers

I'm assuming you just want the best score from the history object.

hist = model.fit(...)

print(hist.history) # this will print a dictionary object, now you need to grab the metrics / score you're looking for

# if your score == 'acc', if not replace 'acc' with your metric

best_score = max(hist.history['acc'])

print(best_score)

If your metric is 'accuracy', use that instead.

If you want the best model, you can just use ModelCheckPoint https://keras.io/callbacks/

user_46220460

The fit() method on keras return a history object. The history.history attribute is a dictionary recording training loss values and metrics values at successive epochs, as well as validation loss values and validation metrics values (if applicable). You can call "history.history['loss']" or "history.history['val_loss']" to access it. Here is an example of it.

model.compile(loss = 'mean_squared_error', optimizer =optimizer,metrics=['accuracy'])
History = model.fit(Xtrain, ytrain, epochs=1, validation_data=(Xtest, ytest), verbose=1)
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])

For the best params, I think using callbacks.ModelCheckpoint might be helpful. This method has an argument 'save_best_only' to the latest best model.

keras.callbacks.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', period=1)

Kindly refer to the Training history visualization and callback for more information.

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