How to get accuracy of model using keras?

Question

After fitting the model (which was running for a couple of hours), I wanted to get the accuracy with the following code:

train_loss=hist.history['loss']
val_loss=hist.history['val_loss']
train_acc=hist.history['acc']
val_acc=hist.history['val_acc']
xc=range(nb_epoch)

of the trained model, but was getting an error, which is caused by the deprecated methods I was using.

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-233-081ed5e89aa4> in <module>()
      3 train_loss=hist.history['loss']
      4 val_loss=hist.history['val_loss']
----> 5 train_acc=hist.history['acc']
      6 val_acc=hist.history['val_acc']
      7 xc=range(nb_epoch)

KeyError: 'acc'

The code I used to fit the model before trying to read the accuracy, is the following:

hist = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,
            verbose=1, validation_data=(X_test, Y_test))


hist = model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch, 
            verbose=1, validation_split=0.2)

Which produces this output when running it:

Epoch 1/20
237/237 [==============================] - 104s 440ms/step - loss: 6.2802 - val_loss: 2.4209
    .....
    .....
    .....
Epoch 19/20
    189/189 [==============================] - 91s 480ms/step - loss: 0.0590 - val_loss: 0.2193
    Epoch 20/20
    189/189 [==============================] - 85s 451ms/step - loss: 0.0201 - val_loss: 0.2312

I've noticed that I was running deprecated methods & arguments.

So how can I read the accuracy and val_accuracy without having to fit again, and waiting for a couple of hours again? I tried to replace train_acc=hist.history['acc'] with train_acc=hist.history['accuracy'] but it didn't help.

Answer 1

You probably didn't add "acc" as a metric when compiling the model.

model.compile(optimizer=..., loss=..., metrics=['accuracy',...])

You can get the metrics and loss from any data without training again with:

model.evaluate(X, Y)

Answer 2

1. add a metrics = ['accuracy'] when you compile the model

2. simply get the accuracy of the last epoch . hist.history.get('acc')[-1]

3. what i would do actually is use a GridSearchCV and then get the best_score_ parameter to print the best metrics