How to see the loss of the best epoch from early stopping in Keras?

I have managed to implement early stopping into my Keras model, but I am not sure how I can view the loss of the best epoch.

es = EarlyStopping(monitor='val_out_soft_loss', 
            mode='min',
            restore_best_weights=True, 
            verbose=2, 
            patience=10)

model.fit(tr_x,
          tr_y,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          callbacks=[es],
          validation_data=(val_x, val_y))
loss = model.history.history["val_out_soft_loss"][-1]
return model, loss

The way I have defined the loss score, means that the returned score comes from the final epoch, not the best epoch.

Example:

from sklearn.model_selection import train_test_split, KFold
losses = []
models = []
for k in range(2):
    kfold = KFold(5, random_state = 42 + k, shuffle = True)
    for k_fold, (tr_inds, val_inds) in enumerate(kfold.split(train_y)):
        print("-----------")
        print("-----------")
        model, loss = get_model(64, 100)
        models.append(model)
        print(k_fold, loss)
        losses.append(loss)
print("-------")
print(losses)
print(np.mean(losses))

Epoch 23/100
18536/18536 [==============================] - 7s 362us/step - loss: 0.0116 - out_soft_loss: 0.0112 - out_reg_loss: 0.0393 - val_loss: 0.0131 - val_out_soft_loss: 0.0127 - val_out_reg_loss: 0.0381

Epoch 24/100
18536/18536 [==============================] - 7s 356us/step - loss: 0.0116 - out_soft_loss: 0.0112 - out_reg_loss: 0.0388 - val_loss: 0.0132 - val_out_soft_loss: 0.0127 - val_out_reg_loss: 0.0403

Restoring model weights from the end of the best epoch
Epoch 00024: early stopping
0 0.012735568918287754

So in this example, I would like to see the loss at Epoch 00014 (which is 0.0124).

I also have a separate question: How can I set the decimal places for the val_out_soft_loss score?

Answer

Assign the fit() call in Keras to a variable so you can track the metrics through the epochs.

history = model.fit(tr_x, ...

It will return a dictionary, access it like this:

loss_hist = history.history['loss']

And then get the min() to get the minimum loss, and argmin() to get the best epoch (zero-based).

np.min(loss_hist)
np.argmin(loss_hist)