Callback where logs are recorded

Callback Metrics Spect.py

# start an Azure ML run
run = Run.get_context()

class LogRunMetrics(Callback):
    # callback at the end of every epoch
    def on_epoch_end(self, epoch, log):
        # log a value repeated which creates a list
        run.log('Loss', log['loss'])
        run.log('Accuracy', log['acc'])
        run.log('Val_Loss', log['val_loss'])
        run.log('Val_Accuracy', log['val_acc'])
        # Reduce LR
        ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, verbose=0, cooldown=2, min_lr=0.0001)
        run.log('LearningRate',self.model.optimizer.lr)