prmodel.py 文件源码

def fit_model(self, logging_uuid, model=None, epochs=1000, batch_size=10):
        if model is not None:
            self.model = model
        X, y, _ = self.get_formulation_training_data()
        scaler = StandardScaler().fit(X)
        lcb = LambdaCallback(
            on_epoch_end=
            lambda epoch, logs:
            r.set(logging_uuid, json.dumps({'model_state': 'training',
                                            'epoch': epoch,
                                            'epochs': epochs,
                                            'loss': logs['loss']})),
            on_train_end=
            lambda logs:
            r.set(logging_uuid, json.dumps({'model_state': 'training',
                                            'epoch': epochs,
                                            'epochs': epochs})),
        )
        self.fit_history = self.model.fit(scaler.transform(X), y,
                                          epochs=epochs,
                                          batch_size=batch_size,
                                          verbose=0,
                                          callbacks=[lcb])
        return self.model, self.fit_history