def generate_tf_history(model, hyperparameters, accuracy, loss, val_accuracy, val_loss, val_precision, val_recall): H = History() H.set_model(model) H.set_params({ 'batch_size': hyperparameters.batch_size, 'epochs': hyperparameters.epochs, 'metrics': ['loss', 'accuracy', 'val_loss', 'val_accuracy', 'val_precision', 'val_recall'] }) history = {'loss': [], 'accuracy': [], 'val_loss': [], 'val_accuracy': [],'val_precision': [], 'val_recall': []} history['loss'] = loss history['accuracy'] = accuracy history['val_loss'] = val_loss history['val_accuracy'] = val_accuracy history['val_precision'] = val_precision history['val_recall'] = val_recall H.history = history return H
def __getstate__(self): state = self.__dict__.copy() if hasattr(self, "model") and self.model is not None: buf = io.BytesIO() with h5py.File(buf, compression="lzf", mode="w") as h5: save_model(self.model, h5, overwrite=True, save_format="h5") buf.seek(0) state["model"] = buf if hasattr(self.model, "history"): from tensorflow.python.keras.callbacks import History history = History() history.history = self.model.history.history history.params = self.model.history.params history.epoch = self.model.history.epoch state["history"] = history return state