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
