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How can I calculate the time spent for overall training a model in Tensorflow (for all epochs)?
Show Estimated remaining time to train a model Tensorflow with large epochs
代码：

y = to_categorical(self.ydata, num_classes=self.vocab_size)
model = Sequential()
model.add(Embedding(self.vocab_size, 10, input_length=1))
model.add(LSTM(1000, return_sequences=True))
model.add(LSTM(1000))
model.add(Dense(1000, activation="relu"))
model.add(Dense(self.vocab_size, activation="softmax"))
keras.utils.plot_model(model, show_layer_names=True)
checkpoint = ModelCheckpoint(modelFilePath, monitor='loss', verbose=1,save_best_only=True, mode='auto')
reduce = ReduceLROnPlateau(monitor='loss', factor=0.2,patience=3, min_lr=0.0001, verbose=1)
tensorboard_Visualization = TensorBoard(log_dir=logdirPath)
model.compile(loss="categorical_crossentropy", optimizer=Adam(lr=0.001))
history = model.fit(self.Xdata, y, epochs=epochs, batch_size=64, callbacks=[checkpoint, reduce, tensorboard_Visualization]).history

灵感来源：

1. https://www.analyticsvidhya.com/blog/2021/08/predict-the-next-word-of-your-text-using-long-short-term-memory-lstm/
2. https://towardsdatascience.com/building-a-next-word-predictor-in-tensorflow-e7e681d4f03f
   这段代码需要一个单词“问题”和“答案”的列表来训练。如果你在阅读这段代码之前就猜到了模型的目标，那么背景知识会给你留下深刻的印象。无论如何，这段代码是有效的。我只想在这一点上增强它。
   如何在设定的时间内训练模型？一个时期所需的时间因我输入给此AI的文本而异。它变化很大，通常在10秒到4分钟左右。我可以使用它来根据时间估算时期，但如果存在其他方法，我希望从TensorFlow的资源中获得更具体的想法。