I'm exploring how an LSTM solves the problem of vanishing gradients. I have created a simple LSTM model on keras. I know that model.fit() returns a history object that stores model loss and accuracy which can then be plotted with respect to the epochs. Is there a similar way to plot gradient descent. Does History store the value of the gradients after every epoch?
closed as off-topic by Sycorax, kjetil b halvorsen, Michael Chernick, mdewey, Ben Mar 14 at 5:40
This question appears to be off-topic. The users who voted to close gave this specific reason:
- "This question appears to be off-topic because EITHER it is not about statistics, machine learning, data analysis, data mining, or data visualization, OR it focuses on programming, debugging, or performing routine operations within a statistical computing platform. If the latter, you could try the support links we maintain." – Sycorax, kjetil b halvorsen, Michael Chernick, Ben