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 monitoring and checkpointing


Monitoring and Checkpointing in TensorFlow

@machinelearnbot

In our last post we gave a basic introduction to TensorFlow 1.0. What we want to do now is take our foundation and move it forward. One of the most important parts of deep learning is understanding what is going on while the code is running. As our problems get more complicated and our datasets get larger, training time can go from minutes to days. If we've picked a model with poor hyper-parameters or just a bad model in general, we don't want to have to wait hours to make an adjustment to our model.