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How is TensorFlow packaged keras different from vanilla keras itself?
To be more specific, the inference results from the session in which the model was built is much better compared to results from a different session using the same model. How is tensorflow packaged keras different from vanilla keras itself ?
How does training and evaluation work in keras?
In general, whether you are using built-in loops or writing your own, model training & evaluation works strictly in the same way across every kind of Keras model — Sequential models, models built with the Functional API, and models written from scratch via model subclassing.
How to use TensorFlow for training and evaluation?
Connect with the community at the TensorFlow Forum Visit Forum This guide covers training, evaluation, and prediction (inference) models when using built-in APIs for training & validation (such as Model.fit () , Model.evaluate () and Model.predict () ).
Why does keras implicitly run tf.global variables initializer?
It turns out that Keras implicitly runs tf.global_variables_initializer if you don’t tell it that you will do so manually. This means that in trying to save my model, it was first re-initializing all of the weights. The flag to prevent Keras from doing this is _MANUAL_VAR_INIT in the tensorflow backend.
Is there an issue with model save and load in keras?
I do not see any issue with model serialization using the save_model () and load_model () functions from the latest Tensorflow packaged Keras. For example:
How to fix model.save and load giving different result?
When you load the keras model, it might reinitialize the weights. I avoided tf.global_variables_initializer () and used load_weights (‘saved_model.h5’). Then model got the saved weights and I was able to reproduce correct results. I did not have to do the _manual_var_init step. (its a very good answer for just Keras)