Will the deep learning toolbox train convert double-precision training data into single-precision during training in default?
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Hello,
I am using the MATLAB deep learning toolbox to build and train a neural network. I trained the network using the provided function 'trainNetwork' using my double-precision data. But the obtained trained weights are in single-precision. My question is that does MATLAB deep learning tool automatically convert the double-precision training data into single-precision first and then do the consequent training in single-precision?
If so, does the two following cases will produce the same results (ignoring the uncertainty brought by SGD)?
1. Training data in double-precision and single-precision network weights.
2. Training data in single-precision and single-precision network weights.
The MATLAB doc says that 'When you train a network using the trainNetwork function, or when you use prediction or validation functions with DAGNetwork and SeriesNetwork objects, the software performs these computations using single-precision, floating-point arithmetic.' I checked the trainNetwork function and found that the network weights were initialized in single-precision but did not see any command on changing the training data precision.
Thank you! Any feedback and suggestion will be much appreciated.
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