MNIST training time in CPU

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I have created a simple feed forward Neural Network library in Java - and I need a benchmark to compare and troubleshoot my library.

Computer specs:

  • AMD Ryzen 7 2700X Eight-Core Processor
  • RAM 16.0 GB
  • WINDOWS 10 OS
  • JVM args: -Xms1024m -Xmx8192m

Note that I am not using a GPU.

Please list the following specs:

  • Computer specs?
  • GPU or CPU (CPU is proffered but GPU is good info)
  • Number of inputs 784 (this is fixed)
  • For each layer:
    • How many nodes?
    • What activation function?
  • Output layer:
    • How many nodes? (10 if classification or 1 as regression)
    • What activation function?
  • What loss function?
  • What gradient descent algorithm (i.e.: vanilla)
  • What batch size?
  • How many epochs? (not iterations)
  • And finally, what is the training time and accuracy?

Thank you so much

Edit

Just to give an idea of what I am dealing with. I created a network with

  • 784 input nodes
  • 784 in hidden layer 0
  • 256 in hidden layer 1
  • 128 in hidden layer 2
  • 1 output nodes
  • mini-batch size 5
  • 16 threads for backprop And it has been training for ~8 hours and has only completed 694 iterations - that is not even 20% of one epoch.

How is this done in minutes as I've seen some claims?

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