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I am new to deep learning as well as using Theano and tensorflow. I was able to understand how the tensor flow example code of a convolution neural network to recognize MNIST benchmark works. But since all inbuilt libraries are used I was not able to get a full grip of it.

I would like to write a fast training algorithm for a convolution neural network that recognizes MNIST benchmark but without tensorflow or Theano. I have already coded the network and I can access the output of each layer as well as the weights and bias used in each layer. I need to use these outputs to update the weights and bias of each layer but I’m not sure how.

I would really appreciate if someone could guide me in this regard.

The network that I have used is the network specified in the tensorflow example. It has two convolution pool layers and two fully connected layers.

Thanks

Abhinav George
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  • Unfortunately SO is not a forum, all the details about your code and problem have to be in the question. You have to be very explicit about the problem. – Dr. Snoopy Dec 24 '17 at 16:48
  • You can be interested in this question - https://stackoverflow.com/q/34254679/712995 – Maxim Dec 25 '17 at 06:12
  • There are plenty of guides for setting up a neural network 'by hand' using `numpy`. Do a web search for "numpy" "neural network". – A T Aug 29 '19 at 04:19

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