Amazing progress has been made in deep learning. I have been Tensorflow for a while now. I started out with tf0.6 then upgraded to tf0.12 then to tf1.0. The latest version is tf1.10 which is supposed to provide a stable API. I have a lot of code which has now become incompatible. The tf0.6’s saver API was totally different. tf.slim was added in tf0.12 (which I loved btw). But as of tf1.10 it is gone. It is recommended to use Keras instead. In this blog post, I am organizing some of my old examples and other useful code and making as much as possible tf1.10 compatible.

A while ago I had written a few articles on neural networks from first principles. All of them can be accessed here.

My tensorflow examples are available. They are compatible with tf1.10.

https://github.com/mpkuse/tensorflow-funicular

Here are the links to all my public code related to Neural Nets basics. I no more actively maintain it at this moment.

  1. ResNet – https://gist.github.com/mpkuse/6f9dcd419effa707422eb2c5097f51b4. Still useful in some of my projects. But I do not recommend using it anymore. Best is to move to Keras to build your networks.
  2. Toy Neural Net from first principles (in Matlab) to classify XOR: https://github.com/mpkuse/learning_xor. A useful resource to understanding the core.
  3. 3-Category classification from scratch: https://github.com/mpkuse/nn_classify.
  4. MNIST with caffe : https://github.com/mpkuse/mnist_in_memory_training. I no
    longer use caffe.
  5. CIFAR with Tensorflow: https://github.com/mpkuse/cifar_tf. This was working with tf0.06. Pretty sure it is incompatible with tf1.10.

 

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