Mnist siamese example returning wrong accuracy values

I am executing the mnist siamese example straight away from the keras examples from the version 1.2.0. I am getting an accuracy value of

* Accuracy on training set: 0.42%
* Accuracy on test set: 2.64%

instead of the correct values

Gets to 99.5% test accuracy after 20 epochs.

At first I thought at a wrong image dimension ordering and tryed different configuration for keras.json But the problem persist. After inspecting prediction against the true values I found the vector of prediction is shifted by exactly by one, so I checked for possible code typos with some switch between 1 and 0 in the loss function and in the pair construction, but everything seems ok. I am running the code in a mac book pro with python 2.7 and tensorflow 0.12.1 Anyone who experienced the same issue?

• Check that you are up-to-date with the master branch of Keras. You can update with: pip install git+git://github.com/fchollet/keras.git --upgrade --no-deps

• If running on TensorFlow, check that you are up-to-date with the latest version. The installation instructions can be found here.

• If running on Theano, check that you are up-to-date with the master branch of Theano. You can update with: pip install git+git://github.com/Theano/Theano.git --upgrade --no-deps

• [] Provide a link to a GitHub Gist of a Python script that can reproduce your issue (or just copy the script here if it is short).

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[oak-tree]

After digging, it looks like @Tokukawa is right. There is a switching problem. According to Dimensionality Reduction by Learning an Invariant Mapping Y=0 if the pair deemd simliar and Y=1 the pair deemed disimilar.

This is exactly what create_paris does. Moreover, the loss function is reverse indeed. The max between 0 and m-D_w should be the loss for dissimilar pairs and therefore the loss function is also reversed in the terms of y_true