Quession about loss calculation

[mrgloom]

Seem in the last layers you reshape all to [2,65536] (assuming this is row x col) and 256*256 is 65536, so it's just image with 2 'channels' (assuming background/foreground) reshaped to flat vector. So in general case it will be [n_classes, h*w]. And than softmax is putted on top. https://github.com/imlab-uiip/keras-segnet/blob/master/SegNet.py#L19 https://github.com/imlab-uiip/keras-segnet/blob/master/model_5l.json#L1266

Also here you use categorical_crossentropy loss. https://github.com/imlab-uiip/keras-segnet/blob/master/SegNet.py#L108

My confusion is how softmax is applied and how loss is computed, as I understand it should be like: compute softmax for each [2,i] (and not to [i,65536]) subvector separately of matrix and loss should be computed for each pixel, so as I understand Keras just do it all in some vectorized form? So what is the rules to pass matrix/tensor to softmax+categorical_crossentropy? what shape shoul it be? row represent dimensions and cols represent pixels? Documentation is not rich about this topics: https://keras.io/activations/#softmax https://keras.io/losses/#categorical_crossentropy

BTW what is profit of store model as .json ? I find this solution less readable.