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Tag: tensorflow

Ordering of batch normalization and dropout?

The original question was in regard to TensorFlow implementations specifically. However, the answers are for implementations in general. This general answer is also the correct answer for TensorFlow. When using batch normalization and dropout in TensorFlow (specifically using the contrib.layers) do I need to be worried about the ordering? It seems possible that if I use dropout followed immediately by

tensorflow.train.import_meta_graph does not work?

I try to simply save and restore a graph, but the simplest example does not work as expected (this is done using version 0.9.0 or 0.10.0 on Linux 64 without CUDA using python 2.7 or 3.5.2) First I save the graph like this: This creates a file “file” that is non-empty and also sets g1 to something that looks like

tflearn / tensorflow does not learn xor

Following code was written to learn the XOR function, but about half of the time the network does not learn and the loss after each epoch stays the same. Sometimes I get correct results like this: But often this: My 2x2x1 network should be able to perform XOR, and there is even some evidence that suggests that this network should

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