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I would like to train a caffe network with the python interface. The main reason behind this is I use multi dimentional input of a few Tbs of data and I dont want to convert all this to LMDB and train it.

I have found a this one answer on stack overflow.

But his loads this complete data at once and has initialized weights.

I would like to load data to a numpy and then pass it to the caffe.

And save the weights of the caffemodel to a .caffemodel file once every 1000 iterations.

the print_network() get_accuracy() & load_data() are very useful. And gives me a good inside.

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    have you considered using `type: "Python"` layer as your input layer? See [this example](http://stackoverflow.com/a/34996628/1714410) how to do this – Shai May 30 '16 at 10:24

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Beside using PythonLayer , one thing you can do is use MemoryData layer and feed in each batch of data at a time by using solver.net.set_input_arrays(your_data) after however many iteration is needed to go through one batch of data.

Remember, you can always restore the training state by using .solverstate file from your snapshots.

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