This library allows you to read and write Tensor protobufs (specifically the tensors that are used by the Google TensorFlow library). The tensors are encoded/decoded from multidimensional JS arrays.
To install this:
$ npm install
If you want to rebuild the protobuf implementations, feel free to recompile them by running (but there should be no reason to do this):
$ ./compile.sh
make_tensor(array, type)
array
: A multidimensional array representing the tensor you want to encodetype
: One of lib.types.INT or lib.types.FLOAT representing which type of tensor it isRETURNS
: A string encoding the binary value of the tensor protobuf (NOTE: There is some nonsense about encoding the strings as UINT8s but JS using utf-16. I think I've solved all the bugs, although it is possible there are still more.)
const tensorjs = require('tensorjs');
tensorjs.make_tensor([[1, 2], [3, 4]], tensorjs.types.INT);
// returns '\b\u0003\u0012\b\u0012\u0002\b\u0002\u0012\u0002\b\u0002:\u0004\u0001\u0002\u0003\u0004'
We provide two convenience functions so that you don't have to pass in the type:
tensorjs.intTensor([[1, 2], [3, 4]]);
tensorjs.floatTensor([1.5, 2.5]);
make_array(tensor_pb)
tensor_pb
: A string encoding the tensor protobuf.RETURNS
: A multidimensional array representing the tensor.
const tensorjs = require('tensorjs');
tensorjs.make_array('\b\u0003\u0012\b\u0012\u0002\b\u0002\u0012\u0002\b\u0002:\u0004\u0001\u0002\u0003\u0004');
// returns [[1, 2], [3, 4]]
Copyright (c) 2017 Tomas Reimers
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