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Allow users to provide different dtypes in binary arithmetic ops (add/sub/mul/div/...) and matmul, just like in numpy.
The dtype of the result is upcasted i.e. matMul(float32, int32) => float32
This will result in release patch 0.14.1, which will fix the breakage in 0.14.0 caused by #1408 due to improved dtype inference where tensor(new Int32Array()) is inferred to be int32, and was float32.
Fixestensorflow/tfjs#934, tensorflow/tfjs#966
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TensorFlow.js version
0.13.3
Describe the problem or feature request
Results in:
Error: The dtypes of the first(int32) and second(float32) input must match
This does work:
const mse = tf.losses.meanSquaredError(tensor1.toFloat(), tensor2.toFloat());
Code to reproduce the bug / link to feature request
http://jsfiddle.net/5c94d3tf/
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