Equivalent for np.add.at in tensorflow


How do I convert a np.add.at statement into tensorflow?

np.add.at(dW, self.x.ravel(), dout.reshape(-1, self.D))


self.dW.shape is (V, D), self.D.shape is (N, D) and self.x.size is N


For np.add.at, you probably want to look at tf.SparseTensor, which represents a tensor by a list of values and a list of indices (which is more suitable for sparse data, hence the name).

So for your example:

np.add.at(dW, self.x.ravel(), dout.reshape(-1, self.D))

that would be (assuming dW, x and dout are tensors):

tf.sparse_add(dW, tf.SparseTensor(x, tf.reshape(dout, [-1])))

This is assuming x is of shape [n, nDims] (i.e. x is a ‘list’ of n indices, each of dimension nDims), and dout has shape [n].

Answered By – Lars Mennen

This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0

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