Theano differentiation through scan

code

import theano, theano.tensor as T

inpt = T.vector('inpt')
W = T.matrix('weights')
output = T.dot(inpt, W)

d_output_wrt_inpt, _ = theano.map(lambda i: T.grad(output[i], inpt),
                          T.arange(output.shape[0]))

f = theano.function([inpt, W], d_output_wrt_inpt)
print theano.pp(f.maker.env.outputs[0])

output

for{inplace,cpu,scan_fn}(Shape_i{1}(weights), 1[int64:int64:int8], Shape_i{1}(weights), weights.T, DimShuffle{1}(_dot22(inpt, weights)), alloc(TensorConstant{(1,) of 0.0}, Shape_i{1}(weights)))