output

hidden value: (2, 3, 3)
[[[  2.43871672e-316   4.07723682e-316   3.54132749e-312]
  [  1.62581115e-316   2.44505736e-316   2.36086774e-312]
  [  8.12905574e-317  -6.42364390e-319   1.18035621e-312]]

 [[  0.00000000e+000   8.19301551e-317   5.17748386e-317]
  [  8.12905574e-317   8.12877907e-317   1.18040798e-312]
  [  1.62581115e-316   2.44505736e-316   2.36086774e-312]]]
out value at step 0: (3, 3)
[[ 1.  1.  1.]
 [ 1.  1.  1.]
 [ 1.  1.  1.]]
Traceback (most recent call last):
  File "test.py", line 36, in <module>
    outputs_info=[hidden0])
  File "/u/lamblinp/code/Theano_work/theano/scan_module/scan.py", line 910, in scan
    scan_outs = local_op(* scan_inputs  )
  File "/u/lamblinp/code/Theano_work/theano/gof/op.py", line 389, in __call__
    node.op.perform(node, input_vals, output_storage)
  File "/u/lamblinp/code/Theano_work/theano/scan_module/scan_op.py", line 530, in perform
    outs[j][0][pos[j]] = output_storage[offset_out+j].storage[0]
ValueError: array dimensions are not compatible for copy

test.py

import numpy
import theano
from theano import tensor as T


theano.config.compute_test_value = 'warn'
insize = 2
hiddensize = 3
outsize = 2

inweights = T.shared(numpy.empty((2, 3)))
hidden0 = T.shared(numpy.empty(3))
X = numpy.array([
  [[2, 3], [1, 2], [-1, 1]],
  [[1, 0], [0, 1], [1, 2]],
])

inpts = T.tensor3('input-sequences')
inpts.tag.test_value = X

hidden = T.tensordot(inpts, inweights, axes=([2], [0]))
print 'hidden value:', hidden.tag.test_value.shape
print hidden.tag.test_value

# The function will be executed once, to build the inner
# graph of the scan node. We can use that
def fn(x, h_tm1):
    out = x + 1
    print 'out value at step 0:', out.tag.test_value.shape
    print out.tag.test_value
    return out

hidden_rec, _ = theano.scan(
        fn,
        sequences=hidden,
        outputs_info=[hidden0])

print 'hidden_rec value:', hidden_rec.tag.test_value.shape
print hidden_rec.tag.test_value

f = theano.function([inpts], hidden_rec)
print f(X)