Hi, I am having trouble getting the resnet to work. Do you mind helping?

[yxchng]

This is the error I get when I try to use the resnet implemented by you.

Traceback (most recent call last): File "resnet.py", line 297, in main() File "resnet.py", line 229, in main history = model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=(X_test, y_test), callbacks=[lrate, early_stop, hist, checkpoint]) File "/usr/local/lib/python2.7/site-packages/keras/engine/training.py", line 1082, in fit callback_metrics=callback_metrics) File "/usr/local/lib/python2.7/site-packages/keras/engine/training.py", line 801, in _fit_loop outs = f(ins_batch) File "/usr/local/lib/python2.7/site-packages/keras/backend/theano_backend.py", line 531, in call return self.function(*inputs) File "/usr/local/lib/python2.7/site-packages/theano/compile/function_module.py", line 875, in call storage_map=getattr(self.fn, 'storage_map', None)) File "/usr/local/lib/python2.7/site-packages/theano/gof/link.py", line 325, in raise_with_op reraise(exc_type, exc_value, exc_trace) File "/usr/local/lib/python2.7/site-packages/theano/compile/function_module.py", line 862, in call self.fn() if output_subset is None else\ File "/usr/local/lib/python2.7/site-packages/theano/gof/op.py", line 908, in rval r = p(n, [x[0] for x in i], o) File "/usr/local/lib/python2.7/site-packages/theano/tensor/signal/pool.py", line 849, in perform raise NotImplementedError()

NotImplementedError: Apply node that caused the error: AveragePoolGrad{ds=(7, 7), ignore_border=True, st=(1, 1), padding=(5, 5), mode='average_exc_pad'}(Elemwise{add,no_inplace}.0, IncSubtensor{InplaceInc;::, ::, :int64:, :int64:}.0) Toposort index: 1209 Inputs types: [TensorType(float32, 4D), TensorType(float32, 4D)] Inputs shapes: [(192, 8, 4, 4), (192, 8, 8, 8)] Inputs strides: [(512, 64, 16, 4), (2048, 256, 32, 4)] Inputs values: ['not shown', 'not shown'] Outputs clients: [[Sum{axis=[0, 2, 3], acc_dtype=float64}(AveragePoolGrad{ds=(7, 7), ignore_border=True, st=(1, 1), padding=(5, 5), mode='average_exc_pad'}.0), CorrMM_gradWeights{half, (1, 1)}(Elemwise{Composite{(i0 * (Abs(i1) + i2 + i3))}}[(0, 2)].0, AveragePoolGrad{ds=(7, 7), ignore_border=True, st=(1, 1), padding=(5, 5), mode='average_exc_pad'}.0, Subtensor{int64}.0, Subtensor{int64}.0), CorrMM_gradInputs{half, (1, 1)}(Subtensor{::, ::, ::int64, ::int64}.0, AveragePoolGrad{ds=(7, 7), ignore_border=True, st=(1, 1), padding=(5, 5), mode='average_exc_pad'}.0), Elemwise{add,no_inplace}(Elemwise{Composite{Switch(i0, i1, (i2 / i3))}}.0, Elemwise{Composite{((i0 * i1 * i2) / i3)}}.0, Elemwise{Composite{(((i0 / i1) / i2) / i3)}}.0, AveragePoolGrad{ds=(7, 7), ignore_border=True, st=(1, 1), padding=(5, 5), mode='average_exc_pad'}.0, Elemwise{Switch}[(0, 1)].0), Elemwise{add,no_inplace}(Elemwise{Composite{Switch(i0, i1, (i2 / i3))}}.0, Elemwise{Composite{((i0 * i1 * i2) / i3)}}.0, Elemwise{Composite{(((i0 / i1) / i2) / i3)}}.0, Elemwise{switch,no_inplace}.0, Elemwise{Composite{Switch(i0, i1, (i2 / i3))}}.0, Elemwise{Composite{((i0 * i1 * i2) / i3)}}.0, Elemwise{Composite{(((i0 / i1) / i2) / i3)}}.0, AveragePoolGrad{ds=(7, 7), ignore_border=True, st=(1, 1), padding=(5, 5), mode='average_exc_pad'}.0, Elemwise{Switch}[(0, 1)].0), Elemwise{Composite{(Switch(i0, i1, (i2 / i3)) + ((i4 * i5 * i6) / i7) + i8 + Switch(i0, i9, i1) + i10 + i11 + i12 + i13 + i14 + i15 + i16 + i17 + i18)}}[(0, 2)](InplaceDimShuffle{x,x,x,x}.0, TensorConstant{%281, 1, 1, 1%29 of 0}, Elemwise{mul}.0, Elemwise{add,no_inplace}.0, Elemwise{Composite{AND%28GE%28i0, i1%29, LE%28i0, i2%29%29}}.0, InplaceDimShuffle{x,0,x,x}.0, Elemwise{sub,no_inplace}.0, Elemwise{mul,no_inplace}.0, Elemwise{Composite{%28%28%28i0 / i1%29 / i2%29 / i3%29}}.0, Elemwise{true_div}.0, Elemwise{Composite{Switch%28i0, i1, %28i2 / i3%29%29}}.0, Elemwise{Composite{%28%28i0 i1 i2%29 / i3%29}}.0, Elemwise{Composite{%28%28%28i0 / i1%29 / i2%29 / i3%29}}.0, Elemwise{switch,no_inplace}.0, Elemwise{Composite{Switch%28i0, i1, %28i2 / i3%29%29}}.0, Elemwise{Composite{%28%28i0 i1 i2%29 / i3%29}}.0, Elemwise{Composite{%28%28%28i0 / i1%29 / i2%29 / i3%29}}.0, AveragePoolGrad{ds=%287, 7%29, ignore_border=True, st=%281, 1%29, padding=%285, 5%29, mode='average_exc_pad'}.0, Elemwise{Switch}[%280, 1%29].0)]]

Backtrace when the node is created(use Theano flag traceback.limit=N to make it longer): File "/usr/local/lib/python2.7/site-packages/theano/gradient.py", line 1267, in access_grad_cache term = access_term_cache(node)[idx] File "/usr/local/lib/python2.7/site-packages/theano/gradient.py", line 961, in access_term_cache output_grads = [access_grad_cache(var) for var in node.outputs] File "/usr/local/lib/python2.7/site-packages/theano/gradient.py", line 1267, in access_grad_cache term = access_term_cache(node)[idx] File "/usr/local/lib/python2.7/site-packages/theano/gradient.py", line 961, in access_term_cache output_grads = [access_grad_cache(var) for var in node.outputs] File "/usr/local/lib/python2.7/site-packages/theano/gradient.py", line 1267, in access_grad_cache term = access_term_cache(node)[idx] File "/usr/local/lib/python2.7/site-packages/theano/gradient.py", line 961, in access_term_cache output_grads = [access_grad_cache(var) for var in node.outputs] File "/usr/local/lib/python2.7/site-packages/theano/gradient.py", line 1267, in access_grad_cache term = access_term_cache(node)[idx] File "/usr/local/lib/python2.7/site-packages/theano/gradient.py", line 1101, in access_term_cache input_grads = node.op.grad(inputs, new_output_grads)

[yxchng]

I found out that it has to do with the average pooling layer. Apparently, "same" is only supported in Tensorflow. Do you not face the problem in Theano?

[raghakot]

Which version of theano are you using? I dont seem to encounter the same issue