Feywell opened a new issue #11150: Hope for adding sample about mx.rnn.ConvGRUCell() URL: https://github.com/apache/incubator-mxnet/issues/11150 I need to use `ConvGRU `(symbol interface) to complete a task. But I just learn to use `contrib.rnn.Conv2DGRUCell` . How can I use the interface `mx.rnn.ConvGRUCell()`([code](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/rnn/rnn_cell.py))? ** my code detail：** ```python def data_process(batch_size): train_lst = ‘/home/feywell/demo/train.lst’ val_lst = ‘/home/feywell/demo/val.lst’ train_data = ImageSeqIter( path_imglist=train_lst, data_shape=(3,512,512), label_shape=(3,512,512), resize=512, mean=np.array([[0.4914],[0.4822],[0.4465]]), std=np.array([[0.2023],[0.1994],[0.2010]]), data_name=‘data’, label_name=‘label’, batch_size=batch_size, rand_crop = False, rand_mirror = False, ) valid_data = ImageSeqIter( path_imglist=val_lst, data_shape=(3,512,512), label_shape=(3,512,512), resize=512, mean=np.array([[0.4914],[0.4822],[0.4465]]), std=np.array([[0.2023],[0.1994],[0.2010]]), data_name='data', label_name='label', batch_size=batch_size, rand_crop = False, rand_mirror = False ) return train_data,valid_data train_loader,val_loader = data_process(4) **test net** input_shape = (4,3,512,512) ctx = mx.gpu() data = mx.sym.Variable(‘data’) states = mx.sym.Variable(‘states’) net = ConvGRUCell(input_shape=input_shape, num_hidden=12,i2h_kernel=(3,3), h2h_kernel=(3,3),i2h_pad=(1,1)) print(net) output,states = net(data,states) print(output) print(output.list_arguments()) print(states) model = mx.mod.Module(symbol=output, context=ctx,label_names=None) model.fit(train_loader, # train data eval_data=val_loader, # validation data optimizer=‘sgd’, # use SGD to train optimizer_params={‘learning_rate’:0.1}, # use fixed learning rate eval_metric=‘acc’, # report accuracy during training batch_end_callback = mx.callback.Speedometer(4, 100), # output progress for each 100 data batches num_epoch=10) # train for at most 10 dataset passes ``` **error like following:** ```python <__main__.ConvGRUCell object at 0x2b122c5bbfd0> <Symbol ConvGRU_t0_out> ['data', 'ConvGRU_i2h_weight', 'ConvGRU_i2h_bias', 'states', 'ConvGRU_h2h_weight', 'ConvGRU_h2h_bias'] [<Symbol ConvGRU_t0_out>] RuntimeErrorTraceback (most recent call last) <ipython-input-66-430f77b3b8f1> in <module>() 27 eval_metric='acc', # report accuracy during training 28 batch_end_callback = mx.callback.Speedometer(4, 100), # output progress for each 100 data batches ---> 29 num_epoch=10) # train for at most 10 dataset passes 30 # model = mx.mod.Module(output, data_names=['data',], label_names=None, context=mx.gpu()) 31 print(model) /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/module/base_module.pyc in fit(self, train_data, eval_data, eval_metric, epoch_end_callback, batch_end_callback, kvstore, optimizer, optimizer_params, eval_end_callback, eval_batch_end_callback, initializer, arg_params, aux_params, allow_missing, force_rebind, force_init, begin_epoch, num_epoch, validation_metric, monitor) 458 459 self.bind(data_shapes=train_data.provide_data, label_shapes=train_data.provide_label, --> 460 for_training=True, force_rebind=force_rebind) 461 if monitor is not None: 462 self.install_monitor(monitor) anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/module/module.pyc in bind(self, data_shapes, label_shapes, for_training, inputs_need_grad, force_rebind, shared_module, grad_req) 427 fixed_param_names=self._fixed_param_names, 428 grad_req=grad_req, group2ctxs=self._group2ctxs, --> 429 state_names=self._state_names) 430 self._total_exec_bytes = self._exec_group._total_exec_bytes 431 if shared_module is not None: /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/module/executor_group.pyc in init(self, symbol, contexts, workload, data_shapes, label_shapes, param_names, for_training, inputs_need_grad, shared_group, logger, fixed_param_names, grad_req, state_names, group2ctxs) 262 self.num_outputs = len(self.symbol.list_outputs()) 263 –> 264 self.bind_exec(data_shapes, label_shapes, shared_group) 265 266 def decide_slices(self, data_shapes): /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/module/executor_group.pyc in bind_exec(self, data_shapes, label_shapes, shared_group, reshape) 358 else: 359 self.execs.append(self._bind_ith_exec(i, data_shapes_i, label_shapes_i, --> 360 shared_group)) 361 362 self.data_shapes = data_shapes /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/module/executor_group.pyc in _bind_ith_exec(self, i, data_shapes, label_shapes, shared_group) 636 type_dict=input_types, shared_arg_names=self.param_names, 637 shared_exec=shared_exec, group2ctx=group2ctx, –> 638 shared_buffer=shared_data_arrays, **input_shapes) 639 self._total_exec_bytes += int(executor.debug_str().split(’\n’)[-3].split()[1]) 640 return executor anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/symbol/symbol.pyc in simple_bind(self, ctx, grad_req, type_dict, stype_dict, group2ctx, shared_arg_names, shared_exec, shared_buffer, **kwargs) 1513 error_msg += "%s: %s\n" % (k, v) 1514 error_msg += "%s" % e -> 1515 raise RuntimeError(error_msg) 1516 1517 # update shared_buffer RuntimeError: simple_bind error. Arguments: data: (4, 3, 512, 512) label: (4, 3, 512, 512) Error in operator ConvGRU_t0_h2h: [20:55:03] src/operator/nn/./convolution-inl.h:625: Check failed: dtype != -1 (-1 vs. -1) First input must have specified type Stack trace returned 10 entries: [bt] (0) /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/libmxnet.so(_ZN4dmlc10StackTraceB5cxx11Ev+0x48) [0x2b11487cbc68] [bt] (1) /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/libmxnet.so(_ZN4dmlc15LogMessageFatalD1Ev+0x18) [0x2b11487cc678] [bt] (2) /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/libmxnet.so(_ZNK5mxnet2op15ConvolutionProp9InferTypeEPSt6vectorIiSaIiEES5_S5_+0x990) [0x2b1148953ab0] [bt] (3) /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/libmxnet.so(+0x2ed4735) [0x2b114aed4735] [bt] (4) /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/libmxnet.so(+0x2ccacf8) [0x2b114accacf8] [bt] (5)/anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/libmxnet.so(+0x2cd1d61) [0x2b114acd1d61] [bt] (6) /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet4exec9InferTypeEON4nnvm5GraphEOSt6vectorIiSaIiEERKNSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE+0x11f) [0x2b114acd2bcf] [bt] (7) /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet4exec13GraphExecutor4InitEN4nnvm6SymbolERKNS_7ContextERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES4_St4lessISD_ESaISt4pairIKSD_S4_EEERKSt6vectorIS4_SaIS4_EESR_SR_RKSt13unordered_mapISD_NS2_6TShapeESt4hashISD_ESt8equal_toISD_ESaISG_ISH_ST_EEERKSS_ISD_iSV_SX_SaISG_ISH_iEEES17_RKSN_INS_9OpReqTypeESaIS18_EERKSt13unordered_setISD_SV_SX_SaISD_EEPSN_INS_7NDArrayESaIS1I_EES1L_S1L_PSS_ISD_S1I_SV_SX_SaISG_ISH_S1I_EEEPNS_8ExecutorERKSS_INS2_9NodeEntryES1I_NS2_13NodeEntryHashENS2_14NodeEntryEqualESaISG_IKS1S_S1I_EEE+0x7d5) [0x2b114acb68a5] [bt] (8) /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/libmxnet.so(_ZN5mxnet8Executor10SimpleBindEN4nnvm6SymbolERKNS_7ContextERKSt3mapINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEES3_St4lessISC_ESaISt4pairIKSC_S3_EEERKSt6vectorIS3_SaIS3_EESQ_SQ_RKSt13unordered_mapISC_NS1_6TShapeESt4hashISC_ESt8equal_toISC_ESaISF_ISG_SS_EEERKSR_ISC_iSU_SW_SaISF_ISG_iEEES16_RKSM_INS_9OpReqTypeESaIS17_EERKSt13unordered_setISC_SU_SW_SaISC_EEPSM_INS_7NDArrayESaIS1H_EES1K_S1K_PSR_ISC_S1H_SU_SW_SaISF_ISG_S1H_EEEPS0_+0xcd) [0x2b114acb714d] [bt] (9) /anaconda2/lib/python2.7/site-packages/mxnet-1.0.0-py2.7.egg/mxnet/libmxnet.s ```
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