InvalidArgumentError: No OpKernel was registered to support Op 'CudnnRNN' used by node cu_dnnlstm/CudnnRNN (defined at :19) with these attrs: > 1348 raise type(e)(node_def, op, message)
anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)ġ327 return self._do_call(_run_fn, feeds, fetches, targets, options,ġ330 return self._do_call(_prun_fn, handle, feeds, fetches)ġ347 message = error_interpolation.interpolate(message, self._graph)
> 1152 feed_dict_tensor, options, run_metadata) anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)ġ150 if final_fetches or final_targets or (handle and feed_dict_tensor):ġ151 results = self._do_run(handle, final_targets, final_fetches, anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)ĩ28 result = self._run(None, fetches, feed_dict, options_ptr,ĩ31 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr) > 758 )ħ60 for flag, v in zip(is_initialized, candidate_vars): anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/backend.py in _initialize_variables(session) anaconda3/lib/python3.7/site-packages/tensorflow/python/keras/backend.py in get_session() InvalidArgumentError: No OpKernel was registered to support Op 'CudnnRNN' used by )
> 1352 tf_session.ExtendSession(self._session) anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in _extend_graph(self)ġ351 with self._graph._session_run_lock(): # pylint: disable=protected-access anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)ġ316 # Ensure any changes to the graph are reflected in the runtime. anaconda3/lib/python3.7/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args) InvalidArgumentError Traceback (most recent call last) Train on 60000 samples, validate on 10000 samples Model.add(Dense(10, activation='softmax')) Model.add(LSTM(128, input_shape=(x_train.shape), activation='relu', return_sequences=True)) (x_train, y_train),(x_test, y_test) = mnist.load_data() # unpacks images to x_train/x_test and labels to y_train/y_test Mnist = tf. # mnist is a dataset of 28x28 images of handwritten digits and their labels From import Sequentialįrom import Dense, Dropout, LSTM #, CuDNNLSTM