More from tensorflow.python.util.nest .flatten() .pack_sequence_as() .map_structure() .is_sequence() .assert_same_structure() .flatten_dict_items() Related Methods time.time() numpy.array() numpy.zeros() numpy.float32() tensorflow.concat() tensorflow.reshape() ...
函数功能：Basic RNN sequence-to-sequence model.def basic_rnn_seq2seq(encoder_inputs, decoder_inputs, cell, dtype=dtypes.float32, scope=None): """ 这一部分具体描述就看英文的吧，更通俗易懂一些 This model first runs an RNN to encode encoder_inputs into a state vector, then runs decoder,...
An Open Source Machine Learning Framework for Everyone - tensorflow/tensorflow/python/util/nest.py at master · tensorflow/tensorflow
(ts) for ts in nest.flatten(dataset_ops.get_structure(self._input_dataset)) ] known_input_batch_dims = [d for d in input_batch_dims if d is not None] if not known_input_batch_dims: return True known_input_batch_dims = np.asarray(known_input_batch_dims) return known_input_batch...
(self, *args, **kwargs):ifvalues_util.is_saving_non_distributed():# 非分布式情况# 直接返回本地数值returnself._primary.scatter_update(*args, **kwargs)# 否则进行分布式处理returnsuper(MirroredVariable, self).scatter_update(*args, **kwargs)def_get_cross_replica(self):# Return identity, to ...
nest 1: nest.assert_same_structure 2: nest.flatten 3: nest.is_nested 4: nest.map_structure 5: nest.pack_sequence_as 49: nn 1: nn.all_candidate_sampler 2: nn.approx_max_k 3: nn.approx_min_k 4: nn.atrous_conv2d 5: nn.atrous_conv2d_transpose 6: nn.avg_pool 7: nn.avg_pool...
tf.cond支持在tensorflow.python.util.nest中实现的嵌套结构。 true_fn和false_fn都必须返回列表,元组和/或命名元组的相同(可能是嵌套的)值结构。单例列表和元组构成了对此的唯一例外:当由true_fn和/或false_fn返回时,它们被隐式解包为单个值。通过传递strict = True禁用此行为。
stitch_values( ((dense_results, dense_indices), (sparse_results, sparse_indices))) return nest.pack_sequence_as(value, flat_results) def _all_reduce_per_replica_values(self, reduce_op, per_replica_values, options): """All reduce a list of per_replica_value.""" values_by_device = [...
`outputs` is an objectcontaining the decoder output, `next_state` is a (structure of) statetensors and TensorArrays, `next_inputs` is the tensor that should be usedas input for the next step, `finished` is a boolean tensor telling whetherthe sequence is complete, for each sequence in th...
File “/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/checkpoint_management.py”, line 813, in save save_path = self._checkpoint.write(prefix) File “/usr/local/lib/python3.8/dist-packages/tensorflow/python/training/tracking/util.py”, line 2105, in write ...