概述
tfa.seq2seq.TrainingSampler，简略读取输出的训练采样器。
调用trainingSampler.initialize(input_tensors)时，取各batch中time_step=0的数据，拼接成一个数据集，返回。
下一次调用sampler.next_inputs函数时，会取各batch中time_step++的数据，拼接成一个数据集，返回。
举例说明
官网例子修改版:
import tensorflow_addons as tfaimport tensorflow as tfdef tfa_seq2seq_TrainingSampler_test(): batch_size = 2 max_time = 3 word_vector_len = 4 hidden_size = 5 sampler = tfa.seq2seq.TrainingSampler() cell = tf.keras.layers.LSTMCell(hidden_size) input_tensors = tf.random.uniform([batch_size, max_time, word_vector_len]) initial_finished, initial_inputs = sampler.initialize(input_tensors) cell_input = initial_inputs cell_state = cell.get_initial_state(initial_inputs) for time_step in tf.range(max_time): cell_output, cell_state = cell(cell_input, cell_state) sample_ids = sampler.sample(time_step, cell_output, cell_state) finished, cell_input, cell_state = sampler.next_inputs( time_step, cell_output, cell_state, sample_ids) if tf.reduce_all(finished): break print(time_step)if __name__ == '__main__': pass; tfa_seq2seq_TrainingSampler_test()以下面的代码为例，
# 假如输出数值上如下所示, 输出各维度含意, [batch_size, time_step, feature_length(或者word_vector_length)]input_tensors = tf.Tensor([[[0.9346709 0.13170087 0.6356932 0.13167298] [0.4919318 0.44602418 0.49046385 0.28244007] [0.9263021 0.9984634 0.10324025 0.653986 ]] [[0.8260417 0.269673 0.37965262 0.86320114] [0.88838446 0.28112316 0.5868691 0.4174199 ] [0.61980057 0.2420206 0.17553246 0.9765543 ]]], shape=(2, 3, 4), dtype=float32)当运行完sampler.initialize(input_tensors)时，失去如下的采样后果，即两个batch中，每个batch中time_step=0的数据，拼接而成。
initial_inputs = tf.Tensor([[0.9346709 0.13170087 0.6356932 0.13167298] [0.8260417 0.269673 0.37965262 0.86320114]], shape=(2, 4), dtype=float32)第一次运行完sampler.next_inputs时，失去如下的采样后果，即两个batch中，每个batch中time_step=1的数据，拼接而成。
initial_inputs = tf.Tensor([[0.4919318 0.44602418 0.49046385 0.28244007] [0.88838446 0.28112316 0.5868691 0.4174199 ]], shape=(2, 4), dtype=float32)第二次运行完sampler.next_inputs时，失去如下的采样后果，即两个batch中，每个batch中time_step=2的数据，拼接而成。
initial_inputs = tf.Tensor([[0.9263021 0.9984634 0.10324025 0.653986 ] [0.61980057 0.2420206 0.17553246 0.9765543 ]], shape=(2, 4), dtype=float32)sample_ids的含意，RNN输入，每一批中，数值最大的逻辑位对应的下标。
# 当LSTMCell的输入如下所示时，cell_output = tf.Tensor([[-0.07552935 0.07034459 0.12033001 -0.1792231 0.05634112] [-0.10488522 0.06370427 0.17486209 -0.10092633 0.09584342]], shape=(2, 5), dtype=float32) # 显然，第一批与第二批中都是下标=2的逻辑位数值最大sample_ids = tf.Tensor([2 2], shape=(2,), dtype=int32)参考文献
https://www.tensorflow.org/addons/api_docs/python/tfa/seq2seq/Sampler?hl=zh-cn (tfa.seq2seq.Sampler | TensorFlow Addons)
https://tensorflow.google.cn/addons/api_docs/python/tfa/seq2seq/TrainingSampler (tfa.seq2seq.TrainingSampler | TensorFlow Addons)