tensorflow summary

定义summary

writer = tf.summary.FileWriter(logdir=self.han_config.log_path, graph=session.graph)

1.scalar存储结果

　　a.先在训练的循环外定义:

test_accuracy_summary = tf.summary.scalar('test_accuracy', self.han_model.accuracy)
            test_loss_summary = tf.summary.scalar('test_loss', self.han_model.loss)
            test_scalar = tf.summary.merge([test_accuracy_summary, test_loss_summary])

　　b.在session run的时候run test_scalar，获得值，然后再添加。

 writer.add_summary(summary=train_scalar_, global_step=steps)

2.histogram存储权重，偏执。

　　a.先在训练的循环外定义:

            W_w_attention_word_histogram = tf.summary.histogram('W_w_attention_word', self.han_model.W_w_attention_word)
            W_b_attention_word_histogram = tf.summary.histogram('W_w_attention_word', self.han_model.W_b_attention_word)
            context_vecotor_word_histogram = tf.summary.histogram('context_vecotor_word',
                                                                  self.han_model.context_vecotor_word)
            W_w_attention_sentence_histogram = tf.summary.histogram('W_w_attention_sentence',
                                                                    self.han_model.W_w_attention_sentence)
            W_b_attention_sentence_histogram = tf.summary.histogram('W_b_attention_sentence',
                                                                    self.han_model.W_b_attention_sentence)
            context_vecotor_sentence_histogram = tf.summary.histogram('context_vecotor_sentence',
                                                                      self.han_model.context_vecotor_sentence)
            train_variable_histogram = tf.summary.merge([W_w_attention_word_histogram, W_b_attention_word_histogram,
                                                    context_vecotor_word_histogram, W_w_attention_sentence_histogram,
                                                W_b_attention_sentence_histogram, context_vecotor_sentence_histogram])

　　b.在session run的时候run test_scalar，获得值，然后再添加。

writer.add_summary(summary=train_variable_histogram_, global_step=steps)