Вы можете использовать SummarySaverHook
eval_hooks = []
eval_summary_hook = tf.train.SummarySaverHook(
save_steps=1,
output_dir='model_dir',
summary_op=tf.summary.histogram(logits.name, logits))
eval_hooks.append(eval_summary_hook)
return tf.estimator.EstimatorSpec(mode=mode,
loss=loss,
eval_metric_ops=eval_metric_ops,
evaluation_hooks=evaluation_hooks
)