這裏出錯的原因是定義的 loss 採用 sparse_softmax_cross_entropy_with_logits
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=tf.argmax(labels_holder,1), logits=label_predict)
查閱 TensorFlow API：https://tensorflow.google.cn/api_docs/python/tf/losses/sparse_softmax_cross_entropy
sparse_softmax_cross_entropy_with_logits 返回：Weighted loss Tensor of the same type as logits. If reduction is NONE, this has the same shape as labels; otherwise, it is scalar.
即返回和輸入標籤具有相同形狀的張量
將 tf.summary.scalar(name="loss", tensor=loss) 改爲 tf.summary.histogram(name="loss", tensor=loss)
