寫了一個TensorFlow卷積神經網路的訓練程式。

基於mnist資料集進行訓練和測試。

但是在程式執行的時候報出了下面的錯誤。

Traceback (most recent call last):
  File "nn_eg.py", line 104, in <module>
    train_loss, train_op = sess.run([loss, train_op], {input_x: batch[0], output_y: batch[1]})
  File "/home/zhonghangalex/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 929, in run
    run_metadata_ptr)
  File "/home/zhonghangalex/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1137, in _run
    self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)
  File "/home/zhonghangalex/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 471, in __init__
    self._fetch_mapper = _FetchMapper.for_fetch(fetches)
  File "/home/zhonghangalex/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 261, in for_fetch
    return _ListFetchMapper(fetch)
  File "/home/zhonghangalex/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 370, in __init__
    self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
  File "/home/zhonghangalex/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 370, in <listcomp>
    self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
  File "/home/zhonghangalex/venv/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 258, in for_fetch
    type(fetch)))
TypeError: Fetch argument None has invalid type <class 'NoneType'>
 

 這裡我們看到錯誤指向的是程式碼的104行，我將這部分的程式碼貼出來：

# 訓練神經網路
for i in range(20000):
	batch = mnist.train.next_batch(50) #從Train（訓練)資料集裡取下一個50樣本
	train_loss, train_op = sess.run([loss, train_op], {input_x: batch[0], output_y: batch[1]})
	if i % 100 == 0:
		test_accuracy = sess.run(accuracy, {input_x: test_x, output_y: test_y})
		print("Step=%d, Train loss=%.4f, [Test accuracy=%.2f]" % (i, train_loss, test_accuracy))
 

這是對神經網路訓練的過程，指定訓練20000步，有一個奇怪的現象就是，迴圈的第一步進行得很順暢，可是從第二步開始就報了這個錯誤：

這就說明了應該是變量出現了問題。

查閱資料後發現是因為：

train_op變數重新分配給結果的第二個元素sess.run()（恰好是None）。因此，在第二次迭代中，train_op是None，這導致錯誤。 解決的方法很簡單，就是把兩個變數的第二個變數改為“_”：

# 訓練神經網路
for i in range(20000):
	batch = mnist.train.next_batch(50) #從Train（訓練)資料集裡取下一個50樣本
	train_loss, _ = sess.run([loss, train_op], {input_x: batch[0], output_y: batch[1]})
	if i % 100 == 0:
		test_accuracy = sess.run(accuracy, {input_x: test_x, output_y: test_y})
		print("Step=%d, Train loss=%.4f, [Test accuracy=%.2f]" % (i, train_loss, test_accuracy))
 

這樣便成功開始了訓練：