Python Tensorflow TypeError:获取参数None的类型无效<；类型'；非类型'>；？

我正在构建一个松散基于的RNN

我的模型的相关部分如下：

input_sequence = tf.placeholder(tf.float32, [BATCH_SIZE, TIME_STEPS, PIXEL_COUNT + AUX_INPUTS])
output_actual = tf.placeholder(tf.float32, [BATCH_SIZE, OUTPUT_SIZE])

lstm_cell = tf.nn.rnn_cell.BasicLSTMCell(CELL_SIZE, state_is_tuple=False)
stacked_lstm = tf.nn.rnn_cell.MultiRNNCell([lstm_cell] * CELL_LAYERS, state_is_tuple=False)

initial_state = state = stacked_lstm.zero_state(BATCH_SIZE, tf.float32)
outputs = []

with tf.variable_scope("LSTM"):
    for step in xrange(TIME_STEPS):
        if step > 0:
            tf.get_variable_scope().reuse_variables()
        cell_output, state = stacked_lstm(input_sequence[:, step, :], state)
        outputs.append(cell_output)

final_state = state

而喂食：

cross_entropy = tf.reduce_mean(-tf.reduce_sum(output_actual * tf.log(prediction), reduction_indices=[1]))
train_step = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(prediction, 1), tf.argmax(output_actual, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

with tf.Session() as sess:
    sess.run(tf.initialize_all_variables())
    numpy_state = initial_state.eval()

    for i in xrange(1, ITERATIONS):
        batch = DI.next_batch()

        print i, type(batch[0]), np.array(batch[1]).shape, numpy_state.shape

        if i % LOG_STEP == 0:
            train_accuracy = accuracy.eval(feed_dict={
                initial_state: numpy_state,
                input_sequence: batch[0],
                output_actual: batch[1]
            })

            print "Iteration " + str(i) + " Training Accuracy " + str(train_accuracy)

        numpy_state, train_step = sess.run([final_state, train_step], feed_dict={
            initial_state: numpy_state,
            input_sequence: batch[0],
            output_actual: batch[1]
            })

当我运行此操作时，会出现以下错误：

Traceback (most recent call last):
  File "/home/agupta/Documents/Projects/Image-Recognition-with-LSTM/RNN/feature_tracking/model.py", line 109, in <module>
    output_actual: batch[1]
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 698, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 838, in _run
    fetch_handler = _FetchHandler(self._graph, fetches)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 355, in __init__
    self._fetch_mapper = _FetchMapper.for_fetch(fetches)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 181, in for_fetch
    return _ListFetchMapper(fetch)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 288, in __init__
    self._mappers = [_FetchMapper.for_fetch(fetch) for fetch in fetches]
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 178, in for_fetch
    (fetch, type(fetch)))
TypeError: Fetch argument None has invalid type <type 'NoneType'>

回溯（最近一次呼叫最后一次）：
文件“/home/agupta/Documents/Projects/Image Recognition with LSTM/RNN/feature_tracking/model.py”，第109行，在
实际输出：批次[1]
文件“/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py”，第698行，正在运行
运行_元数据_ptr）
文件“/usr/local/lib/python2.7/dist packages/tensorflow/python/client/session.py”，第838行，正在运行
fetch\u handler=\u FetchHandler（self.\u图形，fetches）
文件“/usr/local/lib/python2.7/dist packages/tensorflow/python/client/session.py”，第355行，在__
self.\u fetch\u mapper=\u FetchMapper.for\u fetch（fetches）
文件“/usr/local/lib/python2.7/dist packages/tensorflow/python/client/session.py”，第181行，for_fetch
return\u ListFetchMapper（fetch）
文件“/usr/local/lib/python2.7/dist packages/tensorflow/python/client/session.py”，第288行，在__
self._mappers=[_FetchMapper.for_fetch（fetch）for fetch in fetches]
文件“/usr/local/lib/python2.7/dist packages/tensorflow/python/client/session.py”，第178行，for_fetch
（fetch，键入（fetch）））
TypeError:获取参数None的类型无效

---

也许最奇怪的是，这个错误会在第二次迭代中抛出，而第一次迭代完全正常。我正在努力解决这个问题，因此非常感谢您的帮助。

您正在将

train\u step

变量重新分配给

sess.run（）

（恰好是

None

）结果的第二个元素。因此，在第二次迭代中，

train_step

是

None

，这导致了错误

幸运的是，解决方法很简单：

for i in xrange(1, ITERATIONS):

    # ...

    # Discard the second element of the result.
    numpy_state, _ = sess.run([final_state, train_step], feed_dict={
        initial_state: numpy_state,
        input_sequence: batch[0],
        output_actual: batch[1]
        })

---

出现此错误的另一个常见原因是，如果包含摘要获取操作，但尚未编写任何摘要

例如：

# tf.summary.scalar("loss", loss) # <- uncomment this line and it will work fine
summary_op = tf.summary.merge_all()
sess = tf.Session()
# ...
summary = sess.run([summary_op, ...], feed_dict={...}) # TypeError, summary_op is "None"!

---

#tf.summary.scalar（“失去”，失去）#先生，你是有史以来最伟大的人。非常感谢。李先生，你能用一般的语言解释一下什么时候会出现这种错误吗？我无法理解，因为我在不同的上下文中有相同的错误。。。