Tensorflow TypeError：获取参数None具有无效的类型 ？

Question

我正在根据TensorFlow教程松散地构建RNN 。

我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

和喂养：

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]
            })

运行此命令时，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'>

也许最奇怪的部分是该错误在 第二 次迭代中抛出，并且第一次迭代完全正常。我正在尝试修复此问题，因此，我们将不胜感激。

Answer 1

您正在将train_step变量重新分配给结果的第二个元素sess.run()（恰好是None）。因此，在第二次迭代中，train_stepis
None，导致错误。

修复很简单：

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]
        })