Tensorflow图是否可以在会话之外运行

时间：2017-08-31 17:17:44

标签： python tensorflow convolution

有人可以解释以下情况：

我使用Tensorflow创建了一个简单的卷积神经网络。我正在使用课程，并且已经在构造函数中创建了我的图表。然后，我使用我写过的火车方法训练网络。我也使用队列和馈入机制。这是代码的摘录：

 class Super_res:
'Create a CNN model which augments the resolution of an image'
# object initialization (python) - constructor
def __init__(self, input, output, batch_size, record_size, weights, biases):    # input (neurons), output (no. neurons), batch_size (batches to process before registering delta), record_size ()
    print("Initializing object")
    self.input = input
    self.output = output
    self.batch_size = batch_size
    self.record_size = record_size
    self.weights = weights
    self.biases = biases

    # initialize data batch readers. Parameters: [Path], record_size, batch_size
    self.data_batch = data_reader3.batch_generator([DATA_PATH_OPTICAL_TRAIN],self.record_size, self.batch_size) # train set
    self.data_batch_eval = data_reader3.batch_generator([DATA_PATH_EVAL],self.record_size, self.batch_size) # eval set

    # this returns a [batch_size, 2, n_input] tensor. The second dimension is comprised of the low-res image and the GT high-res image. Each of these images is comprised of n_input entries (flat vector)
    self.data1 = tf.placeholder_with_default(tf.transpose(self.data_batch, [1, 0, 2]), [2, batch_size, n_input]) # one for optical and another for GT image [batch_size, n_input] each

    self.keep_prob = tf.placeholder(tf.float32) #dropout (keep probability) - this placeholder can accept a Tensor of arbitrary shape

    # create network model
    self.pred = self.cnn_model(self.data1[0], self.weights,     self.biases)    # self.data1[0] is the low-res data 

 def train(self):
    #self.low_res = self.data1[0]
    #self.high_res = self.data1[1]

    # define loss and optimizer
    #self.cost = tf.reduce_mean(tf.pow(self.data1[1] - self.pred, 2))
    #self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.cost)

    # Initializing the variables
    init = tf.global_variables_initializer()

    # Initialize session
    with tf.Session() as sess:
        sess.run(init)
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        step = 1
        print("Entering training")

        # Keep  training until reach max iterations
        while step * batch_size < training_iters:
            #_, c = sess.run([self.optimizer, self.cost])
            conv_result = sess.run(self.pred)
            print(conv_result)
            #data2 = self.data1[0]
            #print(data2)

            if step % display_step == 0:
                print("Step:", '%04d' % (step+1))
            #   "cost=", c)
            step = step + 1

        coord.request_stop()
        coord.join(threads)

当我运行此代码时，我收到以下错误输出：

 Entering training
 Traceback (most recent call last):
 File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages  \tensorflow\python\client\session.py", line 1139, in _do_call
return fn(*args)
File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages \tensorflow\python\client\session.py", line 1121, in _run_fn
status, run_metadata)
File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\contextlib.py", line 66, in __exit__
next(self.gen)
File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 466, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.OutOfRangeError:    RandomShuffleQueue '_2_shuffle_batch/random_shuffle_queue' is closed and has   insufficient elements (requested 512, current size 0)
     [[Node: shuffle_batch = QueueDequeueManyV2[component_types=  [DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"]  (shuffle_batch/random_shuffle_queue, shuffle_batch/n)]]
     [[Node: shuffle_batch/_25 = _Recv[client_terminated=false,   recv_device="/job:localhost/replica:0/task:0/gpu:0",   send_device="/job:localhost/replica:0/task:0/cpu:0",   send_device_incarnation=1, tensor_name="edge_5_shuffle_batch",    tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]

 During handling of the above exception, another exception occurred:

 Traceback (most recent call last):
 File "super_res_class.py", line 137, in <module>
 p.train()
 File "super_res_class.py", line 106, in train
 conv_result = sess.run(self.pred)
 File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages  \tensorflow\python\client\session.py", line 789, in run
 run_metadata_ptr)
 File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages \tensorflow\python\client\session.py", line 997, in _run
 feed_dict_string, options, run_metadata)
 File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages  \tensorflow\python\client\session.py", line 1132, in _do_run
 target_list, options, run_metadata)
 File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages  \tensorflow\python\client\session.py", line 1152, in _do_call
 raise type(e)(node_def, op, message)
 tensorflow.python.framework.errors_impl.OutOfRangeError:    RandomShuffleQueue '_2_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 512, current size 0)
     [[Node: shuffle_batch = QueueDequeueManyV2[component_types=   [DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"] (shuffle_batch/random_shuffle_queue, shuffle_batch/n)]]
      [[Node: shuffle_batch/_25 = _Recv[client_terminated=false,     recv_device="/job:localhost/replica:0/task:0/gpu:0",    send_device="/job:localhost/replica:0/task:0/cpu:0",    send_device_incarnation=1, tensor_name="edge_5_shuffle_batch",     tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]

Caused by op 'shuffle_batch', defined at:
File "super_res_class.py", line 136, in <module>
p = Super_res(1024,1024,512,record_size, weights, biases)   # params  (n_input, n_output, batch_size)
File "super_res_class.py", line 50, in __init__
self.data_batch =   data_reader3.batch_generator([DATA_PATH_OPTICAL_TRAIN],self.record_size, self.batch_size) # train set
 File "E:\google_drive\Doctorate\matlab code\Tensorflow\doctorate_CNN\dong_recreation\data_reader3.py", line 156, in batch_generator
 capacity=capacity, min_after_dequeue=min_after_dequeue)
 File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages\tensorflow\python\training\input.py", line 1217, in shuffle_batch
 name=name)
 File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages\tensorflow\python\training\input.py", line 788, in _shuffle_batch
dequeued = queue.dequeue_many(batch_size, name=name)
File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages \tensorflow\python\ops\data_flow_ops.py", line 457, in dequeue_many
 self._queue_ref, n=n, component_types=self._dtypes, name=name)
File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages \tensorflow\python\ops\gen_data_flow_ops.py", line 946, in _queue_dequeue_many_v2
 timeout_ms=timeout_ms, name=name)
 File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 767, in apply_op
op_def=op_def)
 File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages\tensorflow\python\framework\ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
 File "C:\Users\divin\Miniconda3\envs\tensorflow_gpu\lib\site-packages\tensorflow\python\framework\ops.py", line 1269, in __init__
self._traceback = _extract_stack()

 OutOfRangeError (see above for traceback): RandomShuffleQueue '_2_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 512, current size 0)
     [[Node: shuffle_batch = QueueDequeueManyV2[component_types=[DT_FLOAT], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](shuffle_batch/random_shuffle_queue, shuffle_batch/n)]]
     [[Node: shuffle_batch/_25 = _Recv[client_terminated=false,   recv_device="/job:localhost/replica:0/task:0/gpu:0", send_device="/job:localhost/replica:0/task:0/cpu:0", send_device_incarnation=1, tensor_name="edge_5_shuffle_batch", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]


 (tensorflow_gpu) E:\google_drive\Doctorate\matlab code\Tensorflow\doctorate_CNN\dong_recreation>

当我从pred输出中删除sess.run（）时，代码似乎正常运行。 

 def train(self):
    #self.low_res = self.data1[0]
    #self.high_res = self.data1[1]

    # define loss and optimizer
    #self.cost = tf.reduce_mean(tf.pow(self.data1[1] - self.pred, 2))
    #self.optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.cost)

    # Initializing the variables
    init = tf.global_variables_initializer()

    # Initialize session
    with tf.Session() as sess:
        sess.run(init)
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)
        step = 1
        print("Entering training")

        # Keep  training until reach max iterations
        while step * batch_size < training_iters:
            #_, c = sess.run([self.optimizer, self.cost])
            conv_result = self.pred
            print(conv_result)
            #data2 = self.data1[0]
            #print(data2)

            if step % display_step == 0:
                print("Step:", '%04d' % (step+1))
            #   "cost=", c)
            step = step + 1

        coord.request_stop()
        coord.join(threads)

有人可以向我解释一下吗？通常，仅在会话下运行时才会评估图表！这里给出了什么？

1 个答案:

答案 0 :(得分：1)

只是说conv_result = self.pred无法做任何事情 - 确实需要sess.run(self.pred)才能让它执行。您获得的错误与您的模型有关。正如他们所说，你的InputProducer有一个空队列。根据您提供的信息，无法对其进行诊断，但我会在网站上进一步搜索您的InputProducer未填充/零大小的原因。

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