Python 整数或布尔值的TensorFlow占位符变量isn'；行不通

我使用TensorFlow中的以下代码片段有条件地从一个源或另一个源提取数据：

if __name__ == '__main__':



with tf.device("/gpu:0"):

    with tf.Graph().as_default():

        with tf.variable_scope("cifar_conv_model"):

            is_train = tf.placeholder(tf.int32) # placeholder for whether to pull from train or val data
            keep_prob = tf.placeholder(tf.float32) # dropout probability

            x, y = tf.cond(tf.equal(is_train, tf.constant(1, dtype=tf.int32)), distorted_inputs, inputs)

            output = inference(x, keep_prob)

            cost = loss(output, y)

            global_step = tf.Variable(0, name='global_step', trainable=False)

            train_op = training(cost, global_step)

            eval_op = evaluate(output, y)

            summary_op = tf.merge_all_summaries()

            saver = tf.train.Saver()
            summary_writer = tf.train.SummaryWriter("conv_cifar_logs/", graph_def=sess.graph_def)
            init_op = tf.initialize_all_variables()

            sess = tf.Session()

            sess.run(init_op)

            tf.train.start_queue_runners(sess=sess)

            # Training cycle
            for epoch in range(training_epochs):

                avg_cost = 0.
                total_batch = int(cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN/batch_size)
                # Loop over all batches
                for i in range(total_batch):
                    # Fit training using batch data
                    _, new_cost = sess.run([train_op, cost], feed_dict={is_train: 1, keep_prob: 0.5})
                    # Compute average loss
                    avg_cost += new_cost/total_batch
                    print "Epoch %d, minibatch %d of %d. Average cost = %0.4f." %(epoch, i, total_batch, avg_cost)

随着线程的爆发，我不断获得错误呕吐物，但重复出现的主题是以下错误：

InvalidArgumentError: You must feed a value for placeholder tensor 'cifar_conv_model/Placeholder' with dtype int32
     [[Node: cifar_conv_model/Placeholder = Placeholder[dtype=DT_INT32, shape=[], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]
     [[Node: cifar_conv_model/Placeholder/_151 = _HostRecv[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_407_cifar_conv_model/Placeholder", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
Caused by op u'cifar_conv_model/Placeholder', defined at:
  File "convnet_cifar.py", line 134, in <module>
    is_train = tf.placeholder(tf.int32) # placeholder for whether to pull from train or val data
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/array_ops.py", line 743, in placeholder
    name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_array_ops.py", line 607, in _placeholder
    name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/op_def_library.py", line 655, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2040, in create_op
    original_op=self._default_original_op, op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 1087, in __init__
    self._traceback = _extract_stack()

InvalidArgumentError:必须为数据类型为int32的占位符张量“cifar\U conv\U模型/占位符”提供一个值
[[Node:cifar\u conv\u model/Placeholder=Placeholder[dtype=DT\u INT32，shape=[]，[u device=“/job:localhost/replica:0/task:0/cpu:0”]（）]
[[Node:cifar\u conv\u model/Placeholder/\u 151=\u HostRecv[client\u terminated=false，recv\u device=“/job:localhost/replica:0/task:0/gpu:0”，send\u device=“/job:localhost/replica:0/task:0/cpu:0”，send\u device\u化身=1，tensor\u name=“edge\u 407\u cifar\u conv\u model/Placeholder”，tensor=DT\u INT32，\u device=“/job:localhost/replica:0/task:0/gpu:0”]
由以下位置定义的“cifar\u conv\u模型/占位符”引起：
文件“convnet_cifar.py”，第134行，在
is_train=tf.placeholder（tf.int32）#是否从train或val数据中提取的占位符
占位符中的文件“/usr/local/lib/python2.7/dist packages/tensorflow/python/ops/array_ops.py”，第743行
名称=名称）
文件“/usr/local/lib/python2.7/dist packages/tensorflow/python/ops/gen_array_ops.py”，第607行，在占位符中
名称=名称）
文件“/usr/local/lib/python2.7/dist packages/tensorflow/python/ops/op_def_library.py”，第655行，在apply_op
op_def=op_def）
文件“/usr/local/lib/python2.7/dist packages/tensorflow/python/framework/ops.py”，第2040行，在create_op中
初始值=自身值。\默认值\初始值，初始值=初始值）
文件“/usr/local/lib/python2.7/dist packages/tensorflow/python/framework/ops.py”，第1087行，在__
self.\u traceback=\u extract\u stack（）

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任何帮助都将不胜感激

这里提到了一种解决方案：

您必须为tf.变量更改占位符

is_train=tf.placeholder（tf.int32）

：

is_train=tf.Variable（True，name='training'）

。 因此，在进行调用之前，您可以通过调用sess.run（tf.initialize\u all\u variables（））进行初始化

tf.train.start\u queue\u runner（sess=sess）