Unable to feed value for placeholder tensor
<p>I have written a simple version bidirectional lstm for sentence classification. But it keeps giving me "You must feed a value for placeholder tensor 'train_x'" error and it seems this come from the variable initialization step. </p>

<pre><code>data = load_data(FLAGS.data)
model = RNNClassifier(FLAGS)
init = tf.initialize_all_variables()

with tf.Session() as sess:
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    sess.run(init)
    print("Graph initialized..")
    print()
    np.random.seed(FLAGS.random_state)
    for epoch in range(FLAGS.max_max_epoch):

        loss = sess.run(model.cost, feed_dict={model.train_x: data.train_x, model.train_y: data.train_y, 
                                        model.embedding_placeholder: data.glove_vec})
        print("Epoch {:2d}: Loss = {:.6f} = {:.5f}".format(epoch+1, loss))
    coord.request_stop()
    coord.join(threads)
</code></pre>

<p>And the <code>RNNClassifier</code> class code (in a different directory):</p>

<pre><code>class RNNClassifier:

    def __init__(self, FLAGS):
        self.params = FLAGS
        with tf.device("/cpu:0"):
            self.train_x = tf.placeholder(tf.int32, [6248, 42], name='train_x')
            self.train_y = tf.placeholder(tf.int32, [6248, 3], name='train_y')
            self.embedding_placeholder = tf.placeholder(tf.float32, [1193515, 100])

        with tf.variable_scope('forward_lstm'):
            lstm_fw_cell = tf.nn.rnn_cell.LSTMCell(num_units=self.params.num_hidden, use_peepholes=False, 
                                                activation=tf.nn.relu, forget_bias=0.0, 
                                                state_is_tuple=True)
        with tf.variable_scope('backward_lstm'):
            lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(num_units=self.params.num_hidden, use_peepholes=False, 
                                                activation=tf.nn.relu, forget_bias=0.0, 
                                                state_is_tuple=True)

        fw_initial_state = lstm_fw_cell.zero_state(self.params.batch_size, tf.float32)
        bw_initial_state = lstm_bw_cell.zero_state(self.params.batch_size, tf.float32)
        self._initial_state = [fw_initial_state, bw_initial_state]

        with tf.device("/cpu:0"), tf.variable_scope('softmax'):
            self.W = tf.get_variable('W', [self.params.num_hidden*2, self.params.num_classes])
            self.b = tf.get_variable('b', [self.params.num_classes], initializer=tf.constant_initializer(0.0))

        batched_inputs, batched_labels = self.batch_data()
        embed_inputs = self.use_embedding(batched_inputs)


        rnn_outputs, output_state_fw, output_state_bw  = tf.nn.bidirectional_rnn(
            cell_fw=lstm_fw_cell,
            cell_bw=lstm_bw_cell,
            inputs=embed_inputs,
            initial_state_fw=fw_initial_state,
            initial_state_bw=bw_initial_state
            )


        logits = tf.matmul(rnn_outputs[-1], self.W) + self.b

        self._cost = cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf.cast(batched_labels, tf.float32)))
        optimizer = tf.train.AdamOptimizer(learning_rate=0.05).minimize(cost)


    def batch_data(self):
        # inputs = tf.convert_to_tensor(train_x, dtype=tf.int32)
        # labels = tf.convert_to_tensor(train_y, dtype=tf.int32)
        batched_inputs, batched_labels = tf.train.batch(
            tensors=[self._train_x, self._train_y],
            batch_size=self.params.batch_size,
            dynamic_pad=True,
            enqueue_many=True,
            name='batching'
    )
    return batched_inputs, batched_labels


    def use_embedding(self, batched_inputs):
        with tf.device("/cpu:0"), tf.name_scope("input_embedding"):
            embedding = tf.get_variable("embedding", shape=[1193515, 100], trainable=False)
            embedding_init = embedding.assign(self.embedding_placeholder)
            embed_inputs = tf.split(1, self.params.seq_len, tf.nn.embedding_lookup(embedding_init, batched_inputs))
            embed_inputs = [tf.squeeze(input_, [1]) for input_ in embed_inputs]
    return embed_inputs

    @property
    def cost(self):
        return self._cost
</code></pre>

<p><strong>The output (including the error):</strong></p>

<pre><code>I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties: 
name: GeForce GTX 750 Ti
major: 5 minor: 0 memoryClockRate (GHz) 1.0845
pciBusID 0000:01:00.0
Total memory: 2.00GiB
Free memory: 1.41GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:839] Creating TensorFlow device (/gpu:0) -&gt; (device: 0, name: GeForce GTX 750 Ti, pci bus id: 0000:01:00.0)
E tensorflow/core/client/tensor_c_api.cc:485] You must feed a value for placeholder tensor 'train_x' with dtype int32 and shape [6248,42]
     [[Node: train_x = Placeholder[dtype=DT_INT32, shape=[6248,42], _device="/job:localhost/replica:0/task:0/cpu:0"]()]]

Graph initialized..

W tensorflow/core/framework/op_kernel.cc:936] Out of range: PaddingFIFOQueue '_0_batching/padding_fifo_queue' is closed and has insufficient elements (requested 50, current size 0)
     [[Node: batching = QueueDequeueMany[_class=["loc:@batching/padding_fifo_queue"], component_types=[DT_INT32, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](batching/padding_fifo_queue, batching/n)]]
W tensorflow/core/framework/op_kernel.cc:936] Out of range: PaddingFIFOQueue '_0_batching/padding_fifo_queue' is closed and has insufficient elements (requested 50, current size 0)
     [[Node: batching = QueueDequeueMany[_class=["loc:@batching/padding_fifo_queue"], component_types=[DT_INT32, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](batching/padding_fifo_queue, batching/n)]]
E tensorflow/core/client/tensor_c_api.cc:485] PaddingFIFOQueue '_0_batching/padding_fifo_queue' is closed and has insufficient elements (requested 50, current size 0)
     [[Node: batching = QueueDequeueMany[_class=["loc:@batching/padding_fifo_queue"], component_types=[DT_INT32, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](batching/padding_fifo_queue, batching/n)]]
     [[Node: batching/_9 = _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_1191_batching", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
Traceback (most recent call last):
  File "train_lstm.py", line 66, in &lt;module&gt;
    model.embedding_placeholder: data.glove_vec})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 382, in run
    run_metadata_ptr)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 655, in _run
    feed_dict_string, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 723, in _do_run
    target_list, options, run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 743, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors.OutOfRangeError: PaddingFIFOQueue '_0_batching/padding_fifo_queue' is closed and has insufficient elements (requested 50, current size 0)
     [[Node: batching = QueueDequeueMany[_class=["loc:@batching/padding_fifo_queue"], component_types=[DT_INT32, DT_INT32], timeout_ms=-1, _device="/job:localhost/replica:0/task:0/cpu:0"](batching/padding_fifo_queue, batching/n)]]
     [[Node: batching/_9 = _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_1191_batching", tensor_type=DT_INT32, _device="/job:localhost/replica:0/task:0/gpu:0"]()]]
Caused by op u'batching', defined at:
  File "train_lstm.py", line 49, in &lt;module&gt;
    model = RNNClassifier(FLAGS)
  File "/home/ccrmad/Code/TDLSTM/models/rnn_classifier.py", line 34, in __init__
    batched_inputs, batched_labels = self.batch_data()
  File "/home/ccrmad/Code/TDLSTM/models/rnn_classifier.py", line 74, in batch_data
    name='batching'
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/training/input.py", line 595, in batch
    dequeued = queue.dequeue_many(batch_size, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/data_flow_ops.py", line 435, in dequeue_many
    self._queue_ref, n=n, component_types=self._dtypes, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gen_data_flow_ops.py", line 867, in _queue_dequeue_many
    timeout_ms=timeout_ms, name=name)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/op_def_library.py", line 703, in apply_op
    op_def=op_def)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/ops.py", line 2310, 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 1232, in __init__
    self._traceback = _extract_stack()
</code></pre>

<p>I have tried move the <code>train_x</code> and <code>train_y</code> placeholder initialization before <code>init = tf.initialize_all_variables()</code> and feed them to RNNClassifier() as two args but it still give the same error. Why?</p>

<p>It looks like the problem is in this method, in <code>RNNClassifier</code>:</p>

<pre><code>def batch_data(self):
    # ...
    batched_inputs, batched_labels = tf.train.batch(
        tensors=[self._train_x, self._train_y],
        batch_size=self.params.batch_size,
        dynamic_pad=True,
        enqueue_many=True,
        name='batching')
</code></pre>

<p>The two tensor arguments to <a href="https://www.tensorflow.org/versions/r0.10/api_docs/python/io_ops.html#batch" rel="nofollow"><code>tf.train.batch()</code></a> are <code>self._train_x</code> and <code>self._train_y</code>. In the <code>RNNClassifier</code> constructor, it seems like you create these as <a href="https://www.tensorflow.org/versions/r0.10/api_docs/python/io_ops.html#placeholder" rel="nofollow"><code>tf.placeholder()</code></a> tensors:</p>

<pre><code>def __init__(self, FLAGS):
    # ...
    with tf.device("/cpu:0"):
        self.train_x = tf.placeholder(tf.int32, [6248, 42], name='train_x')
        self.train_y = tf.placeholder(tf.int32, [6248, 3], name='train_y')
</code></pre>

<p>...though I'm assuming that the difference between <code>self._train_x</code> and <code>self.train_x</code> is a copy-paste error, because <code>self._train_x</code> doesn't seem to be defined anywhere else.</p>

<p>Now, one of the surprising things about <code>tf.train.batch()</code> is that it consumes its inputs in a completely separate thread, called a "queue runner", and started when you call <a href="https://www.tensorflow.org/versions/r0.10/api_docs/python/train.html#start_queue_runners" rel="nofollow"><code>tf.train.start_queue_runners()</code></a>. This thread calls <a href="https://www.tensorflow.org/versions/r0.10/api_docs/python/client.html#Session.run" rel="nofollow"><code>Session.run()</code></a> on a subgraph that depends on your placeholders, but doesn't know how to feed these placeholders, so it is this call that fails, leading to the error you are seeing.</p>

<p>How then should you fix it? One option would be to use a <a href="https://github.com/tensorflow/tensorflow/blob/450e9386e0cc4b81b49adb9184859411f43ef259/tensorflow/contrib/learn/python/learn/dataframe/queues/feeding_queue_runner.py" rel="nofollow">"feeding queue runner"</a>, for which there is experimental support. A simpler option would be to use the <a href="https://www.tensorflow.org/versions/r0.10/api_docs/python/io_ops.html#slice_input_producer" rel="nofollow"><code>tf.train.slice_input_producer()</code></a> to produce slices from your input data, as follows:</p>

<pre><code>def batch_data(self, train_x, train_y):
    input_slice, label_slice = tf.train.slice_input_producer([train_x, train_y])
    batched_inputs, batched_labels = tf.train.batch(
        tensors=[input_slice, label_slice],
        batch_size=self.params.batch_size,
        dynamic_pad=False,   # All rows are the same shape.
        enqueue_many=False,  # tf.train.slice_input_producer() produces one row at a time.
        name='batching')
    return batched_inputs, batched_labels

# ...
# Create batches from the entire training data, where `array_input` and
# `array_labels` are two NumPy arrays.
batched_inputs, batched_labels = model.batch_data(array_input, array_labels)
</code></pre>

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