Blue482 Blue482 - 2 months ago 63
Python Question

Unable to feed value for placeholder tensor

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.

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)


And the
RNNClassifier
class 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


The output (including the error):

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) -> (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 <module>
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 <module>
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()


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

Answer

This is what I did..

I could change the way I initialised my input variables as:

data = load_data(FLAGS.data)
model = RNNClassifier(FLAGS, data)
init = tf.initialize_all_variables()

with tf.Session() as sess:
    sess.run(init)
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    for epoch in range(FLAGS.max_max_epoch):
        sess.run(model.train_step)
        loss, acc = sess.run([model.mean_cost, model.accuracy])
        print("Epoch {:2d}: Loss = {:.6f}; Training Accuracy = {:.5f}".format(epoch+1, loss, acc))
    print()
    coord.request_stop()
    coord.join(threads)

And in RNNClassifier class I could replace

    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])

(and remove use_embedding()) to

def __init__(self, FLAGS, data):
    self._train_x = tf.convert_to_tensor(data.train_x, dtype=tf.int32)
    self._train_y = tf.convert_to_tensor(data.train_y, dtype=tf.int32)
    embedding = tf.get_variable("embedding", shape=self.embedding_shape, trainable=False)
    self.embedding_init = embedding.assign(data.glove_vec)

This way everything is initialised with RNNClassifier(FLAGS, data) before making the call to the queue runners.

Or I could initialise my input variables outside of the class RNNClassifier and feed them once I decide to run my session:

_train_x = tf.Variable(tf.zeros([6248, 42], tf.int32), dtype=tf.int32, name='train_x')
_train_y = tf.Variable(tf.zeros([6248, 3], tf.int32), dtype=tf.int32, name='train_y')
model = RNNClassifier(FLAGS, data, _train_x, _train_y)
init = tf.initialize_all_variables()
with tf.Session() as sess:
    sess.run(init)
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    for epoch in range(FLAGS.max_max_epoch):
        sess.run(model.train_step, feed_dict={_train_x: data.train_x, _train_y: data.train_y})
        loss, acc = sess.run([model.mean_cost, model.accuracy], feed_dict={_train_x: data.train_x, _train_y: data.train_y})
        print("Epoch {:2d}: Loss = {:.6f}; Training Accuracy = {:.5f}".format(epoch+1, loss, acc))
    print()
    coord.request_stop()
    coord.join(threads)

You're probably as confused as I am, as why I made my two inputs _train_x and _train_y tensorflow variables not placeholders. Here suggests this is to initialise through sess.run(init) before start all queue runners. I am still not sure how to take this? If you can explain if do so, much appreciated. Thanks.

ps: Can I just say I do find the Tensorflow community on Stackoverflow quieter than I expected. I have posted two other questions and neither has a answer.

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