need help deciphering tensorflow feed InvalidArgumentError - tensorflow

Question

I don't think I understand this feed error from tensorflow
Debug: [[ 0. 0.]]
Debug: (1, 2)
Debug: float64
2018-05-09 09:56:34.615561: W tensorflow/core/kernels/queue_base.cc:295] _0_input_producer: Skipping cancelled enqueue attempt with queue not closed
Traceback (most recent call last):
File "/home/kiran/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1323, in _do_call
return fn(*args)
File "/home/kiran/anaconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1302, in _run_fn
status, run_metadata)
File "/home/kiran/anaconda3/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'seqModel/a_prev' with dtype double and shape [1,2]
[[Node: seqModel/a_prev = Placeholder[dtype=DT_DOUBLE, shape=[1,2], _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
the way I feed the placeholder is:
self.a_prev = tf.placeholder(tf.float64, shape=[1,2], name='a_prev')
batch = tf.train.batch([self.x_acc, self.y_acc,
self.prev_pos],
batch_size=1, capacity=20000, num_threads=1)
x_acc, y_acc, prev_pos = sess.run(batch)
test = np.array([[ x_acc[0,0], y_acc[0,0] ]])
print("Debug: ",test)
print("Debug:",test.shape)
print("Debug:",test.dtype)
_,X_hat_val,loss_val, X_val = sess.run([train,X_hat,loss, self.X],
feed_dict={self.a_prev : np.array([[x_acc[0,0],y_acc[0,0] ]]),
self.pos1 : np.array([[ prev_pos[0,0] ]])
})
The error does not make sense because I am feeding values to the placeholder but it says that there are no values. What does that mean?

Answer 1

NB: I didn't run your code, as it depends on unavailable data.
However, it's probable your error is caused by reassigning the self.a_prev attribute, line 173. With this line, self.a_prev doesn't point to the tf.placeholder(..., name='a_prev') anymore, but to a different Tensor (from self.new_evidence) - so the actual placeholder doesn't get fed when running.
Toy example for this supposition
import tensorflow as tf
import numpy as np
x_acc = np.random.rand(2, 2)
y_acc = np.random.rand(2, 2)
a_prev = tf.placeholder(tf.float64, shape=[1,2], name='a_prev')
some_results = tf.add(a_prev, 1.)
a_prev = tf.constant([[-1, -1]])
# ... now "a_prev" the python variable isn't pointing to the placeholder anymore,
# so "a_prev" the placeholder exists in the graph with no python pointer to it.
with tf.Session() as sess:
res = sess.run(some_results, feed_dict={a_prev : np.array([[x_acc[0,0],y_acc[0,0] ]])})
# "a_prev" the constant is assigned the values, not "a_prev" the placeholder,
# hence an error.
InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'a_prev' with dtype double and shape [1,2]
[[Node: a_prev = Placeholderdtype=DT_DOUBLE, shape=[1,2],
_device="/job:localhost/replica:0/task:0/device:GPU:0"]] [[Node: Add/_1 = _Recvclient_terminated=false,
recv_device="/job:localhost/replica:0/task:0/device:CPU:0",
send_device="/job:localhost/replica:0/task:0/device:GPU:0",
send_device_incarnation=1, tensor_name="edge_8_Add",
tensor_type=DT_DOUBLE,
_device="/job:localhost/replica:0/task:0/device:CPU:0"]]