Error feeding a placeholder - tensorflow

Question

I´m having issues feeding one of my placeholders (keep_prob). The error says that I have to feed a float value, but I´m already doing it. I´ve been trying to solve it, but I can´t figure out a solution. My code is here:
Error while running a convolutional network using my own data in Tensorflow
And my error is:
File "<ipython-input-81-fd184c90091e>", line 4, in <module>
keep_prob = tf.placeholder(tf.float32)
File "c:\python36\lib\site-packages\tensorflow\python\ops\array_ops.py",
line 1530, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "c:\python36\lib\site-packages\tensorflow\python\ops\gen_array_ops.py",
line 1954, in _placeholder
name=name)
File "c:\python36\lib\site-
packages\tensorflow\python\framework\op_def_library.py", line 767, in
apply_op
op_def=op_def)
File "c:\python36\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:\python36\lib\site-packages\tensorflow\python\framework\ops.py",
line 1269, in __init__
self._traceback = _extract_stack()
InvalidArgumentError (see above for traceback): You must feed a value for
placeholder tensor 'Placeholder_17' with dtype float
[[Node: Placeholder_17 = Placeholder[dtype=DT_FLOAT, shape=<unknown>,
_device="/job:localhost/replica:0/task:0/cpu:0"]()]]
Thank you.

Answer 1

I suspect that your error comes not from the line that you think. I can't run your code, but looking at it, my guess is the following:
You are feeding the keep_prob value here, which is OK for training:
train_step.run(feed_dict={x: image_batch_eval, y_: label_batch_eval,
keep_prob: 0.5})
But you are not feeding the keep_prob value here, which you also have to do:
print('Precisión %g' % accuracy.eval(feed_dict={x: image_test_batch_eval,
y_: label_test_batch_eval}))
If you look at your code, the accuracy operation ultimately leeds up to this operation, which needs the placeholder:
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
So, if you are evaluating for example the validation or testing accuracy, then feed value 1.0 otherwise feed whatever you are feeding for training.
Give it a try?