How to feed data properly in tensorflow - dictionary

Question

I have been learning Tensorflow and understanding feed_dict has been a challenge. Take for example the following piece of code i am working on
p=0
self.sequence_length=25
with tf.Session() as sess:
init.run()
char_to_ix={ch:ix for ix,ch in enumerate(self.words)}
ix_to_char={ix:ch for ix,ch in enumerate(self.words)}
words_in_input=self.data[p:p+self.sequence_length]
inputs=[char_to_ix[ix] for ix in words_in_input]
words_in_target=self.data[p+1:p+self.sequence_length+1]
targets=[char_to_ix[ix] for ix in words_in_target]
onex=sess.run([selected_next_letter],feed_dict={self.X:inputs,self.y:targets})
p=p+1
This gives the error: Shapes of all inputs must match: values[0].shape = [25] != values[1].shape = []
However, when I edit the code to
with tf.Session() as sess:
init.run()
char_to_ix={ch:ix for ix,ch in enumerate(self.words)}
ix_to_char={ix:ch for ix,ch in enumerate(self.words)}
words_in_input=self.data[p:p+self.sequence_length]
inputs=[char_to_ix[ix] for ix in words_in_input]
words_in_target=self.data[p+1:p+self.sequence_length+1]
targets=[char_to_ix[ix] for ix in words_in_target]
for x,y in zip(inputs,targets):
onex=sess.run([selected_next_letter],feed_dict={self.X:x,self.y:y})
It executes.
My questions is: Is it possible to feed the whole list such as inputs and targets in the feed_dict or must I input it through a loop one by one. I ask this because the tutorials I have been reading, I see a whole list being passed in a feed_dict such as
loss_val = sess.run([train_op, loss_mean], feed_dict={
images_batch:images_batch_val,
labels_batch:labels_batch_val
})

Answer 1

Usually the reason for that error is because your input array(x) isn’t the same size as your labels array(y). As the error states it looks like your labels array is empty. Before doing anything tensorflowy make sure both x and y arrays have values in them and that they are of the same size.
To answer your question, yes you can use lists when training and is the preferred way of using tensorflow.