feed_dict in for loop - tensorflow

Question

I'm trying to make this old code work. Tensorflow has deleted train and next_batch.
I adjusted the code as much as I can but faced a stone hard wall here.
from keras.datasets import mnist
import keras as K
(Xtr, Ytr), (Xte, Yte) = mnist.load_data()
#Xtr, Ytr = mnist.ds_train.batch(5000) #5000 for training (nn candidates)
#Xte, Yte = mnist.test.next_batch(200) #200 for testing
# tf Graph Input
xtr = tf.placeholder("float", [None, 784])
xte = tf.placeholder("float", [784])
# Nearest Neighbor calculation using L1 Distance
# Calculate L1 Distance
distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices=1)
# Prediction: Get min distance index (Nearest neighbor)
pred = tf.arg_min(distance, 0)
accuracy = 0.
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
xtr = tf.reshape(xtr, (-1, 28, 28))
xte = tf.reshape(xtr, (-1, 28, 28))
Training start
# Start training
with tf.Session() as sess:
sess.run(init)
# loop over test data
for i in range(Xte.size):
# Get nearest neighbor\
n_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[i, :]})
# Get nearest neighbor class label and compare it to its true label
print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), "True Class:", np.argmax(Yte[i]))
# Calculate accuracy
if np.argmax(Ytr[nn_index]) == np.argmax(Yte[i]):
accuracy += 1./len(Xte)
it seems to be impossible to have an index in an array size in loop.
However, The OUTPUT is like this
(10000, 28, 28)
(28, 28)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-31-4c49a9166101> in <module>
9 Xtee = Xte[i, :, :]
10 print(Xtee.shape)
---> 11 nn_index = sess.run(pred, feed_dict={xtr: Xtr, xte: Xte[50, :]})
12 # Get nearest neighbor class label and compare it to its true label
13 print("Test", i, "Prediction:", np.argmax(Ytr[nn_index]), "True Class:", np.argmax(Yte[i]))
1 frames
/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1163 not subfeed_t.get_shape().is_compatible_with(np_val.shape)):
1164 raise ValueError(
-> 1165 f'Cannot feed value of shape {str(np_val.shape)} for Tensor '
1166 f'{subfeed_t.name}, which has shape '
1167 f'{str(subfeed_t.get_shape())}')
ValueError: Cannot feed value of shape (28, 28) for Tensor Reshape_3:0, which has shape (?, 28, 28)