tensorflow TypeError: unhashable type: 'numpy.ndarray'

So I was trying to make a tensorflow model with my own data but it seems like numpy array isnt a feedable type in tf.Session.run()?

I checked the website and it is supposed to be feedable as shown in one of their example:

x = tf.placeholder(tf.float32, shape=(1024, 1024))
y = tf.matmul(x, x)

with tf.Session() as sess:
  print(sess.run(y))  # ERROR: will fail because x was not fed.

  rand_array = np.random.rand(1024, 1024) #HERE IS THE NUMPY ARRAY

  print(sess.run(y, feed_dict={x: rand_array}))  # Will succeed.

After searching a couple of time, it seems like most of the time the problem is with renaming placeholder variable during the code which mess stuff up but it isn't the case in my code, at least I don't think so.

Here is the full working code and the input file data:

import tensorflow as tf
import numpy as np

data = np.load("test_data.npz")
trng_input = np.array(data['Input'], dtype=np.float64)
trng_output = np.array(data['Output'], dtype=np.float64)

nhl1 = 16
nhl2 = 8
n_classes = 4


x = tf.placeholder(dtype=tf.float64, shape=[len(trng_input),24])
y = tf.placeholder(dtype=tf.float64, shape=[len(trng_output),n_classes])

def NN(data):
    hl1 = {"weights":tf.Variable(tf.random_normal([24, nhl1], dtype=tf.float64)),
           "biases":tf.Variable(tf.random_normal([nhl1], dtype=tf.float64))}

    hl2 = {"weights":tf.Variable(tf.random_normal([nhl1, nhl2], dtype=tf.float64)),
           "biases":tf.Variable(tf.random_normal([nhl2], dtype=tf.float64))}

    output_layer = {"weights":tf.Variable(tf.random_normal([nhl2, n_classes], dtype=tf.float64)),
                    "biases":tf.Variable(tf.random_normal([n_classes], dtype=tf.float64))}

    l1 = tf.add(tf.matmul(data, hl1["weights"]), hl1["biases"])
    l1 = tf.nn.leaky_relu(l1, alpha=0.2)

    l2 = tf.add(tf.matmul(l1, hl2["weights"]), hl2["biases"])
    l2 = tf.nn.leaky_relu(l2, alpha=0.2)

    output = tf.add(tf.matmul(l2, output_layer["weights"]), output_layer["biases"])
##    output = tf.nn.leaky_relu(l1, alpha=0.2)

    return output

def train(x):
    prediction = NN(x)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction, labels=y))
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    epochs = 100

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        for epoch in range(epochs):
            epoch_loss = 0
            _, c = sess.run([optimizer, cost], feed_dict={x: trng_input, y: trng_output})
            epoch_loss += c
            print(F"Epoch {epoch} completed out of {epochs}. \nloss:{epoch_loss}")

        correct = tf.equal(tf.argmax(prediction,1), tf.argmax(y, 1))

        accuracy = tf.reduce_mean(tf.cast(correct,"float"))
        Eval = accuracy.eval({x:trng_input, y:trng_output})
        print(F"Accuracy:{Eval}")

train(trng_input)

Full error as requested:

Traceback (most recent call last):
  File "C:\Python36\machine_learning\real_tf_folder\test.py", line 59, in <module>
    train(trng_input)
  File "C:\Python36\machine_learning\real_tf_folder\test.py", line 49, in train
    _, c = sess.run([optimizer, cost], feed_dict={x: trng_input, y: trng_output})
 TypeError: unhashable type: 'numpy.ndarray'