FailedPreconditionError: GetNext() failed because the iterator has not been initialized - tensorflow

Question

I am new to tensorflow and trying to create a simple MLP. My model is running fine but is not giving the desired performance. I tried to create summaries but am now getting this error:
FailedPreconditionError: GetNext() failed because the iterator has not been initialized. Ensure that you have run the initializer operation for this iterator before getting the next element.
My code:
def fc_layer(input, channels_in,channels_out, name = "fc"):
with tf.name_scope(name):
W = tf.Variable(tf.zeros([channels_in, channels_out]), name="weights")
clip_op = tf.assign(W, tf.clip_by_norm(W, 1, axes = None))
b = tf.Variable(tf.zeros([channels_out]), name="biases")
act = tf.matmul(input, W) + b
tf.summary.histogram("weights", W)
tf.summary.histogram("biases", b)
tf.summary.histogram("activations", act)
return act
# Setup placeholders, and reshape the data
y = tf.placeholder(tf.float32, shape=[None,128], name = 'y')
x = tf.placeholder(tf.float32, shape=[None,256], name = 'x')
dataset = tf.data.Dataset.from_tensor_slices((y, x)).batch(batch_size).repeat()
iter = dataset.make_initializable_iterator()
input_features, output_features = iter.get_next()
fc_1 = tf.nn.relu(fc_layer(input_features, 128,512, name = "fc1"))
fc_2 = tf.nn.relu(fc_layer(fc_1, 512,256, name = "fc1"))
out_layer = fc_layer(fc_2, 256,256, name = "out")
with tf.name_scope('loss'):
loss_op =
tf.sqrt(tf.reduce_mean(tf.squared_difference(out_layer,output_features)))
tf.summary.scalar("loss", loss_op)
with tf.name_scope('train'):
train_op =
tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss_op)
#Summary writer
merged_summary = tf.summary.merge_all()
writer = tf.summary.FileWriter(r'C:\Users\Jaweria\Documents\Code_logs',
graph=tf.get_default_graph())
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# initialise iterator with train data
sess.run(iter.initializer, feed_dict={ y: train_data[0], x: train_data[1], batch_size: Batch_Size})
print('Training...')
for i in range(training_epochs):
tot_loss = 0
for _ in range(n_batches):
_, loss_value = sess.run([train_op, loss_op])
tot_loss += loss_value
s = sess.run(merged_summary)
writer.add_summary(s,i*n_batches+ _)
print("Iter: {}, Loss: {:.4f}".format(i, tot_loss / n_batches))
# initialise iterator with test data
sess.run(iter.initializer, feed_dict={ y: test_data[0], x: test_data[1],
batch_size: test_data[0].shape[0]})
print('Test Loss: {:4f}'.format(sess.run(loss_op)))