[Python] Augment MNIST dataset tensorflow

I am trying to augment the MNIST dataset. This is what I tried. Can't get any success.

from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf

X = mnist.train.images
y = mnist.train.labels

def flip_images(X_imgs):
X_flip = []
tf.reset_default_graph()
X = tf.placeholder(tf.float32, shape = (28, 28, 1))
input_d = tf.reshape(X_imgs, [-1, 28, 28, 1])
tf_img1 = tf.image.flip_left_right(X)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for img in input_d:
flipped_imgs = sess.run([tf_img1], feed_dict = {X: img})
X_flip.extend(flipped_imgs)
X_flip = np.array(X_flip, dtype = np.float32)
return X_flip

flip = flip_images(X)


What am I doing wrong? I can't seem to figure out.

Error:

Line: for img in input_d:
raise TypeError("'Tensor' object is not iterable.")
TypeError: 'Tensor' object is not iterable

Continue reading...