## get 100 random rows

random100 = tests.random_batch(100)
x_batch = pre_process_images(images_train[random100, :, :, :], istrain=True)

img_size = 28
num_channels = 3
x = tf.placeholder(tf.float32, shape=[100,
                                      img_size,
                                      img_size,
                                      num_channels], name='x')

## making it float32 -  i was getting an error here - this casting did the fix
loss = tf.cast(x_batch, tf.float32)

#x_batch = x
test = main_network(loss)
session.run(x_batch)