What’s the difference between tf.placeholder and tf.Variable?

In short, you use tf.Variable for trainable variables such as weights (W) and biases (B) for your model.

weights = tf.Variable(
    tf.truncated_normal([IMAGE_PIXELS, hidden1_units],
                    stddev=1.0 / math.sqrt(float(IMAGE_PIXELS))), name="weights")

biases = tf.Variable(tf.zeros([hidden1_units]), name="biases")

tf.placeholder is used to feed actual training examples.

images_placeholder = tf.placeholder(tf.float32, shape=(batch_size, IMAGE_PIXELS))
labels_placeholder = tf.placeholder(tf.int32, shape=(batch_size))

This is how you feed the training examples during the training:

for step in xrange(FLAGS.max_steps):
    feed_dict = {
       images_placeholder: images_feed,
       labels_placeholder: labels_feed,
     }
    _, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)

Your tf.variables will be trained (modified) as the result of this training.

See more at https://www.tensorflow.org/versions/r0.7/tutorials/mnist/tf/index.html. (Examples are taken from the web page.)