What is the difference between [], [None], None and () for the shape of a placeholder?

Question

I have seen pieces of code using either [], [None], None or () as the shape for a placeholder, that is

x = tf.placeholder(..., shape=[], ...) y = tf.placeholder(..., shape=[None], ...) z = tf.placeholder(..., shape=None, ...)  w = tf.placeholder(..., shape=(), ...) 

What's the difference between these?

Answer 1

TensorFlow uses arrays rather than tuples. It converts tuples to arrays. Therefore [] and () are equivalent.

Now, consider this code example:

x = tf.placeholder(dtype=tf.int32, shape=[], name="foo1") y = tf.placeholder(dtype=tf.int32, shape=[None], name="foo2") z = tf.placeholder(dtype=tf.int32, shape=None, name="foo3")  val1 = np.array((1, 2, 3)) val2 = 45  with tf.Session() as sess:     sess.run(tf.global_variables_initializer())      #print(sess.run(x, feed_dict = {x: val1}))  # Fails     print(sess.run(y, feed_dict = {y: val1}))     print(sess.run(z, feed_dict = {z: val1}))      print(sess.run(x, feed_dict = {x: val2}))     #print(sess.run(y, feed_dict = {y: val2}))  # Fails     print(sess.run(z, feed_dict = {z: val2})) 

As can be seen, placeholder with [] shape takes a single scalar value directly. Placeholder with [None] shape takes a 1-dimensional array and placeholder with None shape can take in any value while computation takes place.