tf.rank function in Tensorflow

Question

I ma trying to understand tf.rank function in tensorflow. From the documentation here, I understood that rank should return the number of distinct elements in the tensor.

Here x and weights are 2 distinct 2*2 tensors with 4 distinct elemnts in each of them. However, rank() function outputs are:

Tensor("Rank:0", shape=(), dtype=int32) Tensor("Rank_1:0", shape=(), dtype=int32)

Also, for the tensor x, I used tf.constant() with dtype = float to convert ndarray into float32 tensor but the rank() still outputs as int32.

g = tf.Graph()
with g.as_default():
    weights = tf.Variable(tf.truncated_normal([2,2]))
    x = np.asarray([[1 , 2], [3 , 4]])
    x = tf.constant(x, dtype = tf.float32)
    y = tf.matmul(weights, x)
    print (tf.rank(x), tf.rank(weights))


with tf.Session(graph = g) as s:
    tf.initialize_all_variables().run()
    print (s.run(weights), s.run(x))
    print (s.run(y))

How should I interpret the output.

Answer 1

Firstly, tf.rank returns the dimension of a tensor, not the number of elements. For instance, the output from tf.rank called for the 2x2 matrix would be 2.

To print the rank of a tensor, create an appropriate node, e.g. rank = tf.rank(x) and then evaluate this node using a Session.run(), as you've done for weights and x. Execution of print (tf.rank(x), tf.rank(weights)) expectedly prints out description of tensors, as tf.rank(x), tf.rank(weights) are nodes of the graph, not the variables with defined values.