Tensorflow Shape must be rank 1 but is rank 2

Question

When I declare my variable like this:

x = tf.Variable([len(_ELEMENT_LIST), 4], dtype=tf.float32)

I get the following error:

E0622 20:04:25.241938   21886 app.py:544] Top-level exception: Shape must be rank 1 but is rank 2 for 'input_layer/concat' (op: 'ConcatV2') with input shapes: [5], [5,1], [5,1], [].
E0622 20:04:25.252672   21886 app.py:545] Traceback (most recent call last):

When I do it like this:

x = tf.get_variable("x", [len(_ELEMENT_LIST), 4])

It works

I'm trying to compute Tensors using concat.

tf.concat([
        x, features["y"],
        features["z"]
    ], 1)

Answer 1

x = tf.Variable([len(_ELEMENT_LIST), 4], dtype=tf.float32)

the first parameter of tf.Variable is the initial value of the variable, so in the upper statement x is a Variable with value [len(_ELEMENT_LIST), 4] , and it's rank of shape is 1.

x = tf.get_variable("x", [len(_ELEMENT_LIST), 4])

the second parameter of tf.get_variable is the shape of Variable, so the rank of shape of Variable X is 2.