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What does TensorFlow shape (?,) mean?

I'm getting the shape of a TensofFlow tensor as:

(?,)

This answer says that the ? means that the dimension is not fixed in the graph and it can vary between run calls.

What does the ? mean in conjuction with the trailing comma?

Documentation chapter and verse would be appreciated. I find syntax very difficult to google.

like image 973
Tom Hale Avatar asked Aug 22 '18 06:08

Tom Hale


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What does shape mean in TensorFlow?

shape returns a 1-D integer tensor representing the shape of input . For a scalar input, the tensor returned has a shape of (0,) and its value is the empty vector (i.e. []).

How do you read a TensorFlow shape?

The shape is the number of elements in each dimension, e.g.: a scalar has a rank 0 and an empty shape () , a vector has rank 1 and a shape of (D0) , a matrix has rank 2 and a shape of (D0, D1) and so on.

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tf. reshape(t, []) reshapes a tensor t with one element to a scalar.

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Reading about the terminology you can see that shape (3, ) means you have a vector of 3 elements. And this is exactly what you provide [1, 3, 8] . Reading about shapes you would see that you want your placeholder to be a matrix of size (3 x something). So adjust either a placeholder or a feeding value.


1 Answers

The comma means that the dimension is represented as a 1-elem tuple instead an int.

Each tensor, when created, is by default a n-dim:

import tensorflow as tf
t = tf.constant([1, 1, 1])
s = tf.constant([[1, 1, 1],[2,2,2]])

print("0) ", tf.shape(t))
print("1) ", tf.shape(s))

0)  Tensor("Shape_28:0", shape=(1,), dtype=int32)
1)  Tensor("Shape_29:0", shape=(2,), dtype=int32)

However, you can reshape it to get a more "whole" shape (i.e. nXm / nXmXr... dim):

print("2) ", tf.reshape(t, [3,1]))
print("3) ", tf.reshape(s, [2,3]))

2)  Tensor("Reshape_12:0", shape=(3, 1), dtype=int32)
3)  Tensor("Reshape_13:0", shape=(2, 3), dtype=int32)
like image 172
CIsForCookies Avatar answered Oct 01 '22 21:10

CIsForCookies