How to get Tensorflow tensor dimensions (shape) as int values?

Question

Suppose I have a Tensorflow tensor. How do I get the dimensions (shape) of the tensor as integer values? I know there are two methods, tensor.get_shape() and

Answer 1

Another simple solution is to use map() as follows:

tensor_shape = map(int, my_tensor.shape)

This converts all the Dimension objects to int

Answer 2

Another way to solve this is like this:

tensor_shape[0].value

This will return the int value of the Dimension object.

Answer 3

In later versions (tested with TensorFlow 1.14) there's a more numpy-like way to get the shape of a tensor. You can use tensor.shape to get the shape of the tensor.

tensor_shape = tensor.shape
print(tensor_shape)

Answer 4

for a 2-D tensor, you can get the number of rows and columns as int32 using the following code:

rows, columns = map(lambda i: i.value, tensor.get_shape())

Answer 5

2.0 Compatible Answer: In Tensorflow 2.x (2.1), you can get the dimensions (shape) of the tensor as integer values, as shown in the Code below:

Method 1 (using tf.shape):

import tensorflow as tf
c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
Shape = c.shape.as_list()
print(Shape)   # [2,3]

Method 2 (using tf.get_shape()):

import tensorflow as tf
c = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
Shape = c.get_shape().as_list()
print(Shape)   # [2,3]

Answer 6

To get the shape as a list of ints, do tensor.get_shape().as_list().

To complete your tf.shape() call, try tensor2 = tf.reshape(tensor, tf.TensorShape([num_rows*num_cols, 1])). Or you can directly do tensor2 = tf.reshape(tensor, tf.TensorShape([-1, 1])) where its first dimension can be inferred.