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

In TensorFlow, it is sometimes necessary to obtain integer values for the tensor's dimensions (shape) for certain computations. Obtaining the tensor's shape can be done via the .shape attribute; however, this typically returns a TensorShape object whose dimension values may include None (if a dimension is not fixed during graph construction). To obtain specific integer values, several methods can be employed:

Method One: Using tf.shape Function

The tf.shape function can be used to obtain the tensor's shape at runtime as a new tensor, returning a 1-dimensional integer tensor. If you need to use these specific dimension values as integers for computation, you can convert them or use tf.get_static_value.

python
import tensorflow as tf

tensor = tf.zeros([10, 20, 30])
shape_tensor = tf.shape(tensor)  # Returns a tensor
print("Shape tensor:", shape_tensor)

# If you need to use these values outside the graph, you can use the following:
shape_list = shape_tensor.numpy()  # Convert to numpy array (only valid in Eager mode or outside tf.function)
print("Shape as list of integers:", shape_list)

Method Two: Using .get_shape() and .as_list()

If the tensor's shape is fully known during graph construction, you can directly obtain the integer shape list using .get_shape() and .as_list().

python
tensor = tf.zeros([10, 20, 30])
static_shape = tensor.get_shape().as_list()  # Returns the shape as an integer list
print("Static shape:", static_shape)

Method Three: Through Tensor Attributes

If the tensor's shape is explicitly defined when creating the tensor, you can directly access it via tensor attributes:

python
tensor = tf.zeros([10, 20, 30])
height, width, depth = tensor.shape[0], tensor.shape[1], tensor.shape[2]
print("Height:", height, "Width:", width, "Depth:", depth)

Each method has its pros and cons. Generally, if you are unsure about specific dimension values during graph construction, the first method is more flexible. If dimensions are known at compile time, the second and third methods are simpler and more direct. In practice, choose the appropriate method based on the specific context.