What is Tensor Shape?
Tensor shape refers to the layout or structure of a tensor, which defines the number of dimensions and the size of each dimension in the tensor. It describes how many elements are along each axis of the tensor.
Python3
# Create a tensortensor = tf.constant([[1, 2, 3], [4, 5, 6]])# Get the shape of the tensorshape = tensor.shapeprint("Tensor shape:", shape) |
Output:
Tensor shape: (2, 3)
Tensor shape is crucial in TensorFlow because it determines how the data is organized and how operations can be applied to the tensor. TensorFlow provides methods to get and manipulate the shape of a tensor, allowing developers to work with tensors effectively in machine learning models and other numerical computations.
What is Tensor and Tensor Shapes?
Tensors are multidimensional arrays, fundamental to TensorFlow’s operations and computations. Understanding key concepts like tensor shape, size, rank, and dimension is crucial for effectively using TensorFlow in machine learning projects. In this article, we are going to understand tensor and its properties.