What are tensors in TensorFlow and what are their roles?

In TensorFlow, tensors are multi-dimensional arrays similar to NumPy’s ndarray objects. Tensors are the core data structure in TensorFlow, used to represent input and output data in computation graphs. Tensors can be scalars (0 dimensions), vectors (1 dimension), matrices (2 dimensions), or arrays of higher dimensions.

In TensorFlow, tensors are used to store and pass data, as well as perform various mathematical operations. Within a computational graph, tensors can serve as inputs and outputs between nodes, allowing for processing and transformation through different operations to accomplish complex computation tasks.

In general, tensors in TensorFlow are the core data structure used to represent and process data, serving as the fundamental unit of operations in TensorFlow. Tensors allow for the construction of various complex deep learning models and enable efficient computation and optimization.