关于tf.constant([1,2,3])与tf.constant([[1,2,3]])的区别及类型疑问

Great question! Let's break this down clearly and correct a common misunderstanding in your query.

Core Differences: Rank (Dimension) and Shape #

The two tensors differ entirely in their rank (number of dimensions) and shape:

• tf.constant([1,2,3]):

  • This creates a 1-dimensional tensor (rank=1)—what we usually call a vector.
  • Its shape is (3,), meaning it has one single axis with 3 elements. Think of it as a straight list of values with no row/column structure.
  • To access elements: Use a single index, e.g., t1[0] returns 1, t1[2] returns 3.

• tf.constant([[1,2,3]]):

  • This creates a 2-dimensional tensor (rank=2)—a matrix.
  • Its shape is (1, 3), meaning it has two axes: the first axis (rows) holds 1 element, and the second axis (columns) holds 3 elements. You can visualize this as a single row of 3 values.
  • To access elements: Use two indices, e.g., t2[0, 0] returns 1, t2[0, 2] returns 3.

Correcting Your Misunderstanding #

You asked if tf.constant([1,2,3]) creates a scalar and the other creates an array—this isn't accurate:

• A scalar is a 0-dimensional tensor, like tf.constant(5) (shape ()). The [1,2,3] version is a 1D vector, not a scalar.
• Both are TensorFlow tensors (multi-dimensional arrays at their core)—the only difference is their number of dimensions. There's no "scalar vs array" split here; both are array-like structures, just with different ranks.

In short: The extra set of square brackets adds an entire dimension, turning a 1D vector into a 2D single-row matrix.

内容的提问来源于stack exchange，提问作者David_tut