How to understand the term `tensor` in TensorFlow?

Question

I am new to TensorFlow. While I am reading the existing documentation, I found the term tensor really confusing. Because of it, I need to clarify the following

Answer 1

From the glossary:

A Tensor is a typed multi-dimensional array. For example, a 4-D array of floating point numbers representing a mini-batch of images with dimensions [batch, height, width, channel].

Basically, every data is a Tensor in TensorFlow (hence the name):

• placeholders are Tensors to which you can feed a value (with the feed_dict argument in sess.run())
• Variables are Tensors which you can update (with var.assign()). Technically speaking, tf.Variable is not a subclass of tf.Tensor though
• tf.constant is just the most basic Tensor, which contains a fixed value given when you create it

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However, in the graph, every node is an operation, which can have Tensors as inputs or outputs.