### 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 questions:

- What is the relationship between
`tensor`

and`Variable`

,`tensor`

vs.`tf.constant`

, ‘tensor’ vs.`tf.placeholder`

? - Are they all types of tensors?

##
Answer #1:

TensorFlow doesn’t have first-class Tensor objects, meaning that there are no notion of `Tensor`

in the underlying graph that’s executed by the runtime. Instead the graph consists of op nodes connected to each other, representing operations. An operation allocates memory for its outputs, which are available on endpoints `:0`

, `:1`

, etc, and you can think of each of these endpoints as a `Tensor`

. If you have `tensor`

corresponding to `nodename:0`

you can fetch its value as `sess.run(tensor)`

or `sess.run('nodename:0')`

. Execution granularity happens at operation level, so the `run`

method will execute op which will compute all of the endpoints, not just the `:0`

endpoint. It’s possible to have an Op node with no outputs (like `tf.group`

) in which case there are no tensors associated with it. It is not possible to have tensors without an underlying Op node.

You can examine what happens in underlying graph by doing something like this

```
tf.reset_default_graph()
value = tf.constant(1)
print(tf.get_default_graph().as_graph_def())
```

So with `tf.constant`

you get a single operation node, and you can fetch it using `sess.run("Const:0")`

or `sess.run(value)`

Similarly, `value=tf.placeholder(tf.int32)`

creates a regular node with name `Placeholder`

, and you could feed it as `feed_dict={"Placeholder:0":2}`

or `feed_dict={value:2}`

. You can not feed and fetch a placeholder in the same `session.run`

call, but you can see the result by attaching a `tf.identity`

node on top and fetching that.

For variable

```
tf.reset_default_graph()
value = tf.Variable(tf.ones_initializer()(()))
value2 = value+3
print(tf.get_default_graph().as_graph_def())
```

You’ll see that it creates two nodes `Variable`

and `Variable/read`

, the `:0`

endpoint is a valid value to fetch on both of these nodes. However `Variable:0`

has a special `ref`

type meaning it can be used as an input to mutating operations. The result of Python call `tf.Variable`

is a Python `Variable`

object and there’s some Python magic to substitute `Variable/read:0`

or `Variable:0`

depending on whether mutation is necessary. Since most ops have only 1 endpoint, `:0`

is dropped. Another example is `Queue`

— `close()`

method will create a new `Close`

op node which connects to `Queue`

op. To summarize — operations on python objects like `Variable`

and `Queue`

map to different underlying TensorFlow op nodes depending on usage.

For ops like `tf.split`

or `tf.nn.top_k`

which create nodes with multiple endpoints, Python’s `session.run`

call automatically wraps output in `tuple`

or `collections.namedtuple`

of `Tensor`

objects which can be fetched individually.

##
Answer #2:

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

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

##
Answer #3:

As already mentioned by others, yes they are all tensors.

The way I understood those is to first visualize and understand 1D, 2D, 3D, 4D, 5D, and 6D tensors as in the picture below. (source: *knoldus*)

Now, in the context of TensorFlow, you can imagine a computation graph like the one below,

Here, the `Op`

s take two tensors `a`

and `b`

as *input*; *multiplies* the tensors with itself and then *adds* the result of these multiplications to produce the result tensor `t3`

. And these *multiplications* and *addition* `Op`

s happen at the nodes in the computation graph.

And these tensors `a`

and `b`

can be constant tensors, Variable tensors, or placeholders. It doesn’t matter, as long as they are of the same *data type* and compatible shapes(or `broadcast`

able to it) to achieve the operations.

##
Answer #4:

TensorFlow’s central data type is the tensor. Tensors are the underlying components of computation and a fundamental data structure in TensorFlow. Without using complex mathematical interpretations, we can say a tensor (in TensorFlow) describes a multidimensional numerical array, with zero or n-dimensional collection of data, determined by rank, shape, and type.Read More: What is tensors in TensorFlow?