Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How to get the dimensions of a tensor (in TensorFlow) at graph construction time** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

## How to get the dimensions of a tensor (in TensorFlow) at graph construction time?

**How to get the dimensions of a tensor (in TensorFlow) at graph construction time?**`tensor.get_shape`

is used for fixed shapes, which means the tensor's**shape can be deduced**in the graph.**get the dimensions of a tensor (in TensorFlow) at graph construction time**`tensor.get_shape`

is used for fixed shapes, which means the tensor's**shape can be deduced**in the graph.

## Method 1

I see most people confused about `tf.shape(tensor)`

and `tensor.get_shape()`

Let’s make it clear:

`tf.shape`

`tf.shape`

is used for dynamic shape. If your tensor’s shape is **changable**, use it. An example: a input is an image with changable width and height, we want resize it to half of its size, then we can write something like:`new_height = tf.shape(image)[0] / 2`

`tensor.get_shape`

`tensor.get_shape`

is used for fixed shapes, which means the tensor’s **shape can be deduced** in the graph.

Conclusion: `tf.shape`

can be used almost anywhere, but `t.get_shape`

only for shapes can be deduced from graph.

## Method 2

A function to *access* the values:

def shape(tensor): s = tensor.get_shape() return tuple([s[i].value for i in range(0, len(s))])

Example:

batch_size, num_feats = shape(logits)

**Conclusion**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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