You need to import the TensorFlow module first in order for it to work properly.
Module ‘TensorFlow’ has no attribute ‘placeholder’. You must include “import TensorFlow” when using this package, or else errors will occur while trying to do so!
We have much software to make it easy to do our work, from which, we get the easiness in our complicated or lengthy works. But sometimes, we do face problems when we don’t have the knowledge of the right method to fix any error.
AttributeError: module ‘TensorFlow’ has no attribute ‘placeholder’
We are going to guide you about the same thing, we will tell you about the method to fix the module TensorFlow, which has not had the placeholder attribute.
When we face the error while using the module TensorFlow which has no attribute placeholder, we can use these ways to solve it, but first, we will define the TensorFlow and the placeholder attribute.
TensorFlow is the second generation system of Google Brain. Version 1.0.0 was released on February 11, 2017.
While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units).
TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS.
The placeholder attribute
The placeholder attribute is a cord that provides a brief clue to the operator as to what kind of information is anticipated in the field.
The AttributeError placeholder TensorFlow is an error that occurs when you try to access a non-existent attribute on your model or script.
The best way I’ve found for fixing this issue though if all else fails then just return None instead of setting it back up again with another key point!
It should be a word or short phrase that provides a clue as to the expected type of data, rather than a detailed answer or clue.
AttributeError: module ‘tensorflow’ has no attribute ‘placeholder’
There are two ways to get around this issue.
Solution 1: to follow the update scheme of Tensorflow 2.0
- The first method is applying changes in Tensorflow 2.0. Please refer to the details on the update this link.
- The placeholder can be replaced by a variable.
Solution 2: to use compatibility mode
- The second method is to disable the updated features in 2.0 by applying compatibility mode **. This can be implemented as follows, then you will still be able to declare **tf. placeholder.
Or follow these two ways
Solution 1. Follow TensorFlow migration guide
- Migrate your code following this guide.
- The Solution for the title problem is to use variables instead of placeholders.
Let’s see the following example:
import tensorflow as tf
x = tf.placeholder(shape=[None, 2], dtype=tf.float32)
Solution 2. Use TensorFlow 1.x compatibility mode
- The second approach is to use TensorFlow v1 compatibility mode.
- To do it you have to use import TensorFlow.compat.v1 as tf instead of import TensorFlow as tf and add tf.disable_v2_behavior().