While running my project, **I found this error Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_placeholder’.**

In this TensorFlow tutorial, I will share my approach to solving this error.

So, I have covered two approaches to solve this error: the compatibility mode and the API of the latest version of Tensorflow.

Let’s begin,

## Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_placeholder’

The error Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_placeholder’ means you are trying to use an attribute **‘sparse_plceholder’**, which doesn’t exist in the TensorFlow module.

First, I will explain what the **sparse_placehoder()** function is.

- A placeholder is simply a variable to which we will subsequently give information.
- It enables us to design our computation graph and produce our operations without requiring the data. Then, we use these placeholders to feed data into the graph in the TensorFlow language.
- The
**sparse_placeholder**inserts a placeholder for a sparse tensor that will always be fed in the order of the dictionary, and it will return the sparse tensor that may be used as a handle for feeding a value.

Let me show you an example of how this error can arise.

As you can when you try to create a **sparse_placeholder** with **shape (3,3)**, it returns the error **Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_placeholder**‘.

Before the solution, you must know the reason behind this error, and there are multiple reasons.

The first reason is that you might be trying to use TensorFlow API or a function that either never existed or has been deprecated and removed in the new version.

The second reason is the **tensorflow.sparse_placehoder()** function was in TensorFlow version 1.x.x but has been removed or replaced in TensorFlow version 2.x.x.

Here, this error occurs because you have updated to the latest version of tensorflow around 2.x something. The Tensorflow 2.x.x introduced many changes and improvements.

To fix this error, you have two options: **use the new API of the latest tensorflow** and **use compatibility mode.**

Let’s start with compatibility mode; to fix the issue, use the **tf.compat.v1.sparse_placeholder()** function, available in the TensorFlow 2.x.x version.

That means if your project depends heavily on TensorFlow version 1.x.x features and you can’t update or change all your code to TensorFlow version 2.x.x ( or the latest version), you can use TensorFlow version 1.x.x compatibility mode in TensorFlow version 2.x.x.

Here is the Syntax of **tf.compat.v1.sparse_placeholder()** function in Python TensorFlow.

```
tf.compat.v1.sparse_placeholder(
dtype, shape=None, name=None
)
```

Where parameters are:

**dtype**: This parameter defines the type of value elements in the input tensor to be fed in the order of the dictionary.**shape**: It defines the shape of the input tensor and will check the condition. If the given shape is not specified, you can easily feed a sparse tensor of any shape.**name:**It specifies the operation’s name, and by default, it takes no value.

Now let’s see with an example how to use the sparse_placholder() function from the **tf.compat.v1** module. So, for that, use the code below.

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
result= tf.compat.v1.sparse_placeholder(dtype=tf.float32, shape=(3,3))
print(result)
```

In this example, we have imported the tensorflow library with the alias name **‘tf’** and then used the** tf.compat.v1.sparse_placeholder()** function If this sparse tensor is evaluated, an error will result. Feeding its value into Session.run requires the optional feed dict argument.

You don’t get an error because you use TensorFlow 1.x compatibility mode in TensorFlow 2.x.

Now, if you want to fix the error without using the compatibility mode, you must follow the API of the latest version of Tensorflow.

In TensorFlwo version 2.x.x uses **tensorflow.sparse.SparseTensor **is for sparse data handling instead of placeholds.

You can create a sparse tensor in TensorFlow 2.x.x, as shown below.

```
import tensorflow as tf
sparse_tensor = tf.sparse.SparseTensor(indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4])
print(sparse_tensor)
```

You have successfully created a sparse tensor using the SparseTensor from the tf. sparse module.

This is how to resolve the error Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_placeholder’ using the compatibility mode and SparseTensor from the tf.sparse module.

## Conclusion

In this TensorFlow tutorial, you learned how to resolve the error **Attributeerror: module ‘tensorflow’ has no attribute ‘sparse_placeholder’.**

To resolve that error, you have followed the two methods: in the first method, you have used the **tf.compat.v1.sparse_placeholder()**, and in the second method, you have used the **SparseTensor()** function from the **tf.sparse **submodule.

You may like to read:

- Module ‘tensorflow’ has no attribute ‘optimizers’
- Attributeerror: module ‘tensorflow’ has no attribute ‘count_nonzero’
- Attributeerror: module ‘tensorflow’ has no attribute ‘reduce_sum’

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.