In this Python tutorial, we will focus on how to fix the **Attributeerror: module tensorflow has no attribute ‘**

*div*

**‘ in TensorFlow**. and also we will look at some examples of how we can use the

**function in**

*tf.div()***. And we will cover these topics.**

*TensorFlow*- Attributeerror: module ‘tensorflow’ has no attribute ‘div’
- Attributeerror: module ‘tensorflow’ has no attribute ‘dimension’
- Attributeerror: module ‘tensorflow’ has no attribute ‘count_nonzero’
- Attributeerror: module ‘tensorflow’ has no attribute ‘lin_space’
- Attributeerror: module ‘tensorflow’ has no attribute ‘sigmoid’
- Attributeerror module ‘tensorflow’ has no attribute ‘placeholder’
- Attributeerror module ‘tensorflow’ has no attribute ‘isnan’

**Table of Contents**show

## Attributeerror module ‘tensorflow’ has no attribute ‘div’

- In this section, we will discuss how to solve the attributeerror module ‘tensorflow’ that has no attribute ‘div’.
- The two given input Tensors are divided element by element using the tf.div() function.

**Example**:

```
import tensorflow as tf
tens_1 = tf.constant([36, 56, 21])
tens_2 = tf.constant([6, 8, 7])
result=tf.div(tens_1,tens_2)
print(result)
```

In the following given code first, we imported the TensorFlow library with the alias name ‘tf’ and then create a tensor by using the **tf.constant()** function within this function we have assigned the integer numbers as an argument.

Next, we used the **tf.div()** function, and this method is available in Tensorflow 1.x version and it does not support the latest version.

Here is the Implementation of the following given code.

Here is the Solution to this error.

**Syntax**:

Here is the syntax of ** tf.math.divide()** function in Python TensorFlow

```
tf.math.divide(
x, y, name=None
)
```

- It consists of a few parameters
**x**: This parameter defines the first input tensor.**y**: This parameter specifies the second input tensor.**name**: By default, it takes no value and specifies the operation’s name.

```
import tensorflow as tf
tens_1 = tf.constant([36, 56, 21])
tens_2 = tf.constant([6, 8, 7])
result=tf.divide(tens_1,tens_2)
print(result)
```

You can refer to the below Screenshot

This is how we can solve the attributeerror module ‘tensorflow’ has no attribute ‘div’.

Read: TensorFlow Tensor to numpy

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

- Here we will use the attributeerror module ‘tensorflow.math’ has no attribute ‘divide’.
- Tensors are used in every computation of Tensorflow. A tensor is an n-dimensional vector or matrix that can represent any data. A tensor’s values all have the same data type and known (or at least partially known) shape.
- The dimensions of the matrix or array are determined by the geometry of the data.

**Example**:

```
import tensorflow as tf
tens_1 = tf.constant([[36, 56, 21],[12,56,7]])
result=tf.dimension(tens_1)
print(result)
```

Here is the execution of the following given code

Here is the Solution to this error.

In this example we are going to use the **tf.compat.v1.Dimension()** and it will Represent the value of one dimension in a TensorShape.

Read: Python TensorFlow reduce_sum

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

- The multidimensional tensor’s nonzero element count is calculated using this function. This function allows us to determine the number of elements in the multidimensional tensors that are not zero.
- The number of nonzero values in the given tensors along the specified axis is the function’s return value. If not, the array’s nonzero value count is returned.

**Example**:

```
import tensorflow as tf
# Creation of input tensor
new_tens = tf.constant([16,0,24,78,0], dtype = tf.int32)
print("Input: ",new_tens)
new_output = tf.count_nonzero(new_tens)
# Display the Content
print("Number of non-zero elements: ",new_output)
```

In the following given code first, we imported the TensorFlow library as tf and then created a tensor by using the **tf.constant()** function, and within this function, we assigned the integer values as an argument.

After that, we used the **tf.count_nonzero()** function, and within this function, we assigned the input tensor as an argument. This function will work in TensorFlow 1.x version.

Here is the implementation of the following given code.

The solution to this error.

In this example, we will use the **tf.math.count_nonzero()** function and this function generates the number of non-zeros values across the dimensions of an input tensor.

**Syntax**:

```
tf.math.count_nonzero(
input,
axis=None,
keepdims=None,
dtype=tf.dtypes.int64,
name=None
)
```

- It consists of a few parameters
**input**: This parameter reduces the tensor. Should be of the bool, string, or numeric type.**axis**: By default, it takes none value The reduced dimensions. If None (the default), all dimensions are reduced. range [-rank(input), rank(input)] is required.**keepdims**: It will check the condition if it is true then it will reduce dimensions with length 1.**dtype**: This parameter specifies the data type of the input tensor.**name**: It specifies the name of the operation.

```
import tensorflow as tf
# Creation of input tensor
new_tens = tf.constant([16,0,24,78,0], dtype = tf.int32)
print("Input: ",new_tens)
new_output = tf.math.count_nonzero(new_tens)
# Display the Content
print("Number of non-zero elements: ",new_output)
```

Here is the implementation of the following given code

As you can see in the Screenshot we have solved the error solution of the attributeerror module tensorflow has no attribute ‘count_nonzero’.

Read: Python TensorFlow random uniform

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

- In this section, we will discuss how to solve the attributeerror module tensorflow that has no attribute ‘lin_space’.
- Linspace is used to create an evenly spaced sequence in a specified interval.
- The TensorFlow linspace function generates sequences of values that are evenly spaced over a specified range.
- In essence, you provide a beginning point, an ending point, and the total number of breakpoints you want to have within that interval (including the start and end points). A series of values on that interval that is equally spaced will be returned by the tf.linspace function.

**Syntax**:

Here is the Syntax of tf.linspace() function in Python TensorFlow

```
tf.linspace(
start, stop, num, name=None, axis=0
)
```

- It consists of a few parameters
**start**: This parameter defines the starting value of an input tensor.**stop**: It specifies the ending value of the input tensor.

**Note**: This function will work in tensorflow 1.x and 2.x versions.

**Example**:

```
import tensorflow as tf
# Creation of input tensors
new_start_val = tf.constant(12, dtype = tf.float64)
new_end_val = tf.constant(25, dtype = tf.float64)
num = 5
new_output = tf.linspace(new_start_val, new_end_val, num)
# Display the Content
print("Result: ", new_output)
```

In this example, we have used the tf.linspace() function and within this function, we assigned the starting and ending values to it.

You can refer to the below Screenshot

This is how we can solve the attributerror module tensorflow has no attribute lin_space.

Read: TensorFlow Multiplication

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

- In this section, we will discuss the attributeerror module ‘tensorflow’ has no attribute ‘sigmoid’.
- When we need to determine the likelihood that some set of data belongs to a particular class in binary classification issues, the sigmoid function, which produces results in the range (0, 1), is the best tool to use. At any point, the sigmoid function can be differentiated, and its derivative is produced.
- The sigmoid function has a “vanishing gradients” issue because it flattens out at both ends, causing extremely minor changes in the weights during backpropagation. This may cause the neural network to become trapped and stop adapting.

**Example**:

```
import tensorflow as tf
# Creation of input tensor
new_tens = tf.constant([15.0, -13.5, 3.4, -42.1, 17.9, -34.5], dtype = tf.float32)
# By using the sigmoid() function
result = tf.Sigmoid(new_tens, name ='sigmoid')
with tf.compat.v1.Session() as val:
new_output=val.run(result)
print(new_output)
```

Here is the Screenshot of the following given code

Here is the Solution to this error.

In this example, we are going to use the **tf.sigmoid()** function and this function computes the sigmoid of x element-wise.

**Syntax**:

Let’s have a look at the Syntax and understand the working of the tf.sigmoid() function

```
tf.math.sigmoid(
x, name=None
)
```

- It consists of a few parameters
**x**: This parameter defines the input tensor on which the sigmoid function will be applied.**name**: By default, it takes none value and it defines the name of the operation.

Read: TensorFlow mean squared error

## Attributeerror module ‘tensorflow’ has no attribute ‘placeholder’

- When we attempt to access an attribute for an object that doesn’t exist, we get an AttributeError in a Python program. The statement “‘module ‘tensorflow’ has no attribute ‘placeholder'” informs us that the placeholder attribute is not present in the TensorFlow module().
- The TensorFlow 1.x API contains the placeholder() function. In most cases, the feature is either known by a different name or is deprecated if the AttributeError refers to a module that lacks an attribute.

**Example**:

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_tensor=tf.placeholder(dtype=tf.int32,shape=(300,300))
print(new_tensor)
```

Here is the execution of the following given code

The solution to this error

**Reason: **The possible reason for this error is that the **tf.placeholder()** attribute is not available in Tensorflow’s latest version (TensorFlow2.0).

**Example**:

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_tensor=tf.compat.v1.placeholder(dtype=tf.int32,shape=(300,300))
print(new_tensor)
```

You can refer to the below Screenshot

Read: Python TensorFlow one_hot

## Attributeerror module ‘tensorflow’ has no attribute ‘isnan’

- The built-in math function
in Python can be used to determine whether an input is a legitimate number or not.*isnan()* - To use the
function, which is part of the math library, it returns true if the element is NaN otherwise it will returns false.*isnan()*

**Example**:

```
import tensorflow as tf
import numpy as np
# Creation of input tensor
tens = tf.constant([16, 67, 34, 178, np.inf], dtype = tf.float64)
# Calculating the nan value
new_output = tf.is_nan(tens)
# Printing the result
print('Result: ', new_output)
```

Here is the Screenshot of the following given code.

Here is the Solution to this error.

```
import tensorflow as tf
import numpy as np
# Creation of input tensor
tens = tf.constant([16, 67, 34, 178, np.inf], dtype = tf.float64)
# Calculating the nan value
new_output = tf.math.is_nan(tens)
# Printing the result
print('Result: ', new_output)
```

In the following given code first, we have imported the input tensor by using the tf.constant() function and within this function, we have assigned datatype as an argument.

After that we used the **tf.math.is_nan()** and within this function, we passed the input tensor as an argument.

Here is the execution of the following given code.

You may also like to read the following Python TensorFlow tutorials.

- Python TensorFlow random uniform
- TensorFlow Tensor to numpy
- Module ‘TensorFlow’ has no attribute ‘session’
- Import error no module named TensorFlow
- Module ‘tensorflow’ has no attribute ‘get_variable’

In this Python tutorial, we have focused on how to fix the attributeerror: module tensorflow has no attribute ** ‘div’** in TensorFlow, and also we will look at some examples of how we can use the

**tf.div()**function in

**. And we have covered these topics.**

*TensorFlow*- Attributeerror: module ‘tensorflow’ has no attribute ‘div’
- Attributeerror: module ‘tensorflow’ has no attribute ‘dimension’
- Attributeerror: module ‘tensorflow’ has no attribute ‘count_nonzero’
- Attributeerror: module ‘tensorflow’ has no attribute ‘lin_space’

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