In this article, we will discuss how to solve the attributeerror module ‘tensorflow’ that has no attribute ‘scaler_summmary’. Let us discuss how to use the scalar_summary() function in TensorFlow. And we’ll also cover the following topics:

- Attributeerror: module ‘tensorflow’ has no attribute ‘scalar_summary’
- Attributeerror: module ‘tensorflow’ has no attribute ‘scaler’
- Attributeerror: module ‘tensorflow’ has no attribute ‘nest’
- Attributeerror: module ‘tensorflow’ has no attribute ‘Confusion_matrix’

## attributeerror: module ‘tensorflow’ has no attribute ‘scalar_summary’

- In this section, we will discuss how to solve the attributeerror module ‘tensorflow’ has no attribute ‘scalar_summary’.
- This name, prefixed by any active name scopes, will be the summary tag used for TensorBoard.

**Example**:

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tf.compat.v1.reset_default_graph()
new_scalar_value = tf.compat.v1.get_variable('x_scalar' , shape=[], initializer=tf.compat.v1.truncated_normal_initializer(mean=0, stddev=1))
new_summary_scalar = tf.scalar_summary(name='scaler_value_first', tensor=new_scalar_value )
new_initializer = tf.global_variables_initializer()
with tf.compat.v1.Session() as val:
writer = tf.summary.FileWriter('./graphs', val.graph)
for step in range(50):
val.run(new_initializer)
summary = val.run(new_summary_scalar)
writer.add_summary(summary, step)
print('Done with the new_scalar summary')
```

In the following given code, we imported the tensorflow library with the alias name ‘tf’ and then used the tf.compat.v1.get_variable() function and this function tf.get variable() produces a variable with the requested shape and initializer if one does not already exist and returns the variable with the requested shape and initializer if one does not already exist.

Here is the implementation of the following given code

Here is the Solution to this error

Reason: The reason for this error is tf.scalar_summary() function is not available in Tensorflow any version. Now in this case we use the** tf.s ummary.scalar()** function.

**Syntax**:

```
tf.summary.scalar(
name,
data,
step=None,
description=None
)
```

- It consists of a few parameters
**name**: The title of this summary. This name plus any current name scopes will be the summary tag for TensorBoard.**data**: a real scalar number that can be converted to a float32 tensor.**step**: It explicit int64 and monotonic step value for this summary. By default, it takes none value.- Description: By default, it takes none value and it is a long-form description for this summary.

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tf.compat.v1.reset_default_graph()
new_scalar_value = tf.compat.v1.get_variable('x_scalar' , shape=[], initializer=tf.compat.v1.truncated_normal_initializer(mean=0, stddev=1))
new_summary_scalar =tf.summary.scalar(name='scaler_value_first', tensor=new_scalar_value )
new_initializer = tf.global_variables_initializer()
with tf.compat.v1.Session() as val:
writer = tf.summary.FileWriter('./graphs', val.graph)
for step in range(50):
val.run(new_initializer)
summary = val.run(new_summary_scalar)
writer.add_summary(summary, step)
print('Done with the new_scalar summary')
```

In this example, we used the **tf.summary.scalar()** function and within this function, we assigned the name of the summary and tensor name. Next, we will be creating the writer inside the session. In the end, add the summary to the writer.

You can refer to the below Screenshot

This is how to solve the attributeerror module tensorflow has no attribute scalar_summary.

Read Attributeerror: module ‘tensorflow’ has no attribute ‘mul’

## attributeerror: module ‘tensorflow’ has no attribute ‘scaler’

- Here we will discuss how to solve the attributeerror module ‘tensorflow’ has no attribute session.
- A scalar type of tensor means is produced by the
**scalar()**function. A scaler, also known as a rank-0 Tensor, is a zero-dimension array. Using the**scalar()**function, a scalar is produced.

**Example**:

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tf.compat.v1.reset_default_graph()
new_scalar_value = tf.compat.v1.get_variable('x_scalar' , shape=[], initializer=tf.compat.v1.truncated_normal_initializer(mean=0, stddev=1))
new_summary_scalar =tf.scalar.summary(name='scaler_value_first', tensor=new_scalar_value )
new_initializer = tf.global_variables_initializer()
with tf.compat.v1.Session() as val:
writer = tf.summary.FileWriter('./graphs', val.graph)
for step in range(50):
val.run(new_initializer)
summary = val.run(new_summary_scalar)
writer.add_summary(summary, step)
print('Done with the new_scalar summary')
```

Here is the Screenshot of the following given code

The solution to this error

In this example we are going to use the tf.math.scalar_mul() function.

**Syntax**:

```
tf.math.scalar_mul(
scalar,
x,
name=None
)
```

- It consists of a few parameters
**scalar**: This parameter specifies the known shape.**x**: This defines the input tensor or indexed slices to be scaled.**name**: By default, it takes none value and specifies the name of the operation.

```
import tensorflow as tf
new_tensor = tf.reshape(tf.range(20, dtype=tf.float32), [10, 2])
with tf.GradientTape() as w:
w.watch(new_tensor)
m = tf.gather(new_tensor, [2, 3])
result = tf.math.scalar_mul(20.0, m)
print(result)
```

In the following given code we used the **tf.reshape()** function To reshape a given tensor into the desired shape, use the **tf.reshape()** function. Next, we used the **tf.math.scalar_mul()** and thus multiplies a scalar times.

You can refer to the below Screenshot

As you can see in the Screenshot we have solved the attributeerror module tensorflow has no attribute scaler.

Read Module ‘tensorflow’ has no attribute ‘log’

## attributeerror: module ‘tensorflow’ has no attribute ‘nest’

- Let us discuss how to solve the attributeerror module ‘tensorflow’ has no attribute ‘nest’.
- To perform this task we are going to use the
**tf.Nest()**function and a value of any other type, usually one of the compatible types like int, float, ndarray, or a TensorFlow data type like Tensor, Variable. commonly referred to as the structure’s atoms.

**Example**:

```
import tensorflow as tf
result= tf.Nest.is_nested("1234")
print(result)
```

Here is the implementation of the following given code

The solution to this error

Reason: The reason behind this error is the** tf.Nest() **function does not work in the Tensorflow version and this function has been modified in the latest version of tensorflow 2.9 which is **tf.nest()**.

**Source Code**:

```
import tensorflow as tf
result= tf.nest.is_nested("7564")
print(result)
```

You can refer to the below Screenshot

This is how to solve the attributeerror module tensorflow has no attribute nest

Read Module ‘tensorflow’ has no attribute ‘truncated_normal’

## attributeerror: module ‘tensorflow’ has no attribute ‘Confusion_matrix’

- In this section, we will discuss how to solve the attributerror module ‘tensorflow’ has no attribute ‘confusion_matrix’.
- In Python TensorFlow, the confusion matrix is used to find the confusion matrix from predictions and labels.
- A table called a confusion matrix is used to describe how well a classification system performs. The output of a classification algorithm is visualized and summarised in a confusion matrix.

**Syntax**:

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

```
tf.math.confusion_matrix(
labels,
predictions,
num_classes=None,
weights=None,
dtype=tf.dtypes.int32,
name=None
)
```

- It consists of a few parameter
**labels**: For the classification task, a 1-D tensor of real labels is used.**prediction**: This parameter defines the prediction for the given classification.**num_classes**: This parameter defines how many labels a classification task would be able to use. If this value is not specified, it will be determined using the labels array and predictions.**weights**: By default, it takes none value and it will check the shape matches predictions.**dtype**: By default it takes tf.dtypes.int32() and it specifies the data type of the confusion matrix.

**Example**:

```
import tensorflow as tf
new_label_val = tf.constant([2,6,7],dtype = tf.int32)
new_predict_val = tf.constant([15,78,2],dtype = tf.int32)
# Evaluating confusion matrix
new_output = tf.Confusion_matrix(new_label_val,new_predict_val)
# Printing the result
print('Confusion_matrix: ',new_output)
```

Here is the implementation of the following given code

Here is the Solution to this error

**Reason**: The reason for this error this function is not available in TensorFlow’s latest version and this function takes a few parameters.

```
import tensorflow as tf
new_label_val = tf.constant([2,6,7],dtype = tf.int32)
new_predict_val = tf.constant([15,78,2],dtype = tf.int32)
# Evaluating confusion matrix
new_output = tf.confusion_matrix(new_label_val,new_predict_val)
# Printing the result
print('Confusion_matrix: ',new_output)
```

Here is the Screenshot of the following given code

As you can see in the Screenshot we have solved the attributeerror: module ‘tensorflow’ has no attribute ‘Confusion_matrix’

In this Python tutorial, we have discussed how to solve the attributeerror module ‘tensorflow’ that has no attribute ‘scaler_summmary’. Let us discuss how to use the **scalar_summary() **function in TensorFlow. And also we covered the following topics:

- Attributeerror: module ‘tensorflow’ has no attribute ‘scalar_summary’
- Attributeerror: module ‘tensorflow’ has no attribute ‘scaler’
- Attributeerror: module ‘tensorflow’ has no attribute ‘nest’
- Attributeerror: module ‘tensorflow’ has no attribute ‘Confusion_matrix’

You may like the following Python Tensorflow tutorials:

- Module ‘tensorflow’ has no attribute ‘get_variable’
- Module ‘tensorflow’ has no attribute ‘div’
- Module ‘tensorflow’ has no attribute ‘sparse_placeholder’
- Module ‘tensorflow’ has no attribute ‘optimizers’
- Tensorflow convert sparse tensor to tensor

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.