In this Python tutorial, we will discuss the error “**module ‘TensorFlow’ has no attribute ‘truncated_normal’**“. And we’ll also cover the following topics:

- Attributeerror: module ‘tensorflow’ has no attribute ‘truncated_normal’
- Attributeerror: module ‘tensorflow’ has no attribute ‘truncated_normal_initializer’

Also, check the latest tutorial on TensorFlow: Module ‘tensorflow’ has no attribute ‘optimizers’

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

- In this section, we will discuss how to solve the error module
**‘tensorflow’ has no attribute ‘truncated_normal’**. - To perform this particular task we are going to use the truncated_normal Any samples that deviate more than two standard deviations from the mean are discarded and reduced, and the values are taken from a normal distribution with a known mean and standard deviation, and this method is used to generate random values from a normal distribution and normal distribution means probability distribution that occurs in many events.

**Example**:

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
input_tens = tf.constant(50,dtype="int32",name="input_tensor")
result=tf.truncated_normal(shape=[input_tens], mean=4, stddev=1)
with tf.compat.v1.Session() as val:
new_output=val.run(result)
print(new_output)
```

Here is the Screenshot of the following given code

The solution to this error

In this example, we will use the **tf.random.truncated_normal() **function and this function return a output random values from a truncated normal distribution.

**Syntax**:

Here is the Syntax of **tf.random.truncated_normal()** function

```
tf.random.truncated_normal(
shape,
mean=0.0,
stddev=1.0,
dtype=tf.dtypes.float32,
seed=None,
name=None
)
```

- It consists of a few parameters
**shape**: This parameter defines a Python or Tensor array of 1-D integers. The output tensor’s form.**mean**: By default, it takes a 0.0 value and a Python value of type dtype or a 0-D tensor. the average of a normal distribution that was shortened.**stddev**: a Python value of type dtype or a 0-D tensor and the initial standard deviation of the normal distribution.**dtype**: By default, it takes tf.dtypes.float32 and it specifies the data type of the tensor.**seed**: an integer in Python, and it is used to produce the distribution’s random seed.**name**: It defines the name of the operation and by default, it takes none value.

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
input_tens = tf.constant(50,dtype="int32",name="input_tensor")
result=tf.random.truncated_normal(shape=[input_tens], mean=4, stddev=1)
with tf.compat.v1.Session() as val:
new_output=val.run(result)
print(new_output)
```

In the above code, we have imported the Tensorflow library and then use the **tf.compat.v1.disable_eager_execution** function for creating the session.

After that, we applied the constant function for creating a tensor shape and then use the **tf.random.truncated_normal() **function and within this function we have assigned the **shape, mean, and stddev** as an argument. Once you will execute this code the output displays random values from a normal distribution.

You can refer to the below Screenshot.

This is how we can solve the attribute error module ‘tensorflow’ has no attribute ‘truncated_normal’.

Read: Module ‘tensorflow’ has no attribute ‘log’

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

- Let us discuss how to solve the error module ‘tensorflow’ has no attribute ‘truncated_normal_initializer’.
- To perform this particular task we are going to use the
**tf.compat.v1.truncated_normal_initializer()**function and this function initializer generates a truncated normal distribution. - The only difference between these values and those from a random normal initializer is that values that deviate by more than two standard deviations are discarded and reduced. The weights and filters of neural networks should be initialized using this method.

**Syntax**:

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

```
tf.compat.v1.truncated_normal_initializer(
mean=0.0,
stddev=1.0,
seed=None,
dtype=tf.dtypes.float32
)
```

**Example**:

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_trunc = tf.truncated_normal_initializer(mean=14,stddev=1,seed=4)
print(new_trunc)
```

Here is the Screenshot of the following given code

The solution to this error.

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_trunc = tf.compat.v1.truncated_normal_initializer(mean=14,stddev=1,seed=4)
print(new_trunc)
```

You can refer to the below Screenshot

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

You may also like to read the following TensorFlow tutorials.

- TensorFlow Fully Connected Layer
- Module ‘tensorflow’ has no attribute ‘Function’
- Pandas dataframe to tensorflow dataset
- TensorFlow Natural Language Processing
- Tensorflow custom loss function
- Tensorflow get static value
- attributeerror: module ‘tensorflow’ has no attribute ‘matrix_transpose’

In this Python tutorial, we have discussed the error “**module ‘TensorFlow’ has no attribute ‘truncated_normal’**“. And we have also covered the following topics:

- Attributeerror: module ‘tensorflow’ has no attribute ‘truncated_normal’
- Attributeerror: module ‘tensorflow’ has no attribute ‘truncated_normal_initializer’

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