Module ‘tensorflow’ has no attribute ‘get_variable’

In this Python tutorial, we will focus on how to fix the attributeerror: module tensorflow has no attribute ‘get_variable’ in our model, and also we will look at some examples of how we can use the tf.get_variable() function in TensorFlow. And we will cover these topics.

• Attributeerror module ‘tensorflow’ has no attribute ‘get_variable’
• Attributeerror module ‘tensorflow’ has no attribute ‘get_variable_scope’
• Attributeerror module ‘tensorflow’ has no attribute ‘variable_scope’
• Attributeerror module ‘tensorflow’ has no attribute ‘variable’
• Attributeerror module ‘tensorflow’ has no attribute ‘variables_initializer’
• Attributeerror module ‘tensorflow’ has no attribute ‘report_uninitialized_variables’
• Attributeerror module ‘tensorflow’ has no attribute ‘trainable_variables’
• Attributeerror module ‘tensorflow’ has no attribute ‘initalize_all_variables’
• Attributeerror module ‘tensorflow’ has no attribute ‘local_variables_initializer’

Attributeerror module ‘tensorflow’ has no attribute ‘get_variable’

• Here we are going to discuss the error Attributeerror module ‘TensorFlow’ has no attribute ‘get_variable’.
• Instead of calling tf.Variable directly, use the function tf.get variable() to get or create a variable. As opposed to tf.Variable, which passes the value directly, utilizes an initializer. A function called an initializer accepts a shape and outputs a tensor with that shape. Here are a few initializers that TensorFlow offers.

Example:

import tensorflow as tf

new_tens = tf.get_variable(name='tens',shape=[2],dtype=tf.int32)
print(new_tens)

Here is the Screenshot of the following given code.

Reason: The reason for this error is the tf.get_variable() function is not available in the latest version of TensorFlow.

Solution of this error code:

In this example we are going to use the concept of tf.compat.v1.get_variable() function.

Syntax:

Here is the Syntax of tf.compat.v1.get_variable() function.

tf.compat.v1.get_variable(
    name,
    shape=None,
    dtype=None,
    initializer=None,
    regularizer=None,
    trainable=None,
    collections=None,
    caching_device=None,
    partitioner=None,
    validate_shape=True,
    use_resource=None,
    custom_getter=None,
    constraint=None,
    synchronization=tf.VariableSynchronization.AUTO,
    aggregation=tf.compat.v1.VariableAggregation.NONE
)

• It consists of a few parameters
  • name: It specifies the name of the operation.
  • shape: By default, it takes none value and defines the shape of the input tensor.
  • dtype: It defines the datatype of the input tensor.
  • initializer: If a variable initializer is created, it will be used. can either be a Tensor or an initializer object. Its shape must be known if it’s a Tensor unless validate shape is False.
  • regularizer: tf.GraphKeys will receive the outcome of applying it to a freshly formed variable. You can regularise using REGULARIZATION LOSSES.
  • trainable: Add the variable to the GraphKeys collection if True. TRAINABLE VARIABLES (see tf.Variable).
  • collection: List of graph collections keys to add the Variable.

import tensorflow as tf

new_tens = tf.compat.v1.get_variable(name='new_tens',shape=[2],dtype=tf.int32)
print(new_tens)

In the above code we have imported the tensorflow library with the alias name ‘tf’ and then used the tf.compat.v1.get_variable() function and within this function, we assigned the shape and datatype as an argument.

You can refer to the below Screenshot

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

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

Attributeerror module ‘tensorflow’ has no attribute ‘get_variable_scope’

• Let us discuss and understand how we can solve the error Attributeerror module ‘TensorFlow’ has no attribute ‘get_variable_scope’.
• By using the tf.get_variable_scope() function, we can easily get the variable scope. Variable scope provides checks to prevent accidental creation or sharing while allowing you to share already generated variables and create new ones.

Example:

import tensorflow as tf

with tf.get_variable_scope('new_tens_1'):
    new_output = tf.zeros((), name='new_tens_2')
    print(new_output)

Here is the implementation of the following given code

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

Now let’s see the solution to this error.

In this example we are going to use the concept of tf.compat.v1.get_variable_scope() function

Syntax:

Let’s have a look at the syntax and understand the working of tf.compat.v1.get_variable_scope() function

tf.compat.v1.get_variable_scope()

Note: It does not take any parameters.

Example:

import tensorflow as tf

with tf.compat.v1.variable_scope('new_tens_1'):
    new_output = tf.zeros((), name='new_tens_2')
    print(new_output)

In the above code we have imported the TensorFlow library and then used the tf.compat.v1.variable_scope() function and within this function, we have assigned the tensor name.

Here is the implementation of the following given code

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

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

Attributeerror module ‘tensorflow’ has no attribute ‘variable_scope’

• In this section, we will discuss how to solve the attribute error module ‘tensorflow’ has no attribute ‘variable_scope’.
• To perform this particular task we are going to use the tf.variable_scope() function and this function is only available in the tensorflow 1.x version.

Example:

In the above example, we have used the tf.variable_scope() function and within this function, we have mentioned the shape and datatype as an argument.

Here is the Solution to this error

Here we will use the tf.compat.v1.variable_scope() function.

Syntax:

tf.compat.v1.variable_scope(
    name_or_scope,
    default_name=None,
    values=None,
    initializer=None,
    regularizer=None,
    caching_device=None,
    partitioner=None,
    custom_getter=None,
    reuse=None,
    dtype=None,
    use_resource=None,
    constraint=None,
    auxiliary_name_scope=True
)

• It consists of a few parameters
  • name_or_scope: This parameter defines the string or variable scope.
  • default_name: The name that will be used by default if the name or scope parameter is None will be uniquified. Name or Scope is not necessary and can be None because it won’t be used if it is provided.
  • Values: The list of arguments for Tensors that the op function receives.
  • initializer: By default, it takes none value and initializer for variables within this scope.
  • regularize regularizer by default for variables in this scope.

import tensorflow as tf

new_tens = tf.compat.v1.variable_scope(default_name='tens',values=[2,3],dtype=tf.int32, name_or_scope='tens')
print(new_tens)

You can refer to the below Screenshot

This is how we can solve this attributeerror module tensorflow has no attribute variable_scope.

Read: Batch Normalization TensorFlow

Attributeerror module ‘tensorflow’ has no attribute ‘variable’

• Any kind or shape of Tensor may be used as the initial value for the variable in the Variable() function Object() { [native code] }. The initial value of the variable determines its type and shape.
• Once built, the shape and the variables cannot be changed. Let’s have a look at a couple of TensorFlow variable creation examples.
• Variables are needed in TensorFlow to express the parameters of a deep learning model. Tensor-based in-memory buffers known as TensorFlow Variables are persistent across several graph executions.
• A variable’s value will fluctuate as training goes on, and each variation will bring the model one step closer to the ideal system.

Example:

import tensorflow as tf

new_tens = tf.variable([2, 2])
print("The shape of the given variable: ",
	new_tens.shape)

# The size of the variable
print("The size of the tensorflow variable:",
	tf.size(new_tens).numpy())

Here is the implementation of the following given code.

The solution to this error.

import tensorflow as tf

new_tens = tf.Variable([2, 2])
print("The shape of the given variable: ",
	new_tens.shape)

# The size of the variable
print("The size of the tensorflow variable:",
	tf.size(new_tens).numpy())

You can refer to the below Screenshot

Read: Tensorflow custom loss function

Attributeerror module ‘tensorflow’ has no attribute ‘variables_initializer’

To initialize global variables, use the tf. global variables initializer() function. To initialize local variables, use the tf. local variables initializer() function.

Example:

import tensorflow as tf

new_tens = tf.variables_initializer(name='tens')
print(new_tens)

In this example, we have used the tf.variables_initializer() function and this function is available in the tensorflow 1.x version.

Here is the implementation of the following given code

Here is the Solution to the given code

Here we use the tf.compat.v1.variables_initializer() function

Syntax:

tf.compat.v1.variables_initializer(
    var_list, name='init'
)

Example:

import tensorflow as tf

new_tens = tf.compat.v1.variables_initializer(var_list=[12,3,4],name='tens')
print(new_tens)

Here is the Output of the following given code.

In the above example, we have solved the attributeerror module ‘tensorflow’ has no attribute ‘variables_initializer’.

Read: TensorFlow global average pooling

Attributeerror module ‘tensorflow’ has no attribute ‘report_uninitialized_variables’

In this example, we are going to use the tf.report_uninitialized_variables() function and within this function, we have set the var_list and the name of the tensor.

Example:

import tensorflow as tf

new_tens = tf.report_uninitialized_variables(var_list=[12,3,4],name='tens')
print(new_tens)

Here is the implementation of the following given code

The solution to this error

import tensorflow as tf

new_tens = tf.compat.v1.report_uninitialized_variables(var_list=None,name='tens')
print(new_tens)

In this example, we have used the tf.compat.v1.report_uninitialized_variables() function If there are any uninitialized variables, it returns a 1-D tensor with their names; otherwise, it returns an empty array.

Here is the Screenshot of the following given code

Read: Binary Cross Entropy TensorFlow

Attributeerror module ‘tensorflow’ has no attribute ‘trainable_variables’

• All of the trainable variables in the model are returned by trainable variables. Here, “trainable” refers to the variables that should be improved to boost the model’s effectiveness.
• The Variable() function Object automatically adds new variables to the graph collection GraphKeys when the argument trainable=True is supplied. TRAINABLE VARIABLES. The contents of that collection are returned by this function.

Example:

import tensorflow as tf

tensor = tf.trainable_variables(scope=None)
print(tensor)

Here is the implementation of the following given code

Reason: The reason for this error is the tf.trainable_variables() function is not available in the latest version of TensorFlow.

Solution of this error code:

In this example we are going to use the concept of tf.compat.v1.trainable_variables() function.

Syntax:

tf.compat.v1.trainable_variables(
    scope=None
)

• It consists of only one parameter
  • scope: It is an optional parameter and if it is provided, re.match is used to filter the output list so that it only contains objects whose name attribute matches scope. If a scope is given, items without a name attribute are never returned. Because of the selection of re.match, a scope without unique tokens filters by the prefix.

Example:

import tensorflow as tf

tensor = tf.compat.v1.trainable_variables(scope=None)
print(tensor)

Here we used the tf.compat.v1.trainable_variables() function and this function is available in tensorflow latest version 2.x.

You can refer to the below Screenshot

This is how we can solve this attributeerror module ‘tensorflow’ has no attribute ‘trainable_variables’.

Read: Module ‘TensorFlow’ has no attribute ‘get_default_graph’

Attributeerror module ‘tensorflow’ has no attribute ‘initalize_all_variables’

• In this section, we will discuss how to solve the error Attributeerror module ‘tensorflow’ has no attribute ‘initalize_all_variables’.
• To perform this task we are going to use the concept of tf.initialize_all_variables() function and this function is available in tensorflow 1.x version.

Example:

import tensorflow as tf

tensor = tf.initialize_all_variables()
print(tensor)

Here is the Output of the following given code.

Here is the Solution to this error

import tensorflow as tf

tensor = tf.compat.v1.initialize_all_variables()
print(tensor)

You can refer to the below Screenshot.

Read: TensorFlow Graph – Detailed Guide

Attributeerror module ‘tensorflow’ has no attribute ‘local_variables_initializer’

• Let us discuss and understand how we can solve the error Attributeerror module ‘TensorFlow’ has no attribute ‘local_variables_initializer’.
• By using the ‘local_variables_initializer‘ function, we can easily get the variable scope. Variable scope provides checks to prevent accidental creation or sharing while allowing you to share already generated variables and create new ones.

Example:

import tensorflow as tf

tensor = tf.local_variables_initializer()
print(tensor)

You can refer to the below Screenshot

Solution

import tensorflow as tf

tensor = tf.compat.v1.local_variables_initializer()
print(tensor)

Here is the Implementation of the following given code

Also, take a look at some more Python TensorFlow tutorials.

• TensorFlow mean squared error
• Convert list to tensor TensorFlow
• Python TensorFlow one_hot
• Module ‘tensorflow’ has no attribute ‘sparse_placeholder’

In this Python tutorial, we have focused on how to fix the attribute error: module tensorflow has no attribute ‘get_variable’ in our model, and also we will look at some examples of how we can use the tf.get_variable() function in TensorFlow. And we have covered these topics.

• Attributeerror module ‘tensorflow’ has no attribute ‘get_variable’
• Attributeerror module ‘tensorflow’ has no attribute ‘get_variable_scope’
• Attributeerror module ‘tensorflow’ has no attribute ‘variable_scope’
• Attributeerror module ‘tensorflow’ has no attribute ‘variable’
• Attributeerror module ‘tensorflow’ has no attribute ‘variables_initializer’
• Attributeerror module ‘tensorflow’ has no attribute ‘report_uninitialized_variables’
• Attributeerror module ‘tensorflow’ has no attribute ‘trainable_variables’
• Attributeerror module ‘tensorflow’ has no attribute ‘initalize_all_variables’
• Attributeerror module ‘tensorflow’ has no attribute ‘local_variables_initializer’