Module ‘TensorFlow’ has no attribute ‘get_default_graph’

In this Python tutorial, we will discuss the error “module ‘TensorFlow‘ has no attribute ‘get_default_graph’“. Here we’ll cover the reason related to this error using TensorFlow. And we’ll also cover the following topics:

• module ‘TensorFlow’ has no attribute ‘get_default_graph’
• module ‘tensorflow’ has no attribute ‘get_variable’
• module ‘tensorflow’ has no attribute ‘get_default_session’
• module ‘tensorflow’ has no attribute ‘get_default_graph’ keras
• module ‘tensorflow’ has no attribute ‘get_variable_scope’
• module ‘tensorflow’ has no attribute ‘get_shape’
• module ‘tensorflow’ has no attribute ‘get_tensor_by name’
• module ‘tensorflow’ has no attribute ‘make_tensor_proto’
• module ‘tensorflow’ has no attribute ‘get_collection’
• module ‘tensorflow’ has no attribute ‘mean_squared_error’
• module ‘tensorflow’ has no attribute ‘placeholder’

Module ‘tensorflow’ has no attribute ‘get_default_graph’

In this section, we will discuss the error AttributeError: module ‘Tensorflow’ has no attribute ‘get_default_graph’ in Python.

Example:

import tensorflow as tf

tensor1 = tf.Variable(4)
tensor2 = tf.Variable(6)
tensor3 = tf.Variable(3)
result = (tensor1 + tensor2) * tensor3

for result in tf.get_default_graph().get_operations():
    print (result.name)

Here is the implementation of the following given code.

As you can see in the Screenshot the output displays the error AttributeError: module ‘TensorFlow’ has no attribute ‘get_default_graph’.

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

Now let’s see the solution to this error

Example:

import tensorflow as tf

tensor1 = tf.Variable(4)
tensor2 = tf.Variable(6)
tensor3 = tf.Variable(3)
result = (tensor1 + tensor2) * tensor3

for result in tf.compat.v1.get_default_graph().get_operations():
    print (result.name)

In the following given code, we have imported the TensorFlow library and then created the operation by using the tf.variable() function. After that, we have used the tf.compat.v1.get_default_graph() function and store the result variable as an argument.

Here is the execution of the following given code.

Also, read: Import error no module named TensorFlow

Module ‘TensorFlow’ has no attribute ‘get_variable’

• Here we are going to discuss the error Attributeerror module ‘TensorFlow’ has no attribute ‘get_variable’.
• To perform this particular task, we are going to use the tf.get_variable() function and this function is used to get the given variable with these arguments. But this function works only in TensorFlow 1.x version.
• If you are going to execute this function on TensorFlow 2.x version then it will raise an attribute error.

Example:

import tensorflow as tf

tensor = tf.get_variable(name='tens',shape=[1],dtype=tf.int32)
print(tensor)

Here is the implementation 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:

import tensorflow as tf

tensor = tf.compat.v1.get_variable(name='tens',shape=[1],dtype=tf.int32)
print(tensor)

In the following given code we have created a variable named ‘tensor’ and assigned the tf.compat.v1.get_variable() function, and within this function, we have assigned the name and dtype parameter to it.

Here is the Screenshot of the following given code.

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

Module ‘tensorflow’ has no attribute ‘get_default_session’

• In this section, we will discuss the error AttributeError:module ‘Tensorflow’ has no attribute ‘get_default_session’ in Python.
• To do this task, we are going to use the tf.get_default_session() function for creating the session. In this example we will perform multiplication operation by using the * operator.

Example:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tens1 = tf.constant(38,dtype="int32",name="tens1")
tens2 = tf.constant(98,dtype="int32",name="tens2")
with tf.get_default_session() as val:
    new_result=val.run(tens1*tens2)
    print(new_result)

In the above code we have imported the TensorFlow library and then use the tf.constant() function and within this function, we have assigned the values and type as an argument.

You can refer to the below Screenshot.

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

Solution:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tens1 = tf.constant(38,dtype="int32",name="tens1")
tens2 = tf.constant(98,dtype="int32",name="tens2")
with tf.compat.v1.Session() as val:
    new_result=val.run(tens1*tens2)
    print(new_result)

Here is the implementation of the following given code.

Read: TensorFlow Tensor to NumPy

Module ‘tensorflow’ has no attribute ‘get_variable_scope’

• Here we are going to discuss 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. But in this Program, this function does not work because in Tensorflow 2.x this function does not exist.

Example:

import tensorflow as tf

with tf.get_variable_scope('tens1'):
    result = tf.zeros((), name='tens2')
    print(result)

Here is the Screenshot of the following given code.

As you can see in the Screenshot the output displays the error AttributeError: module ‘TensorFlow’ has no attribute ‘get_variable_scope’.

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.

Solution:

import tensorflow as tf

with tf.compat.v1.variable_scope('tens1'):
    result = tf.zeros((), name='tens2')
    print(result)

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 execution of the following given code.

Read: Python TensorFlow reduce_sum

Module ‘tensorflow’ has no attribute ‘get_shape’

• In this section, we will discuss the error AttributeError: module ‘Tensorflow’ has no attribute ‘get_shape’ in Python.
• To do this task, we are going to use the tf.get_shape() function and this function will help the user to get the shape of input tensor.
• But in this Program, this function does not work in Tensorflow 2.x version.

Example:

import tensorflow as tf

tensor = tf.constant([[[15, 67, 89], [34, 27, 89]], 
                [[45, 89, 189], [68, 91, 46]]])

result=tf.get_shape(tensor)
result

Here is the implementation of the following given code.

As you can see in the Screenshot the output displays the error AttributeError: module ‘TensorFlow’ has no attribute ‘get_shape’.

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

Solution:

import tensorflow as tf

tensor = tf.constant([[[15, 67, 89], [34, 27, 89]], 
                [[45, 89, 189], [68, 91, 46]]])

result=tf.shape(tensor)
result

Here is the execution of the following given code.

Read: TensorFlow mean squared error

Module ‘tensorflow’ has no attribute ‘get_tensor_by name’

• In this section, we will discuss the error AttributeError: module ‘Tensorflow’ has no attribute ‘get_tensor_by name’ in Python.
• To perform this particular task we are going to use the tf.get_tensor_by_name() and this function return all the tensor name.
• This function does not exist in TensorFlow 2.x version instead of that we are going to use the tf.compat.v1.get_default_graph() function.

Example:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()
tensor1 = tf.constant([[17, 18], [19, 20]])
tensor2 = tf.constant([[21, 22], [23, 24]])
tensor3 = tf.matmul(tensor1, tensor2, name='tens')

with tf.compat.v1.Session() as sess:
    new_output =  sess.run(tensor3)
    print (tensor3.name) 
    new_output = tf.get_tensor_by_name("tens")
    print (new_output)

Here is the Screenshot of the following given code.

As you can see in the Screenshot the output displays the error AttributeError: module ‘TensorFlow’ has no attribute ‘get_tensor_by_name’.

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

Solution:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()

tensor1 = tf.constant([[17, 18], [19, 20]])
tensor2 = tf.constant([[21, 22], [23, 24]])
tensor3 = tf.matmul(tensor1, tensor2, name='example')

with tf.compat.v1.Session() as val:
    result =  val.run(tensor3)
    print (tensor3.name)
    result = tf.compat.v1.get_default_graph().get_tensor_by_name("example:0")
    print (result)

In the following given code, we have imported the TensorFlow library and then created the tensors by using the tf.constant() function. After creating the tensors, we have applied the matmul() function for multiplication.

Here is the Output of the following given code.

Read: Python TensorFlow Placeholder

Module ‘tensorflow’ has no attribute ‘make_tensor_proto’

• Here we are going to discuss the error module ‘TensorFlow’ has no attribute ‘make_tensor_proto’ in Python.
• This function will help the user to create a TensorProto and this is used to compute a numpy array.

Example:

import tensorflow as tf

new_val = tf.constant([[25,37,89],[56,14,90]])
result = tf.make_tensor_proto_(new_val)
print(result)

Here is the Screenshot of the following given code.

As you can see in the Screenshot the output displays the error AttributeError: module ‘TensorFlow’ has no attribute ‘make_tensor_proto’.

Solution:

import tensorflow as tf

new_val = tf.constant([[25,37,89],[56,14,90]])
result = tf.make_tensor_proto(new_val)
print(result)

In the following given code we have imported the TensorFlow library and then created a tensor by using the tf.constant() function and within this function, we have assigned only integer numbers.

After creating the tensor we have used the tf.make_tensor_proto() function and within this function, we have passed the tensor as an argument.

You can refer to the below Screenshot.

Read: Tensorflow iterate over tensor

Module ‘tensorflow’ has no attribute ‘get_collection’

• In this section, we will discuss the error module ‘tensorflow’ has no attribute ‘get_collection’ in Python.
• To do this task, we are going to use the tf.get_collection() function and this function is using the default graph.

Example:

import tensorflow as tf

with tf.compat.v1.variable_scope('my_scope'):
    tens = tf.Variable(0)
    
print (tf.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope='my_scope'))

In the above code, we have imported the TensorFlow library and then use the tf.compat.v1.variable_scope() function (‘my_scope’).

Here is the implementation of the following given code.

As you can see in the Screenshot the output displays the error AttributeError: module ‘TensorFlow’ has no attribute ‘get_collection’.

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

Solution:

import tensorflow as tf

with tf.compat.v1.variable_scope('my_scope'):
    tens = tf.Variable(0)
    
print (tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope='my_scope'))

In the following given code we have used the tf.compat.v1.get_collection() method.

Here is the execution of the following given code.

Read: Convert list to tensor TensorFlow

Module ‘tensorflow’ has no attribute ‘mean_squared_error’

• Here we are going to discuss the error module ‘tensorflow’ has no attribute ‘mean_squared_error’ in Python.
• To perform this particular task, we are going to use the tf.mean_squared_error() function and this function is used to insert a sum of squares from given labels and prediction.

Example:

import tensorflow as tf

y_true = tf.constant([[4.6, 7.3, 3.2],
                      [4.1,5.8,7.2]])
y_pred = tf.constant([[2.4, 4.6, 9.7],
                      [1.2,2.3,1.6]])


result=tf.mean_squared_error(y_true,y_pred)
print("Reduce mean squared error:",result)

Here is the Screenshot of the following given code.

As you can see in the Screenshot the output displays the error AttributeError: module ‘TensorFlow’ has no attribute ‘mean_squared_error’.

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

Solution:

import tensorflow as tf

y_true = tf.constant([[4.6, 7.3, 3.2],
                      [4.1,5.8,7.2]])
y_pred = tf.constant([[2.4, 4.6, 9.7],
                      [1.2,2.3,1.6]])


result=tf.compat.v1.losses.mean_squared_error(y_true,y_pred)
print("Reduce mean squared error:",result)

In the following given code we have used the tf.compat.v1.losses.mean_squared_error() function.

Here is the Output of the following given code.

Read: Python TensorFlow truncated normal

Module ‘tensorflow’ has no attribute ‘placeholder’

• In this section, we will discuss the error module ‘tensorflow’ has no attribute ‘placeholder’ in Python.
• This function is used to provide the data for operation and generate our computation graph.

Example:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()

tens=tf.placeholder(dtype=tf.int32,shape=(300,300))
print(tens)

In the above code, we have imported the TensorFlow library and then created the session by using the tf.compat.v1.disable_eager_execution() function.

Here is the implementation of the following given code.

As you can see in the Screenshot the output displays the error AttributeError: module ‘TensorFlow’ has no attribute ‘placeholder’.

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

Solution:

import tensorflow as tf
tf.compat.v1.disable_eager_execution()

tens=tf.compat.v1.placeholder(dtype=tf.int32,shape=(300,300))
print(tens)

Here is the execution of the following given code.

Also, check the following Python TensorFlow tutorials.

• Python TensorFlow expand_dims
• Python TensorFlow one_hot
• Module ‘tensorflow’ has no attribute ‘mul’
• Module ‘tensorflow’ has no attribute ‘get_variable’
• TensorFlow clip_by_value – Complete tutorial

In this tutorial, we have covered the error “module ‘TensorFlow‘ has no attribute ‘get_default_graph’“. Here we have covered the reason related to this error using TensorFlow. And we have also covered the following topics:

• module ‘TensorFlow’ has no attribute ‘get_default_graph’
• module ‘tensorflow’ has no attribute ‘get_variable’
• module ‘tensorflow’ has no attribute ‘get_default_session’
• module ‘tensorflow’ has no attribute ‘get_default_graph’ keras
• module ‘tensorflow’ has no attribute ‘get_variable_scope’
• module ‘tensorflow’ has no attribute ‘get_shape’
• module ‘tensorflow’ has no attribute ‘get_tensor_by name’
• module ‘tensorflow’ has no attribute ‘make_tensor_proto’
• module ‘tensorflow’ has no attribute ‘get_collection’
• module ‘tensorflow’ has no attribute ‘mean_squared_error’
• module ‘tensorflow’ has no attribute ‘placeholder’