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

Do you know how to solve the attributeerror module ‘tensorflow’ that has no attribute ‘matrix_transpose’. Let us discuss how to use the matrix_transpose() function in TensorFlow. And we’ll also cover the following topics:

• Attributeerror: module ‘tensorflow’ has no attribute ‘matrix_transpose’
• Attributeerror: module ‘tensorflow’ has no attribute ‘Range’
• Attributeerror: ‘module’ object has no attribute ‘decode_raw’

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

• In this section, we will discuss how to solve the attributeerror module ‘tensorflow’ has no attribute ‘matrix_transpose’.
• This method is used to transpose the input tensor By interchanging rows into columns or columns into rows, you can find a matrix’s transpose. In the superscript of the provided matrix, the letter “T” designates the transpose of the matrix. For example, if “B” is the given matrix, then B’ or BT represents the matrix’s transposition.

Example:

``````import tensorflow as tf
# Creation of input tensor
population_of_USA= tf.constant([[6782, 4623, 9654, 9356],
[15672, 45621, 99452, 47892]])
result = tf.matrix_transpose(population_of_USA)
print("transpose of matrix",result)``````

Here is the Screenshot of the following given code

Here is the Solution to this error

Reason: The reason behind this error is matrix_transpose is not available in TensorFlow latest version.

Now we are going to use the latest version of transpose() function that is tf.transpose().

Syntax:

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

``````tf.transpose(
a,
perm=None,
conjugate=False,
name='transpose'
)``````
• It consists of a few parameters
• a: This parameter defines the input tensor.
• perm: By default it takes none value and specifies the permutation of the dimensions of tensor.
• conjugate: optional bool The mathematical equivalent of setting it to True is tf.math.conj(tf.transpose(input)).
• name: This parameter defines the name of the operation and by default it takes ‘transpose’ value.

Example:

``````import tensorflow as tf
# Creation of input tensor
population_of_USA= tf.constant([[6782, 4623, 9654, 9356],
[15672, 45621, 99452, 47892]])
result = tf.transpose(population_of_USA)
print("transpose of matrix",result)``````

In the following given code first, we created an input tensor ‘population_of_USA’ by using the tf.constant() function and within this function, we assigned the value of the integer as an argument.

Next, we used the tf.transpose() function and assign the input tensor as a parameter and it will transpose all the column elements to row elements.

Here is the implementation of the following given code

This is how we can solve the attributeerror module TensorFlow has no attribute matrix_transpose

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

• Here we will discuss how to solve the attributeerror module ‘tensorflow’ has no attribute ‘Range’.
• The TensorFlow range function provides the start, stop, step, and dtype, range() creates a new tf.Tensor1D filled with the values in the given range. In simple words, we can say that it will create a sequence of numbers.

Syntax:

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

``````tf.range
(
start,
limit,
delta=1,
dtype=None,
name='range'
)``````
• It consists of a few parameters
• start: The 0-D Tensor (scalar). functions as range limit and the first entry defaults to 0 if limit is None. Otherwise, acts as range limit.
• limit: 0-D Tensor (scalar). Uppermost possible sequence, exclusive. Defaults to the value of start if None is present, while the first entry in the range defaults to 0.
• delta: By default, it takes 1 value and specifies the number that increment will be start.
• dtype: By default, it takes none value and specifies the data type of the input tensor.

Example:

``````import tensorflow as tf
start = 4
limit = 20
delta = 5
result=tf.Range(start, limit, delta)
print(result)``````

Here is the execution of the following given code

Here is the Solution to this error

``````import tensorflow as tf
start = 4
limit = 20
delta = 5
result=tf.range(start, limit, delta)
print(result)``````

In the above code, we set the range value of start, limit, and delta. Next, we used the tf.range() function and assigned all the parameters to it. Once you will execute this code it will return a tensor with a given range.

You can refer to the below Screenshot

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

## Attributeerror: ‘module’ object has no attribute ‘decode_raw’

• Let us discuss how to solve the attributeerror ‘module’ object has no attribute ‘decode_raw’.
• In Python TensorFlow the decode_raw() is used to convert raw bytes strings into tensors.

Syntax:

``````tf.io.decode_raw(
input_bytes, out_type, little_endian=True, fixed_length=None, name=None
)``````
• It consists of a few parameters
• input_bytes: The input Tensor’s elements are each transformed into an array of bytes.
• out_type: The output’s DType. Half, float, double, int32, uint16, int8, int16, int8, and int64 are all acceptable types.
• little_endian: Whether the input_bytes data is in little-endian format. Data will be converted into host byte order if necessary.
• fixed_length: By default, it takes none value, If it is, each element’s initial fixed-length bytes will be transformed. Data will either be trimmed or zero-padded to the desired length.

Example:

``````import tensorflow as tf

result= tf.decode_raw(tf.constant([["78"],["67"]]), tf.uint8, fixed_length=4)
print(result)``````

Here is the Screenshot of the following given code

Here is the Solution of the error

``````import tensorflow as tf

result= tf.io.decode_raw(tf.constant([["78"],["67"]]), tf.uint8, fixed_length=4)
print(result)``````

Here is the Screenshot of the following given code