# Tensorflow convert sparse tensor to tensor

Do you know how to convert the sparse tensor into a tensor, we will discuss the sparse tensor and a tensor in Python. A sparse tensor dataset is one in which most of the entries are zero; an illustration of this would be a big diagonal matrix. (the majority of which are zero).

• Tensorflow convert sparse tensor to tensor
• Tensorflow convert tensor to sparse tensor
• Convert TensorFlow tensor to torch tensor
• Tensorflow convert Tensor to a number
• Tensorflow convert Tensor to dimensions
• TensorFlow tensor length

## Tensorflow convert sparse tensor to tensor

• In this section, we will discuss how to convert the sparse tensor to a tensor in Python TensorFlow.
• A dataset called a sparse tensor is one in which the majority of the entries are zero; an illustration of this would be a big diagonal matrix. (the majority of which are zero). The non-zero values and their related coordinates are stored rather than the entire set of values for the tensor object.

Syntax:

Here is the Syntax of the following given code

``````tf.sparse.to_dense(
sp_input, default_value=None, validate_indices=True, name=None
)``````

Example:

``````import tensorflow as tf
new_tensor = tf.SparseTensor(dense_shape=[3, 5],values=[7, 8, 9],indices =[[0, 1],[0, 3],[2, 0]])
result=tf.sparse.to_dense(new_tensor).numpy()
print(result)``````

In this example, we have imported the tensorflow library and then created a tensor by using the tf.SparseTensor() function and within this function we assigned the dense_shape, values, and indices.

Next, to convert the sparse tensor to a tensor we used the .numpy() function and display the result.

Here is the Screenshot of the following given code

This is how we can convert the sparse tensor to a tensor in Python TensorFlow.

## Convert tensor to sparse tensor

• Here we will discuss how to convert the tensor to a sparse tensor in Python TensorFlow.
• In this example, we are going to use the tf.sparse.from_dense() function and this function is used to convert the dense tensor to a sparse tensor.

Syntax:

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

``````tf.sparse.from_dense(
tensor, name=None
)``````
• It consists of a few parameters
• tensor: This parameter defines the input tensor and dense Tensor to be converted to a SparseTensor.
• name: This parameter defines the name of the operation and by default, it takes none value.

Example:

``````import tensorflow as tf

new_tens = tf.sparse.from_dense([1, 0, 1, 0, 0])
new_tens.shape.as_list()
new_tens.values.numpy()
new_tens.indices.numpy()``````

You can refer to the below Screenshot

As you can see in the Screenshot the output displays the conversion of tensor to sparse tensor.

## How to Convert tensorflow tensor to torch tensor

• Let us discuss how to convert the TensorFlow tensor to a torch tensor in Python TensorFlow.
• In this example, we are going to use the torch.from_numpy() function and this NumPy array can be converted into a tensor in PyTorch using the function torch.from numpy().
• It anticipates an input of a NumPy array (numpy.ndarray). Tensor is the output format. The memory used by the returned tensor and ndarray is the same. There is no way to resize the returned tensor.

Syntax:

Here is the Syntax of the torch.from_numpy() function in Python

``torch.from_numpy(ndarray)``

Note: It consists of only one parameter and it specifies the NumPy array.

Example

``````import tensorflow as tf
import torch

tensorflow_tens=tf.constant([12,3,4,5,6])
py_tensors = torch.from_numpy(tensorflow_tens.numpy())
print(py_tensors)``````

In the following given code first, we imported all the necessary libraries and then created a tensor by using the tf.constant() function. Next, we want to convert the TensorFlow tensor to a PyTorch tensor. For this, we have used the torch.from_numpy() function and within this function, we assigned the tensor to it.

Here is the Screenshot of the following given code

This is how we can convert the tensorflow tensor to torch tensor in Python TensorFlow.

## Convert Tensor to a number

• In this section, we will discuss how to convert the tensor to a number.
• To perform this task we are going to use the tf.reduce_sum() function this function is used to calculate tensor elements sum across all dimensions.

Syntax:

Let’s have a look at the Syntax and understand the working of the tf.reduce_sum() function in Python TensorFlow.

``````tf.math.reduce_sum(
input_tensor, axis=None, keepdims=False, name=None
)``````
• It consists of a few parameters
• input_tensor: This parameter defines the input tensor and it is used to reduce the tensor.
• axis: By default, it takes none value and is reduced to dimension and it will check the condition if it is none then it will reduce all the dimensions Must be in the range [-rank(input_tensor), rank(input_tensor)].
• keepdims: If it is true then it will retain reduced dimension with length 1.
• name: By default, it takes none value and specifies the name of the operation.

Example:

Let’s take an example and check how to convert the input tensor to a number.

Source Code:

``````import tensorflow as tf
# Creation of input tensor
new_tensor = tf.constant([[12, 14, 16], [19, 20, 21]])
# By using the tf.reduce_sum() function
new_output_reduce_sum = tf.reduce_sum(new_tensor)
print(new_output_reduce_sum )
print(new_output_reduce_sum .numpy())``````

In the following given code first, we imported the TensorFlow library and then created an input tensor by using the tf.constant() function and within this function, we assigned the integer values as an argument.

Next, we want to convert the tensor into a number for this we used the tf.reduce_sum() function and assigned the tensor as a parameter.

Here is the Screenshot of the following given code.

In this example, we have discussed the conversion of tensorflow to a number.

## Convert Tensor to dimensions

• Here we will discuss how to convert the input tensor to dimensions in Python TensorFlow.
• To perform this task we are going to use the tf.shape() function and this function returns a tensor that contains the shape of the given tensor.

Syntax:

Let’s have a look at the Syntax and understand the working of tf.shape() function in Python TensorFlow.

``````tf.shape(
input,
out_type=tf.dtypes.int32,
name=None
)``````
• It consists of a few parameters
• input: This parameter defines the input tensor and is applied to the function.
• out_type: By default, it takes tf.dtypes.int32 and it is The specified output type of the operation.
• name: This parameter defines the name of the operation and by default, it takes none value.

Example:

``````import tensorflow as tf

new_tens = tf.constant([[12, 25, 87],
[18, 29, 28]])
print(tf.shape(new_tens).numpy())``````

In this example, we have created a tensor by using the tf.constant() function and then used the tf.shape() function to get the dimension of the tensor.

Here is the implementation of the following given code.

As you can see in the Screenshot we have discussed the conversion of tensor to dimensions.

## TensorFlow tensor length

• In this section, we will discuss how to get the length of an input tensor in Python TensorFlow.
• By using the tf.size() function and it will return the size of the input tensor and this function takes three parameters.

Syntax:

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

``````tf.size(
input,
out_type=tf.dtypes.int32,
name=None
)``````
• It consists of a few parameters
• input: This parameter defines the input tensor and it could be a sparse tensor.
• out_type: By default it takes tf.dtypes.int32 and it specified non-quantized numeric output.
• name: It specifies the name of the operation and by default, it takes none value.

Example:

Let’s take an example and understand how we can display the length of the input tensor.

``````import tensorflow as tf

new_tens = tf.constant([[12, 25, 87],
[18, 29, 28]])
print(tf.shape(new_tens).numpy())``````

You can refer to the below Screenshot.