In this Python tutorial, we will learn** how to convert the Tensorflow Tensor to NumPy array**. Also, we will cover the following topics.

- TensorFlow tensor to numpy array without session
- TensorFlow eager tensor to numpy
- TensorFlow 2 tensor to numpy
- Tensorflow transform tensor to numpy array
- Tensorflow sparse tensor to numpy
- Tensorflow tensor vs numpy array

**Table of Contents**show

## Tensorflow Tensor to numpy

- In this section, we will learn the conversion of Tensor to numpy array in TensorFlow Python.
- In Tensorflow
**2.0**,**tf.session()**module has been removed and instead of session, we are going to use the**tf.compat.v1.disable_eager_execution()**for running the session. To convert the tensor into a numpy array first we will import the**eager_execution**function along with the TensorFlow library. - Next, we will create the constant values by using the
**tf.constant()**function and, then we are going to run the session by using the syntax**session=tf.compat.v1.Session()**in**eval()**function.

**Example:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
new_val = tf.constant([[15,78,24],[17,27,39]])
new_result = new_val.eval(session=tf.compat.v1.Session())
new_result
```

Here is the implementation of the following given code

Read: Python TensorFlow reduce_sum

## TensorFlow tensor to numpy array without session

- In this section, we will learn how to convert the tensor with numpy array in Tensorflow Python without session.
- To do this task first we are going to use the
**.numpy()**function in tensor and this is an in-built method and it will help the user to convert the tensor into a numpy array. - Now to create a tensor first, we will import the TensorFlow library and then declare a variable. Next, we will use the
**tf.constant()**function for creating a tensor of constant values. By using the**new_tens_val.numpy()**function you can easily get the numpy array values.

**Syntax:**

Let’s have a look at the Syntax and understand the working of the **tf.constant()** function

```
tf.constant
(
value,
dtype=None,
shape=None,
name='const'
)
```

**Example:**

Let’s, take an example and check how to convert the tensor to a numpy array in Tensorflow Python without session

**Source Code:**

```
import tensorflow as tf
new_tens_val=tf.constant([[24,56,78],
[16,18,29],
[23,46,78]])
result=new_tens_val.numpy()
result
```

Here is the Screenshot of the following given code

As you can see in the Screenshot the output displays the NumPy array.

Read: TensorFlow get shape

## TensorFlow eager tensor to numpy

- Here we are going to discuss how to convert the tensor to numpy array in TensorFlow by using
**eager_execution()**function. - This function is not used for graphs it basically calculates the values of tensors and it supports both
**TPU and GPU**. If you are using TensorFlow**2.0**version then it can easily work on the Program. - To create the session we will used the
**session=tf.compat.v1.Session()**syntax and it will help the user to convert the tensor values into numpy array but first you have to import**tf.compat.v1.disable_eager_execution()**function.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
values = tf.constant([[23,34,96],[27,99,89]])
output = values.eval(session=tf.compat.v1.Session())
output
```

Here is the execution of the following given code

Read: Python TensorFlow reduce_mean

## TensorFlow 2 tensor to numpy

- In this example we are going to learn how to convert tensor with numpy in TensorFlow
**2.0**version. - To perform this particular task we are going to use the tf.make_tensor_proto() method. This method will help the user to get the numpy array from the tensor and this method is available in Tensorflow
**2.0**. If you are using the old version of TensorFlow**1.x**then you can easily use the**eval()**or**session()**function. - In this example, we have created the
**tensorproto**and it is an object that has certain types and shapes and it will check the condition if the shape value is none then it specifies the numpy array.

**Syntax:**

Let’s have a look at the Syntax and understand the working of the **tf.make_tensor_proto() **method

```
tf.make_tensor_proto
(
values,
dtype=None,
shape=None,
verify_shape=False,
allow_broadcast=False
)
```

- It consists of a few parameters
**values:**This parameter indicates the values we have to insert in this method.**dtype:**By default it takes none value and it is an optional parameter and it represents with**tensor_pb2**.**shape:**This parameter specifies the dimension of tensor.**verify_shape:**By default, it takes**‘False’**value and it verify the shape of values.

**Example:**

```
import tensorflow as tf
new_val = tf.constant([[25,37,89],[56,14,90]])
result = tf.make_tensor_proto(new_val)
d=tf.make_ndarray(result)
d
```

In the following given code, we have used the **tf.constant()** function for the constant values and then we have declared a variable named** ‘result’** and assigned the **tf.make_tensor_proto() **function. Once you will execute this code the output displays the conversion of the tensor into a numpy array.

Here is the Output of the following given code

Read: Import error no module named TensorFlow

## Tensorflow transform tensor to numpy array

- Here we are going to learn how to transform the tensor with a numpy array in TensorFlow Python.
- To perform this particular task we are going to use the
**.numpy()**function and this method is available in the TensorFlow module package. In this example, we will create a Tensor Object, and then we are going to apply the**tensor.numpy()**function.

**Example:**

```
import tensorflow as tf
cons_values=tf.constant([[14,28,48],
[67,92,189],
[45,98,178]]) #tensor object
new_output=cons_values.numpy()
new_output
```

In the above example, the cons_values represent the tensor object in which we have assigned the **tf.constant()** function and within this function, we have passed the integer values.

After that, we declared a variable named** ‘new_output’ **and assigned the t**ensor.numpy()** function. Once you will print **‘new_output’ **then the result displays the NumPy array.

Here is the implementation of the following given code

As you can see in the Screenshot, the tensor values have been transformed into a numpy array.

Read: Python TensorFlow one_hot

## Tensorflow sparse tensor to numpy

- In this Program, we will learn how to convert the sparse tensor to a numpy array in TensorFlow Python.
- In Python spare tensors stores lots of zeros elements and to contain the zero values in the tensor we have to store them in a storage manner this method is used when we are working in Natural language processing. In this function, we will not encode the non-zero values.
- While in the case of dense tensors the maximum values are nonzeros and it is faster in indices as compared to sparse.
- In this example, we will discuss how to convert sparse tensor values to numpy. To do this task we are going to use the
**eager_execution()**function for running the session.

**Syntax:**

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

```
tf.sparse.SparseTensor
(
indices,
values,
dense_shape
)
```

- It consists of a few parameter
- indices: This parameter indicates the indices of non-zero values and it takes only a 2-dimensional array shape.
- values: These are the actual values and it can be only a 1-dimensional array that passes the values for each element in indices.
**dense_shape:**This parameter specifies the dimension and it can be only a 1-dimensional array int64.

**Example:**

Let’s take an example and check how to convert the sparse tensor to a numpy array in TensorFlow Python.

**Source Code:**

```
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
with tf.compat.v1.Session() as val:
new_output = val.run(tf.SparseTensor([[0,16],[24,56]],[15,25],[3,4]))
print(new_output)
```

Here is the Screenshot of the following given code

As you can see in the Screenshot the output displays the NumPy array.

Read: Convert list to tensor TensorFlow

## Tensorflow tensor vs numpy array

- In this section, we will learn the comparison between tensor and numpy array in Python.

Numpy array | Tensor |
---|---|

It is a collection of values of the same data type and it is a library that is available on Python and is mostly used in linear algebra or numerical computation problems. | In Python tensors are immutable and a tensor has a rank tensor that indicates it has only one input. If the function has two inputs then the function will be a second rank tensor. |

NumPy library has many functions like np.sum, np.diff, etc that perform some operation and it always returns as ndarray. | Tensors have a memory like GPU and it takes both scaler and vector values.Tensors are a multidimensional array and to perform some operations on tensor object we can easily use the tf.constant() function,tf,variable(),tf.placeholder. |

You can easily convert the numpy array to tensor by using the tf.convert_to_tensor() method. | While in the case of tensor you can easily convert the tensor into a numpy array by using the tensor.numpy() function. |

In Python NumPy, you can do indexing to use numpy.where() function or slicing method. | In Python TensorFlow, you can use the colon: for slices indices. |

In Python to get the shape of an array, we can easily use the numpy.reshape() function. | In Python TensorFlow, you can use the colon : for slices indices. To get the new shape of the tensor we can easily use the tf.reshape() function. |

**Example:**

Let’s take an example and we will see the difference between the numpy array and tensor in Python

**Source Code:**

```
#Tensor
import tensorflow as tf
tensor_value = tf.constant([1,2,34,5,6])
tensor_value
#numpy array
import numpy as np
new_val = np.array([34,5,7,8,9])
new_val
#if you are using other software then use print statement
#print(new_val)
```

In the above code, we have just simply created a tensor by importing the TensorFlow library and then using the **tf.constant()** function in which we have assigned the constant values.

Now, if you are using the **Jupyter notebook** then, just simply write the variable name it will display the output as well as a datatype. After that, we have created an array by using the **np.array()** function.

Here is the execution of the following given code

You may also like to read the following TensorFlow tutorials.

- Python TensorFlow truncated normal
- Tensorflow iterates over tensor
- Python TensorFlow random uniform
- Tensorflow custom loss function
- TensorFlow next_batch + Examples

So, in this tutorial, we have learned **how to convert the Tensorflow Tensor to NumPy** and, we have covered these topics.

- TensorFlow tensor to numpy array without session
- TensorFlow tensor to numpy keras
- TensorFlow eager tensor to numpy
- TensorFlow 2 tensor to numpy
- Tensorflow transform tensor to numpy array
- Tensorflow sparse tensor to numpy
- Tensorflow tensor vs numpy array

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