# Convert Tensor to NumPy Array in Python

This tutorial will introduce the methods to convert a Tensor to a NumPy array in Python.

## Convert a Tensor to a NumPy Array With the `Tensor.numpy()` Function in Python

The Eager Execution of the TensorFlow library can be used to convert a tensor to a NumPy array in Python. With Eager Execution, the behavior of the operations of TensorFlow library changes, and the operations execute immediately. We can also perform NumPy operations on Tensor objects with Eager Execution. The `Tensor.numpy()` function converts the Tensor to a NumPy array in Python. In TensorFlow 2.0, the Eager Execution is enabled by default. So, this approach works best for the TensorFlow version 2.0. See the following code example.

``````import tensorflow as tf
tensor = tf.constant([[1,2,3],[4,5,6],[7,8,9]])
print("Tensor = ",tensor)
array = tensor.numpy()
print("Array = ",array)
``````

Output:

``````Tensor =  tf.Tensor(
[[1 2 3]
[4 5 6]
[7 8 9]], shape=(3, 3), dtype=int32)
Array =  [[1 2 3]
[4 5 6]
[7 8 9]]
``````

In the above code, we first created and initialized the Tensor object `tensor` with the `tf.constant()` function in Python. We printed the `tensor` and converted it to a NumPy array `array` with the `tensor.numpy()` function in Python. In the end, we printed the `array`.

## Convert a Tensor to a NumPy Array With the `Tensor.eval()` Function in Python

We can also use the `Tensor.eval()` function to convert a Tensor to a NumPy array in Python. This method is not supported in the TensorFlow version 2.0. So, we have to either keep the previous version 1.0 of the TensorFlow or disable all the behavior of version 2.0 of the TensorFlow library. See the following code example.

``````import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
tensor = tf.constant([[1,2,3],[4,5,6],[7,8,9]])
print("Tensor = ",tensor)
array = tensor.eval(session=tf.Session())
print("Array = ",array)
``````

Output:

``````Tensor =  Tensor("Const_1:0", shape=(3, 3), dtype=int32)
Array =  [[1 2 3]
[4 5 6]
[7 8 9]]
``````

In the above code, we converted the Tensor object `tensor` to the NumPy array `array` with the `tensor.eval()` function in Python. We first imported version 1.0 of the TensorFlow library and disabled all the behavior of version 2.0. We then created and initialized the `tensor` with the `tf.constant()` function and printed the values in `tensor`. We then executed the `tensor.eval()` function and saved the returned value inside the `array`, and printed the values in `array`.

## Convert a Tensor to a NumPy Array With the `TensorFlow.Session()` Function in Python

The `TensorFlow.Session()` is another method that can be used to convert a Tensor to a NumPy array in Python. This method is very similar to the previous approach with the `Tensor.eval()` function. This approach is also not supported by version 2.0 of the TensorFlow library. We either have to install version 1.0 of the TensorFlow library or disable all the behavior of version 2.0 of the TensorFlow library. We can pass our Tensor object to the `TensorFlow.Session().run()` function to convert that Tensor object to a NumPy array in Python. See the following code example.

``````import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
tensor = tf.constant([[1,2,3],[4,5,6],[7,8,9]])
print("Tensor = ",tensor)
array = tf.Session().run(tensor)
print("Array = ",array)
``````

Output:

``````Tensor =  Tensor("Const_6:0", shape=(3, 3), dtype=int32)
Array =  [[1 2 3]
[4 5 6]
[7 8 9]]
``````

In the above code, we converted the Tensor object `tensor` to the NumPy array `array` with the `tf.Session.run(tensor)` function in Python. We first imported the version 1.0 compatible TensorFlow library and disabled all the behavior of version 2.0. We then created the Tensor object `tensor` and printed the values of `tensor`. We then converted the `tensor` Tensor to the `array` NumPy array with the `tf.Session.run(tensor)` function and printed the values in `array`.

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