How to Convert TensorFlow Tensors to NumPy Arrays?

How to Convert Tensors to NumPy Arrays in TensorFlow

In Python bindings for TensorFlow, converting tensors into NumPy arrays is a necessary step for further data manipulation or integration with third-party libraries.

In TensorFlow 2.x:

TensorFlow 2.x enables eager execution by default, allowing you to simply call .numpy() on the Tensor object. This method returns a NumPy array:

<code class="python">import tensorflow as tf

a = tf.constant([[1, 2], [3, 4]])
b = tf.add(a, 1)

a.numpy()  # [array([[1, 2], [3, 4]], dtype=int32)]
b.numpy()  # [array([[2, 3], [4, 5]], dtype=int32)]</code>

In TensorFlow 1.x:

Eager execution is not enabled by default. To convert a tensor to a NumPy array in TensorFlow 1.x:

• Use .eval() method within a session:

<code class="python">a = tf.constant([[1, 2], [3, 4]])
b = tf.add(a, 1)

with tf.Session() as sess:
    out = sess.run([a, b])
    # out[0] contains the NumPy array representation of a
    # out[1] contains the NumPy array representation of b</code>

• Use tf.compat.v1.numpy_function:

<code class="python">a = tf.constant([[1, 2], [3, 4]])
b = tf.add(a, 1)

out = tf.compat.v1.numpy_function(lambda x: x.numpy(), [a, b])
# out[0] contains the NumPy array representation of a
# out[1] contains the NumPy array representation of b</code>

Note: The NumPy array may share memory with the Tensor object. Any changes to one may be reflected in the other.

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