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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:
<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>
<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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