TensorFlow 2.x
Eager Execution is enabled by default, so just call .numpy()
on the Tensor object.
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)
tf.multiply(a, b).numpy()
# array([[ 2, 6],
# [12, 20]], dtype=int32)
See NumPy Compatibility for more. It is worth noting (from the docs),
Numpy array may share a memory with the Tensor object. Any changes to one may be reflected in the other.
Bold emphasis mine. A copy may or may not be returned, and this is an implementation detail based on whether the data is in CPU or GPU (in the latter case, a copy has to be made from GPU to host memory).
But why am I getting the AttributeError: 'Tensor' object has no attribute 'numpy'
?.
A lot of folks have commented about this issue, there are a couple of possible reasons:
- TF 2.0 is not correctly installed (in which case, try re-installing), or
- TF 2.0 is installed, but eager execution is disabled for some reason. In such cases, call
tf.compat.v1.enable_eager_execution()
to enable it, or see below.
If Eager Execution is disabled, you can build a graph and then run it through tf.compat.v1.Session
:
a = tf.constant([[1, 2], [3, 4]])
b = tf.add(a, 1)
out = tf.multiply(a, b)
out.eval(session=tf.compat.v1.Session())
# array([[ 2, 6],
# [12, 20]], dtype=int32)
See also TF 2.0 Symbols Map for a mapping of the old API to the new one.