["python","numpy","tensorflow"]
Convert a tensor to numpy array in Tensorflow?

How to convert a tensor into a numpy array when using Tensorflow with Python bindings?
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.