How to get value of a Keras tensor in TensorFlow 2?

Question

TF1 had sess.run() and .eval() to get values of tensors - and Keras had K.get_value(); now, neither work the same (former two at all).

K.eager(K.get_value)(tensor) appears to work inside Keras graph by exiting it, and K.get_value(tensor) outside the graph - both w/ TF2's default eagerly (which is off in former). However, this fails if tensor is a Keras backend operation:

import keras.backend as K
def tensor_info(x):
    print(x)
    print("Type: %s" % type(x))
    try:        
        x_value = K.get_value(x)
    except:
        try:    x_value = K.eager(K.get_value)(x)
        except: x_value = x.numpy()
    print("Value: %s" % x_value)  # three methods

ones = K.ones(1)
ones_sqrt = K.sqrt(ones)

tensor_info(ones); print()
tensor_info(ones_sqrt)

<tf.Variable 'Variable:0' shape=(1,) dtype=float32, numpy=array([1.], dtype=float32)>
Type: <class 'tensorflow.python.ops.resource_variable_ops.ResourceVariable'>
Value: [1.]

Tensor("Sqrt:0", shape=(1,), dtype=float32)
Type: <class 'tensorflow.python.framework.ops.Tensor'>
# third print fails w/ below

AttributeError: 'Tensor' object has no attribute 'numpy' 

---

This is a non-issue in TF < 2.0. Github's been silent. I'm aware of ways to rewrite the code as a workaround, but it'll eliminate Keras' backend-neutrality and work akin to tf.keras. Is there a way to get Keras 2.3 tensor values in TensorFlow 2.0 while retaining backend-neutrality?

Answer 1

I think you want K.eval:

>>> v = K.ones(1)
>>> K.eval(v)
array([1.], dtype=float32)
>>> K.eval(K.sqrt(v))
array([1.], dtype=float32)

Note that K.get_value is reserved for use with variables (e.g. v here) while K.eval works with any tensor.