CODEWITHSUNDEEP
pythontensorflowkerastensorflow2.0
OverLordGoldDragon
OverLordGoldDragon

Reputation: 19836

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

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?

Upvotes: 10

Views: 27136

Answers (4)

Ibraheem
Ibraheem

Reputation: 55

In my case tensorflow 2.0 this works when printing the loss:

 import tensorflow as tf
 from tensorflow import keras

...

  print(loss_value)
  print(float(loss_value) )

output:

    tf.Tensor(2.3782592, shape=(), dtype=float32)
    2.3782591819763184

Upvotes: 0

DesiKeki
DesiKeki

Reputation: 696

I think what you are looking for is tf.keras.backend.get_value API.

print(x)
>>tf.Tensor([1.], shape=(1,), dtype=float32)
print(tf.keras.backend.get_value(x))
>>[1.]

Upvotes: 1

OverLordGoldDragon
OverLordGoldDragon

Reputation: 19836

Per my PR, this is the more reliable (but not guaranteed) workaround:

def K_eval(x):
    try:
        return K.get_value(K.to_dense(x))
    except:
        eval_fn = K.function([], [x])
        return eval_fn([])[0]

Update: mind the distrubution context under which a Tensor is to be evaluated; in TF2.2, a tf.Variable or tf.Tensor created under tf.python.distribute.distribution_strategy_context.in_replica_context() == True will fail any K.eval-etc attempts. Looks like tensors simply aren't meant to be evaluated there.

Upvotes: 1

Sergei Lebedev
Sergei Lebedev

Reputation: 2679

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.

Upvotes: 9

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