Incorrect list - I guess the script somehow also needs to check return values?

[TheCrazyT]

I think currently the list of differentiable OPs is incorrect.

Take ArgMax as an example:

https://github.com/tensorflow/tensorflow/blob/87462bfac761435a46641ff2f10ad0b6e5414a4b/tensorflow/python/ops/math_grad.py#L37

It returns [None, None]. And I guess, that is why I'm still getting: ValueError: No gradients provided for any variable when I use a Lambda-Layer with tf.math.argmax within keras. So my guess is that ArgMax is indeed not differentiable.

But maybe I'm just missing something?

Example code:

import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras.layers import *
from tensorflow.keras.models import *
from tensorflow.keras.optimizers import *

def ArgMax():
    return Lambda(lambda q:tf.math.argmax(q, 2))
inputs1 = Input(shape = (1,1,))
d = Dense(3)(inputs1)
out = ArgMax()(d)

model = Model(inputs = [inputs1], outputs = [out], name = "test")

model.summary()
model.compile(SGD(0.1),loss="mae")
model.fit(
        x = [np.ones((100,1,1))],
        y = [np.zeros((100,1))],
        batch_size = 100,
        verbose = 2,
        epochs = 10)