Tensorflow 2.0: Optimizer.minimize ('Adam' object has no attribute 'minimize')

Question

For my Reinforcement Learning application, I need to be able to apply custom gradients / minimize changing loss function. According to documentation, it should be possible with Optimizer.minimize() function. However, my pip-installed version appears not to have this feature at all.

My code:

from tensorflow.python.keras.optimizers import Adam, SGD
print(tf.version.VERSION)
optim = Adam()
optim.minimize(loss, var_list=network.weights)

output:

2.0.0-alpha0
Traceback (most recent call last):
  File "/Users/ikkamens/Library/Preferences/PyCharmCE2018.3/scratches/testo.py", line 18, in <module>
    optim.minimize(loss, var_list=network.weights)
AttributeError: 'Adam' object has no attribute 'minimize'

Answer 1

Actually there is a difference. If you print both classes, you'll see:

from tensorflow.python.keras.optimizers import Adam

print(Adam)
print(tf.optimizers.Adam)

<class 'tensorflow.python.keras.optimizers.Adam'>
<class 'tensorflow.python.keras.optimizer_v2.adam.Adam'>

So in the first case Adam inherits from some other class. It's meant to be used inside Keras training loop, therefore, it doesn't have minimize method. To make sure, let's get all class methods

import inspect
from tensorflow.python.keras.optimizers import Adam

print(inspect.getmembers(Adam(), predicate=inspect.ismethod))

Output shows that this class doesn't even have minimize