In TF-v2.12 there are no methods called `_set_hyper` and `_get_hyper`

TensorFlow General Discussion
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1

https://github.com/tensorflow/tensorflow/issues/62021

I’m trying to play with optimizers in TF-v2.12:

import tensorflow as tf

class MyAdamOptimizer(tf.keras.optimizers.Optimizer):
    def __init__(self, learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-7, name="MyAdamOptimizer", **kwargs):
        super(MyAdamOptimizer, self).__init__(name, **kwargs)
        
        self._set_hyper("learning_rate", kwargs.get("lr", learning_rate))
        self._set_hyper("beta_1", beta_1)
        self._set_hyper("beta_2", beta_2)
        self._set_hyper("epsilon", epsilon)
        
    def _create_slots(self, var_list):
        for var in var_list:
            self.add_slot(var, "m")
            self.add_slot(var, "v")
            
    def _resource_apply_dense(self, grad, var):
        lr = self._get_hyper("learning_rate", var_dtype=var.dtype.base_dtype)
        beta_1 = self._get_hyper("beta_1", var_dtype=var.dtype.base_dtype)
        beta_2 = self._get_hyper("beta_2", var_dtype=var.dtype.base_dtype)
        epsilon = self._get_hyper("epsilon", var_dtype=var.dtype.base_dtype)
        
        m = self.get_slot(var, "m")
        v = self.get_slot(var, "v")
        
        m.assign_add((1 - beta_1) * (grad - m))
        v.assign_add((1 - beta_2) * (tf.square(grad) - v))
        
        m_hat = m / (1 - tf.math.pow(beta_1, tf.cast(self.iterations + 1, tf.float32)))
        v_hat = v / (1 - tf.math.pow(beta_2, tf.cast(self.iterations + 1, tf.float32)))
        
        var_update = lr * m_hat / (tf.sqrt(v_hat) + epsilon)
        
        var.assign_sub(var_update)
        
        return var_update
        
    def _resource_apply_sparse(self, grad, var):
        raise NotImplementedError("Sparse gradient updates are not supported.")

    
optimizer = MyAdamOptimizer(learning_rate=0.001)

But I can’t find _set_hyper and _get_hyper. Can anyone give me the latest implementation strategy for Optimizers?

2

Hi @maifeeulasad

Can you please try out the following?

import tensorflow as tf
from tensorflow.python.keras.optimizer_v2 import optimizer_v2

class MyAdamOptimizer(optimizer_v2.OptimizerV2):
def init(self, learning_rate=0.001, beta_1=0.9,
beta_2=0.999, epsilon=1e-7,
name=“MyAdamOptimizer”, **kwargs):
super(MyAdamOptimizer, self).init(name, **kwargs)

    self._set_hyper("learning_rate", kwargs.get("lr", learning_rate))
    self._set_hyper("beta_1", beta_1)
    self._set_hyper("beta_2", beta_2)
    self._set_hyper("epsilon", epsilon)
    
def _create_slots(self, var_list):
    for var in var_list:
        self.add_slot(var, "m")
        self.add_slot(var, "v")
        
def _resource_apply_dense(self, grad, var):
    lr = self._get_hyper("learning_rate", var_dtype=var.dtype.base_dtype)
    beta_1 = self._get_hyper("beta_1", var_dtype=var.dtype.base_dtype)
    beta_2 = self._get_hyper("beta_2", var_dtype=var.dtype.base_dtype)
    epsilon = self._get_hyper("epsilon", var_dtype=var.dtype.base_dtype)
    
    m = self.get_slot(var, "m")
    v = self.get_slot(var, "v")
    
    m.assign_add((1 - beta_1) * (grad - m))
    v.assign_add((1 - beta_2) * (tf.square(grad) - v))
    
    m_hat = m / (1 - tf.math.pow(beta_1, tf.cast(self.iterations + 1, tf.float32)))
    v_hat = v / (1 - tf.math.pow(beta_2, tf.cast(self.iterations + 1, tf.float32)))
    
    var_update = lr * m_hat / (tf.sqrt(v_hat) + epsilon)
    
    var.assign_sub(var_update)
    
    return var_update
    
def _resource_apply_sparse(self, grad, var):
    raise NotImplementedError("Sparse gradient updates are not supported.")

optimizer = MyAdamOptimizer(learning_rate=0.001)

(for some reason, I’m unable to format my text as I wish. Sorry for the mess on your screen)

3

@tagoma Really appreaciate your response.

But, it leads to ValueError: Could not interpret optimizer identifier: <__main__.MyAdamOptimizer object at 0x79f2a00cba30>. Due to version mis-match as far as I know.

4

I don’t get any error message locally using TF 2.13.
I created a Colab using TF 2.12.
Can you please have a look at it?