set iteration count and optimizer type, change learning rate in runtime

scripts/experiment.py

+import numpy as np 
+import tensorflow as tf
+tf.compat.v1.disable_eager_execution() 
+import os 
+
+class Experiment:
+    
+    def __init__(self, optimizer, learning_rate, loss, steps=int(1e5)):
+        self.optimizer = optimizer
+        self.steps = int(steps)
+        
+    def change_optimizer(self, learning_rate, loss, keyword='adam'):
+        if keyword == 'adam':
+            self.optimizer = self.optimizer
+        elif keyword == 'sgd':
+            self.optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate).minimize(loss)
+        elif keyword == 'rmsprop':
+            self.optimizer = tf.compat.v1.train.RMSPropOptimizer(learning_rate).minimize(loss)
+        else:
+            raise NotImplementedError('Undefined optimizer!')
+            
+    def get_optimizer(self):
+        return self.optimizer
+    
+    def set_iteration_count(self, iteration_count):
+        self.steps = iteration_count
+        
+    def get_iteration_count(self):
+        return self.steps
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