Import Geant4 11.4.0.beta source tree

examples/extended/parameterisations/Par04/training_vae/utils/optimizer.py

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+from enum import IntEnum
+
+from tensorflow.keras.optimizers import Optimizer, Adadelta, Adagrad, Adam, Adamax, Ftrl, SGD, Nadam, RMSprop
+
+
+class OptimizerType(IntEnum):
+    """ Enum class of various optimizer types.
+
+    This class must be IntEnum to be JSON serializable. This feature is important because, when Optuna's study is
+    saved in a relational DB, all objects must be JSON serializable.
+    """
+
+    SGD = 0
+    RMSPROP = 1
+    ADAM = 2
+    ADADELTA = 3
+    ADAGRAD = 4
+    ADAMAX = 5
+    NADAM = 6
+    FTRL = 7
+
+
+class OptimizerFactory:
+    """Factory of optimizer like Stochastic Gradient Descent, RMSProp, Adam, etc.
+    """
+
+    @staticmethod
+    def create_optimizer(optimizer_type: OptimizerType, learning_rate: float) -> Optimizer:
+        """For a given type and a learning rate creates an instance of optimizer.
+
+        Args:
+            optimizer_type: a type of optimizer
+            learning_rate: a learning rate that should be passed to an optimizer
+
+        Returns:
+            An instance of optimizer.
+
+        """
+        if optimizer_type == OptimizerType.SGD:
+            return SGD(learning_rate)
+        elif optimizer_type == OptimizerType.RMSPROP:
+            return RMSprop(learning_rate)
+        elif optimizer_type == OptimizerType.ADAM:
+            return Adam(learning_rate)
+        elif optimizer_type == OptimizerType.ADADELTA:
+            return Adadelta(learning_rate)
+        elif optimizer_type == OptimizerType.ADAGRAD:
+            return Adagrad(learning_rate)
+        elif optimizer_type == OptimizerType.ADAMAX:
+            return Adamax(learning_rate)
+        elif optimizer_type == OptimizerType.NADAM:
+            return Nadam(learning_rate)
+        else:
+            # i.e. optimizer_type == OptimizerType.FTRL
+            return Ftrl(learning_rate)