Add python API for Adadelta optimizer.

python/paddle/fluid/optimizer.py

@@ -24,7 +24,9 @@ from layer_helper import LayerHelper
 from regularizer import append_regularization_ops
 from clip import append_gradient_clip_ops, error_clip_callback
 
-__all__ = ['SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad']
+__all__ = [
+    'SGD', 'Momentum', 'Adagrad', 'Adam', 'Adamax', 'DecayedAdagrad', 'Adadelta'
+]
 
 
 class Optimizer(object):
@@ -575,6 +577,58 @@ class DecayedAdagradOptimizer(Optimizer):
         return decayed_adagrad_op
 
 
+class AdadeltaOptimizer(Optimizer):
+    """Simple Adadelta optimizer with average squared grad state and
+    average squared update state.
+    """
+    _avg_squared_grad_acc_str = "_avg_squared_grad"
+    _avg_squared_update_acc_str = "_avg_squared_update"
+
+    def __init__(self, learning_rate, epsilon=1.0e-6, rho=0.95, **kwargs):
+        assert learning_rate is not None
+        assert epsilon is not None
+        assert rho is not None
+        super(AdadeltaOptimizer, self).__init__(
+            learning_rate=learning_rate, **kwargs)
+        self.type = "adadelta"
+        self._epsilon = epsilon
+        self._rho = rho
+
+    def _create_accumulators(self, block, parameters):
+        assert isinstance(block, framework.Block)
+
+        for p in parameters:
+            self._add_accumulator(self._avg_squared_grad_acc_str, p)
+            self._add_accumulator(self._avg_squared_update_acc_str, p)
+
+    def _append_optimize_op(self, block, param_and_grad):
+        assert isinstance(block, framework.Block)
+
+        avg_squared_grad_acc = self._get_accumulator(
+            self._avg_squared_grad_acc_str, param_and_grad[0])
+        avg_squared_update_acc = self._get_accumulator(
+            self._avg_squared_update_acc_str, param_and_grad[0])
+
+        # Create the adadelta optimizer op
+        adadelta_op = block.append_op(
+            type=self.type,
+            inputs={
+                "Param": param_and_grad[0],
+                "Grad": param_and_grad[1],
+                "AvgSquaredGrad": avg_squared_grad_acc,
+                "AvgSquaredUpdate": avg_squared_update_acc
+            },
+            outputs={
+                "ParamOut": param_and_grad[0],
+                "AvgSquaredGradOut": avg_squared_grad_acc,
+                "AvgSquaredUpdateOut": avg_squared_update_acc
+            },
+            attrs={"epsilon": self._epsilon,
+                   "rho": self._rho})
+
+        return adadelta_op
+
+
 # We short the class name, since users will use the optimizer with the package
 # name. The sample code:
 #
@@ -589,3 +643,4 @@ Adagrad = AdagradOptimizer
 Adam = AdamOptimizer
 Adamax = AdamaxOptimizer
 DecayedAdagrad = DecayedAdagradOptimizer
+Adadelta = AdadeltaOptimizer