Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into detection_output

python/paddle/v2/fluid/optimizer.py

@@ -18,8 +18,9 @@ class Optimizer(object):
     but need to use one of it's implementation.
     """
 
-    def __init__(self, global_step=None):
+    def __init__(self, global_step=None, regularization=None):
         self._global_step = global_step
+        self.regularization = regularization
         # Dictionary of accumulators. Some optimizer subclasses need to
         # allocate and manage extra variables associated with the parameters
         # to train. These variables are called accumulators.
@@ -199,7 +200,8 @@ class Optimizer(object):
         """
         params_grads = append_backward_ops(loss, parameter_list, no_grad_set)
         # Add regularization if any
-        params_grads = append_regularization_ops(params_grads)
+        params_grads = append_regularization_ops(params_grads,
+                                                 self.regularization)
         optimize_ops = self.create_optimization_pass(params_grads, loss,
                                                      startup_program)
         return optimize_ops
@@ -209,9 +211,9 @@ class SGDOptimizer(Optimizer):
     """ Simple SGD optimizer without any state.
     """
 
-    def __init__(self, learning_rate, global_step=None):
+    def __init__(self, learning_rate, **kwargs):
         assert learning_rate is not None
-        super(SGDOptimizer, self).__init__(global_step)
+        super(SGDOptimizer, self).__init__(**kwargs)
         self.type = "sgd"
         self._learning_rate = learning_rate
 
@@ -236,14 +238,10 @@ class MomentumOptimizer(Optimizer):
     """
     _velocity_acc_str = "velocity"
 
-    def __init__(self,
-                 learning_rate,
-                 momentum,
-                 use_nesterov=False,
-                 global_step=None):
+    def __init__(self, learning_rate, momentum, use_nesterov=False, **kwargs):
         assert learning_rate is not None
         assert momentum is not None
-        super(MomentumOptimizer, self).__init__(global_step)
+        super(MomentumOptimizer, self).__init__(**kwargs)
         self.type = "momentum"
         self._learning_rate = learning_rate
         self._momentum = momentum
@@ -284,10 +282,10 @@ class AdagradOptimizer(Optimizer):
     """
     _moment_acc_str = "moment"
 
-    def __init__(self, learning_rate, epsilon=1.0e-6, global_step=None):
+    def __init__(self, learning_rate, epsilon=1.0e-6, **kwargs):
         assert learning_rate is not None
         assert epsilon is not None
-        super(AdagradOptimizer, self).__init__(global_step)
+        super(AdagradOptimizer, self).__init__(**kwargs)
         self.type = "adagrad"
         self._learning_rate = learning_rate
         self._epsilon = epsilon
@@ -331,12 +329,12 @@ class AdamOptimizer(Optimizer):
                  beta1=0.9,
                  beta2=0.999,
                  epsilon=1e-8,
-                 global_step=None):
+                 **kwargs):
         assert learning_rate is not None
         assert beta1 is not None
         assert beta2 is not None
         assert epsilon is not None
-        super(AdamOptimizer, self).__init__(global_step)
+        super(AdamOptimizer, self).__init__(**kwargs)
         self.type = "adam"
         self._learning_rate = learning_rate
         self._beta1 = beta1
@@ -436,12 +434,12 @@ class AdamaxOptimizer(Optimizer):
                  beta1=0.9,
                  beta2=0.999,
                  epsilon=1e-8,
-                 global_step=None):
+                 **kwargs):
         assert learning_rate is not None
         assert beta1 is not None
         assert beta2 is not None
         assert epsilon is not None
-        super(AdamaxOptimizer, self).__init__()
+        super(AdamaxOptimizer, self).__init__(**kwargs)
         self.type = "adamax"
         self._learning_rate = learning_rate
         self._beta1 = beta1
@@ -514,16 +512,12 @@ class DecayedAdagradOptimizer(Optimizer):
     """
     _moment_acc_str = "moment"
 
-    def __init__(self,
-                 learning_rate,
-                 decay=0.95,
-                 epsilon=1.0e-6,
-                 global_step=None):
+    def __init__(self, learning_rate, decay=0.95, epsilon=1.0e-6, **kwargs):
         assert learning_rate is not None
         assert decay is not None
         assert epsilon is not None
 
-        super(DecayedAdagradOptimizer, self).__init__(global_step)
+        super(DecayedAdagradOptimizer, self).__init__(**kwargs)
         self.type = "decayed_adagrad"
         self._learning_rate = learning_rate
         self._decay = decay

python/paddle/v2/fluid/regularizer.py

@@ -3,7 +3,7 @@ import framework
 __all__ = ['append_regularization_ops', 'L1Decay', 'L2Decay']
 
 
-def append_regularization_ops(parameters_and_grads):
+def append_regularization_ops(parameters_and_grads, regularization=None):
     """Create and add backward regularization Operators
 
     Creates and adds backward regularization operators in the BlockDesc.
@@ -14,6 +14,8 @@ def append_regularization_ops(parameters_and_grads):
     Args:
         parameters_and_grads: A list of (parameters, gradients) pairs
                               that need to be regularized.
+        regularization: A global regularizer. If the parameter is not
+                        set. It will be applied with regularizer.
 
     Returns:
         list of (parameters, gradients) pair with the regularized gradient
@@ -23,14 +25,19 @@ def append_regularization_ops(parameters_and_grads):
     """
     params_and_grads = []
     for param, grad in parameters_and_grads:
+        regularization_term = None
+        if param.regularizer is not None:
+            # Add variable for regularization term in grad block
+            regularization_term = param.regularizer(param, grad.block)
+        elif regularization is not None:
+            regularization_term = regularization(param, grad.block)
+
         # If no gradient or no regularization specified,
         # then we don't need to do anything
-        if grad is None or param.regularizer is None:
+        if grad is None or regularization_term is None:
             params_and_grads.append((param, grad))
             continue
 
-        # Add variable for regularization term in grad block
-        regularization_term = param.regularizer(param, grad.block)
         assert grad.shape == regularization_term.shape
 
         grad.block.append_op(