Add Sqrt and Reciprocal activation

paddle/gserver/activations/ActivationFunction.cpp

@@ -396,6 +396,44 @@ Error __must_check backward(Argument& act) {
 }
 END_DEFINE_ACTIVATION(exponential)
 
+/**
+ * @brief Reciprocal Activation.
+ * \f[
+ * f(z) = 1/z
+ * \f]
+ */
+BEGIN_DEFINE_ACTIVATION(reciprocal)
+Error __must_check forward(Argument& act) {
+  act.value->reciprocal2();
+  return Error();
+}
+
+Error __must_check backward(Argument& act) {
+  act.grad->dotMulSquare(*act.value);
+  act.grad->neg();
+  return Error();
+}
+END_DEFINE_ACTIVATION(reciprocal)
+
+/**
+ * @brief Square Root Activation.
+ * \f[
+ * f(z) = sqrt(z)
+ * \f]
+ */
+BEGIN_DEFINE_ACTIVATION(sqrt)
+Error __must_check forward(Argument& act) {
+  act.value->sqrt2();
+  return Error();
+}
+
+Error __must_check backward(Argument& act) {
+  act.grad->dotDiv(*act.grad, *act.value);
+  act.grad->mulScalar(0.5);
+  return Error();
+}
+END_DEFINE_ACTIVATION(sqrt)
+
 /**
  * @brief Logarithm Activation.
  * \f[

python/paddle/trainer_config_helpers/activations.py

@@ -17,7 +17,7 @@ __all__ = [
     "IdentityActivation", "LinearActivation", 'SequenceSoftmaxActivation',
     'ExpActivation', "ReluActivation", "BReluActivation", "SoftReluActivation",
     "STanhActivation", "AbsActivation", "SquareActivation", "BaseActivation",
-    "LogActivation"
+    "LogActivation", "SqrtActivation", "ReciprocalActivation"
 ]
 
 
@@ -224,3 +224,27 @@ class LogActivation(BaseActivation):
 
     def __init__(self):
         BaseActivation.__init__(self, 'log', False)
+
+
+class SqrtActivation(BaseActivation):
+    """
+    Square Root Activation.
+
+    .. math::
+       f(z) = sqrt(z)
+    """
+
+    def __init__(self):
+        BaseActivation.__init__(self, 'sqrt', False)
+
+
+class ReciprocalActivation(BaseActivation):
+    """
+    Reciprocal Activation.
+
+    .. math::
+       f(z) = 1/z
+    """
+
+    def __init__(self):
+        BaseActivation.__init__(self, 'reciprocal', False)

python/paddle/trainer_config_helpers/layer_math.py

@@ -40,6 +40,8 @@ register_unary_math_op('sigmoid', act.SigmoidActivation())
 register_unary_math_op('tanh', act.TanhActivation())
 register_unary_math_op('square', act.SquareActivation())
 register_unary_math_op('relu', act.ReluActivation())
+register_unary_math_op('sqrt', act.SqrtActivation())
+register_unary_math_op('reciprocal', act.ReciprocalActivation())
 
 
 def add(layeroutput, other):

python/paddle/trainer_config_helpers/tests/configs/math_ops.py

@@ -4,6 +4,8 @@ settings(batch_size=1000, learning_rate=1e-5)
 
 x = data_layer(name='data', size=100)
 x = layer_math.exp(x)
+x = layer_math.sqrt(x)
+x = layer_math.reciprocal(x)
 x = layer_math.log(x)
 x = layer_math.abs(x)
 x = layer_math.sigmoid(x)

python/paddle/trainer_config_helpers/tests/configs/protostr/math_ops.protostr

@@ -20,13 +20,43 @@ layers {
     }
   }
 }
+layers {
+  name: "__sqrt_0__"
+  type: "mixed"
+  size: 100
+  active_type: "sqrt"
+  inputs {
+    input_layer_name: "__exp_0__"
+    proj_conf {
+      type: "identity"
+      name: "___sqrt_0__.w0"
+      input_size: 100
+      output_size: 100
+    }
+  }
+}
+layers {
+  name: "__reciprocal_0__"
+  type: "mixed"
+  size: 100
+  active_type: "reciprocal"
+  inputs {
+    input_layer_name: "__sqrt_0__"
+    proj_conf {
+      type: "identity"
+      name: "___reciprocal_0__.w0"
+      input_size: 100
+      output_size: 100
+    }
+  }
+}
 layers {
   name: "__log_0__"
   type: "mixed"
   size: 100
   active_type: "log"
   inputs {
-    input_layer_name: "__exp_0__"
+    input_layer_name: "__reciprocal_0__"
     proj_conf {
       type: "identity"
       name: "___log_0__.w0"
@@ -351,6 +381,8 @@ sub_models {
   name: "root"
   layer_names: "data"
   layer_names: "__exp_0__"
+  layer_names: "__sqrt_0__"
+  layer_names: "__reciprocal_0__"
   layer_names: "__log_0__"
   layer_names: "__abs_0__"
   layer_names: "__sigmoid_0__"

python/paddle/v2/trainer.py

@@ -177,7 +177,7 @@ class SGD(object):
         Testing method. Will test input data.
 
         :param reader: A reader that reads and yeilds data items.
-        :type reader: collections.Iterable  
+        :type reader: collections.Iterable
         :param feeding: Feeding is a map of neural network input name and array
                         index that reader returns.
         :type feeding: dict