Fix default value for decay parameter in cycl-SGD (#1361)

clab · Apr 25, 2018 · f129862 · f129862
1 parent 4f46736
commit f129862
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diff --git a/dynet/training.h b/dynet/training.h
@@ -234,7 +234,7 @@ struct CyclicalSGDTrainer : public Trainer {
    * \param gamma Learning rate upper bound decay parameter
    * \param edecay Learning rate decay parameter. Ideally you shouldn't use this with cyclical learning rate since decay is already handled by \f$\gamma\f$
    */
-  explicit CyclicalSGDTrainer(ParameterCollection& m, float learning_rate_min = 0.01, float learning_rate_max = 0.1, float step_size = 2000, float gamma = 0.0, float edecay = 0.0) : Trainer(m, learning_rate_min), e_min(learning_rate_min), e_max(learning_rate_max), step_size(step_size), gamma(gamma), it(0) {}
+  explicit CyclicalSGDTrainer(ParameterCollection& m, float learning_rate_min = 0.01, float learning_rate_max = 0.1, float step_size = 2000, float gamma = 1.0, float edecay = 0.0) : Trainer(m, learning_rate_min), e_min(learning_rate_min), e_max(learning_rate_max), step_size(step_size), gamma(gamma), it(0) {}
   void restart() override {};
   using Trainer::restart;
   void update() override {

diff --git a/python/_dynet.pyx b/python/_dynet.pyx
@@ -6061,10 +6061,10 @@ cdef class CyclicalSGDTrainer(Trainer):
         learning_rate_min (number): Lower learning rate (default: {0.01})
         learning_rate_max (number): Upper learning rate (default: {0.1})
         step_size (number): Period of the triangular function in number of iterations (__not__ epochs). According to the original paper, this should be set around (2-8) x (training iterations in epoch) (default: {2000})
-        gamma (number): Learning rate upper bound decay parameter (default: {0.0})
+        gamma (number): Learning rate upper bound decay parameter (1.0 = no decay) (default: {1.0})
     """
     cdef CCyclicalSGDTrainer *thischildptr
-    def __cinit__(self, ParameterCollection m, float learning_rate_min = 0.01, float learning_rate_max = 0.1, float step_size = 2000, float gamma = 0.0):
+    def __cinit__(self, ParameterCollection m, float learning_rate_min = 0.01, float learning_rate_max = 0.1, float step_size = 2000, float gamma = 1.0):
         self.thischildptr = self.thisptr = new CCyclicalSGDTrainer(m.thisptr, learning_rate_min, learning_rate_max, step_size, gamma)
     cpdef update(self):
         self.thischildptr.update()