wipe out compiler warinngs (Visual Studio /W4)

Author: nyanp

include/activation_function.h

@@ -40,7 +40,7 @@ class activation {
 class identity_activation : public activation {
 public:
     float_t f(float_t x) const { return x; }
-    float_t df(float_t f_x) const { return 1; }  
+    float_t df(float_t /*f_x*/) const { return 1; }  
     std::pair<float_t, float_t> scale() const { return std::make_pair(0.1, 0.9); }
 };
 

include/image.h

@@ -79,7 +79,7 @@ class image {
 
         // color palette (256*3byte)
         for (int i = 0; i < 256; i++) {
-            const char v = i;
+            const char v = static_cast<const char>(i);
             ofs.write(&v, 1);//R
             ofs.write(&v, 1);//G
             ofs.write(&v, 1);//B

include/layer.h

@@ -225,7 +225,7 @@ class input_layer : public layer<N, identity_activation> {
         return this->next_ ? this->next_->forward_propagation(in, index) : this->output_[index];
     }
 
-    const vec_t& back_propagation(const vec_t& current_delta, int index) {
+    const vec_t& back_propagation(const vec_t& current_delta, int /*index*/) {
         return current_delta;
     }
 

include/network.h

@@ -283,6 +283,7 @@ class network {
 
     template<typename Activation, typename Loss>
     bool is_canonical_link(const Activation& h, const Loss& E) {
+        CNN_UNREFERENCED_PARAMETER(E);
         if (typeid(h) == typeid(sigmoid_activation) && typeid(E) == typeid(cross_entropy)) return true;
         if (typeid(h) == typeid(tanh_activation) && typeid(E) == typeid(cross_entropy)) return true;
         if (typeid(h) == typeid(identity_activation) && typeid(E) == typeid(mse)) return true;

include/optimizer.h

@@ -37,7 +37,7 @@ struct optimizer {
 struct gradient_descent_levenberg_marquardt : public optimizer {
 public:
     gradient_descent_levenberg_marquardt() : alpha(0.00085), mu(0.02) {}
-    gradient_descent_levenberg_marquardt(float_t alpha, float_t lambda, float_t mu) : alpha(alpha), mu(mu) {}
+    gradient_descent_levenberg_marquardt(float_t alpha, float_t mu) : alpha(alpha), mu(mu) {}
 
     void update(float_t dW, float_t H, float_t *W) {
         *W -= (alpha / (H + mu)) * (dW); // 7.2%
@@ -55,7 +55,7 @@ struct gradient_descent : public optimizer {
     gradient_descent() : alpha(0.01), lambda(0.0) {}
     gradient_descent(float_t alpha, float_t lambda) : alpha(alpha), lambda(lambda) {}
 
-    void update(float_t dW, float_t H, float_t *W) {
+    void update(float_t dW, float_t /*H*/, float_t *W) {
         *W -= alpha * ((dW) + *W * lambda); // 7.2%
     }
 

include/partial_connected_layer.h

@@ -188,13 +188,13 @@ class partial_connected_layer : public layer<N, Activation> {
         for (int i = 0; i < this->out_size_; i++) {
             wi_connections& wi = out2wi_[i];
             for (size_t j = 0; j < wi.size(); j++)
-                wi[j].first = swaps[wi[j].first];
+                wi[j].first = static_cast<unsigned short>(swaps[wi[j].first]);
         }
 
         for (int i = 0; i < this->in_size_; i++) {
             wo_connections& wo = in2wo_[i];
             for (size_t j = 0; j < wo.size(); j++)
-                wo[j].first = swaps[wo[j].first];
+                wo[j].first = static_cast<unsigned short>(swaps[wo[j].first]);
         }
 
         std::vector<io_connections> weight2io_new(n);

include/product.h

@@ -45,7 +45,7 @@ namespace detail {
 
 
 template<typename T>
-inline bool is_aligned(T, const typename T::value_type* p) {
+inline bool is_aligned(T, const typename T::value_type* /*p*/) {
     return true;
 }
 

include/util.h

@@ -38,6 +38,8 @@
 #endif
 #include "fixed_point.h"
 
+#define CNN_UNREFERENCED_PARAMETER(x) (void)(x)
+
 namespace tiny_cnn {
 
 typedef double float_t;
@@ -168,7 +170,7 @@ void parallel_for(int begin, int end, const Func& f) {
 }
 
 template<typename Func>
-void for_(bool parallelize, int begin, int end, Func f) {
+void for_(bool /*parallelize*/, int begin, int end, Func f) { // ignore parallelize if you don't define CNN_USE_TBB
     parallel_for(begin, end, f);
 }