tidied up initializers

src/param_initializers.h

@@ -10,25 +10,42 @@
 namespace marian {
 
 void zeros(Tensor t) {
-  std::vector<float> vals(t.size(), 0.0f);
-  thrust::copy(vals.begin(), vals.end(), t.begin());
+  t.set(0.f);
 }
 
 void ones(Tensor t) {
-  std::vector<float> vals(t.size(), 1.0f);
-  thrust::copy(vals.begin(), vals.end(), t.begin());
+  t.set(1.0f);
 }
 
-void randreal(Tensor t) {
+template <class Distribution>
+void distribution(Tensor t, float a=0.0, float b=0.1) {
   std::random_device device;
   std::default_random_engine engine(device());
-  std::uniform_real_distribution<> dist(0, 0.1);
+  Distribution dist(a, b);
   auto gen = std::bind(dist, engine);
 
   std::vector<float> vals(t.size());
   std::generate(begin(vals), end(vals), gen);
 
-  thrust::copy(vals.begin(), vals.end(), t.begin());
+  t << vals;
 }
 
+std::function<void(Tensor)> normal(float mean = 0.0, float std = 0.1) {
+  return [mean, std](Tensor t) {
+    distribution<std::normal_distribution<float>>(t, mean, std);
+  }; 
+}
+
+std::function<void(Tensor)> uniform(float a = 0.0, float b = 0.1) {
+  return [a, b](Tensor t) {
+    distribution<std::uniform_real_distribution<float>>(t, a, b);
+  };  
+}
+
+std::function<void(Tensor)> from_vector(const std::vector<float>& v) {
+  return [&v](Tensor t) {
+    t << v;
+  };
+}
+  
 } // namespace marian

src/validate_mnist.cu

@@ -9,7 +9,7 @@ using namespace keywords;
 
 int main(int argc, char** argv) {
   
-  cudaSetDevice(0);
+  cudaSetDevice(1);
   
   const size_t IMAGE_SIZE = 784;
   const size_t LABEL_SIZE = 10;
@@ -20,7 +20,6 @@ int main(int argc, char** argv) {
   std::vector<float> testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", BATCH_SIZE, LABEL_SIZE);
   std::cerr << "Done." << std::endl;
 
-  
   std::cerr << "Loading model params...";
   NpzConverter converter("../scripts/test_model/model.npz");
 
@@ -36,9 +35,9 @@ int main(int argc, char** argv) {
   auto y = input(shape={whatevs, LABEL_SIZE});
   
   auto w = param(shape={IMAGE_SIZE, LABEL_SIZE},
-                 init=[wData](Tensor t) { t.set(wData); });
+                 init=from_vector(wData));
   auto b = param(shape={1, LABEL_SIZE},
-                 init=[bData](Tensor t) { t.set(bData); });
+                 init=from_vector(bData));
 
   auto probs = softmax(dot(x, w) + b, axis=1);
   auto cost = -mean(sum(y * log(probs), axis=1), axis=0);