finish training mnist

src/test.cpp

@@ -257,10 +257,111 @@ int main()
  // Test MNIST dataset.
  Tensor ** train_image=dataset::Read_MNIST_Train_Image("../dataset/train-images-idx3-ubyte");
  Tensor ** train_label=dataset::Read_MNIST_Train_Label("../dataset/train-labels-idx1-ubyte");
- dataset::Visualize_Grayscale(*train_image[59999],cout);
- cout<<train_label[59999]->p[0]<<endl;
+ Tensor ** test_image=dataset::Read_MNIST_Test_Image("../dataset/t10k-images-idx3-ubyte");
+ Tensor ** test_label=dataset::Read_MNIST_Test_Label("../dataset/t10k-labels-idx1-ubyte");
+
+ // //Visualize images.
+ // dataset::Visualize_Grayscale(*train_image[59999],cout);
+ // cout<<train_label[59999]->p[0]<<endl;
+
+ //initalize random seed
+ Init::set_seed(std::chrono::system_clock::now().time_since_epoch().count());
+
+ // Define a 2-layer NN.
+ class MyNet{
+ public:
+ nn::Linear fc1;
+ nn::Linear fc2;
+ MyNet():fc1(28*28,500),fc2(500,10){}
+
+ Variable & operator()(Variable & x)
+ {
+ Variable & out1=fc1(x);
+ Variable & out2=Graph::ReLU(out1);
+ Variable & out3=fc2(out2);
+ return out3;
+ }
+ };
+
+ MyNet mynet;
+
+ Variable x,y; // Use default constructor, otherwise you need to clear_data to release the initial tensor.
+
+ //Reshape train and test images
+
+ for (int i=0;i<60000;++i)
+ {
+ train_image[i]->reshape({28*28,1});
+ }
+ for (int j=0;j<10000;++j)
+ {
+ test_image[j]->reshape({28*28,1});
+ }
+
+ //Build Graph
+
+ Variable & out = mynet(x);
+ Variable & loss = Graph::SoftmaxCrossEntropy(out,y);
+
+ // Define an optimizer
+ int batch_size=100;
+ float learning_rate=0.1;
+ float weight_decay=5e-4;
+ SGDOptimizer myoptim(batch_size, learning_rate, weight_decay);
+
+
+ for (int epoch=0;epoch<1;++epoch) 
+ {
+ for (int inner_loop=0; inner_loop<10000; ++inner_loop)
+ {
+ //Manual Assign tensor to x and y.
+ // Random SHUFLLE !!!!!!!!!!!
+ x.data=train_image[inner_loop];
+ y.data=train_label[inner_loop];
+ Graph::eval(loss);
+ if ((inner_loop+1) % batch_size ==0)
+ cout<<loss.data->p[0]<<endl;
+ Graph::zero_grad();
+ Graph::backward(loss);
+ myoptim.step();
+ }
+ }
+
+ //Eval on Test
+
+ int total_correct=0;
+ for (int inner_loop=0; inner_loop<1000; ++ inner_loop)
+ {
+ x.data=test_image[inner_loop];
+ Graph::eval(out);
+ Tensor u=functional::softmax(*out.data);
+ auto argmax = std::max_element(u.p,u.p+u.length);
+ if ((argmax - u.p) == (int)(test_label[inner_loop]->p[0]))
+ ++total_correct;
+ }
+ cout<<"Numbers of correctness: "<<total_correct<<endl;
+
+
+
+ // Free the resources.
+ x.data=nullptr;
+ y.data=nullptr;
+
+ for (int i=0;i<60000;++i)
+ {
+ delete train_image[i];
+ delete train_label[i];
+ }
  delete train_image;
  delete train_label;
 
+ for (int j=0;j<10000;++j)
+ {
+ delete test_image[j];
+ delete test_label[j];
+ } 
+ delete test_image;
+ delete test_label;
+
  return 0;
 }