mnist tensor

tests/mnist/test_mnist.cpp

@@ -0,0 +1,54 @@
+#include<iostream>
+#include "tkdnn.h"
+
+const char *input_bin   = "../tests/mnist/input.bin";
+const char *c0_bin      = "../tests/mnist/layers/c0.bin";
+const char *c1_bin      = "../tests/mnist/layers/c1.bin";
+const char *d2_bin      = "../tests/mnist/layers/d2.bin";
+const char *d3_bin      = "../tests/mnist/layers/d3.bin";
+const char *output_bin   = "../tests/mnist/output.bin";
+
+int main() {
+
+    // Network layout
+    tkDNN::Network net;
+    tkDNN::dataDim_t dim(1, 1, 28, 28, 1);
+    tkDNN::Layer *l;
+    l = new tkDNN::Conv2d     (&net, dim, 20, 5, 5, 1, 1, 0, 0, c0_bin);
+    l = new tkDNN::Pooling    (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    l = new tkDNN::Conv2d     (&net, l->output_dim, 50, 5, 5, 1, 1, 0, 0, c1_bin);
+    l = new tkDNN::Pooling    (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+    l = new tkDNN::Dense      (&net, l->output_dim, 500, d2_bin);
+    l = new tkDNN::Activation (&net, l->output_dim, CUDNN_ACTIVATION_RELU);
+    l = new tkDNN::Dense      (&net, l->output_dim, 10, d3_bin);
+    l = new tkDNN::Softmax    (&net, l->output_dim);
+ 
+    // Load input
+    value_type *data;
+    value_type *input_h;
+    readBinaryFile(input_bin, dim.tot(), &input_h, &data);
+
+    printDeviceVector(dim.tot(), data);
+    dim.print(); //print initial dimension
+    
+    TIMER_START
+
+    // Inference
+    data = net.infer(dim, data);
+    
+    TIMER_STOP
+    dim.print();   
+
+    // Print result
+    std::cout<<"\n======= RESULT =======\n";
+    printDeviceVector(dim.tot(), data);
+ 
+    // Print real test
+    std::cout<<"\n==== CHECK RESULT ====\n";
+    value_type *out;
+    value_type *out_h;
+    readBinaryFile(output_bin, dim.tot(), &out_h, &out);
+    printDeviceVector(dim.tot(), out);
+ 
+    return 0;
+}