mmr/tkDNN
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better network model

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This commit is contained in:
Francesco Gatti
2017-08-01 23:03:02 +02:00
parent 300b0af5dd
commit e8355cee67
22 changed files with 166 additions and 179 deletions
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tests/mnist/test_mnistRT.cpp
+13 -14
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@@ -27,17 +27,16 @@ int main() {
std::cout<<"\n==== CUDNN ====\n";
// 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);
tkDNN::Network net(dim);
tkDNN::Conv2d l0(&net, 20, 5, 5, 1, 1, 0, 0, c0_bin);
tkDNN::Pooling l1(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tkDNN::Conv2d l2(&net, 50, 5, 5, 1, 1, 0, 0, c1_bin);
tkDNN::Pooling l3(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
tkDNN::Dense l4(&net, 500, d2_bin);
tkDNN::Activation l5(&net, CUDNN_ACTIVATION_RELU);
tkDNN::Dense l6(&net, 10, d3_bin);
tkDNN::Softmax l7(&net);
// Load input
value_type *data;
@@ -72,7 +71,7 @@ int main() {
auto input = network->addInput("data", dt, DimsCHW{ 1, 28, 28});
assert(input != nullptr);
tkDNN::Conv2d *c0 = (tkDNN::Conv2d*) (net.layers[0]);
tkDNN::Conv2d *c0 = &l0;
Weights w { dt, c0->data_h, c0->inputs*c0->outputs*c0->kernelH*c0->kernelW};
Weights b { dt, c0->bias_h, c0->outputs};
// Add a convolution layer with 20 outputs and a 5x5 filter.
@@ -85,7 +84,7 @@ int main() {
assert(pool1 != nullptr);
pool1->setStride(DimsHW{2, 2});
tkDNN::Conv2d *c1 = (tkDNN::Conv2d*) (net.layers[2]);
tkDNN::Conv2d *c1 = &l2;
Weights w1 { dt, c1->data_h, c1->inputs*c1->outputs*c1->kernelH*c1->kernelW};
Weights b1 { dt, c1->bias_h, c1->outputs};
// Add a second convolution layer with 50 outputs and a 5x5 filter.
@@ -98,7 +97,7 @@ int main() {
assert(pool2 != nullptr);
pool2->setStride(DimsHW{2, 2});
tkDNN::Dense *d2 = (tkDNN::Dense*) (net.layers[4]);
tkDNN::Dense *d2 = &l4;
Weights w2 { dt, d2->data_h, d2->inputs*d2->outputs};
Weights b2 { dt, d2->bias_h, d2->outputs};
// Add a fully connected layer with 500 outputs.
@@ -109,7 +108,7 @@ int main() {
auto relu1 = network->addActivation(*ip1->getOutput(0), ActivationType::kRELU);
assert(relu1 != nullptr);
tkDNN::Conv2d *d3 = (tkDNN::Conv2d*) (net.layers[6]);
tkDNN::Dense *d3 = &l6;
Weights w3 { dt, d3->data_h, d3->inputs*d3->outputs};
Weights b3 { dt, d3->bias_h, d3->outputs};
// Add a second fully connected layer with 20 outputs.