mmr/tkDNN
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new network declaration method

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This commit is contained in:
Francesco Gatti
2017-07-02 21:02:12 +02:00
parent d5ff332f0c
commit 159aa8aae8
1 changed files with 15 additions and 32 deletions
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tests/test.cpp
+15 -32
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@@ -20,20 +20,21 @@ int main() {
// Network layout
tkDNN::Network net;
tkDNN::dataDim_t dim(1, 1, 100, 100, 4);
tkDNN::MulAdd m0 (&net, dim, 2, -1);
tkDNN::Conv3d c0 (&net, m0.output_dim, 16, 8, 8, 2, 4, 4, 1, c0_bin, c0_bias_bin);
tkDNN::Activation a0 (&net, c0.output_dim, tkDNN::ACTIVATION_ELU);
tkDNN::Pooling p0 (&net, a0.output_dim, 2, 2, 2, 2, tkDNN::POOLING_AVERAGE);
tkDNN::Conv3d c1 (&net, p0.output_dim, 16, 4, 4, 2, 2, 2, 1, c1_bin, c1_bias_bin);
tkDNN::Activation a1 (&net, c1.output_dim, tkDNN::ACTIVATION_ELU);
tkDNN::Conv3d c2 (&net, a1.output_dim, 24, 3, 3, 2, 1, 1, 1, c2_bin, c2_bias_bin);
tkDNN::Activation a2 (&net, c2.output_dim, tkDNN::ACTIVATION_ELU);
tkDNN::Flatten f2 (&net, a2.output_dim);
tkDNN::Dense d3 (&net, f2.output_dim, 256, d3_bin, d3_bias_bin);
tkDNN::Activation a3 (&net, d3.output_dim, tkDNN::ACTIVATION_ELU);
tkDNN::Dense d4 (&net, a3.output_dim, 32, d4_bin, d4_bias_bin);
tkDNN::Activation a4 (&net, d4.output_dim, tkDNN::ACTIVATION_RELU);
tkDNN::Dense d5 (&net, a4.output_dim, 2, d5_bin, d5_bias_bin);
tkDNN::Layer *l;
l = new tkDNN::MulAdd (&net, dim, 2, -1);
l = new tkDNN::Conv3d (&net, l->output_dim, 16, 8, 8, 2, 4, 4, 1, c0_bin, c0_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_ELU);
l = new tkDNN::Pooling (&net, l->output_dim, 2, 2, 2, 2, tkDNN::POOLING_AVERAGE);
l = new tkDNN::Conv3d (&net, l->output_dim, 16, 4, 4, 2, 2, 2, 1, c1_bin, c1_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_ELU);
l = new tkDNN::Conv3d (&net, l->output_dim, 24, 3, 3, 2, 1, 1, 1, c2_bin, c2_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_ELU);
l = new tkDNN::Flatten (&net, l->output_dim);
l = new tkDNN::Dense (&net, l->output_dim, 256, d3_bin, d3_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_ELU);
l = new tkDNN::Dense (&net, l->output_dim, 32, d4_bin, d4_bias_bin);
l = new tkDNN::Activation (&net, l->output_dim, tkDNN::ACTIVATION_RELU);
l = new tkDNN::Dense (&net, l->output_dim, 2, d5_bin, d5_bias_bin);
// Load input
@@ -48,24 +49,6 @@ int main() {
// Inference
data = net.infer(dim, data); dim.print();
/*
//old inference
data = m0.infer(dim, data); dim.print();
data = c0.infer(dim, data); dim.print();
data = a0.infer(dim, data); dim.print();
data = p0.infer(dim, data); dim.print();
data = c1.infer(dim, data); dim.print();
data = a1.infer(dim, data); dim.print();
data = c2.infer(dim, data); dim.print();
data = a2.infer(dim, data); dim.print();
data = f2.infer(dim, data); dim.print();
data = d3.infer(dim, data); dim.print();
data = a3.infer(dim, data); dim.print();
data = d4.infer(dim, data); dim.print();
data = a4.infer(dim, data); dim.print();
data = d5.infer(dim, data); dim.print();
*/
TIMER_STOP
// Print result