mmr/tkDNN
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yolo layers

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Francesco Gatti
2017-08-01 16:08:56 +02:00
parent 8e4b3c6c17
commit b94931f9f7
21 changed files with 522 additions and 84 deletions
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tests/mnist/test.cpp
+4 -8
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@@ -3,13 +3,9 @@
const char *input_bin = "../tests/mnist/input.bin";
const char *c0_bin = "../tests/mnist/layers/Convolution0.bin";
const char *c0_bias_bin = "../tests/mnist/layers/Convolution0.bias.bin";
const char *c1_bin = "../tests/mnist/layers/Convolution1.bin";
const char *c1_bias_bin = "../tests/mnist/layers/Convolution1.bias.bin";
const char *d2_bin = "../tests/mnist/layers/InnerProduct2.bin";
const char *d2_bias_bin = "../tests/mnist/layers/InnerProduct2.bias.bin";
const char *d3_bin = "../tests/mnist/layers/InnerProduct3.bin";
const char *d3_bias_bin = "../tests/mnist/layers/InnerProduct3.bias.bin";
const char *output_bin = "../tests/mnist/output.bin";
int main() {
@@ -18,13 +14,13 @@ int main() {
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, c0_bin, c0_bias_bin);
l = new tkDNN::Conv2d (&net, dim, 20, 5, 5, 1, 1, 1, 1, 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, c1_bin, c1_bias_bin);
l = new tkDNN::Conv2d (&net, l->output_dim, 50, 5, 5, 1, 1, 1, 1, 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, d2_bias_bin);
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, d3_bias_bin);
l = new tkDNN::Dense (&net, l->output_dim, 10, d3_bin);
l = new tkDNN::Softmax (&net, l->output_dim);
// Load input