Francesco Gatti
40 lines
1.3 KiB
C++
40 lines
1.3 KiB
C++
#include<iostream>
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#include "Layer.h"
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const char *input_bin = "../tests/input.bin";
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const char *d0_bin = "../tests/dense0.bin";
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const char *d0_bias_bin = "../tests/dense0.bias.bin";
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const char *d1_bin = "../tests/dense1.bin";
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const char *d1_bias_bin = "../tests/dense1.bias.bin";
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const char *d2_bin = "../tests/dense2.bin";
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const char *d2_bias_bin = "../tests/dense2.bias.bin";
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int main() {
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// Network layout
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tkDNN::Network net;
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tkDNN::dataDim_t dim(1, 512, 1, 1);
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tkDNN::Dense d0 (&net, dim, 256, d0_bin, d0_bias_bin);
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tkDNN::Activation a0 (&net, d0.output_dim, tkDNN::ACTIVATION_ELU);
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tkDNN::Dense d1 (&net, a0.output_dim, 32, d1_bin, d1_bias_bin);
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tkDNN::Activation a1 (&net, d1.output_dim, tkDNN::ACTIVATION_ELU);
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tkDNN::Dense d2 (&net, a1.output_dim, 2, d2_bin, d2_bias_bin);
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// Load input
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value_type *data;
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value_type *input_h;
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readBinaryFile(input_bin, dim.tot(), &input_h, &data);
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dim.print(); //print initial dimension
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// Inference
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data = d0.infer(dim, data); dim.print();
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data = a0.infer(dim, data); dim.print();
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data = d1.infer(dim, data); dim.print();
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data = a1.infer(dim, data); dim.print();
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data = d2.infer(dim, data); dim.print();
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// Print result
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printDeviceVector(dim.tot(), data);
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return 0;
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} |