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Adding missing file.
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143
src/validate_encoder_decoder.cu
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143
src/validate_encoder_decoder.cu
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#include "marian.h"
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#include "mnist.h"
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#if 0
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ExpressionGraph build_graph() {
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std::cerr << "Loading model params...";
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}
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// read parallel corpus from file
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std::fstream sourceFile("../examples/mt/dev/newstest2013.de");
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std::fstream targetFile("../examples/mt/dev/newstest2013.en");
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std::string sourceLine, targetLine;
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while (getline(sourceFile, sourceLine)) {
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getline(targetFile, targetLine);
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std::vector<size_t> sourceIds = sourceVocab.ProcessSentence(sourceLine);
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std::vector<size_t> targetIds = sourceVocab.ProcessSentence(targetLine);
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}
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#endif
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int main(int argc, char** argv) {
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cudaSetDevice(0);
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using namespace marian;
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using namespace keywords;
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int input_size = 10;
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int output_size = 15;
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int batch_size = 25;
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int hidden_size = 5;
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int num_inputs = 8;
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int num_outputs = 6;
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ExpressionGraph g;
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std::vector<Expr*> X(num_inputs+1); // For the stop symbol.
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std::vector<Expr*> Y(num_outputs);
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std::vector<Expr*> H(num_inputs+1); // For the stop symbol.
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std::vector<Expr*> S(num_outputs);
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// For the stop symbol.
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for (int t = 0; t <= num_inputs; ++t) {
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X[t] = new Expr(g.input(shape={batch_size, input_size}));
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}
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// For the stop symbol.
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for (int t = 0; t <= num_outputs; ++t) {
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Y[t] = new Expr(g.input(shape={batch_size, output_size}));
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}
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Expr Wxh = g.param(shape={input_size, hidden_size}, init=uniform(), name="Wxh");
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Expr Whh = g.param(shape={hidden_size, hidden_size}, init=uniform(), name="Whh");
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Expr bh = g.param(shape={1, hidden_size}, init=uniform(), name="bh");
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Expr h0 = g.param(shape={1, hidden_size}, init=uniform(), name="h0");
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std::cerr << "Building encoder RNN..." << std::endl;
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H[0] = new Expr(tanh(dot(*X[0], Wxh) + dot(h0, Whh) + bh));
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for (int t = 1; t <= num_inputs; ++t) {
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H[t] = new Expr(tanh(dot(*X[t], Wxh) + dot(*H[t-1], Whh) + bh));
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}
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Expr Wxh_d = g.param(shape={output_size, hidden_size}, init=uniform(), name="Wxh_d");
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Expr Whh_d = g.param(shape={hidden_size, hidden_size}, init=uniform(), name="Whh_d");
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Expr bh_d = g.param(shape={1, hidden_size}, init=uniform(), name="bh_d");
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std::cerr << "Building decoder RNN..." << std::endl;
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auto h0_d = *H[num_inputs];
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S[0] = new Expr(tanh(dot(*Y[0], Wxh_d) + dot(h0_d, Whh_d) + bh_d));
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for (int t = 1; t < num_outputs; ++t) {
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S[t] = new Expr(tanh(dot(*Y[t], Wxh_d) + dot(*S[t-1], Whh_d) + bh_d));
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}
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Expr Why = g.param(shape={hidden_size, output_size}, init=uniform(), name="Why");
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Expr by = g.param(shape={1, output_size}, init=uniform(), name="by");
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std::cerr << "Building output layer..." << std::endl;
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std::vector<Expr*> Yp(num_outputs+1); // For the stop symbol.
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Expr* cross_entropy = NULL;
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for (int t = 0; t <= num_outputs; ++t) {
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if (t == 0) {
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Yp[t] = new Expr(named(softmax_fast(dot(h0_d, Why) + by), "pred"));
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cross_entropy = new Expr(sum(*Y[t] * log(*Yp[t]), axis=1));
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} else {
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Yp[t] = new Expr(named(softmax_fast(dot(*S[t-1], Why) + by), "pred"));
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*cross_entropy = *cross_entropy + sum(*Y[t] * log(*Yp[t]), axis=1);
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}
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}
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auto graph = -mean(*cross_entropy, axis=0, name="cost");
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// For the stop symbol.
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for (int t = 0; t <= num_inputs; ++t) {
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Tensor Xt({batch_size, input_size});
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float max = 1.;
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std::vector<float> values(batch_size * input_size);
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std::vector<float> classes(batch_size * output_size, 0.0);
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int k = 0;
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for (int i = 0; i < batch_size; ++i) {
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for (int j = 0; j < input_size; ++j, ++k) {
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values[k] = max * (2.0*static_cast<float>(rand()) / RAND_MAX - 1.0);
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}
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}
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thrust::copy(values.begin(), values.end(), Xt.begin());
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*X[t] = Xt;
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}
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for (int t = 0; t < num_outputs; ++t) {
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Tensor Yt({batch_size, output_size});
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std::vector<float> classes(batch_size * output_size, 0.0);
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int l = 0;
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for (int i = 0; i < batch_size; ++i) {
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int gold = output_size * static_cast<float>(rand()) / RAND_MAX;
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classes[l + gold] = 1.0;
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l += output_size;
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}
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thrust::copy(classes.begin(), classes.end(), Yt.begin());
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*Y[t] = Yt;
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}
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g.forward(batch_size);
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g.backward();
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std::cerr << graph.val().Debug() << std::endl;
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std::cerr << X[0]->val().Debug() << std::endl;
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std::cerr << Y[0]->val().Debug() << std::endl;
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std::cerr << Whh.grad().Debug() << std::endl;
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std::cerr << bh.grad().Debug() << std::endl;
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std::cerr << Why.grad().Debug() << std::endl;
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std::cerr << by.grad().Debug() << std::endl;
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std::cerr << Wxh.grad().Debug() << std::endl;
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std::cerr << h0.grad().Debug() << std::endl;
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return 0;
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}
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