Initial implementation of an RNN.

This commit is contained in:
Andre Martins 2016-09-15 17:29:43 +01:00
parent 61d8b3cb83
commit dbd83a5496

View File

@ -5,117 +5,87 @@
int main(int argc, char** argv) {
cudaSetDevice(0);
/*int numImg = 0;*/
/*auto images = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numImg);*/
/*auto labels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numImg);*/
#if 1
using namespace marian;
using namespace keywords;
Expr x = input(shape={1, 2});
Expr y = input(shape={1, 2});
int input_size = 10;
int output_size = 2;
int batch_size = 25;
int hidden_size = 5;
int num_inputs = 8;
Expr w = param(shape={2, 2}, name="W0");
//Expr b = param(shape={1, 2}, name="b0");
std::vector<Expr*> X(num_inputs);
std::vector<Expr*> Y(num_inputs);
std::vector<Expr*> H(num_inputs);
std::cerr << "Building model...";
auto predict = softmax_fast(dot(x, w),
axis=1, name="pred");
auto graph = -mean(sum(y * log(predict), axis=1),
axis=0, name="cost");
for (int t = 0; t < num_inputs; ++t) {
X[t] = new Expr(input(shape={batch_size, input_size}));
Y[t] = new Expr(input(shape={batch_size, output_size}));
}
Tensor x1t({1, 2});
std::vector<float> xv = { 0.6, 0.1 };
thrust::copy(xv.begin(), xv.end(), x1t.begin());
Expr Wxh = param(shape={input_size, hidden_size}, name="Wxh");
Expr Whh = param(shape={hidden_size, hidden_size}, name="Whh");
Expr bh = param(shape={1, hidden_size}, name="bh");
Expr h0 = param(shape={1, hidden_size}, name="h0");
Tensor x2t({1, 2});
std::vector<float> yv = { 0, 1 };
thrust::copy(yv.begin(), yv.end(), x2t.begin());
std::cerr << "Building RNN..." << std::endl;
H[0] = new Expr(tanh(dot(*X[0], Wxh) + dot(h0, Whh) + bh));
for (int t = 1; t < num_inputs; ++t) {
H[t] = new Expr(tanh(dot(*X[t], Wxh) + dot(*H[t-1], Whh) + bh));
}
x = x1t;
y = x2t;
Expr Why = param(shape={hidden_size, output_size}, name="Why");
Expr by = param(shape={1, output_size}, name="by");
graph.forward(1);
std::cerr << "Building output layer..." << std::endl;
std::vector<Expr*> Yp(num_inputs);
Expr* cross_entropy = NULL;
for (int t = 0; t < num_inputs; ++t) {
Yp[t] = new Expr(softmax_fast(dot(*H[t], Why) + by, name="pred"));
if (!cross_entropy) {
cross_entropy = new Expr(sum(*Y[t] * log(*Yp[t]), axis=1));
} else {
*cross_entropy = *cross_entropy + sum(*Y[t] * log(*Yp[t]), axis=1);
}
}
auto graph = -mean(*cross_entropy, axis=0, name="cost");
for (int t = 0; t < num_inputs; ++t) {
Tensor Xt({batch_size, input_size});
Tensor Yt({batch_size, output_size});
float max = 1.;
std::vector<float> values(batch_size * input_size);
std::vector<float> classes(batch_size * output_size, 0.0);
int k = 0;
int l = 0;
for (int i = 0; i < batch_size; ++i) {
for (int j = 0; j < input_size; ++j, ++k) {
values[k] = max * (2.0*static_cast<float>(rand()) / RAND_MAX - 1.0);
}
int gold = output_size * static_cast<float>(rand()) / RAND_MAX;
classes[l + gold] = 1.0;
l += output_size;
}
thrust::copy(values.begin(), values.end(), Xt.begin());
thrust::copy(classes.begin(), classes.end(), Yt.begin());
*X[t] = Xt;
*Y[t] = Yt;
}
graph.forward(batch_size);
graph.backward();
std::cerr << graph.val().Debug() << std::endl;
std::cerr << w.grad().Debug() << std::endl;
//std::cerr << b.grad().Debug() << std::endl;
#else
using namespace marian;
using namespace keywords;
using namespace std;
const size_t BATCH_SIZE = 500;
const size_t IMAGE_SIZE = 784;
const size_t LABEL_SIZE = 10;
Expr x = input(shape={whatevs, IMAGE_SIZE}, name="X");
Expr y = input(shape={whatevs, LABEL_SIZE}, name="Y");
Expr w = param(shape={IMAGE_SIZE, LABEL_SIZE}, name="W0");
Expr b = param(shape={1, LABEL_SIZE}, name="b0");
Expr z = dot(x, w) + b;
Expr lr = softmax(z, axis=1, name="pred");
Expr graph = -mean(sum(y * log(lr), axis=1), axis=0, name="cost");
//cerr << "x=" << Debug(lr.val().shape()) << endl;
int numofdata;
//vector<float> images = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
//vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
vector<float> images = datasets::mnist::ReadImages("../examples/mnist/train-images-idx3-ubyte", numofdata, IMAGE_SIZE);
vector<float> labels = datasets::mnist::ReadLabels("../examples/mnist/train-labels-idx1-ubyte", numofdata, LABEL_SIZE);
cerr << "images=" << images.size() << " labels=" << labels.size() << endl;
cerr << "numofdata=" << numofdata << endl;
size_t startInd = 0;
size_t startIndData = 0;
while (startInd < numofdata) {
size_t batchSize = (startInd + BATCH_SIZE < numofdata) ? BATCH_SIZE : numofdata - startInd;
cerr << "startInd=" << startInd
<< " startIndData=" << startIndData
<< " batchSize=" << batchSize << endl;
Tensor tx({numofdata, IMAGE_SIZE}, 1);
Tensor ty({numofdata, LABEL_SIZE}, 1);
tx.set(images.begin() + startIndData, images.begin() + startIndData + batchSize * IMAGE_SIZE);
ty.set(labels.begin() + startInd, labels.begin() + startInd + batchSize);
//cerr << "tx=" << Debug(tx.shape()) << endl;
//cerr << "ty=" << Debug(ty.shape()) << endl;
x = tx;
y = ty;
cerr << "x=" << Debug(x.val().shape()) << endl;
cerr << "y=" << Debug(y.val().shape()) << endl;
graph.forward(batchSize);
cerr << "w=" << Debug(w.val().shape()) << endl;
cerr << "b=" << Debug(b.val().shape()) << endl;
std::cerr << "z: " << Debug(z.val().shape()) << endl;
std::cerr << "lr: " << Debug(lr.val().shape()) << endl;
std::cerr << "Log-likelihood: " << graph.val().Debug() << endl ;
//std::cerr << "scores=" << scores.val().Debug() << endl;
//std::cerr << "lr=" << lr.val().Debug() << endl;
graph.backward();
std::cerr << w.grad().Debug() << std::endl;
//std::cerr << graph["pred"].val()[0] << std::endl;
startInd += batchSize;
startIndData += batchSize * IMAGE_SIZE;
}
#endif
std::cerr << Whh.grad().Debug() << std::endl;
std::cerr << bh.grad().Debug() << std::endl;
std::cerr << Why.grad().Debug() << std::endl;
std::cerr << by.grad().Debug() << std::endl;
std::cerr << Wxh.grad().Debug() << std::endl;
std::cerr << h0.grad().Debug() << std::endl;
return 0;
}