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Eliminated non-deterministic behaviour.

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This commit is contained in:
Andre Martins 2016-09-17 01:55:09 +01:00
parent 09ce7e6349
commit 22bbac3287
2 changed files with 14 additions and 11 deletions
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7
src/param_initializers.h
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@ -9,6 +9,9 @@
namespace marian {
// Use a constant seed for deterministic behaviour.
std::default_random_engine engine(42);
void zeros(Tensor t) {
t.set(0.f);
}
@ -19,8 +22,8 @@ void ones(Tensor t) {
template <class Distribution>
void distribution(Tensor t, float a, float b) {
std::random_device device;
std::default_random_engine engine(device());
//std::random_device device;
//std::default_random_engine engine(device());
Distribution dist(a, b);
auto gen = std::bind(dist, engine);

18
src/validate_encoder_decoder.cu
View File

@ -43,13 +43,13 @@ ExpressionGraph build_graph(int source_vocabulary_size,
// Source RNN parameters.
Expr Wxh = named(g.param(shape={embedding_size, hidden_size},
init=uniform()), "Wxh");
init=uniform(-0.1, 0.1)), "Wxh");
Expr Whh = named(g.param(shape={hidden_size, hidden_size},
init=uniform()), "Whh");
init=uniform(-0.1, 0.1)), "Whh");
Expr bh = named(g.param(shape={1, hidden_size},
init=uniform()), "bh");
init=uniform(-0.1, 0.1)), "bh");
Expr h0 = named(g.param(shape={1, hidden_size},
init=uniform()), "h0");
init=uniform(-0.1, 0.1)), "h0");
std::cerr << "Building encoder RNN..." << std::endl;
H.emplace_back(tanh(dot(dot(X[0], E), Wxh) + dot(h0, Whh) + bh));
@ -59,11 +59,11 @@ ExpressionGraph build_graph(int source_vocabulary_size,
// Target RNN parameters.
Expr Wxh_d = named(g.param(shape={output_size, hidden_size},
init=uniform()), "Wxh_d");
init=uniform(-0.1, 0.1)), "Wxh_d");
Expr Whh_d = named(g.param(shape={hidden_size, hidden_size},
init=uniform()), "Whh_d");
init=uniform(-0.1, 0.1)), "Whh_d");
Expr bh_d = named(g.param(shape={1, hidden_size},
init=uniform()), "bh_d");
init=uniform(-0.1, 0.1)), "bh_d");
std::cerr << "Building decoder RNN..." << std::endl;
auto h0_d = H[num_inputs];
@ -74,9 +74,9 @@ ExpressionGraph build_graph(int source_vocabulary_size,
// Output linear layer before softmax.
Expr Why = named(g.param(shape={hidden_size, output_size},
init=uniform()), "Why");
init=uniform(-0.1, 0.1)), "Why");
Expr by = named(g.param(shape={1, output_size},
init=uniform()), "by");
init=uniform(-0.1, 0.1)), "by");
std::cerr << "Building output layer..." << std::endl;