public NNBayes(int[] numNeurons, params object[] parameters)
{
float l2Penalty = 0.1F;
_preds = new TFOutput[numSamples];
_session = new TFSession();
_graph = _session.Graph;
_graph.Seed = Global.Random.Next();
_layers = new BayesLayer[numNeurons.Length - 1];
_input = _graph.Placeholder(TFDataType.Float, new TFShape(-1, numNeurons[0]));
_output = _graph.Placeholder(TFDataType.Float, new TFShape(-1));
_decay = _graph.Placeholder(TFDataType.Float, new TFShape(1));
for (int i = 0; i < numNeurons.Length - 1; i++)
{
_layers[i] = new BayesLayer(_graph, numNeurons[i], numNeurons[i + 1]);
}
TFOutput likelihood = _graph.Const(0F);
for (int i = 0; i < numSamples; i++)
{
TFOutput act = _input;
foreach (BayesLayer layer in _layers)
{
// TFOutput W = layer.SampleW(_graph);
// TFOutput b = layer.Sampleb(_graph);
// TFOutput z = _graph.Add(_graph.MatMul(act, W), b);
TFOutput z = layer.Samplez(_graph, act);
if (layer == _layers.Last())
{
TFOutput pred = _graph.Reshape(z, _graph.Const(new TFShape(-1)));
_preds[i] = pred;
TFOutput sample_likelihood = NNOperations.LogOfNormal(_graph, pred, _output, _graph.Const(1F));
likelihood = _graph.Add(likelihood, sample_likelihood);
}
else
{
act = _graph.Relu(z);
}
}
}
TFOutput kl_W = _graph.Const(0F);
TFOutput kl_b = _graph.Const(0F);
foreach (BayesLayer layer in _layers)
{
kl_W = _graph.Add(kl_W, _graph.ReduceSum(NNOperations.KLUnivariateNormal(_graph, layer.Mu_W, NNOperations.LogTrans(_graph, layer.Phi_W), _graph.Const(0F), _graph.Const((float)(1 / (Math.Sqrt(l2Penalty)))))));
kl_b = _graph.Add(kl_b, _graph.ReduceSum(NNOperations.KLUnivariateNormal(_graph, layer.Mu_b, NNOperations.LogTrans(_graph, layer.Phi_b), _graph.Const(0F), _graph.Const((float)(1 / Math.Sqrt(l2Penalty))))));
}
TFOutput kl = _graph.Add(kl_W, kl_b);
likelihood = _graph.Div(likelihood, _graph.Const((float)numSamples));
_cost = _graph.ReduceMean(_graph.Sub(_graph.Mul(_decay, kl), likelihood));
optimizer = new AdamOptimizer();
optimizer.AddBayesLayer(_graph, _layers, _cost);
optimizer.Apply(_graph);
var runner = _session.GetRunner();
foreach (BayesLayer layer in _layers)
{
layer.Init(runner);
}
optimizer.Init(runner);
runner.Run();
}