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DataAnalysis/private/FullyConnectedNN.cpp

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+#include "FullyConnectedNN.hpp"
+#include "NewPlacement.hpp"
+#include "Utils.hpp"
+
+using namespace tp;
+
+static halnf sigmoid(halnf val) { return 0; }
+
+static halnf relu(halnf val) { return val < 0 ? 0 : val; }
+
+void FullyConnectedNN::initializeRandom(Buffer<halni> description) {
+	mLayers.reserve(description.size());
+
+	for (auto i : Range<halni>(0, (halni) description.size())) {
+		mLayers[i].mNeurons.reserve(description[i]);
+		if (i == 0) {
+			continue;
+		}
+		for (auto neuron : mLayers[i].mNeurons) {
+			neuron->mWeights.reserve(description[i - 1]);
+			for (auto weight : neuron->mWeights) {
+				weight.data() = (halnf) randomFloat();
+			}
+			neuron->mBias = (halnf) randomFloat();
+		}
+	}
+}
+
+void FullyConnectedNN::evaluate(const Buffer<halnf>& input, Buffer<halnf>& output) {
+	ASSERT(output.size() == mLayers.last().mNeurons.size() && input.size() == mLayers.first().mNeurons.size())
+
+	for (auto idx : Range(input.size())) {
+		mLayers.first().mNeurons[idx].mActivationValue = input[idx];
+	}
+
+	for (auto layerIdx : Range<halni>(1, (halni) mLayers.size())) {
+		auto& layer = mLayers[layerIdx];
+		auto& layerPrev = mLayers[layerIdx - 1];
+
+		for (auto neuron : layer.mNeurons) {
+			neuron->mActivationValue = 0;
+			for (auto connectionIdx : Range(neuron->mWeights.size())) {
+				neuron->mActivationValue += neuron->mWeights[connectionIdx] * layerPrev.mNeurons[connectionIdx].mActivationValue;
+			}
+			neuron->mActivationValue += neuron->mBias;
+			neuron->mActivationValue = relu(neuron->mActivationValue);
+		}
+	}
+
+	for (auto idx : Range(output.size())) {
+		output[idx] = mLayers.last().mNeurons[idx].mActivationValue;
+	}
+}