Added lr shader and removed old ml shader

shaders/glsl/logisticregression.comp

@@ -0,0 +1,54 @@
+#version 450
+
+layout (constant_id = 0) const uint M = 0;
+
+layout (local_size_x = 1) in;
+
+layout(set = 0, binding = 0) buffer bxi { float xi[]; };
+layout(set = 0, binding = 1) buffer bxj { float xj[]; };
+layout(set = 0, binding = 2) buffer by { float y[]; };
+layout(set = 0, binding = 3) buffer bwin { float win[]; };
+layout(set = 0, binding = 4) buffer bwouti { float wouti[]; };
+layout(set = 0, binding = 5) buffer bwoutj { float woutj[]; };
+layout(set = 0, binding = 6) buffer bbin { float bin[]; };
+layout(set = 0, binding = 7) buffer bbout { float bout[]; };
+layout(set = 0, binding = 8) buffer blout { float lout[]; };
+
+float m = float(M);
+
+float sigmoid(float z) {
+    return 1.0 / (1.0 + exp(-z));
+}
+
+float inference(vec2 x, vec2 w, float b) {
+    // Compute the linear mapping function
+    float z = dot(w, x) + b;
+    // Calculate the y-hat with sigmoid
+    float yHat = sigmoid(z);
+    return yHat;
+}
+
+float calculateLoss(float yHat, float y) {
+    return -(y * log(yHat)  +  (1.0 - y) * log(1.0 - yHat));
+}
+
+void main() {
+    uint idx = gl_GlobalInvocationID.x;
+
+    vec2 wCurr = vec2(win[0], win[1]);
+    float bCurr = bin[0];
+
+    vec2 xCurr = vec2(xi[idx], xj[idx]);
+    float yCurr = y[idx];
+
+    float yHat = inference(xCurr, wCurr, bCurr);
+
+    float dZ = yHat - yCurr;
+    vec2 dW = (1. / m) * xCurr * dZ;
+    float dB = (1. / m) * dZ;
+    wouti[idx] = dW.x;
+    woutj[idx] = dW.y;
+    bout[idx] = dB;
+
+    lout[idx] = calculateLoss(yHat, yCurr);
+}

shaders/glsl/machinelearning.comp

@@ -1,84 +0,0 @@
-#version 450
-
-// This variable is set by the pipeline
-layout (constant_id = 0) const uint BUFFER_ELEMENTS = 32;
-
-layout(binding = 0) buffer Pos {
-   uint values[ ];
-};
-
-layout (local_size_x = 4, local_size_y = 1, local_size_z = 1) in;
-
-float learningRate = 0.01;
-
-uint fibonacci(uint n) {
-	if(n <= 1){
-		return n;
-	}
-	uint curr = 1;
-	uint prev = 1;
-	for(uint i = 2; i < n; ++i) {
-		uint temp = curr;
-		curr += prev;
-		prev = temp;
-	}
-	return curr;
-}
-
-float sigmoid(float z) {
-    return 1.0 / (1.0 + exp(-z));
-}
-
-float forwardProp(float x, float w, float b) {
-    float z = dot(w, x) + b;
-    float yHat = sigmoid(z);
-    return yHat;
-}
-
-void backwardProp(float x, float yHat, float y, float m, out float w, out float b) {
-    float dZ = yHat - y;
-    float dW = (1/m) * x * dZ;
-    float dB = (1/m) * dZ;
-    w -= (learningRate * dW);
-    b -= (learningRate * dB);
-}
-
-float calculateLoss(float yHat, float y) {
-    return -(y * log(yHat) + (1.0 - y) * log(1.0 - yHat));
-}
-
-shared uint sharedTotal[1];
-
-void main() 
-{
-	uint index = gl_GlobalInvocationID.x;
-	if (index >= BUFFER_ELEMENTS) 
-		return;	
-
-//    float m = int(BUFFER_ELEMENTS);
-//    float w = 0.001;
-//    float b = 0.0;
-//
-//    float x = values[index];
-//
-//    float yHat = forwardProp(x, w, b);
-//    float loss = calculateLoss(yHat, x);
-//
-//    backwardProp(x, yHat, x, m, w, b);
-//
-//	values[index] = fibonacci(values[index]);
-
-    sharedTotal[0] = 0;
-
-    barrier();
-    memoryBarrierShared();
-
-    atomicAdd(sharedTotal[0], values[index]);
-
-    barrier();
-    memoryBarrierShared();
-
-    values[index] = int(sharedTotal[0]);
-}
-
-