typos

browser/vue-flow/src/assets/doc/appinfo.md

@@ -7,9 +7,9 @@
 
 #### Flow
 
-* **Visualise sockets**: Press `shift` key to temporarily visualize sockets
+* **Visualize sockets**: Press `shift` key to temporarily visualize sockets
 * **New Node**: Create a node by dragging a fn from left panel into area
-* **Remove Node**: Middle click in a node to remove a node
+* **Remove Node**: Middle click on a node to remove it
 * **Inspect node**: Double click on a node to get detailed information
 * **Move Node**: Mouse click and drag
 * **Links**: Press `shift` and Drag from a node/socket to a socket highlighted in green

go/src/demos/cmd/noui/flow.go

@@ -26,9 +26,10 @@ func main() {
 	// Try binary operations first
 	// 2 inputs 1 output
 	// Load minst somehow 28x28 784
-	nSample := 1
+	nSample := 2
 
 	learningRate := float64(1.0)
+	_ = learningRate
 	// Load minst into floats array
 
 	// Make a matrix from the weights variable
@@ -37,24 +38,39 @@ func main() {
 	// Layer 1
 	//
 	sample := samples[nSample]
+	label := labels[nSample] // Label output
+
+	process := func(op flow.Operation) flow.Data { // just an helper
+		res, err := op.Process(sample, []float64{label})
+		if err != nil {
+			panic(err)
+		}
+		return res
+	}
 
 	// Define input
 	// Make a matrix out of the input
-	inp := f.Op("matNew", 2, 1, f.In(0))
+	input := f.Op("matNew", 2, 1, f.In(0))
+	log.Println("inMat:", process(input))
+
+	output := f.Op("matNew", 1, 1, f.In(1))
+	log.Println("inRes:", process(output))
 
-	// Layer1 weights
+	// Layer1 weights 2 in 3 out
 	w1 := f.Op("matNew", 3, 2, f.Var("w1", f.Op("normFloat", 3*2)))
+	log.Println("w1 weights:", process(w1))
 	// Layour 1 result
-	l1res := f.Op("matSigmoid", f.Op("matMul", w1, inp))
+	l1res := f.Op("matSigmoid", f.Op("matMul", w1, input))
 
-	// weights 2
-	w2 := f.Op("matNew", 2, 3, f.Var("w2", f.Op("normFloat", 2*3)))
+	// weights 2 // 3 in 1 out
+	w2 := f.Op("matNew", 1, 3, f.Var("w2", f.Op("normFloat", 1*3)))
+	log.Println("w2 weights:", process(w2))
 
 	// Layour 2 result
 	l2res := f.Op("matSigmoid", f.Op("matMul", w2, l1res))
 
+	log.Println("Layer2 result:", process(l2res))
 	// Network output
-	netRes := l2res
 
 	//////////////////////
 	// Create trainer ops
@@ -62,33 +78,30 @@ func main() {
 	// Backpropagation
 
 	log.Println("Grab error from output layer")
-	errOp := f.Op("matSub", inp, netRes)
-	log.Println(errOp.Process(sample))
+	errOp := f.Op("matSub", output, l2res)
+	log.Println("ErrorOp:", process(errOp))
 
 	log.Println("Calculate deltas")
 	deltaOp := f.Op("matMulElem", f.Op("matSigmoidD", l2res), errOp)
-	log.Println(deltaOp.Process(sample))
+	log.Println("Delta:", process(deltaOp))
 
-	log.Println("Multiplying the following matrixes")
-	log.Println(l2res.Process(sample))
-	log.Println(deltaOp.Process(sample))
-	log.Println("Calculate changes to apply to hidden layer")
 	outChangesOp := f.Op("matMul", l2res, deltaOp)
-	log.Println(outChangesOp.Process(sample))
+	log.Println("Changes to be made", process(outChangesOp))
 
-	outChangesOp = f.Op("matScale", outChangesOp, learningRate)
+	/*
+		outChangesOp = f.Op("matScale", outChangesOp, learningRate)
 
-	train1 := f.SetVar("w2", f.Op("matAdd", outChangesOp, w1))
-	// Set Var w1
-	//
-	log.Println("Training 1")
-	log.Println(train1.Process(sample))
+		train1 := f.SetVar("w2", f.Op("matAdd", outChangesOp, w1))
+		// Set Var w1
+		//
+		log.Println("Training 1")
+		log.Println(train1.Process(sample))
 
-	log.Println("deltaOutputLayer")
-	log.Println(deltaOp.Process(sample))
+		log.Println("deltaOutputLayer")
+		log.Println(deltaOp.Process(sample))
 
-	// Do we need this?
-	//netOp := f.Op("toFloatArr", l2op)
+		// Do we need this?
+		//netOp := f.Op("toFloatArr", l2op)*/
 
 	/*netResI, _ := netOp.Process(sample)
 	res := netResI.([]float64)