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132
sam-neurons-words.js
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132
sam-neurons-words.js
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class NeuralNetwork {
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constructor(inputNodes, hiddenNodes, outputNodes) {
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this.inputNodes = inputNodes;
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this.hiddenNodes = hiddenNodes;
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this.outputNodes = outputNodes;
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// Initialize weights with random values between -1 and 1
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this.weights_ih = this.initializeWeights(this.hiddenNodes, this.inputNodes);
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this.weights_ho = this.initializeWeights(this.outputNodes, this.hiddenNodes);
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// Biases
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this.bias_h = this.initializeBiases(this.hiddenNodes);
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this.bias_o = this.initializeBiases(this.outputNodes);
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// Learning rate
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this.learningRate = 0.1;
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}
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// Helper method to initialize weights
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initializeWeights(rows, cols) {
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return Array(rows).fill().map(() => Array(cols).fill().map(() => Math.random() * 2 - 1));
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}
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// Helper method to initialize biases
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initializeBiases(size) {
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return Array(size).fill().map(() => Math.random() * 2 - 1);
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}
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// Sigmoid activation function
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sigmoid(x) {
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return 1 / (1 + Math.exp(-x));
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}
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// Sigmoid derivative (used for backpropagation)
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sigmoidDerivative(x) {
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return x * (1 - x);
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}
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// Feedforward function
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feedforward(inputArray) {
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// Calculate hidden layer outputs
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let hidden = this.weights_ih.map((row, i) =>
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this.sigmoid(row.reduce((sum, weight, j) => sum + weight * inputArray[j], 0) + this.bias_h[i])
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);
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// Calculate output layer outputs
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let output = this.weights_ho.map((row, i) =>
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this.sigmoid(row.reduce((sum, weight, j) => sum + weight * hidden[j], 0) + this.bias_o[i])
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);
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return output;
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}
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// Train the network
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train(inputArray, targetArray) {
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// Feedforward
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let hidden = this.weights_ih.map((row, i) =>
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this.sigmoid(row.reduce((sum, weight, j) => sum + weight * inputArray[j], 0) + this.bias_h[i])
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);
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let outputs = this.weights_ho.map((row, i) =>
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this.sigmoid(row.reduce((sum, weight, j) => sum + weight * hidden[j], 0) + this.bias_o[i])
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);
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// Calculate the output errors
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let outputErrors = targetArray.map((target, i) => target - outputs[i]);
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// Calculate output gradients
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let outputGradients = outputs.map((output, i) =>
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this.sigmoidDerivative(output) * outputErrors[i] * this.learningRate
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);
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// Calculate hidden errors
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let hiddenErrors = this.weights_ho[0].map((_, i) =>
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this.weights_ho.reduce((sum, row) => sum + row[i] * outputErrors[0], 0)
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);
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// Calculate hidden gradients
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let hiddenGradients = hidden.map((h, i) =>
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this.sigmoidDerivative(h) * hiddenErrors[i] * this.learningRate
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);
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// Update weights and biases
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this.weights_ho = this.weights_ho.map((row, i) =>
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row.map((weight, j) => weight + outputGradients[i] * hidden[j])
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);
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this.weights_ih = this.weights_ih.map((row, i) =>
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row.map((weight, j) => weight + hiddenGradients[i] * inputArray[j])
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);
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this.bias_o = this.bias_o.map((bias, i) => bias + outputGradients[i]);
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this.bias_h = this.bias_h.map((bias, i) => bias + hiddenGradients[i]);
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}
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}
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// Example usage:
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let nn = new NeuralNetwork(3, 4, 1); // Increased hidden nodes to 4
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// Example dataset (XOR problem)
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let strings = ["hello", "neural", "world"]
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let inputs = [
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[0, 0, 0],
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[1, 0, 0],
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[1, 0, 1],
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[1, 1, 1]
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];
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let targets = [
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[0],
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[1],
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[2],
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[3]
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];
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// Training the neural network
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for (let i = 0; i < 50000; i++) { // Increased training iterations
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let index = Math.floor(Math.random() * 4);
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nn.train(inputs[index], targets[index]);
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}
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// Testing the neural network
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let feeds = inputs;
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feeds.forEach((item, index) => {
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let feededVal = Math.round(nn.feedforward(item))
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console.log([feededVal, item])
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if (Math.round(feededVal) == 1) {
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let sum = 0;
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item.forEach(num => sum += num);
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try { console.log(strings[sum - 1]) } catch {}
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}
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})
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122
sam-neurons.js
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122
sam-neurons.js
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@ -0,0 +1,122 @@
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class NeuralNetwork {
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constructor(inputNodes, hiddenNodes, outputNodes) {
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this.inputNodes = inputNodes;
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this.hiddenNodes = hiddenNodes;
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this.outputNodes = outputNodes;
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// Initialize weights with random values between -1 and 1
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this.weights_ih = this.initializeWeights(this.hiddenNodes, this.inputNodes);
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this.weights_ho = this.initializeWeights(this.outputNodes, this.hiddenNodes);
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// Biases
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this.bias_h = this.initializeBiases(this.hiddenNodes);
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this.bias_o = this.initializeBiases(this.outputNodes);
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// Learning rate
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this.learningRate = 0.1;
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}
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// Helper method to initialize weights
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initializeWeights(rows, cols) {
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return Array(rows).fill().map(() => Array(cols).fill().map(() => Math.random() * 2 - 1));
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}
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// Helper method to initialize biases
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initializeBiases(size) {
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return Array(size).fill().map(() => Math.random() * 2 - 1);
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}
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// Sigmoid activation function
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sigmoid(x) {
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return 1 / (1 + Math.exp(-x));
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}
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// Sigmoid derivative (used for backpropagation)
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sigmoidDerivative(x) {
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return x * (1 - x);
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}
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// Feedforward function
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feedforward(inputArray) {
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// Calculate hidden layer outputs
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let hidden = this.weights_ih.map((row, i) =>
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this.sigmoid(row.reduce((sum, weight, j) => sum + weight * inputArray[j], 0) + this.bias_h[i])
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);
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// Calculate output layer outputs
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let output = this.weights_ho.map((row, i) =>
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this.sigmoid(row.reduce((sum, weight, j) => sum + weight * hidden[j], 0) + this.bias_o[i])
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);
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return output;
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}
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// Train the network
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train(inputArray, targetArray) {
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// Feedforward
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let hidden = this.weights_ih.map((row, i) =>
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this.sigmoid(row.reduce((sum, weight, j) => sum + weight * inputArray[j], 0) + this.bias_h[i])
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);
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let outputs = this.weights_ho.map((row, i) =>
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this.sigmoid(row.reduce((sum, weight, j) => sum + weight * hidden[j], 0) + this.bias_o[i])
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);
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// Calculate the output errors
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let outputErrors = targetArray.map((target, i) => target - outputs[i]);
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// Calculate output gradients
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let outputGradients = outputs.map((output, i) =>
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this.sigmoidDerivative(output) * outputErrors[i] * this.learningRate
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);
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// Calculate hidden errors
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let hiddenErrors = this.weights_ho[0].map((_, i) =>
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this.weights_ho.reduce((sum, row) => sum + row[i] * outputErrors[0], 0)
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);
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// Calculate hidden gradients
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let hiddenGradients = hidden.map((h, i) =>
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this.sigmoidDerivative(h) * hiddenErrors[i] * this.learningRate
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);
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// Update weights and biases
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this.weights_ho = this.weights_ho.map((row, i) =>
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row.map((weight, j) => weight + outputGradients[i] * hidden[j])
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);
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this.weights_ih = this.weights_ih.map((row, i) =>
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row.map((weight, j) => weight + hiddenGradients[i] * inputArray[j])
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);
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this.bias_o = this.bias_o.map((bias, i) => bias + outputGradients[i]);
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this.bias_h = this.bias_h.map((bias, i) => bias + hiddenGradients[i]);
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}
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}
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// Example usage:
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let nn = new NeuralNetwork(2, 4, 1); // Increased hidden nodes to 4
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// Example dataset (XOR problem)
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let inputs = [
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[0, 0],
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[0, 1],
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[1, 0],
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[1, 1]
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];
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let targets = [
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[0],
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[1],
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[1],
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[0]
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];
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// Training the neural network
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for (let i = 0; i < 50000; i++) { // Increased training iterations
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let index = Math.floor(Math.random() * 4);
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nn.train(inputs[index], targets[index]);
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}
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// Testing the neural network
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console.log(nn.feedforward([0, 0]));
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console.log(nn.feedforward([0, 1]));
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console.log(nn.feedforward([1, 0]));
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console.log(nn.feedforward([1, 1]));
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