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// Neuro-Evolution Flappy Bird with TensorFlow.js
class NeuralNetwork {
constructor(a, b, c, d) {
if (a instanceof tf.Sequential) {
this.model = a;
this.input_nodes = b;
this.hidden_nodes = c;
this.output_nodes = d;
} else {
this.input_nodes = a;
this.hidden_nodes = b;
this.output_nodes = c;
this.model = this.createModel();
}
}
copy() {
return tf.tidy(() => {
const modelCopy = this.createModel();
const weights = this.model.getWeights();
const weightCopies = [];
for (let i = 0; i < weights.length; i++) {
weightCopies[i] = weights[i].clone();
}
modelCopy.setWeights(weightCopies);
return new NeuralNetwork(
modelCopy,
this.input_nodes,
this.hidden_nodes,
this.output_nodes
);
});
}
mutate(rate) {
tf.tidy(() => {
const weights = this.model.getWeights();
const mutatedWeights = [];
for (let i = 0; i < weights.length; i++) {
let tensor = weights[i];
let shape = weights[i].shape;
let values = tensor.dataSync().slice();
for (let j = 0; j < values.length; j++) {
if (random(1) < rate) {
let w = values[j];
values[j] = w + randomGaussian();
}
}
let newTensor = tf.tensor(values, shape);
mutatedWeights[i] = newTensor;
}
this.model.setWeights(mutatedWeights);
});
}
dispose() {
this.model.dispose();
}
predict(inputs) {
return tf.tidy(() => {
const xs = tf.tensor2d([inputs]);
const ys = this.model.predict(xs);
const outputs = ys.dataSync();
return outputs;
});
}
createModel() {
const model = tf.sequential();
const hidden = tf.layers.dense({
units: this.hidden_nodes,
inputShape: [this.input_nodes],
activation: "sigmoid"
});
model.add(hidden);
const output = tf.layers.dense({
units: this.output_nodes,
activation: "softmax"
});
model.add(output);
return model;
}
}
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