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1. const model = tf.sequential();
2. model.add(tf.layers.dense({ units: 5, activation: 'sigmoid', inputShape: [1]}));
3. model.add(tf.layers.dense({ units: 1, activation: 'sigmoid'}));
4. model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
5. const xs = tf.tensor2d([[1], [2], [3], [4], [6], [7], [8], [9]]);
6. const ys = tf.tensor2d([[0], [0], [0], [0], [1], [1], [1], [1]]);
7. model.fit(xs, ys);
8. model.predict(xs).print();
9.
10. const model = tf.sequential();
11. model.add(tf.layers.dense({ units: 1, activation: 'sigmoid', inputShape: [1]}));
12. model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
13. const xs = tf.tensor2d([[1], [2], [3], [4], [6], [7], [8], [9]]);
14. const ys = tf.tensor2d([[0], [0], [0], [0], [1], [1], [1], [1]]);
15. model.fit(xs, ys);
16. model.predict(xs).print();
17.
19.
20. const model = tf.sequential();
21. model.add(tf.layers.dense({ units: 5, activation: 'sigmoid', inputShape: [1]}));
22. model.add(tf.layers.dense({ units: 1, activation: 'sigmoid'}));
24. const xs = tf.tensor1d([1, 2, 3, 4, 5, 6, 7, 8, 9]);
25. const ys = tf.tensor1d([0, 0, 0, 0, 0, 1, 1, 1, 1]);
26. model.fit(xs, ys, {
27. epochs: 2000,
28. });
29. model.predict(xs).print();
30.
31. tf.losses.meanSquaredError(ys, model.predict(xs)).print();
32.
33. sgd = keras.optimizers.SGD(lr=1)
34. model.compile(sgd, 'mse')
35.
36. model.fit(xs, ys, epochs=200)
37.