const model = tf.sequential();
model.add(tf.layers.dense({ units: 5, activation: 'sigmoid', inputShape: [1]}));
model.add(tf.layers.dense({ units: 1, activation: 'sigmoid'}));
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
const xs = tf.tensor2d([[1], [2], [3], [4], [6], [7], [8], [9]]);
const ys = tf.tensor2d([[0], [0], [0], [0], [1], [1], [1], [1]]);
model.fit(xs, ys);
model.predict(xs).print();

const model = tf.sequential();
model.add(tf.layers.dense({ units: 1, activation: 'sigmoid', inputShape: [1]}));
model.compile({loss: 'meanSquaredError', optimizer: 'sgd'});
const xs = tf.tensor2d([[1], [2], [3], [4], [6], [7], [8], [9]]);
const ys = tf.tensor2d([[0], [0], [0], [0], [1], [1], [1], [1]]);
model.fit(xs, ys);
model.predict(xs).print();

const AdadeltaOptimizer = tf.train.adadelta();

const model = tf.sequential();
model.add(tf.layers.dense({ units: 5, activation: 'sigmoid', inputShape: [1]}));
model.add(tf.layers.dense({ units: 1, activation: 'sigmoid'}));
model.compile({loss: 'meanSquaredError', optimizer: AdadeltaOptimizer});
const xs = tf.tensor1d([1, 2, 3, 4, 5, 6, 7, 8, 9]);
const ys = tf.tensor1d([0, 0, 0, 0, 0, 1, 1, 1, 1]);
model.fit(xs, ys, {
epochs: 2000,
});
model.predict(xs).print();

tf.losses.meanSquaredError(ys, model.predict(xs)).print();

sgd = keras.optimizers.SGD(lr=1)
model.compile(sgd, 'mse')

model.fit(xs, ys, epochs=200)

model.compile('adadelta', 'mse')
model.fit(xs, ys, epochs=2000)