TensorFlow: %matplotlib inline
import tensorflow as tf
from d2l import tensorflow as d2l
T = 1000
time = tf.range(1, T + 1, dtype=tf.float32)
x = tf.sin(0.01 * time) + tf.random.normal([T], 0, 0.2)
d2l.plot(time, [x], 'time', 'x', xlim=[1, 1000], figsize=(6, 3))
tau = 4
features = tf.Variable(tf.zeros((T - tau, tau)))
for i in range(tau):
    features[:, i].assign(x[i: T - tau + i])
labels = tf.reshape(x[tau:], (-1, 1))
batch_size, n_train = 16, 600
train_iter = d2l.load_array((features[:n_train], labels[:n_train]), batch_size, is_train=True)
def get_net():
    net = tf.keras.Sequential([tf.keras.layers.Dense(10, activation='relu'),
                                tf.keras.layers.Dense(1)])
    return net
loss = tf.keras.losses.MeanSquaredError()
def train(net, train_iter, loss, epochs, lr):
    trainer = tf.keras.optimizers.Adam()
    for epoch in range(epochs):
        for X, y in train_iter:
            with tf.GradientTape() as g:
                out = net(X)
                l = loss(y, out)
                params = net.trainable_variables
                grads = g.gradient(l, params)
            trainer.apply_gradients(zip(grads, params))
net = get_net()
train(net, train_iter, loss, 5, 0.01)
onestep_preds = net(features)
d2l.plot([time, time[tau:]],
        [x.numpy(), onestep_preds.numpy()], 'time',
        'x', legend=['data', '1-step preds'], xlim=[1, 1000],
        figsize=(6, 3))
multistep_preds = tf.Variable(tf.zeros(T))
multistep_preds[:n_train + tau].assign(x[:n_train + tau])
for i in range(n_train + tau, T):
    multistep_preds[i].assign(tf.reshape(net(tf.reshape(multistep_preds[i - tau: i], (1, -1))), ()))
d2l.plot([time, time[tau:], time[n_train + tau:]],
        [x.numpy(), onestep_preds.numpy(),
            multistep_preds[n_train + tau:].numpy()], 'time',
        'x', legend=['data', '1-step preds', 'multistep preds'],
        xlim=[1, 1000], figsize=(6, 3))
max_steps = 64
features = tf.Variable(tf.zeros((T - tau - max_steps + 1, tau + max_steps)))
for i in range(tau):
    features[:, i].assign(x[i: i + T - tau - max_steps + 1].numpy())
for i in range(tau, tau + max_steps):
    features[:, i].assign(tf.reshape(net((features[:, i - tau: i])), -1))
steps = (1, 4, 16, 64)
d2l.plot([time[tau + i - 1: T - max_steps + i] for i in steps],
        [features[:, tau + i - 1].numpy() for i in steps], 'time', 'x',
        legend=[f'{i}-step preds' for i in steps], xlim=[5, 1000],
        figsize=(6, 3))



Paddle: %matplotlib inline
import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
from paddle import nn
T = 1000
time = paddle.arange(1, T + 1, dtype=paddle.float32)
x = paddle.sin(0.01 * time) + paddle.normal(0, 0.2, (T,))
d2l.plot(time, [x], 'time', 'x', xlim=[1, 1000], figsize=(6, 3))
tau = 4
features = paddle.zeros((T - tau, tau))
for i in range(tau):
    features[:, i] = x[i: T - tau + i]
labels = x[tau:].reshape((-1, 1))
batch_size, n_train = 16, 600
train_iter = d2l.load_array((features[:n_train], labels[:n_train]), batch_size, is_train=True)
def init_weights(m):
    if type(m) == nn.Linear:
        nn.initializer.XavierUniform(m.weight)
def get_net():
    net = nn.Sequential(nn.Linear(4, 10),
                        nn.ReLU(),
                        nn.Linear(10, 1))
    net.apply(init_weights)
    return net
loss = nn.MSELoss(reduction='none')
def train(net, train_iter, loss, epochs, lr):
    trainer = paddle.optimizer.Adam(learning_rate=lr, parameters=net.parameters())
    for epoch in range(epochs):
        for i,(X, y) in enumerate (train_iter()):
            trainer.clear_grad()
            l = loss(net(X), y)
            l.sum().backward()
            trainer.step()
net = get_net()
train(net, train_iter, loss, 5, 0.01)
onestep_preds = net(features)
d2l.plot([time, time[tau:]],
        [x.detach().numpy(), onestep_preds.detach().numpy()], 'time',
        'x', legend=['data', '1-step preds'], xlim=[1, 1000],
        figsize=(6, 3))
multistep_preds = paddle.zeros([T])
multistep_preds[: n_train + tau] = x[: n_train + tau]
for i in range(n_train + tau, T):
    multistep_preds[i] = net(multistep_preds[i - tau:i].reshape((1, -1)))
d2l.plot([time, time[tau:], time[n_train + tau:]],
        [x.detach().numpy(), onestep_preds.detach().numpy(),
            multistep_preds[n_train + tau:].detach().numpy()], 'time',
        'x', legend=['data', '1-step preds', 'multistep preds'],
        xlim=[1, 1000], figsize=(6, 3))
max_steps = 64
features = paddle.zeros((T - tau - max_steps + 1, tau + max_steps))
for i in range(tau):
    features[:, i] = x[i: i + T - tau - max_steps + 1]
for i in range(tau, tau + max_steps):
    features[:, i] = net(features[:, i - tau:i]).reshape([-1])
steps = (1, 4, 16, 64)
d2l.plot([time[tau + i - 1: T - max_steps + i] for i in steps],
        [features[:, tau + i - 1].detach().numpy() for i in steps], 'time', 'x',
        legend=[f'{i}-step preds' for i in steps], xlim=[5, 1000],
        figsize=(6, 3))