PyTorch: import torch
from torch import nn
from d2l import torch as d2l
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
def get_params(vocab_size, num_hiddens, device):
    num_inputs = num_outputs = vocab_size
    def normal(shape):
        return torch.randn(size=shape, device=device)*0.01
    def three():
        return (normal((num_inputs, num_hiddens)), normal((num_hiddens, num_hiddens)), torch.zeros(num_hiddens, device=device))
    W_xz, W_hz, b_z = three()
    W_xr, W_hr, b_r = three()
    W_xh, W_hh, b_h = three()
    W_hq = normal((num_hiddens, num_outputs))
    b_q = torch.zeros(num_outputs, device=device)
    params = [W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q]
    for param in params:
        param.requires_grad_(True)
    return params
def init_gru_state(batch_size, num_hiddens, device):
    return (torch.zeros((batch_size, num_hiddens), device=device), )
def gru(inputs, state, params):
    W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q = params
    H, = state
    outputs = []
    for X in inputs:
        Z = torch.sigmoid((X @ W_xz) + (H @ W_hz) + b_z)
        R = torch.sigmoid((X @ W_xr) + (H @ W_hr) + b_r)
        H_tilda = torch.tanh((X @ W_xh) + ((R * H) @ W_hh) + b_h)
        H = Z * H + (1 - Z) * H_tilda
        Y = H @ W_hq + b_q
        outputs.append(Y)
    return torch.cat(outputs, dim=0), (H,)
vocab_size, num_hiddens, device = len(vocab), 256, d2l.try_gpu()
num_epochs, lr = 500, 1
model = d2l.RNNModelScratch(len(vocab), num_hiddens, device, get_params, init_gru_state, gru)
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device)
num_inputs = vocab_size
gru_layer = nn.GRU(num_inputs, num_hiddens)
model = d2l.RNNModel(gru_layer, len(vocab))
model = model.to(device)
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device)


Paddle: import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import paddle
import paddle.nn.functional as F
from paddle import nn
batch_size, num_steps = 32, 35
train_iter, vocab = d2l.load_data_time_machine(batch_size, num_steps)
def get_params(vocab_size, num_hiddens):
    num_inputs = num_outputs = vocab_size
    def normal(shape):
        return paddle.randn(shape=shape)*0.01
    def three():
        return (normal((num_inputs, num_hiddens)), normal((num_hiddens, num_hiddens)), paddle.zeros([num_hiddens]))
    W_xz, W_hz, b_z = three()
    W_xr, W_hr, b_r = three()
    W_xh, W_hh, b_h = three()
    W_hq = normal((num_hiddens, num_outputs))
    b_q = paddle.zeros([num_outputs])
    params = [W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q]
    for param in params:
        param.stop_gradient = False
    return params
def init_gru_state(batch_size, num_hiddens):
    return (paddle.zeros([batch_size, num_hiddens]), )
def gru(inputs, state, params):
    W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q = params
    H,*_ = state
    outputs = []
    for X in inputs:
        Z = F.sigmoid((X @ W_xz) + (H @ W_hz) + b_z)
        R = F.sigmoid((X @ W_xr) + (H @ W_hr) + b_r)
        H_tilda = paddle.tanh((X @ W_xh) + ((R * H) @ W_hh) + b_h)
        H = Z * H + (1 - Z) * H_tilda
        Y = H @ W_hq + b_q
        outputs.append(Y)
    return paddle.concat(outputs, axis=0), (H,*_)
vocab_size, num_hiddens, device = len(vocab), 256, d2l.try_gpu()
num_epochs, lr = 500, 1.0
model = d2l.RNNModelScratch(len(vocab), num_hiddens, get_params, init_gru_state, gru)
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device)
num_inputs = vocab_size
gru_layer = nn.GRU(num_inputs, num_hiddens, time_major=True)
model = d2l.RNNModel(gru_layer, len(vocab))
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device)