TensorFlow: import tensorflow as tf
from d2l import tensorflow 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):
    num_inputs = num_outputs = vocab_size
    def normal(shape):
        return tf.random.normal(shape=shape,stddev=0.01,mean=0,dtype=tf.float32)
    def three():
        return (tf.Variable(normal((num_inputs, num_hiddens)), dtype=tf.float32), tf.Variable(normal((num_hiddens, num_hiddens)), dtype=tf.float32), tf.Variable(tf.zeros(num_hiddens), dtype=tf.float32))
    W_xz, W_hz, b_z = three()
    W_xr, W_hr, b_r = three()
    W_xh, W_hh, b_h = three()
    W_hq = tf.Variable(normal((num_hiddens, num_outputs)), dtype=tf.float32)
    b_q = tf.Variable(tf.zeros(num_outputs), dtype=tf.float32)
    params = [W_xz, W_hz, b_z, W_xr, W_hr, b_r, W_xh, W_hh, b_h, W_hq, b_q]
    return params
def init_gru_state(batch_size, num_hiddens):
    return (tf.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:
        X = tf.reshape(X,[-1,W_xh.shape[0]])
        Z = tf.sigmoid(tf.matmul(X, W_xz) + tf.matmul(H, W_hz) + b_z)
        R = tf.sigmoid(tf.matmul(X, W_xr) + tf.matmul(H, W_hr) + b_r)
        H_tilda = tf.tanh(tf.matmul(X, W_xh) + tf.matmul(R * H, W_hh) + b_h)
        H = Z * H + (1 - Z) * H_tilda
        Y = tf.matmul(H, W_hq) + b_q
        outputs.append(Y)
    return tf.concat(outputs, axis=0), (H,)
vocab_size, num_hiddens, device_name = len(vocab), 256, d2l.try_gpu()._device_name
strategy = tf.distribute.OneDeviceStrategy(device_name)
num_epochs, lr = 500, 1
with strategy.scope():
    model = d2l.RNNModelScratch(len(vocab), num_hiddens, init_gru_state, gru, get_params)
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, strategy)
gru_cell = tf.keras.layers.GRUCell(num_hiddens, kernel_initializer='glorot_uniform')
gru_layer = tf.keras.layers.RNN(gru_cell, time_major=True, return_sequences=True, return_state=True)
device_name = d2l.try_gpu()._device_name
strategy = tf.distribute.OneDeviceStrategy(device_name)
with strategy.scope():
    model = d2l.RNNModel(gru_layer, vocab_size=len(vocab))
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, strategy)


MXNet: from mxnet import np, npx
from mxnet.gluon import rnn
from d2l import mxnet as d2l
npx.set_np()
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 np.random.normal(scale=0.01, size=shape, ctx=device)
    def three():
        return (normal((num_inputs, num_hiddens)), normal((num_hiddens, num_hiddens)), np.zeros(num_hiddens, ctx=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 = np.zeros(num_outputs, ctx=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.attach_grad()
    return params
def init_gru_state(batch_size, num_hiddens, device):
    return (np.zeros(shape=(batch_size, num_hiddens), ctx=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 = npx.sigmoid(np.dot(X, W_xz) + np.dot(H, W_hz) + b_z)
        R = npx.sigmoid(np.dot(X, W_xr) + np.dot(H, W_hr) + b_r)
        H_tilda = np.tanh(np.dot(X, W_xh) + np.dot(R * H, W_hh) + b_h)
        H = Z * H + (1 - Z) * H_tilda
        Y = np.dot(H, W_hq) + b_q
        outputs.append(Y)
    return np.concatenate(outputs, axis=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)
gru_layer = rnn.GRU(num_hiddens)
model = d2l.RNNModel(gru_layer, len(vocab))
d2l.train_ch8(model, train_iter, vocab, lr, num_epochs, device)