import mxnet as mx
from mxnet import gluon
import numpy as np

hiden_sizes=[10,20,1]
batch_size=300
iteration=300000
log_freq = 20
ctx=mx.gpu()
opt = ‘adam‘ # ‘sgd‘

unroll_len =9
t= mx.nd.arange(0,0.01*(1+unroll_len),.01, ctx=ctx)
tt= mx.nd.random.uniform(shape=(iteration,1), ctx=ctx)
t= (t+tt).T   # (unroll_len, iteration)
y= mx.nd.sin(t[-1])/2

model=gluon.rnn.SequentialRNNCell()
with model.name_scope():
    for hidden_size in hiden_sizes:
        model.add(gluon.rnn.LSTMCell(hidden_size))
model.initialize(ctx=ctx)
L=gluon.loss.L2Loss()
Trainer= gluon.Trainer(model.collect_params(),opt)
prev_batch_idx=-1
acc_l = mx.nd.array([0,], ctx=ctx)

for batch_idx in xrange(iteration/batch_size):
    x_list = [x[batch_idx*batch_size:(batch_idx+1)*batch_size].T for x in t[:unroll_len]]
    # e in x_list: (b,1)
    label =   y[batch_idx*batch_size:(batch_idx+1)*batch_size]
    with mx.autograd.record():
        outputs, states = model.unroll(unroll_len, x_list)
        l=L(outputs[-1], label)
        l.backward()
    Trainer.step(batch_size)
    acc_l += l.mean()
    if batch_idx- prev_batch_idx == log_freq:
        print ‘loss:%.4f‘%((acc_l/log_freq).asnumpy())
        prev_batch_idx = batch_idx
        acc_l *= 0