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import torch.nn
cell=torch.nn.RNNCell(input_size=3,hidden_size=2)
for name,param in cell.named_parameters():
print('{}={}'.format(name,param))weight_ih=Parameter containing:
tensor([[ 0.1831, -0.4158, 0.6376],
[-0.6257, 0.1874, -0.2692]], requires_grad=True)
weight_hh=Parameter containing:
tensor([[-0.0016, 0.1171],
[-0.5100, 0.1382]], requires_grad=True)
bias_ih=Parameter containing:
tensor([0.0149, 0.4452], requires_grad=True)
bias_hh=Parameter containing:
tensor([0.1351, 0.6150], requires_grad=True)
#构造LSTM类实例并用其搭建单层RNN
import torch.nn
import torch
seq_len,batch_size=6,2
input_size,hidden_size=3,5
cell=torch.nn.LSTMCell(input_size=input_size,hidden_size=hidden_size)
inputs=torch.randn(seq_len,batch_size,input_size)
h=torch.randn(batch_size,hidden_size)
c=torch.randn(batch_size,hidden_size)
hs=[]
for t in range(seq_len):
h,c=cell(inputs[t],(h,c))
hs.append(h)
outputs=torch.stack(hs)
print(hs)
outputs[tensor([[-0.4300, 0.1251, -0.2122, 0.0323, 0.0519],
[ 0.1709, 0.0652, -0.1586, 0.0737, 0.1276]], grad_fn=<MulBackward0>), tensor([[-0.2693, -0.1088, 0.1333, 0.0473, -0.0404],
[ 0.1984, -0.0178, -0.0659, -0.0233, 0.0239]], grad_fn=<MulBackward0>), tensor([[ 0.0828, -0.0928, -0.0020, -0.0091, -0.0987],
[-0.2648, -0.2424, 0.3205, 0.0800, -0.0951]], grad_fn=<MulBackward0>), tensor([[-0.0695, -0.2347, 0.1941, 0.0126, -0.1140],
[-0.2564, -0.0829, 0.1944, 0.2127, -0.1447]], grad_fn=<MulBackward0>), tensor([[-0.1805, -0.1243, 0.1830, 0.1452, -0.1416],
[-0.1369, -0.1500, 0.2049, 0.0742, -0.0359]], grad_fn=<MulBackward0>), tensor([[-0.1926, -0.3762, 0.3116, 0.0276, 0.0827],
[-0.2320, -0.0868, 0.2628, 0.1773, 0.0027]], grad_fn=<MulBackward0>)]
tensor([[[-0.4300, 0.1251, -0.2122, 0.0323, 0.0519],
[ 0.1709, 0.0652, -0.1586, 0.0737, 0.1276]],
[[-0.2693, -0.1088, 0.1333, 0.0473, -0.0404],
[ 0.1984, -0.0178, -0.0659, -0.0233, 0.0239]],
[[ 0.0828, -0.0928, -0.0020, -0.0091, -0.0987],
[-0.2648, -0.2424, 0.3205, 0.0800, -0.0951]],
[[-0.0695, -0.2347, 0.1941, 0.0126, -0.1140],
[-0.2564, -0.0829, 0.1944, 0.2127, -0.1447]],
[[-0.1805, -0.1243, 0.1830, 0.1452, -0.1416],
[-0.1369, -0.1500, 0.2049, 0.0742, -0.0359]],
[[-0.1926, -0.3762, 0.3116, 0.0276, 0.0827],
[-0.2320, -0.0868, 0.2628, 0.1773, 0.0027]]],
grad_fn=<StackBackward>)import torch
import torch.nn
num_layer=2 #神经网络的层数
seq_len,batch_size=6,2
input_size,hidden_size=3,5
rnn=torch.nn.GRU(input_size,hidden_size,num_layer)
inputs=torch.randn(seq_len,batch_size,input_size)
h0=torch.randn(num_layer,batch_size,hidden_size)
outputs,hn=rnn(inputs,h0)
outputstensor([[[-0.5562, -0.0266, 0.4143, -0.5868, -0.5355],
[-0.9346, 0.1405, 0.6476, 0.1245, 0.2489]],
[[-0.5478, 0.0366, 0.2290, -0.3898, -0.3592],
[-0.5501, 0.2257, 0.3262, 0.0261, 0.1158]],
[[-0.4573, 0.1116, 0.0775, -0.2578, -0.2382],
[-0.3884, 0.2852, 0.1527, -0.0332, 0.0203]],
[[-0.3827, 0.1408, 0.0436, -0.1802, -0.2873],
[-0.3630, 0.3035, 0.0548, -0.1107, 0.0734]],
[[-0.3634, 0.2086, -0.0326, -0.1208, -0.2351],
[-0.3043, 0.3108, -0.0088, -0.0980, -0.0082]],
[[-0.3142, 0.2445, -0.0502, -0.0986, -0.1679],
[-0.2827, 0.3212, -0.0167, -0.0849, -0.0786]]],
grad_fn=<StackBackward>)from pandas_datareader import wb
countries=['BR','CA','CN','FR','DE','IN','IL','JP','SA','GB','US']
dat=wb.download(indicator='NY.GDP.PCAP.KD',country=countries,start=1970,end=2017)
df=dat.unstack().T
df.index=df.index.droplevel(0)
df
import torch.nn
class Net(torch.nn.Module):
def __init__(self,input_size,hidden_size):
super(Net,self).__init__()
self.rnn=torch.nn.LSTM(input_size,hidden_size)
self.fc=torch.nn.Linear(hidden_size,1)
def forward(self,x):
x=x[:,:,None]
x,_=self.rnn(x)
x=self.fc(x)
x=x[:,:,0]
return x
net=Net(input_size=1,hidden_size=5)
print(net)Net(
(rnn): LSTM(1, 5)
(fc): Linear(in_features=5, out_features=1, bias=True)
)