PyTorch实现卷积LSTM核(ConvLSTMCell)
文章图片
简单RNN与LSTM对比 
文章图片
LSTM计算示意 
文章图片
LSTM计算示意
import torch
from torch import nn
import torch.nn.functional as f
from torch.autograd import Variable# Define some constants
KERNEL_SIZE = 3
PADDING = KERNEL_SIZE // 2class ConvLSTMCell(nn.Module):
"""
Generate a convolutional LSTM cell
"""def __init__(self, input_size, hidden_size):
super(ConvLSTMCell, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.Gates = nn.Conv2d(input_size + hidden_size, 4 * hidden_size, KERNEL_SIZE, padding=PADDING)def forward(self, input_, prev_state):# get batch and spatial sizes
batch_size = input_.data.size()[0]
spatial_size = input_.data.size()[2:]# generate empty prev_state, if None is provided
if prev_state is None:
state_size = [batch_size, self.hidden_size] + list(spatial_size)
prev_state = (
Variable(torch.zeros(state_size)),
Variable(torch.zeros(state_size))
)prev_hidden, prev_cell = prev_state# data size is [batch, channel, height, width]
stacked_inputs = torch.cat((input_, prev_hidden), 1)
gates = self.Gates(stacked_inputs)# chunk across channel dimension
in_gate, remember_gate, out_gate, cell_gate = gates.chunk(4, 1)# apply sigmoid non linearity
in_gate = f.sigmoid(in_gate)
remember_gate = f.sigmoid(remember_gate)
out_gate = f.sigmoid(out_gate)# apply tanh non linearity
cell_gate = f.tanh(cell_gate)# compute current cell and hidden state
cell = (remember_gate * prev_cell) + (in_gate * cell_gate)
hidden = out_gate * f.tanh(cell)return hidden, celldef _main():
"""
Run some basic tests on the API
"""# define batch_size, channels, height, width
b, c, h, w = 1, 3, 4, 8
d = 5# hidden state size
lr = 1e-1# learning rate
T = 6# sequence length
max_epoch = 20# number of epochs# set manual seed
torch.manual_seed(0)print('Instantiate model')
model = ConvLSTMCell(c, d)
print(repr(model))print('Create input and target Variables')
x = Variable(torch.rand(T, b, c, h, w))
y = Variable(torch.randn(T, b, d, h, w))print('Create a MSE criterion')
loss_fn = nn.MSELoss()print('Run for', max_epoch, 'iterations')
for epoch in range(0, max_epoch):
state = None
loss = 0
for t in range(0, T):
state = model(x[t], state)
loss += loss_fn(state[0], y[t])print(' > Epoch {:2d} loss: {:.3f}'.format((epoch+1), loss.data[0]))# zero grad parameters
model.zero_grad()# compute new grad parameters through time!
loss.backward()# learning_rate step against the gradient
for p in model.parameters():
p.data.sub_(p.grad.data * lr)print('Input size:', list(x.data.size()))
print('Target size:', list(y.data.size()))
print('Last hidden state size:', list(state[0].size()))if __name__ == '__main__':
_main()
推荐阅读
- 关于QueryWrapper|关于QueryWrapper,实现MybatisPlus多表关联查询方式
- MybatisPlus使用queryWrapper如何实现复杂查询
- python学习之|python学习之 实现QQ自动发送消息
- 孩子不是实现父母欲望的工具——林哈夫
- opencv|opencv C++模板匹配的简单实现
- Node.js中readline模块实现终端输入
- java中如何实现重建二叉树
- 人脸识别|【人脸识别系列】| 实现自动化妆
- paddle|动手从头实现LSTM
- pytorch|使用pytorch从头实现多层LSTM