Pytorch实现RNN原理
Pytorch实现RNN原理
rnn公式如下。
h t = W h h h t − 1 + W i h X t {{\rm{h}}_t} = {W_{hh}}{{\rm{h}}_{t - 1}} + {W_{ih}}{X_t} ht=Whhht−1+WihXt
其中 X t {X_t} Xt表示t时刻的输入序列。Pytorch中RNN的输入 X {X} X大小为[seq, batch_size, embedding]。
所以 X t {X_t} Xt的大小为[batch_size, embedding]。其中embedding维度是要参与运算的维度，batch_size是要保留的信息。所以一般将 X t {X_t} Xt的大小写成转置的形式[embedding, batch_size]。
W i h X t {W_{ih}}{X_t} WihXt 的结果为[hidden_size, batch_size]。从矩阵的角度来理解就是batchsize个维度为hidden_size的列向量产生都是由原本的序列产生，依赖于自身序列。而与其他的句子无关
上次写的还是有很多小错误，进行了改正
import numpy as np
import torch
from torch import nn
from torch.nn.parameter import Parameter
class Rnn(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, bidirectional=False):
super(Rnn, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.bidirectional = bidirectional
Wih = [np.random.random((self.hidden_size, self.hidden_size)) for i in range(1, self.num_layers)]
Whh = [np.random.random((self.hidden_size, self.hidden_size)) for i in range(self.num_layers)]
self.Wih, self.Whh = Parameter(torch.tensor(Wih)), Parameter(torch.tensor(Whh))
Wih0 = np.random.random((self.hidden_size, self.hidden_size))
self.Wih0 = Parameter(torch.tensor(Wih0))
def forward(self, x):
'''
:param x: [seq, batch_size, embedding]
:return: out, hidden
'''
# x.shape [sep, batch, feature]
# hidden.shape [hidden_size, batch]
# Whh0.shape [hidden_size, hidden_size] Wih0.shape [hidden_size, feature]
# Whh1.shape [hidden_size, hidden_size] Wih1.size [hidden_size, hidden_size]
if not isinstance(x, torch.Tensor):
raise TypeError('x is not tensor')
out = []
hidden = [np.zeros((self.hidden_size, x.shape[1])) for i in range(self.num_layers)]
Wih0 = np.random.random((self.hidden_size, x.shape[2]))
# x, hidden, Wih, Whh = torch.from_numpy(x), torch.tensor(hidden), torch.tensor(Wih), torch.tensor(Whh)
hidden = torch.tensor(hidden)
self.Wih0 = Parameter(torch.tensor(Wih0))
time = x.shape[0]
for i in range(time):
hidden[0] = torch.tanh((torch.matmul(self.Wih0, torch.transpose(x[i, ...], 1, 0)) +
torch.matmul(self.Whh[0], hidden[0].clone().detach())
))
for i in range(1, self.num_layers):
hidden[i] = torch.tanh((torch.matmul(self.Wih[i-1], hidden[i-1].clone().detach()) +
torch.matmul(self.Whh[i], hidden[i].clone().detach())
))
out.append(hidden[self.num_layers-1])
# 如果list中的元素为tensor，就无法用torch.tensor()转换，会报错
return torch.stack([i for i in out]).permute(0, 2, 1).contiguous(), hidden.permute(0, 2, 1).contiguous()
if __name__ == '__main__':
a = torch.tensor([1, 2, 3])
print(torch.cuda.is_available(), type(a))
rnn = Rnn(1, 5, 4)
rnn_office = nn.RNN(1, 5, 4)
optimizer = torch.optim.Adam(params=rnn.parameters(), lr=0.1)
# print(list(rnn.parameters()))
input = torch.tensor(np.random.random((6, 3, 1)))
for _ in range(10):
out, h = rnn(input)
# pred = torch.softmax(h.mean(dim=0), dim=1).argmax(dim=1).to(torch.float64)
pred = h.mean(dim=0)
a = rnn.parameters()
# print(rnn)
param = [i for i in a]
label = torch.tensor([1, 2, 3])
criticism = nn.CrossEntropyLoss()
loss = criticism(pred, label)
print(loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(rnn_office(input.to(torch.float32))[1].shape)
print(f'seq is {input.shape[0]}, batch_size is {input.shape[1]} ', 'out.shape ', out.shape, ' h.shape ', h.shape)
# print(sigmoid(np.random.random((2, 3))))
#
# element-wise multiplication
# print(np.array([1, 2])*np.array([2, 1]))
分割线
我又将代码稍微调整，使得其可以进行梯度下降计算。
import numpy as np
import torch
from torch import nn
class Rnn(nn.Module):
def __init__(self, input_size, hidden_size, num_layers, bidirectional=False):
super(Rnn, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.bidirectional = bidirectional
def forward(self, x):
'''
:param x: [seq, batch_size, embedding]
:return: out, hidden
'''
# x.shape [sep, batch, feature]
# hidden.shape [hidden_size, batch]
# Whh0.shape [hidden_size, hidden_size] Wih0.shape [hidden_size, feature]
# Whh1.shape [hidden_size, hidden_size] Wih1.size [hidden_size, hidden_size]
out = []
x, hidden = np.array(x), [np.zeros((self.hidden_size, x.shape[1])) for i in range(self.num_layers)]
Wih = [np.random.random((self.hidden_size, self.hidden_size)) for i in range(1, self.num_layers)]
Wih0 = np.random.random((self.hidden_size, x.shape[2]))
Whh = [np.random.random((self.hidden_size, self.hidden_size)) for i in range(self.num_layers)]
# x, hidden, Wih, Whh = torch.from_numpy(x), torch.tensor(hidden), torch.tensor(Wih), torch.tensor(Whh)
x = torch.from_numpy(x)
hidden = torch.tensor(hidden)
Wih0 = torch.tensor(Wih0, requires_grad=True)
Wih, Whh = torch.tensor(Wih, requires_grad=True), torch.tensor(Whh, requires_grad=True)
time = x.shape[0]
for i in range(time):
hidden[0] = torch.tanh((torch.matmul(Wih0, torch.transpose(x[i, ...], 1, 0)) +
torch.matmul(Whh[0], hidden[0])
))
for i in range(1, self.num_layers):
hidden[i] = torch.tanh((torch.matmul(Wih[i-1], hidden[i-1]) +
torch.matmul(Whh[i], hidden[i])
))
out.append(hidden[self.num_layers-1])
# 如果list中的元素为tensor，就无法用torch.tensor()转换，会报错
return torch.stack([i for i in out]), hidden
def sigmoid(x):
return 1.0/(1.0 + 1.0/np.exp(x))
if __name__ == '__main__':
a = torch.tensor([1, 2, 3])
print(torch.cuda.is_available(), type(a))
rnn = Rnn(1, 5, 4)
input = np.random.random((6, 2, 1))
out, h = rnn(input)
print(f'seq is {input.shape[0]}, batch_size is {input.shape[1]} ', 'out.shape ', out.shape, ' h.shape ', h.shape)
# print(sigmoid(np.random.random((2, 3))))
#
# element-wise multiplication
# print(np.array([1, 2])*np.array([2, 1]))
分割线
首先说明代码只是帮助理解，并未写出梯度下降部分，默认参数已经被固定，不影响理解。代码主要实现RNN原理，只使用numpy库，不可用于GPU加速。
import numpy as np
class Rnn():
def __init__(self, input_size, hidden_size, num_layers, bidirectional=False):
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.bidirectional = bidirectional
def feed(self, x):
'''
:param x: [seq, batch_size, embedding]
:return: out, hidden
'''
# x.shape [sep, batch, feature]
# hidden.shape [hidden_size, batch]
# Whh0.shape [hidden_size, hidden_size] Wih0.shape [hidden_size, feature]
# Whh1.shape [hidden_size, hidden_size] Wih1.size [hidden_size, hidden_size]
out = []
x, hidden = np.array(x), [np.zeros((self.hidden_size, x.shape[1])) for i in range(self.num_layers)]
Wih = [np.random.random((self.hidden_size, self.hidden_size)) for i in range(1, self.num_layers)]
Wih.insert(0, np.random.random((self.hidden_size, x.shape[2])))
Whh = [np.random.random((self.hidden_size, self.hidden_size)) for i in range(self.num_layers)]
time = x.shape[0]
for i in range(time):
hidden[0] = np.tanh((np.dot(Wih[0], np.transpose(x[i, ...], (1, 0))) +
np.dot(Whh[0], hidden[0])
))
for i in range(1, self.num_layers):
hidden[i] = np.tanh((np.dot(Wih[i], hidden[i-1]) +
np.dot(Whh[i], hidden[i])
))
out.append(hidden[self.num_layers-1])
return np.array(out), np.array(hidden)
def sigmoid(x):
return 1.0/(1.0 + 1.0/np.exp(x))
if __name__ == '__main__':
rnn = Rnn(1, 5, 4)
input = np.random.random((6, 2, 1))
out, h = rnn.feed(input)
print(f'seq is {input.shape[0]}, batch_size is {input.shape[1]} ', 'out.shape ', out.shape, ' h.shape ', h.shape)
# print(sigmoid(np.random.random((2, 3))))
#
# element-wise multiplication
# print(np.array([1, 2])*np.array([2, 1]))