Pytorch十二讲 RNN基础（部分笔记）

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我是靠谱客的博主儒雅草莓，最近开发中收集的这篇文章主要介绍Pytorch十二讲 RNN基础（部分笔记），觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

第12讲循环神经网络基础篇（部分笔记）

B站刘二大人传送门：《PyTorch深度学习实践》完结合集

仅把RNN代码复制了过来

################################################################################
# 刘二大人 RNN基础test1
# 1.RNNCELL
################################################################################
import torch
batch_size = 1
seq_len = 3
input_size = 4
hidden_size = 2
cell = torch.nn.RNNCell(input_size=input_size, hidden_size=hidden_size)
dataset = torch.randn(seq_len, batch_size, input_size)
hidden = torch.zeros(batch_size, hidden_size)
for idx, input in enumerate(dataset):
print('='*20, idx, '='*20)
print('Input size:', input.shape)
hidden = cell(input, hidden)
# 当前时刻的输入及hidden
# input of shape (batch_size, input_size)
# hidden of shape (batch_size, hidden_size)
输入和输出隐层h维度相同
print('outputs size:', hidden.shape)
print(hidden)

################################################################################
# 刘二大人@RNN基础test1
# 1.RNNCELL
# 2.RNN
################################################################################
import torch
batch_size = 1
seq_len = 3
input_size = 4
hidden_size = 2
num_layers = 1
cell = torch.nn.RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers)
inputs = torch.randn(seq_len, batch_size, input_size)
hidden = torch.zeros(num_layers, batch_size, hidden_size)
out, hidden = cell(inputs, hidden)
print('output size:', out.shape)
print('output:', out)
print('hidden size:', hidden.shape)
print('hidden:', hidden)

################################################################################
# 刘二大人@RNN基础test1
# 1.RNNCELL
# 2.RNN
# 3.Use RNNcell
################################################################################
import torch
input_size = 4
hidden_size = 4
batch_size = 1
idx2char = ['e', 'h', 'l', 'o']
x_data = [1, 0, 2, 2, 3]
y_data = [3, 1, 2, 3, 2]
one_hot_lookup = [[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]]
x_one_hot = [one_hot_lookup[x] for x in x_data]
inputs = torch.Tensor(x_one_hot).view(-1, batch_size, input_size)
labels = torch.LongTensor(y_data).view(-1, 1)
class Model(torch.nn.Module):
def __init__(self, input_size, hidden_size, batch_size):
super(Model, self).__init__()
self.batch_size = batch_size
self.input_size = input_size
self.hidden_size = hidden_size
self.rnncell = torch.nn.RNNCell(input_size=self.input_size, hidden_size=self.hidden_size)
def forward(self, input, hidden):
hidden = self.rnncell(input, hidden)
return hidden
def init_hidden(self):
return torch.zeros(self.batch_size, self.hidden_size)
net = Model (input_size, hidden_size, batch_size)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.1)
for epoch in range (15):
loss = 0
optimizer.zero_grad()
hidden = net.init_hidden()
print('Predicted string:', end='')
for input, label in zip (inputs, labels):
hidden = net(input, hidden)
loss += criterion(hidden, label)
_, idx = hidden.max(dim=-1)
print(idx2char[idx.item()], end='')
# print(idx)
loss.backward()
optimizer.step()
print(',Epoch [%d/15] loss=%.4f' % (epoch+1, loss.item()))

################################################################################
# 刘二大人@RNN基础test1
# 1.RNN CELL
# 2.RNN
# 3.Using RNN Cell
# 4.Using RNN Module
################################################################################
import torch
input_size = 4
hidden_size = 4
batch_size = 1
seq_len = 5
num_layers = 1
idx2char = ['e', 'h', 'l', 'o']
x_data = [1, 0, 2, 2, 3]
y_data = [3, 1, 2, 3, 2]
one_hot_lookup = [[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 1]]
x_one_hot = [one_hot_lookup[x] for x in x_data]
inputs = torch.Tensor(x_one_hot).view(seq_len, batch_size, input_size)
labels = torch.LongTensor(y_data)
class Model(torch.nn.Module):
def __init__(self, input_size, hidden_size, batch_size, num_layers):
super(Model, self).__init__()
self.num_layers = num_layers
self.batch_size = batch_size
self.input_size = input_size
self.hidden_size = hidden_size
self.rnn = torch.nn.RNN(input_size=self.input_size, hidden_size=self.hidden_size, num_layers=num_layers)
def forward(self, input):
hidden = torch.zeros(self.num_layers, self.batch_size, self.hidden_size)
out, _ = self.rnn(input, hidden)
return out.view(-1, self.hidden_size)
# def init_hidden(self):
#
return torch.zeros(self.batch_size, self.hidden_size)
net = Model (input_size, hidden_size, batch_size, num_layers)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=0.1)
for epoch in range(15):
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
_, idx = outputs.max(dim=1)
idx = idx.data.numpy()
print('Predicted:', ''.join([idx2char[x] for x in idx]), end='')
print(',Epoch [%d/15] loss = %.3f' % (epoch + 1, loss.item()))