循环神经网络RNN让神经网络有了记忆, 对于序列话的数据,循环神经网络能达到更好的效果.
更多可以查看官网 :
* PyTorch 官网
MNIST手写体
import torch
from torch import nn
from torch.autograd import Variable
import torchvision.datasets as dsets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
torch.manual_seed(1) # reproducible
# Hyper Parameters
EPOCH = 1 # 训练整批数据多少次, 为了节约时间, 只训练一次
BATCH_SIZE = 64
TIME_STEP = 28 # rnn 时间步数 / 图片高度
INPUT_SIZE = 28 # rnn 每步输入值 / 图片每行像素
LR = 0.01 # learning rate
DOWNLOAD_MNIST = True # 如果你已经下载好了mnist数据就写上 Fasle
# Mnist 手写数字
train_data = torchvision.datasets.MNIST(
root='./mnist/', # 保存或者提取位置
train=True, # this is training data
transform=torchvision.transforms.ToTensor(), # 转换 PIL.Image or numpy.ndarray 成
# torch.FloatTensor (C x H x W), 训练的时候 normalize 成 [0.0, 1.0] 区间
download=DOWNLOAD_MNIST, # 没下载就下载, 下载了就不用再下了
)
还是他QAQ,来自MNIST数据集的示例图像
黑色的地方的值都是0, 白色的地方值大于0.
同样, 除了训练数据, 还给一些测试数据, 测试看看它有没有训练好.
test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
# 批训练 50samples, 1 channel, 28x28 (50, 1, 28, 28)
train_loader = Data.DataLoader(dataset=train_data, batch_size=BATCH_SIZE, shuffle=True)
# 为了节约时间, 我们测试时只测试前2000个
test_x = Variable(torch.unsqueeze(test_data.test_data, dim=1), volatile=True).type(torch.FloatTensor)[:2000]/255. # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_y = test_data.test_labels[:2000]
RNN模型
和以前一样, 用一个 class 来建立 RNN 模型. 这个 RNN 整体流程是
-
(input0, state0)->LSTM->(output0, state1); -
(input1, state1)->LSTM->(output1, state2); - ...
-
(inputN, stateN)->LSTM->(outputN, stateN+1); -
outputN->Linear->prediction.
通过LSTM分析每一时刻的值, 并且将这一时刻和前面时刻的理解合并在一起, 生成当前时刻对前面数据的理解或记忆. 传递这种理解给下一时刻分析.
class RNN(nn.Module):
def __init__(self):
super(RNN, self).__init__()
self.rnn = nn.LSTM( # LSTM 效果要比 nn.RNN() 好多了
input_size=28, # 图片每行的数据像素点
hidden_size=64, # rnn hidden unit
num_layers=1, # 有几层 RNN layers
batch_first=True, # input & output 会是以 batch size 为第一维度的特征集 e.g. (batch, time_step, input_size)
)
self.out = nn.Linear(64, 10) # 输出层
def forward(self, x):
# x shape (batch, time_step, input_size)
# r_out shape (batch, time_step, output_size)
# h_n shape (n_layers, batch, hidden_size) LSTM 有两个 hidden states, h_n 是分线, h_c 是主线
# h_c shape (n_layers, batch, hidden_size)
r_out, (h_n, h_c) = self.rnn(x, None) # None 表示 hidden state 会用全0的 state
# 选取最后一个时间点的 r_out 输出
# 这里 r_out[:, -1, :] 的值也是 h_n 的值
out = self.out(r_out[:, -1, :])
return out
rnn = RNN()
print(rnn)
"""
RNN (
(rnn): LSTM(28, 64, batch_first=True)
(out): Linear (64 -> 10)
)
"""
训练
将图片数据看成一个时间上的连续数据, 每一行的像素点都是这个时刻的输入, 读完整张图片就是从上而下的读完了每行的像素点,然后拿出 RNN 在最后一步的分析值判断图片是哪一类.
optimizer = torch.optim.Adam(rnn.parameters(), lr=LR) # optimize all parameters
loss_func = nn.CrossEntropyLoss() # the target label is not one-hotted
# training and testing
for epoch in range(EPOCH):
for step, (x, y) in enumerate(train_loader): # gives batch data
b_x = Variable(x.view(-1, 28, 28)) # reshape x to (batch, time_step, input_size)
b_y = Variable(y) # batch y
output = rnn(b_x) # rnn output
loss = loss_func(output, b_y) # cross entropy loss
optimizer.zero_grad() # clear gradients for this training step
loss.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
"""
...
Epoch: 0 | train loss: 0.0945 | test accuracy: 0.94
Epoch: 0 | train loss: 0.0984 | test accuracy: 0.94
Epoch: 0 | train loss: 0.0332 | test accuracy: 0.95
Epoch: 0 | train loss: 0.1868 | test accuracy: 0.96
"""
最后取10个数据, 看看预测的值到底对不对:
test_output = rnn(test_x[:10].view(-1, 28, 28))
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
print(pred_y, 'prediction number')
print(test_y[:10], 'real number')
"""
[7 2 1 0 4 1 4 9 5 9] prediction number
[7 2 1 0 4 1 4 9 5 9] real number
"""
