与RNN一样，输入也是三个维度，由于之前RNN先使用了CNN进行过滤，这次LSTM模型不使用CNN过滤，直接把所有向量放入训练。如下图，没有和RNN一样使用三层卷积层。
定义超参数
BATCH_SIZE = 32 # 每批处理的数据
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
EPOCHS = 15 # 训练数据集的轮次
1. 最后一个epoch（第15个epoch）时候的训练集的损失率
2.最后一个epoch时候的训练准确率
3. 总共15个epoch，每次epoch之后都测试一次，得到15次的loss
4. 每次训练完epoch之后在测试集的准确度
LSTM模型准确度方面表现正常，但是loss太过抖动，需要调参数。比如学习率，loss函数，batch_size，激活函数，优化算法等。
#!/usr/bin/env python
# -#-coding:utf-8 -*-
# author:魏兴源
# datetime:2021/10/17 21:35:59
# software:PyCharm
"""
模型1：Pytorch LSTM 实现流程
1.图片数据处理，加载数据集
2.使得数据集可迭代（每次读取一个Batch）
3.创建模型类
4.初始化模型类
5.初始化损失类
6.训练模型
"""
# 1.加载库
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
# 2.定义超参数
BATCH_SIZE = 32 # 每批处理的数据
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # 放在cuda或者cpu上训练
EPOCHS = 15 # 训练数据集的轮次
# 3.构建pipeline，对图像做处理
pipeline = transforms.Compose([
# 彩色图像转灰度图像num_output_channels默认1
# transforms.Grayscale(num_output_channels=1),
# 分辨率重置为256
transforms.Resize(256),
# 对加载的图像作归一化处理， 并裁剪为[224x224x3]大小的图像(因为这图片像素不一致直接统一)
transforms.CenterCrop(224),
# 将图片转成tensor
transforms.ToTensor(),
# 正则化，模型出现过拟合现象时，降低模型复杂度
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# 图片路径(训练图片和测试图片的)
base_dir_train = 'data/train'
base_dir_test = 'data/val'
# 打印一下训练图片猫狗各多少张图片
print('train dogs total images : %d' % (len(os.listdir(base_dir_train + '\\dog'))))
print('train cats total images : %d' % (len(os.listdir(base_dir_train + '\\cat'))))
print('test cats total images : %d' % (len(os.listdir(base_dir_test + '\\cat'))))
print('test dogs total images : %d' % (len(os.listdir(base_dir_test + '\\dog'))))
# 4. 加载数据集
"""
训练集,猫是0,狗是1，ImageFolder方法自己分类的，关于ImageFolder详见:
"""
train_dataset = datasets.ImageFolder(root=base_dir_train, transform=pipeline)
print("train_dataset=" + repr(train_dataset[1][0].size()))
print("train_dataset.class_to_idx=" + repr(train_dataset.class_to_idx))
# 创建训练集的可迭代对象，一个batch_size地读取数据,shuffle设为True表示随机打乱顺序读取
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)
# 测试集
test_dataset = datasets.ImageFolder(root=base_dir_test, transform=pipeline)
# print(test_dataset)
print("test_dataset=" + repr(test_dataset[1][0].size()))
print("test_dataset.class_to_idx=" + repr(test_dataset.class_to_idx))
# 创建测试集的可迭代对象，一个batch_size地读取数据
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True)
# 获得一批测试集的数据
images, labels = next(iter(test_loader))
print("images shape", images.shape)
print("labels shape", labels.shape)
# 5.定义函数，显示一批图片
def imShow(inp, title=None):
# tensor转成numpy,transpose转成(通道数,长,宽)
inp = inp.numpy().transpose((1, 2, 0))
mean = np.array([0.485, 0.456, 0.406]) # 均值
std = np.array([0.229, 0.224, 0.225]) # 标准差
inp = std * inp + mean
inp = np.clip(inp, 0, 1) # 像素值限制在0-1之间
plt.imshow(inp)
if title is not None:
plt.title(title)
plt.pause(0.001)
# 网格显示
out = torchvision.utils.make_grid(images)
imShow(out)
# 6.定义LSTM网络
class LSTM_Model(nn.Module):
def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):
super(LSTM_Model, self).__init__() # 初始化父类构造方法
self.hidden_dim = hidden_dim
self.layer_dim = layer_dim
# 构建LSTM模型
self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)
# 全连接层
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
# 初始化隐藏层状态全为0
# (layer_dim,batch_size,hidden_dim)
h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_().to(DEVICE)
# 初始化cell state
c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_().to(DEVICE)
x = x.view(x.size(0), 1, -1)
# 分离隐藏状态 避免梯度爆炸
'''
RNN只有一个状态，而LSTM有两个状态，所以两个状态都要分离
'''
out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))
# 只需要最后一层隐层的状态
out = self.fc(out[:, -1, :])
return out
# 7.初始化模型
input_dim = 150528 # 输入维度(输入的节点数量)
hidden_dim = 50 # 隐藏层的维度(每个隐藏层的节点数)
layer_dim = 2 # 2层LSTM(隐藏层的数量 2层)
out_dim = 2 # 输出维度
rnn_model = LSTM_Model(input_dim, hidden_dim, layer_dim, out_dim)
# 8.输出模型参数信息
length = len(list(rnn_model.parameters()))
print(length)
# 9.输出模型参数信息
length = len(list(rnn_model.parameters()))
print(length)
# 优化器
# optimizer = optim.SGD(rnn_model.parameters(), lr=1e-3, momentum=0.9)
optimizer = optim.Adam(rnn_model.parameters(), lr=1e-3, betas=(0.9, 0.99))
# 损失函数,交叉熵损失函数
criterion = nn.CrossEntropyLoss()
# 把损失，准确度，迭代都记录出list，然后讲loss和准确度画出图像
sequence_dim = 53
train_loss_list = []
train_accuracy_list = []
train_iteration_list = []
test_loss_list = []
test_accuracy_list = []
test_iteration_list = []
iteration = 0
# for i, (imgs, labels) in enumerate(test_loader):
# # print("imgs=" + repr(imgs))
# print("labels=" + repr(labels))
# print("i=" + repr(i))
# 训练
# """
for epoch in range(EPOCHS):
# 用来显示训练的loss correct等
train_correct = 0.0
train_total = 0.0
for i, (imgs, labels) in enumerate(train_loader):
# 声明训练，loss等只能在train mode下进行运算
rnn_model.train()
# 把训练的数据集合都扔到对应的设备去
# imgs = imgs.view(-1,1,sequence_dim, input_dim).requires_grad_().to(DEVICE)
# print("imgs shape", imgs.shape)
# print("imgs = ", imgs.data)
imgs = imgs.to(DEVICE)
labels = labels.to(DEVICE)
# 防止梯度爆炸，梯度清零
optimizer.zero_grad()
# 前向传播
rnn_model = rnn_model.cuda() # 这里要从cuda()中取得，不然前面都放在cuda后面放在cpu，会报错，报“不在同一个设备的错误" Input and parameter tensors are not at the same device, found input tensor at cuda:0 and parameter tensor at cpu
output = rnn_model(imgs)
# print("RNN output shape", out.shape)
# print("label shape", labels.shape)
# 计算损失
loss = criterion(output, labels)
# 反向传播
loss.backward()
# 更新参数
optimizer.step()
# 计算训练时候的准确度
train_predict = torch.max(output.data, 1)[1]
if torch.cuda.is_available():
train_correct += (train_predict.cuda() == labels.cuda()).sum()
else:
train_correct += (train_predict == labels).sum()
train_total += labels.size(0)
accuracy = train_correct / train_total * 100.0
# 只画出最后一次epoch的
if (epoch + 1) == EPOCHS:
# 迭代计数器++
iteration += 1
train_accuracy_list.append(accuracy)
train_iteration_list.append(iteration)
train_loss_list.append(loss)
# 打印信息
print("Epoch :%d , Batch : %5d , Loss : %.8f,train_correct:%d,train_total:%d,accuracy:%.6f" % (
epoch + 1, i + 1, loss.item(), train_correct, train_total, accuracy))
print("==========================预测开始===========================")
rnn_model.eval（)
# 验证accuracy
correct = 0.0
total = 0.0
# 迭代测试集 获取数据 预测
for j, (datas, targets) in enumerate(test_loader):
datas = datas.to(DEVICE)
targets = targets.to(DEVICE)
# datas = datas.view(-1, sequence_dim, input_dim).requires_grad_().to(DEVICE)
# datas = datas.reshape(datas.size(0), 1, -1)
# 模型预测
outputs = rnn_model(datas)
# 防止梯度爆炸，梯度清零
optimizer.zero_grad()
# 获取测试概率最大值的下标
predicted = torch.max(outputs.data, 1)[1]
# 统计计算测试集合
total += targets.size(0)
if torch.cuda.is_available():
# print(predicted.cuda() == targets.cuda())
correct += (predicted.cuda() == targets.cuda()).sum()
# print("predicted.cuda()=" + repr(predicted.cuda()))
# print("labels.cuda()=" + repr(targets.cuda()))
else:
correct += (predicted == targets).sum()
accuracy = correct / total * 100.0
test_accuracy_list.append(accuracy)
test_loss_list.append(loss.item())
test_iteration_list.append(iteration)
print("TEST--->loop : {}, Loss : {}, correct:{}, total:{}, Accuracy : {}".format(iteration, loss.item(),
correct,
total, accuracy))
# 可视化训练集loss
plt.figure(1)
plt.plot(train_iteration_list, train_loss_list)
plt.xlabel("number of iteration")
plt.ylabel("loss")
plt.title("RNN train loss")
plt.show()
# 可视化训练集accuracy
plt.figure(2)
plt.plot(train_iteration_list, train_accuracy_list)
plt.xlabel('number of iteration')
plt.ylabel('accuracy')
plt.title('LSTM train accuracy')
plt.show()
# 可视化测试集loss
plt.figure(3)
plt.plot(test_iteration_list, test_loss_list)
plt.xlabel('number of iteration')
plt.ylabel('loss')
plt.title('LSTM test loss')
plt.show()
# 可视化测试集accuracy
plt.figure(4)
plt.plot(test_iteration_list, test_accuracy_list)
plt.xlabel('number of iteration')
plt.ylabel('accuracy')
plt.title('LSTM test accuracy')
plt.show()本文章为转载内容，我们尊重原作者对文章享有的著作权。如有内容错误或侵权问题，欢迎原作者联系我们进行内容更正或删除文章。
