网站开发中制作视频播放器,百度动态排名软件,百度商城网站建设,如何做网站改版文章目录 pytorch 神经网络训练demoResult参考来源 pytorch 神经网络训练demo
数据集#xff1a;MNIST
该数据集的内容是手写数字识别#xff0c;其分为两部分#xff0c;分别含有60000张训练图片和10000张测试图片 图片来源#xff1a;https://tensornews.cn/mnist_intr… 文章目录 pytorch 神经网络训练demoResult参考来源 pytorch 神经网络训练demo
数据集MNIST
该数据集的内容是手写数字识别其分为两部分分别含有60000张训练图片和10000张测试图片 图片来源https://tensornews.cn/mnist_intro/
神经网络RNN, GRU, LSTM
# Imports
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
import torchvision.transforms as transforms# Set device
device torch.device(cuda if torch.cuda.is_available() else cpu)# Hyperparameters
input_size 28
sequence_length 28
num_layers 2
hidden_size 256
num_classes 10
learning_rate 0.001
batch_size 64
num_epochs 2# Create a RNN
class RNN(nn.Module):def __init__(self, input_size, hidden_size, num_layers, num_classes):super(RNN, self).__init__()self.hidden_size hidden_sizeself.num_layers num_layersself.rnn nn.RNN(input_size, hidden_size, num_layers, batch_firstTrue)self.fc nn.Linear(hidden_size*sequence_length, num_classes) # fully connecteddef forward(self, x):h0 torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)# Forward Propout, _ self.rnn(x, h0)out out.reshape(out.shape[0], -1)out self.fc(out)return out# Create a GRU
class RNN_GRU(nn.Module):def __init__(self, input_size, hidden_size, num_layers, num_classes):super(RNN_GRU, self).__init__()self.hidden_size hidden_sizeself.num_layers num_layersself.gru nn.GRU(input_size, hidden_size, num_layers, batch_firstTrue)self.fc nn.Linear(hidden_size*sequence_length, num_classes) # fully connecteddef forward(self, x):h0 torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)# Forward Propout, _ self.gru(x, h0)out out.reshape(out.shape[0], -1)out self.fc(out)return out# Create a LSTM
class RNN_LSTM(nn.Module):def __init__(self, input_size, hidden_size, num_layers, num_classes):super(RNN_LSTM, self).__init__()self.hidden_size hidden_sizeself.num_layers num_layersself.lstm nn.LSTM(input_size, hidden_size, num_layers, batch_firstTrue)self.fc nn.Linear(hidden_size*sequence_length, num_classes) # fully connecteddef forward(self, x):h0 torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)c0 torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)# Forward Propout, _ self.lstm(x, (h0, c0))out out.reshape(out.shape[0], -1)out self.fc(out)return out# Load data
train_dataset datasets.MNIST(rootdataset/, trainTrue, transformtransforms.ToTensor(),downloadTrue)
train_loader DataLoader(datasettrain_dataset, batch_sizebatch_size, shuffleTrue)
test_dataset datasets.MNIST(rootdataset/, trainFalse, transformtransforms.ToTensor(),downloadTrue)
test_loader DataLoader(datasettest_dataset, batch_sizebatch_size, shuffleTrue)# Initialize network 选择一个即可
model RNN(input_size, hidden_size, num_layers, num_classes).to(device)
# model RNN_GRU(input_size, hidden_size, num_layers, num_classes).to(device)
# model RNN_LSTM(input_size, hidden_size, num_layers, num_classes).to(device)# Loss and optimizer
criterion nn.CrossEntropyLoss()
optimizer optim.Adam(model.parameters(), lrlearning_rate)# Train network
for epoch in range(num_epochs):# data: images, targets: labelsfor batch_idx, (data, targets) in enumerate(train_loader):# Get data to cuda if possibledata data.to(device).squeeze(1) # 删除一个张量中所有维数为1的维度 (N, 1, 28, 28) - (N, 28, 28)targets targets.to(device)# forwardscores model(data) # 64*10loss criterion(scores, targets)# backwardoptimizer.zero_grad()loss.backward()# gradient descent or adam stepoptimizer.step()# Check accuracy on training test to see how good our model
def check_accuracy(loader, model):if loader.dataset.train:print(Checking accuracy on training data)else:print(Checking accuracy on test data)num_correct 0num_samples 0model.eval()with torch.no_grad(): # 不计算梯度for x, y in loader:x x.to(device).squeeze(1)y y.to(device)# x x.reshape(x.shape[0], -1) # 64*784scores model(x)# 64*10_, predictions scores.max(dim1) #dim1表示对每行取最大值每行代表一个样本。num_correct (predictions y).sum()num_samples predictions.size(0) # 64print(fGot {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}%)model.train()check_accuracy(train_loader, model)
check_accuracy(test_loader, model)
Result
RNN Result
Checking accuracy on training data
Got 57926 / 60000 with accuracy 96.54%
Checking accuracy on test data
Got 9640 / 10000 with accuracy 96.40%GRU Result
Checking accuracy on training data
Got 59058 / 60000 with accuracy 98.43%
Checking accuracy on test data
Got 9841 / 10000 with accuracy 98.41%LSTM Result
Checking accuracy on training data
Got 59248 / 60000 with accuracy 98.75%
Checking accuracy on test data
Got 9849 / 10000 with accuracy 98.49%参考来源
【1】https://www.youtube.com/watch?vGl2WXLIMvKAlistPLhhyoLH6IjfxeoooqP9rhU3HJIAVAJ3Vzindex5
