网站建设需要摊销吗,备案 网站名称 修改,网站怎么显示被k,佛山招收网站设计一、说明 本篇介绍模型模型的参数#xff0c;模型推理和使用#xff0c;保存加载。 二、训练参数和模型 在本单元中#xff0c;我们将了解如何加载模型及其持久参数状态和推理模型预测。为了加载模型#xff0c;我们将定义模型类#xff0c;其中包含用于训练模型的神经网… 一、说明 本篇介绍模型模型的参数模型推理和使用保存加载。 二、训练参数和模型 在本单元中我们将了解如何加载模型及其持久参数状态和推理模型预测。为了加载模型我们将定义模型类其中包含用于训练模型的神经网络的状态和参数。 %matplotlib inline import torch import onnxruntime from torch import nn import torch.onnx as onnx import torchvision.models as models from torchvision import datasets from torchvision.transforms import ToTensor class NeuralNetwork(nn.Module):def __init__(self):super(NeuralNetwork, self).__init__()self.flatten nn.Flatten()self.linear_relu_stack nn.Sequential(nn.Linear(28*28, 512),nn.ReLU(),nn.Linear(512, 512),nn.ReLU(),nn.Linear(512, 10),nn.ReLU())def forward(self, x):x self.flatten(x)logits self.line 加载模型权重时我们需要首先实例化模型类因为该类定义了网络的结构。接下来我们使用 load_state_dict 方法加载参数。 model NeuralNetwork() model.load_state_dict(torch.load(data/model.pth)) model.eval() 注意请务必在推理之前调用 model.eval 方法以将 dropout 和批量归一化层设置为评估模式。如果不这样做将产生不一致的推理结果。 三、模型推理 优化模型以在各种平台和编程语言上运行是很困难的。在所有不同的框架和硬件组合中最大限度地提高性能是非常耗时的。开放式神经网络交换 ONNX 运行时为您提供了一种解决方案只需训练一次即可在任何硬件、云或边缘设备上加速推理。 ONNX 是许多供应商支持的一种通用格式用于共享神经网络和其他机器学习模型。您可以使用 ONNX 格式在其他编程语言和框架如 Java、JavaScript、C# 和 ML.NET上对模型进行推理。 input_image torch.zeros((1,28,28)) onnx_model data/model.onnx onnx.export(model, input_image, onnx_model) 我们将使用测试数据集作为示例数据以便从 ONNX 模型进行推理以进行预测。 test_data datasets.FashionMNIST(rootdata,trainFalse,downloadTrue,transformToTensor() )classes [T-shirt/top,Trouser,Pullover,Dress,Coat,Sandal,Shirt,Sneaker,Bag,Ankle boot, ] x, y test_data[0][0], test_data[0][1] 我们需要使用 onnxruntime 创建一个推理会话。推理会话。为了推断 onnx 模型我们使用 run 和 pass 输入要返回的输出列表如果需要所有输出请留空和输入值映射。结果是一个输出列表 session onnxruntime.InferenceSession(onnx_model, None) input_name session.get_inputs()[0].name output_name session.get_outputs()[0].nameresult session.run([output_name], {input_name: x.numpy()}) predicted, actual classes[result[0][0].argmax(0)], classes[y] print(fPredicted: {predicted}, Actual: {actual}) 四、torch.utils.data.DataLoader和torch.utils.data.Dataset PyTorch有两个基元来处理数据torch.utils.data.DataLoader和torch.utils.data.Dataset。数据集存储样本及其相应的标签DataLoader 围绕数据集包装一个可迭代对象。 %matplotlib inline import torch from torch import nn from torch.utils.data import DataLoader from torchvision import datasets from torchvision.transforms import ToTensor, Lambda, Compose import matplotlib.pyplot as plt PyTorch提供特定于领域的库如TorchTextTorchVision和TorchAudio所有这些都包括数据集。在本教程中我们将使用TorchVision数据集。 torchvision.datasets 模块包含许多真实世界视觉数据如 CIFAR 和 COCO的数据集对象。在本教程中我们将使用 FashionMNIST 数据集。每个TorchVision数据集都包含两个参数转换和target_transform分别修改样本和标签。 # Download training data from open datasets. training_data datasets.FashionMNIST(rootdata,trainTrue,downloadTrue,transformToTensor(), )# Download test data from open datasets. test_data datasets.FashionMNIST(rootdata,trainFalse,downloadTrue,transformToTensor(), ) 我们将数据集作为参数传递给 DataLoader。这将在我们的数据集上包装一个可迭代对象并支持自动批处理、采样、随机排序和多进程数据加载。这里我们定义一个 64 的批量大小即 dataloader 迭代中的每个元素将返回一批 64 个特征和标签。 batch_size 64# Create data loaders. train_dataloader DataLoader(training_data, batch_sizebatch_size) test_dataloader DataLoader(test_data, batch_sizebatch_size)for X, y in test_dataloader:print(Shape of X [N, C, H, W]: , X.shape)print(Shape of y: , y.shape, y.dtype)break# Display sample data figure plt.figure(figsize(10, 8)) cols, rows 5, 5 for i in range(1, cols * rows 1):idx torch.randint(len(test_data), size(1,)).item()img, label test_data[idx]figure.add_subplot(rows, cols, i)plt.title(label)plt.axis(off)plt.imshow(img.squeeze(), cmapgray) plt.show() Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28]) Shape of y: torch.Size([64]) torch.int64 五、创建模型 为了在 PyTorch 中定义神经网络我们创建一个继承自 nn.Module 的类。我们在 __init__ 函数中定义网络层并在转发函数中指定数据如何通过网络。为了加速神经网络的运算我们将其转移到 GPU如果可用。 # Get cpu or gpu device for training. device cuda if torch.cuda.is_available() else cpu print(Using {} device.format(device))# Define model class NeuralNetwork(nn.Module):def __init__(self):super(NeuralNetwork, self).__init__()self.flatten nn.Flatten()self.linear_relu_stack nn.Sequential(nn.Linear(28*28, 512),nn.ReLU(),nn.Linear(512, 512),nn.ReLU(),nn.Linear(512, 10),nn.ReLU())def forward(self, x):x self.flatten(x)logits self.linear_relu_stack(x)return logitsmodel NeuralNetwork().to(device) print(model) Using cuda device NeuralNetwork((flatten): Flatten()(linear_relu_stack): Sequential((0): Linear(in_features784, out_features512, biasTrue)(1): ReLU()(2): Linear(in_features512, out_features512, biasTrue)(3): ReLU()(4): Linear(in_features512, out_features10, biasTrue)(5): ReLU()) ) 六、优化模型参数 为了训练模型我们需要一个损失函数和一个优化器。我们将使用 nn。交叉熵损失用于损失随机梯度下降用于优化。 loss_fn nn.CrossEntropyLoss() learning_rate 1e-3 optimizer torch.optim.SGD(model.parameters(), lrlearning_rate) 在单个训练循环中模型对训练数据集进行预测批量馈送到它并向后传播预测误差以调整模型的参数。 def train(dataloader, model, loss_fn, optimizer):size len(dataloader.dataset)for batch, (X, y) in enumerate(dataloader):X, y X.to(device), y.to(device)# Compute prediction errorpred model(X)loss loss_fn(pred, y)# Backpropagationoptimizer.zero_grad()loss.backward()optimizer.step()if batch % 100 0:loss, current loss.item(), batch * len(X) 我们还可以对照测试数据集检查模型的性能以确保它正在学习。 def test(dataloader, model):size len(dataloader.dataset)model.eval()test_loss, correct 0, 0with torch.no_grad():for X, y in dataloader:X, y X.to(device), y.to(device)pred model(X)test_loss loss_fn(pred, y).item()correct (pred.argmax(1) y).type(torch.float).sum().item()test_loss / sizecorrect / sizeprint(fTest Error: \n Accuracy: {(100*correct):0.1f}%, Avg loss: {test_loss:8f} \n) 训练过程通过多次迭代纪元进行。在每个时期模型学习参数以做出更好的预测。我们打印模型在每个时期的准确性和损失;我们希望看到精度随着每个时期的增加和损失的减少而减少。 epochs 15 for t in range(epochs):print(fEpoch {t1}\n-------------------------------)train(train_dataloader, model, loss_fn, optimizer)test(test_dataloader, model) print(Done!) Epoch 1 ------------------------------- loss: 2.295450 [ 0/60000] loss: 2.293073 [ 6400/60000] loss: 2.278504 [12800/60000] loss: 2.282501 [19200/60000] loss: 2.273211 [25600/60000] loss: 2.258452 [32000/60000] loss: 2.248237 [38400/60000] loss: 2.228594 [44800/60000] loss: 2.240276 [51200/60000] loss: 2.221318 [57600/60000] Test Error: Accuracy: 51.8%, Avg loss: 0.034745 Epoch 2 ------------------------------- loss: 2.212354 [ 0/60000] loss: 2.207739 [ 6400/60000] loss: 2.160400 [12800/60000] loss: 2.176181 [19200/60000] loss: 2.168270 [25600/60000] loss: 2.146453 [32000/60000] loss: 2.119934 [38400/60000] loss: 2.083791 [44800/60000] loss: 2.126453 [51200/60000] loss: 2.077550 [57600/60000] Test Error: Accuracy: 53.2%, Avg loss: 0.032452 Epoch 3 ------------------------------- loss: 2.082280 [ 0/60000] loss: 2.068733 [ 6400/60000] loss: 1.965958 [12800/60000] loss: 1.997126 [19200/60000] loss: 2.002057 [25600/60000] loss: 1.967370 [32000/60000] loss: 1.910595 [38400/60000] loss: 1.849006 [44800/60000] loss: 1.944741 [51200/60000] loss: 1.861265 [57600/60000] Test Error: Accuracy: 51.6%, Avg loss: 0.028937 Epoch 4 ------------------------------- loss: 1.872628 [ 0/60000] loss: 1.844543 [ 6400/60000] loss: 1.710179 [12800/60000] loss: 1.779804 [19200/60000] loss: 1.737971 [25600/60000] loss: 1.746953 [32000/60000] loss: 1.624768 [38400/60000] loss: 1.575720 [44800/60000] loss: 1.742827 [51200/60000] loss: 1.653375 [57600/60000] Test Error: Accuracy: 58.4%, Avg loss: 0.025570 Epoch 5 ------------------------------- loss: 1.662315 [ 0/60000] loss: 1.636235 [ 6400/60000] loss: 1.508407 [12800/60000] loss: 1.606842 [19200/60000] loss: 1.560728 [25600/60000] loss: 1.606024 [32000/60000] loss: 1.426900 [38400/60000] loss: 1.406240 [44800/60000] loss: 1.619918 [51200/60000] loss: 1.521326 [57600/60000] Test Error: Accuracy: 61.2%, Avg loss: 0.023459 Epoch 6 ------------------------------- loss: 1.527535 [ 0/60000] loss: 1.511209 [ 6400/60000] loss: 1.377129 [12800/60000] loss: 1.494889 [19200/60000] loss: 1.457990 [25600/60000] loss: 1.502333 [32000/60000] loss: 1.291539 [38400/60000] loss: 1.285098 [44800/60000] loss: 1.484891 [51200/60000] loss: 1.414015 [57600/60000] Test Error: Accuracy: 62.2%, Avg loss: 0.021480 Epoch 7 ------------------------------- loss: 1.376779 [ 0/60000] loss: 1.384830 [ 6400/60000] loss: 1.230116 [12800/60000] loss: 1.382574 [19200/60000] loss: 1.255630 [25600/60000] loss: 1.396211 [32000/60000] loss: 1.157718 [38400/60000] loss: 1.186382 [44800/60000] loss: 1.340606 [51200/60000] loss: 1.321607 [57600/60000] Test Error: Accuracy: 62.8%, Avg loss: 0.019737 Epoch 8 ------------------------------- loss: 1.243344 [ 0/60000] loss: 1.279124 [ 6400/60000] loss: 1.121769 [12800/60000] loss: 1.293069 [19200/60000] loss: 1.128232 [25600/60000] loss: 1.315465 [32000/60000] loss: 1.069528 [38400/60000] loss: 1.123324 [44800/60000] loss: 1.243827 [51200/60000] loss: 1.255190 [57600/60000] Test Error: Accuracy: 63.4%, Avg loss: 0.018518 Epoch 9 ------------------------------- loss: 1.154148 [ 0/60000] loss: 1.205280 [ 6400/60000] loss: 1.046463 [12800/60000] loss: 1.229866 [19200/60000] loss: 1.048813 [25600/60000] loss: 1.254785 [32000/60000] loss: 1.010614 [38400/60000] loss: 1.077114 [44800/60000] loss: 1.176766 [51200/60000] loss: 1.206567 [57600/60000] Test Error: Accuracy: 64.3%, Avg loss: 0.017640 Epoch 10 ------------------------------- loss: 1.090360 [ 0/60000] loss: 1.149150 [ 6400/60000] loss: 0.990786 [12800/60000] loss: 1.183704 [19200/60000] loss: 0.997114 [25600/60000] loss: 1.207199 [32000/60000] loss: 0.967512 [38400/60000] loss: 1.043431 [44800/60000] loss: 1.127000 [51200/60000] loss: 1.169639 [57600/60000] Test Error: Accuracy: 65.3%, Avg loss: 0.016974 Epoch 11 ------------------------------- loss: 1.041194 [ 0/60000] loss: 1.104409 [ 6400/60000] loss: 0.947670 [12800/60000] loss: 1.149421 [19200/60000] loss: 0.960403 [25600/60000] loss: 1.169899 [32000/60000] loss: 0.935149 [38400/60000] loss: 1.018250 [44800/60000] loss: 1.088222 [51200/60000] loss: 1.139813 [57600/60000] Test Error: Accuracy: 66.2%, Avg loss: 0.016446 Epoch 12 ------------------------------- loss: 1.000646 [ 0/60000] loss: 1.067356 [ 6400/60000] loss: 0.912046 [12800/60000] loss: 1.122742 [19200/60000] loss: 0.932827 [25600/60000] loss: 1.138785 [32000/60000] loss: 0.910242 [38400/60000] loss: 0.999010 [44800/60000] loss: 1.056596 [51200/60000] loss: 1.114582 [57600/60000] Test Error: Accuracy: 67.5%, Avg loss: 0.016011 Epoch 13 ------------------------------- loss: 0.966393 [ 0/60000] loss: 1.035691 [ 6400/60000] loss: 0.881672 [12800/60000] loss: 1.100845 [19200/60000] loss: 0.910265 [25600/60000] loss: 1.112597 [32000/60000] loss: 0.889558 [38400/60000] loss: 0.982751 [44800/60000] loss: 1.029199 [51200/60000] loss: 1.092738 [57600/60000] Test Error: Accuracy: 68.5%, Avg loss: 0.015636 Epoch 14 ------------------------------- loss: 0.936334 [ 0/60000] loss: 1.007734 [ 6400/60000] loss: 0.854663 [12800/60000] loss: 1.081601 [19200/60000] loss: 0.890581 [25600/60000] loss: 1.089641 [32000/60000] loss: 0.872057 [38400/60000] loss: 0.969192 [44800/60000] loss: 1.005193 [51200/60000] loss: 1.073098 [57600/60000] Test Error: Accuracy: 69.4%, Avg loss: 0.015304 Epoch 15 ------------------------------- loss: 0.908971 [ 0/60000] loss: 0.982067 [ 6400/60000] loss: 0.830095 [12800/60000] loss: 1.064921 [19200/60000] loss: 0.874204 [25600/60000] loss: 1.069008 [32000/60000] loss: 0.856447 [38400/60000] loss: 0.957340 [44800/60000] loss: 0.983547 [51200/60000] loss: 1.055251 [57600/60000] Test Error: Accuracy: 70.3%, Avg loss: 0.015001 Done! 准确性最初不会很好没关系尝试运行循环以获取更多纪元或将learning_rate调整为更大的数字。也可能是我们选择的模型配置可能不是此类问题的最佳配置。 七、保存模型 保存模型的常用方法是序列化内部状态字典包含模型参数。 torch.save(model.state_dict(), data/model.pth) print(Saved PyTorch Model State to model.pth) 八、负载模型 加载模型的过程包括重新创建模型结构并将状态字典加载到其中。 model NeuralNetwork() model.load_state_dict(torch.load(data/model.pth)) 此模型现在可用于进行预测。 classes [T-shirt/top,Trouser,Pullover,Dress,Coat,Sandal,Shirt,Sneaker,Bag,Ankle boot, ]model.eval() x, y test_data[0][0], test_data[0][1] with torch.no_grad():pred model(x)predicted, actual classes[pred[0].argmax(0)], classes[y]print(fPredicted: {predicted}, Actual: {actual}) Predicted: Ankle boot, Actual: Ankle boot 祝贺您已经完成了 PyTorch 初学者教程我们希望本教程能帮助您在 PyTorch 上开始深度学习。