pytorch快速入门实战篇-FashionMnist案例
参考：官网
文章目录
- 1. Working with data
-
- 1.1 Download training data from open datasets.
- 1.2 Download test data from open datasets.
- 1.3 Create data loaders.
- 2. Creating Models
-
- 2.1 Get cpu or gpu device for training.
- 2.2 Define model
- 2.3 Optimizing the Model Parameters
- 3 Saving Models
- 4. Loading Models
- 5 Complete Code in python
-
- 5.1 Get Data
- 5.2 Train
- 5.3 Inference
- 6. Reference
- 7. others
1. Working with data
PyTorch has two primitives to work with data: torch.utils.data.DataLoader and torch.utils.data.Dataset. Dataset stores the samples and their corresponding labels, and DataLoader wraps an iterable around the Dataset.
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor
PyTorch offers domain-specific libraries such as TorchText, TorchVision, and TorchAudio, all of which include datasets. For this tutorial, we will be using a TorchVision dataset.
The torchvision.datasets module contains Dataset objects for many real-world vision data like CIFAR, COCO (full list here). In this tutorial, we use the FashionMNIST dataset. Every TorchVision Dataset includes two arguments: transform and target_transform to modify the samples and labels respectively.
1.1 Download training data from open datasets.
training_data = datasets.FashionMNIST(
root="data",
train=True,
download=True,
transform=ToTensor(),
)
1.2 Download test data from open datasets.
test_data = datasets.FashionMNIST(
root="data",
train=False,
download=True,
transform=ToTensor(),
)
Out:
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz
Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz
Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz
Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz
Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz
Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw
We pass the Dataset as an argument to DataLoader. This wraps an iterable over our dataset, and supports automatic batching, sampling, shuffling and multiprocess data loading. Here we define a batch size of 64, i.e. each element in the dataloader iterable will return a batch of 64 features and labels.
batch_size = 64
1.3 Create data loaders.
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)
for X, y in test_dataloader:
print(f"Shape of X [N, C, H, W]: {X.shape}")
print(f"Shape of y: {y.shape} {y.dtype}")
break
Out:
Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])
Shape of y: torch.Size([64]) torch.int64
Read more about loading data in PyTorch.
2. Creating Models
To define a neural network in PyTorch, we create a class that inherits from nn.Module. We define the layers of the network in the init function and specify how data will pass through the network in the forward function. To accelerate operations in the neural network, we move it to the GPU if available.
2.1 Get cpu or gpu device for training.
device = “cuda” if torch.cuda.is_available() else “cpu”
print(f"Using {device} device")
2.2 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)
)
def forward(self, x):
x = self.flatten(x)
logits = self.linear_relu_stack(x)
return logits
model = NeuralNetwork().to(device)
print(model)
Out:
Using cuda device
NeuralNetwork(
(flatten): Flatten(start_dim=1, end_dim=-1)
(linear_relu_stack): Sequential(
(0): Linear(in_features=784, out_features=512, bias=True)
(1): ReLU()
(2): Linear(in_features=512, out_features=512, bias=True)
(3): ReLU()
(4): Linear(in_features=512, out_features=10, bias=True)
)
)
Read more about building neural networks in PyTorch.
2.3 Optimizing the Model Parameters
To train a model, we need a loss function and an optimizer.
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
In a single training loop, the model makes predictions on the training dataset (fed to it in batches), and backpropagates the prediction error to adjust the model’s parameters.
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
model.train()
for batch, (X, y) in enumerate(dataloader):
X, y = X.to(device), y.to(device)
# Compute prediction error
pred = model(X)
loss = loss_fn(pred, y)
# Backpropagation
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch % 100 == 0:
loss, current = loss.item(), batch * len(X)
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
We also check the model’s performance against the test dataset to ensure it is learning.
def test(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
model.eval()
test_loss, correct = 0, 0
with 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 /= num_batches
correct /= size
print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
The training process is conducted over several iterations (epochs). During each epoch, the model learns parameters to make better predictions. We print the model’s accuracy and loss at each epoch; we’d like to see the accuracy increase and the loss decrease with every epoch.
epochs = 5
for t in range(epochs):
print(f"Epoch {t+1}\n-------------------------------")
train(train_dataloader, model, loss_fn, optimizer)
test(test_dataloader, model, loss_fn)
print("Done!")
Out:
Epoch 1
-------------------------------
loss: 2.299968 [ 0/60000]
loss: 2.291713 [ 6400/60000]
loss: 2.274773 [12800/60000]
loss: 2.272840 [19200/60000]
loss: 2.265114 [25600/60000]
loss: 2.223690 [32000/60000]
loss: 2.246659 [38400/60000]
loss: 2.205027 [44800/60000]
loss: 2.203506 [51200/60000]
loss: 2.183183 [57600/60000]
Test Error:
Accuracy: 40.4%, Avg loss: 2.176064
Epoch 2
-------------------------------
loss: 2.179508 [ 0/60000]
loss: 2.171398 [ 6400/60000]
loss: 2.122458 [12800/60000]
loss: 2.136343 [19200/60000]
loss: 2.099183 [25600/60000]
loss: 2.031877 [32000/60000]
loss: 2.070053 [38400/60000]
loss: 1.988699 [44800/60000]
loss: 1.989894 [51200/60000]
loss: 1.931872 [57600/60000]
Test Error:
Accuracy: 58.0%, Avg loss: 1.927841
Epoch 3
-------------------------------
loss: 1.951685 [ 0/60000]
loss: 1.920792 [ 6400/60000]
loss: 1.815998 [12800/60000]
loss: 1.850795 [19200/60000]
loss: 1.757668 [25600/60000]
loss: 1.698289 [32000/60000]
loss: 1.727373 [38400/60000]
loss: 1.622224 [44800/60000]
loss: 1.637520 [51200/60000]
loss: 1.541725 [57600/60000]
Test Error:
Accuracy: 62.5%, Avg loss: 1.558743
Epoch 4
-------------------------------
loss: 1.615998 [ 0/60000]
loss: 1.577118 [ 6400/60000]
loss: 1.438216 [12800/60000]
loss: 1.503292 [19200/60000]
loss: 1.393710 [25600/60000]
loss: 1.377801 [32000/60000]
loss: 1.397386 [38400/60000]
loss: 1.316668 [44800/60000]
loss: 1.345000 [51200/60000]
loss: 1.248275 [57600/60000]
Test Error:
Accuracy: 63.4%, Avg loss: 1.277212
Epoch 5
-------------------------------
loss: 1.348338 [ 0/60000]
loss: 1.324071 [ 6400/60000]
loss: 1.170114 [12800/60000]
loss: 1.266121 [19200/60000]
loss: 1.148989 [25600/60000]
loss: 1.163925 [32000/60000]
loss: 1.187885 [38400/60000]
loss: 1.124403 [44800/60000]
loss: 1.159600 [51200/60000]
loss: 1.073207 [57600/60000]
Test Error:
Accuracy: 64.3%, Avg loss: 1.099506
Done!
Read more about Training your model.
3 Saving Models
A common way to save a model is to serialize the internal state dictionary (containing the model parameters).
torch.save(model.state_dict(), "model.pth")
print("Saved PyTorch Model State to model.pth")
Out:
Saved PyTorch Model State to model.pth
4. Loading Models
The process for loading a model includes re-creating the model structure and loading the state dictionary into it.
model = NeuralNetwork()
model.load_state_dict(torch.load("model.pth"))
This model can now be used to make predictions.
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(f'Predicted: "{predicted}", Actual: "{actual}"')
Out:
Predicted: "Ankle boot", Actual: "Ankle boot"
Read more about Saving & Loading your model.
Total running time of the script: ( 0 minutes 42.173 seconds)
5 Complete Code in python
5.1 Get Data
如果上面通过命令行的方式下载数据集特别慢，可以直接下载本网盘里的数据集，然后放到对应的文件夹下。
链接：https://pan.baidu.com/s/194eBjl2W98VfcSCNe0x8Ow
提取码：e11r
解压后放入相应的文件夹下：
5.2 Train
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor
# Download training data from open datasets.
training_data = datasets.FashionMNIST(
root="data",
train=True,
download=False, # 数据集已下载，如果未下载，download = True
transform=ToTensor(), # 数据预处理，格式调整
)
# Download test data from open datasets.
test_data = datasets.FashionMNIST(
root="data",
train=False,
download=True, # 数据集已下载，如果未下载，download = True
transform=ToTensor(), # 数据预处理，格式调整
)
# for X, y in test_dataloader:
# print(f"Shape of X [N, C, H, W]: {X.shape}")
# print(f"Shape of y: {y.shape} {y.dtype}")
# break
# 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)
)
def forward(self, x):
x = self.flatten(x)
logits = self.linear_relu_stack(x)
return logits
# print(model)
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
model.train()
for batch, (X, y) in enumerate(dataloader):
X, y = X.to(device), y.to(device)
# Compute prediction error
pred = model(X)
loss = loss_fn(pred, y)
# Backpropagation
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch % 100 == 0:
loss, current = loss.item(), batch * len(X)
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
if __name__ == '__main__':
batch_size = 64
loss_fn = nn.CrossEntropyLoss()
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device} device")
model = NeuralNetwork().to(device) # 实例化模型
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
# Get cpu or gpu device for training.
# Create data loaders.
train_dataloader = DataLoader(training_data, batch_size=batch_size)
test_dataloader = DataLoader(test_data, batch_size=batch_size)
epochs = 5
for t in range(epochs):
print(f"Epoch {t + 1}\n-------------------------------")
train(train_dataloader, model, loss_fn, optimizer)
#test(test_dataloader, model, loss_fn)
print("Done!")
# Saving models
torch.save(model.state_dict(), "model.pth")
print("Saved PyTorch Model State to model.pth")
5.3 Inference
test.py
import torch
from torch import nn
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)
)
def forward(self, x):
x = self.flatten(x)
logits = self.linear_relu_stack(x)
return logits
classes = [
"T-shirt/top",
"Trouser",
"Pullover",
"Dress",
"Coat",
"Sandal",
"Shirt",
"Sneaker",
"Bag",
"Ankle boot",
]
if __name__ == '__main__':
model = NeuralNetwork()
model.load_state_dict(torch.load("model.pth"))
model.eval()
#x, y = test_data[0][0], test_data[0][1] # y=9
x=[[[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0118, 0.0039, 0.0000, 0.0000, 0.0275,
0.0000, 0.1451, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0039, 0.0078, 0.0000,
0.1059, 0.3294, 0.0431, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.4667, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0039, 0.0000, 0.0000,
0.3451, 0.5608, 0.4314, 0.0000, 0.0000, 0.0000, 0.0000, 0.0863,
0.3647, 0.4157, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0157, 0.0000, 0.2078,
0.5059, 0.4706, 0.5765, 0.6863, 0.6157, 0.6510, 0.5294, 0.6039,
0.6588, 0.5490, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0078, 0.0000, 0.0431, 0.5373,
0.5098, 0.5020, 0.6275, 0.6902, 0.6235, 0.6549, 0.6980, 0.5843,
0.5922, 0.5647, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0039, 0.0000,
0.0078, 0.0039, 0.0000, 0.0118, 0.0000, 0.0000, 0.4510, 0.4471,
0.4157, 0.5373, 0.6588, 0.6000, 0.6118, 0.6471, 0.6549, 0.5608,
0.6157, 0.6196, 0.0431, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0039, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0118, 0.0000, 0.0000, 0.3490, 0.5451, 0.3529,
0.3686, 0.6000, 0.5843, 0.5137, 0.5922, 0.6627, 0.6745, 0.5608,
0.6235, 0.6627, 0.1882, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0078, 0.0157,
0.0039, 0.0000, 0.0000, 0.0000, 0.3843, 0.5333, 0.4314, 0.4275,
0.4314, 0.6353, 0.5294, 0.5647, 0.5843, 0.6235, 0.6549, 0.5647,
0.6196, 0.6627, 0.4667, 0.0000],
[0.0000, 0.0000, 0.0078, 0.0078, 0.0039, 0.0078, 0.0000, 0.0000,
0.0000, 0.0000, 0.1020, 0.4235, 0.4588, 0.3882, 0.4353, 0.4588,
0.5333, 0.6118, 0.5255, 0.6039, 0.6039, 0.6118, 0.6275, 0.5529,
0.5765, 0.6118, 0.6980, 0.0000],
[0.0118, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0824,
0.2078, 0.3608, 0.4588, 0.4353, 0.4039, 0.4510, 0.5059, 0.5255,
0.5608, 0.6039, 0.6471, 0.6667, 0.6039, 0.5922, 0.6039, 0.5608,
0.5412, 0.5882, 0.6471, 0.1686],
[0.0000, 0.0000, 0.0902, 0.2118, 0.2549, 0.2980, 0.3333, 0.4627,
0.5020, 0.4824, 0.4353, 0.4431, 0.4627, 0.4980, 0.4902, 0.5451,
0.5216, 0.5333, 0.6275, 0.5490, 0.6078, 0.6314, 0.5647, 0.6078,
0.6745, 0.6314, 0.7412, 0.2431],
[0.0000, 0.2667, 0.3686, 0.3529, 0.4353, 0.4471, 0.4353, 0.4471,
0.4510, 0.4980, 0.5294, 0.5333, 0.5608, 0.4941, 0.4980, 0.5922,
0.6039, 0.5608, 0.5804, 0.4902, 0.6353, 0.6353, 0.5647, 0.5412,
0.6000, 0.6353, 0.7686, 0.2275],
[0.2745, 0.6627, 0.5059, 0.4078, 0.3843, 0.3922, 0.3686, 0.3804,
0.3843, 0.4000, 0.4235, 0.4157, 0.4667, 0.4706, 0.5059, 0.5843,
0.6118, 0.6549, 0.7451, 0.7451, 0.7686, 0.7765, 0.7765, 0.7333,
0.7725, 0.7412, 0.7216, 0.1412],
[0.0627, 0.4941, 0.6706, 0.7373, 0.7373, 0.7216, 0.6706, 0.6000,
0.5294, 0.4706, 0.4941, 0.4980, 0.5725, 0.7255, 0.7647, 0.8196,
0.8157, 1.0000, 0.8196, 0.6941, 0.9608, 0.9882, 0.9843, 0.9843,
0.9686, 0.8627, 0.8078, 0.1922],
[0.0000, 0.0000, 0.0000, 0.0471, 0.2627, 0.4157, 0.6431, 0.7255,
0.7804, 0.8235, 0.8275, 0.8235, 0.8157, 0.7451, 0.5882, 0.3216,
0.0314, 0.0000, 0.0000, 0.0000, 0.6980, 0.8157, 0.7373, 0.6863,
0.6353, 0.6196, 0.5922, 0.0431],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000]]]
x=torch.tensor(x)
#y=9
with torch.no_grad():
pred = model(x)
#predicted, actual = classes[pred[0].argmax(0)], classes[y]
predicted= classes[pred[0].argmax(0)]
print(f'Predicted: "{predicted}"')
out:
Predicted: "Ankle boot"
6. Reference
- 官网：https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html
7. others
完整的数据集和代码可从该处下载：https://download.csdn.net/download/weixin_39107270/85213879