Pytorch学习笔记Day二（全连接层学习）资源-CSDN文库

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Pytorch学习笔记学习笔记Day二（全连接层学习）二（全连接层学习）

MNIST数据集用全连接层实现

import torch

from torch import nn

from torch.nn import functional as F

from torch import optim

import torchvision

from matplotlib import pyplot as plt

def plot_curve(data):

fig = plt.figure()

plt.plot(range(len(data)), data, color='blue')

plt.legend(['value'], loc='upper right')

plt.xlabel('step')

plt.ylabel('value')

plt.show()

def plot_image(img, label, name):

fig = plt.figure()

for i in range(6):

plt.subplot(2, 3, i + 1)

plt.tight_layout()

plt.imshow(img[i][0]*0.3081+0.1307, cmap='gray', interpolation='none')

plt.title("{}: {}".format(name, label[i].item()))

plt.xticks([])

plt.yticks([])

plt.show()

def one_hot(label, depth=10):

out = torch.zeros(label.size(0), depth)

idx = torch.LongTensor(label).view(-1, 1)

out.scatter_(dim=1, index=idx, value=1)

return out

batch_size = 512

# step1. load dataset

train_loader = torch.utils.data.DataLoader(

torchvision.datasets.MNIST('../data', train=True, download=True,

transform=torchvision.transforms.Compose([

torchvision.transforms.ToTensor(),

torchvision.transforms.Normalize(

(0.1307,), (0.3081,))

])),

batch_size=batch_size, shuffle=True)

test_loader = torch.utils.data.DataLoader(

torchvision.datasets.MNIST('../data', train=False, download=True,

transform=torchvision.transforms.Compose([

torchvision.transforms.ToTensor(),

torchvision.transforms.Normalize(

(0.1307,), (0.3081,))

])),

batch_size=batch_size, shuffle=False)

x, y = next(iter(train_loader))

# plot_image(x, y, 'image sample')

class Net(nn.Module):

def __init__(self):

super(Net, self).__init__()

# xw+b

self.fc1 = nn.Linear(28*28, 256)

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