随笔分类 - # pytorch深度学习实践
摘要：import torch from torch.utils.data import Dataset from torch.utils.data import DataLoader import gzip import csv import time import math import matplo
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摘要：Inception Moudel import torch import torch.nn as nn from torchvision import transforms from torchvision import datasets from torch.utils.data import D
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摘要：import torch from torchvision import transforms from torchvision import datasets from torch.utils.data import DataLoader import torch.nn.functional as
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摘要：import torch from torchvision import transforms from torchvision import datasets from torch.utils.data import DataLoader import torch.nn.functional as
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摘要：import torch import numpy as np from torch.utils.data import Dataset, DataLoader class DiabetesDataset(Dataset): def __init__(self, filepath): xy = np
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摘要：import numpy as np import torch import matplotlib.pyplot as plt # prepare dataset xy = np.loadtxt('./diabetes.csv', delimiter=',', dtype=np.float32) x
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摘要：import torch x_data = torch.tensor([[1.0], [2.0], [3.0]]) y_data = torch.tensor([[0.], [0.], [1.]]) class LogisticRessionModel(torch.nn.Module): def _
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摘要：import torch # x, y是矩阵，3行1列，也就是说总共有3个数据，每个数据只有1个特征 x_data = torch.tensor([[1.0], [2.0], [3.0]]) y_data = torch.tensor([[2.0], [4.0], [6.0]]) # design
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摘要：# 小试牛刀 # import torch # a = torch.tensor([1.0]) # a.requires_grad = True # 或者 a.requires_grad_() # print(a) # tensor([1.], requires_grad=True) # print
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摘要：import matplotlib.pylab as plt x_data = [1.0, 2.0, 3.0] y_data = [2.0, 4.0, 6.0] w = 1.0 def forward(x): return x * w def cost(xs, ys): cost = 0 for x
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摘要：import numpy as np import matplotlib.pyplot as plt x_data = [1.0, 2.0, 4.0] y_data = [2.0, 4.0, 8.0] def forward(x): return x * w def loss(x_val, y_va
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摘要：第0讲主要放一张学习深度学习流程图，其实这是在学完课程之后的一个补充。 作为总章，说几个关于本栏的阅读前提 通过观看B站视频学习代码通过看视频和csdn博主错错莫、一荤配一素博主书写从第一讲开始，没有代码介绍，纯代码，只是给自己做一个记录，但上述两位博主已经给出，可找他们文章学习代码整理到githu
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