Iris Classification on PyTorch code # -*- coding:utf8 -*- from sklearn.datasets import load_iris from sklearn.utils import shuffle from sklearn.model_selection import train_test_split import torch import torch.optim as optim import torch.nn as nn imp…

Iris Classification on Tensorflow Neural Network formula derivation \[ \begin{align} a & = x \cdot w_1 \\ y & = a \cdot w_2 \\ & = x \cdot w_1 \cdot w_2 \\ y & = softmax(y) \end{align} \] code (training only) \[ a = x \cdot w_1 \\ y = a \c…

Iris Classification on Keras Installation Python3 版本为 3.6.4 : : Anaconda conda install tensorflow==1.15.0 conda install keras==2.1.6 Code # encoding:utf8 from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from…

Awesome-Pytorch-list 2018-08-10 09:25:16 This blog is copied from: https://github.com/Epsilon-Lee/Awesome-pytorch-list Pytorch & related libraries pytorch : Tensors and Dynamic neural networks in Python with strong GPU acceleration. pytorch extras :…

By default, Dataloader use collate_fn method to pack a series of images and target as tensors (first dimension of tensor is batch size). The default collate_fn expects all the images in a batch to have the same size because it uses torch.stack() to p…

pytorch和tensorflow的爱恨情仇之基本数据类型 pytorch和tensorflow的爱恨情仇之张量 pytorch版本:1.6.0 tensorflow版本:1.15.0 之前我们就已经了解了pytorch和tensorflow中的变量,本节我们深入了解可训练的参数-变量 接下来我们将使用sklearn自带的iris数据集来慢慢品味. 1.pytorch (1)第一种方式,不使用nn.Module或nn.Sequntial()来建立模型的情况下自定义参数: 加载数据集并转换为te…

看了Movan大佬的文字教程让我对pytorch的基本使用有了一定的了解,下面简单介绍一下二分类用pytorch的基本实现! 希望详细的注释能够对像我一样刚入门的新手来说有点帮助! import torch import torch.nn.functional as F import matplotlib.pyplot as plt from torch.autograd import Variable n_data = torch.ones(100,2) #生成一个100行2列的全1矩阵 x0…

论文  < Convolutional Neural Networks for Sentence Classification>通过CNN实现了文本分类. 论文地址: 666666 模型图: 模型解释可以看论文,给出code and comment: # -*- coding: utf-8 -*- # @time : 2019/11/9 13:55 import numpy as np import torch import torch.nn as nn import torch.optim…

import torch import torch.nn.functional as F import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible # make fake data n_data = torch.ones(100, 2) x0 = torch.normal(2*n_data, 1) # class0 x data (tensor), shape=(100, 2) y0 = torch.zeros(1…

import torch from torch.autograd import Variable import torch.nn.functional as F import matplotlib.pyplot as plt n_data = torch.ones(100, 2) # 100个具有2个属性的数据 shape=(100,2) x0 = torch.normal(2*n_data, 1) # 根据原始数据生成随机数据,第一个参数是均值,第二个是方差,这里设置为1了,shape=(10…