Alive_lei的博客

coding=utf-8“”"author:leifunction: 实现的是构建词典，实现方法把句子转换为数字序列并将其翻转“”"class Word2Sequence(object):UNK_TAG = “UNK”PAD_TAG = “PAD”UNK = 0PAD = 1def __init__(self): self.dict = { self.UNK_TAG: self.UNK, self.PAD_TAG: self.PAD }

coding=utf-8“”"author:leifunction: 使用pytorch完成手写数字的识别“”"import numpy as npfrom torch.utils.data import DataLoaderfrom torchvision.datasets import MNISTfrom torchvision.transforms import Compose, ToTensor, Normalizeimport torch.nn as nnimport torc

coding=utf-8“”"author:leifunction:“”"import torchimport matplotlib.pyplot as pltlearning_rate = 0.011、准备数据y = 3x + 0.8x = torch.rand((500, 1))y_true = x*0.3 + 0.82、通过模型计算y_predictw = torch.rand((1, 1), requires_grad=True)b = torch.empty((1, 1

from pylab import mpl设置显示中文字体mpl.rcParams[“font.sans-serif”] = [“SimHei”]设置正常显示符号mpl.rcParams[“axes.unicode_minus”] = Falsex = range(60)y_beijing = [random.uniform(10, 15) for i in x]y_shanghai = [random.uniform(15, 25) for i in x]创建1行两列的画布fig, ax

import randomfrom pylab import mpl设置显示中文字体mpl.rcParams[“font.sans-serif”] = [“SimHei”]设置正常显示符号mpl.rcParams[“axes.unicode_minus”] = False0.生成数据x = range(60)y_beijing = [random.uniform(10, 15) for i in x]y_shanghai = [random.uniform(15, 25) for i in

coding=utf-8“”"author:leifunction:“”"import osimport torchfrom torch.optim import Adamfrom torch.nn import functional as Ffrom torch import nnfrom torch.utils.data import DataLoaderfrom torchvision.datasets import MNISTfrom torchvision.transfor

coding=utf-8“”"author:leifunction:“”"import torchfrom torch.utils.data import Dataset, DataLoaderimport mathdata_path = “./data/SMSSpamCollection”完成数据集类class MyDataset(Dataset):def init(self):self.lines = open(data_path, encoding=“utf8”).readli

coding=utf-8“”"author:leifunction:“”"from torch.optim import SGDimport torchfrom torch import nn定义一个device对象 如果要转换为gpu上运行device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)准备数据x = torch.rand([500, 1]).to(device)y_true = 3 * x

coding=utf-8“”"author:leifunction:“”"import numpy as npimport tensorflow as tf下载并载入Mnist 手写数字库（55000 * 28 * 28）from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets(‘mnist_data’, one_hot=True)one_hot 独热码(enco

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_data载入数据集mnist = input_data.read_data_sets(“mnist_data”, one_hot=True)每个批次的大小batch_size = 100计算一共多少个批次n_batch = mnist.train.num_examples // batch_size定义两个placeholder 一张图片

coding=utf-8“”"author:leifunction:“”"import tensorflow as tfimport numpy as npimport matplotlib.pyplot as pltfrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("./mnist_data/", one_hot=True)trainimgs, trai

import tensorflow as tf定义线性回归def linearregression():“”"实现线性回归:return:“”"# 准备数据集 x[100, 1] 0.8*x + 0.7 = [100, 1]with tf.variable_scope(“orginal_name”):x = tf.random_normal([100, 1], mean=0, stddev=1)y_true = tf.matmul(x, [[0.8]]) + [[0.7]]# 建立

coding=utf-8“”"author:leifunction:迁移学习“”"import tensorflow as tffrom tensorflow.python import kerasfrom tensorflow.python.keras.preprocessing.image import ImageDataGenerator, load_img, img_to_arrayfrom tensorflow.python.keras.applications.vgg16 imp

from tensorflow.python.keras.applications.vgg16 import VGG16, preprocess_input, decode_predictionsfrom tensorflow.python.keras.preprocessing.image import load_img, img_to_arraydef predict():model = VGG16()print(model.summary())# 预测一张图类别# 加载图片 要求是vgg

构建神经网络进行时装模型训练与预测class SinleNN(object):# 建立神经网络模型model = keras.Sequential([ # 将输入数据的形状进行修改为扁平化 keras.layers.Flatten(input_shape=[28, 28]), # 定义隐藏层，128个神经元的网络层 keras.layers.Dense(128, activation=tf.nn.relu), # 10个类别的分类问题，输出神经元的个数和输出类别

coding=utf-8“”"author:leifunction:“”"from tensorflow.python.keras.datasets import cifar100from tensorflow.python import kerasimport tensorflow as tffrom tensorflow.python.keras.applications import VGG16class CNNCifar(object):# 定义模型model = keras.

数据集下载:https://aistudio.baidu.com/aistudio/datasetDetail/16630主要训练模型:coding=utf-8“”"author:leifunction:使用googlenet进行图片分类“”"import tensorflow as tffrom tensorflow.contrib import slimimport nump...

-- coding: utf-8 --“”"@Time : 19-9-12 上午10:51@Author : lei@Site :@File : crx信誉分析.py@Software: PyCharm“”"import pandas as pdfrom sklearn.feature_extraction import DictVectorizerfrom sklearn.m...

-- coding: utf-8 --“”"@Time : 19-10-1 上午7:32@Author : lei@Site :@File : 神经网络预测股票.py@Software: PyCharm“”"股票预测import pandas as pdimport numpy as npfrom sklearn.model_selection import train_te...

-- coding: utf-8 --“”"@Time : 19-9-19 下午2:50@Author : lei@Site :@File : dermatology皮肤病.py@Software: PyCharm“”"import pandas as pdimport numpy as npfrom sklearn.ensemble import RandomForestCl...

-- coding: utf-8 --“”"@Time : 19-9-21 下午7:09@Author : lei@Site :@File : 房价预测.py@Software: PyCharm“”"房价预测from sklearn.linear_model import LinearRegressionfrom sklearn.model_selection import t...

-- coding: utf-8 --“”"@Time : 19-9-26 下午2:39@Author : lei@Site :@File : 情感分析.py@Software: PyCharm“”"情感分析from sklearn.model_selection import train_test_splitfrom sklearn.feature_extraction.te...

-- coding: utf-8 --“”"@Time : 19-9-20 下午8:12@Author : lei@Site :@File : captcha_train.py@Software: PyCharm“”"import tensorflow as tfimport os定义一个初始化权重的函数def weight_variables(shape):weight ...

-- coding: utf-8 --“”"@Time : 19-10-8 下午4:12@Author : lei@Site :@File : CNN3_mnist.py@Software: PyCharm“”"CNN实现mnist识别import tensorflow as tffrom tensorflow.contrib.layers import fully_conne...

-- coding: utf-8 --“”"@Time : 19-10-17 下午4:06@Author : lei@Site :@File : Cheng.py@Software: PyCharm“”"ai自己写成语import tensorflow as tfimport collectionsimport numpy as np成语数量 47247字的个数 49...

-- coding: utf-8 --“”"@Time : 19-10-19 下午7:39@Author : lei@Site :@File : squirrel_image.py@Software: PyCharm“”"松鼠识别 0 1from sklearn.linear_model import LogisticRegressionfrom sklearn.metric...

coding=utf-8“”"author:leifunction: 机器人写唐诗“”"import tensorflow as tfimport numpy as npimport collectionsimport re提取数据def extract_data(file_path):poems = []with open(file_path, “r”, encoding...