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tormozzzz's questions

Martin Hope
tormozzzz
Asked: 2022-02-18 04:05:16 +0800 CST
  • 0

有谁知道如何修复错误？很有意思 ）

From dataclasses import dataclass

@dataclass
class цел:
   число: int = 0

переменная1: цел

фун = def # ошибка
класс = class # ошибка
если = if # ошибка
лябда = lambda # ошибка
импорт = import # ошибка
иначе = else # ошибка

def вывод (выводимое):
    print(выводимое)

вывод (переменная1)
python
Martin Hope
tormozzzz
Asked: 2021-10-14 02:54:21 +0800 CST
  • 2

我们从https://www.sravni.ru/bank/gazprombank/debetovaja-karta/visagold/umnaya/?currency=RUB 拿一张桌子在此处输入图像描述 有这样的代码：

import requests
from lxml import html
from bs4 import BeautifulSoup

url = 'https://www.sravni.ru/bank/gazprombank/debetovaja-karta/visagold/umnaya/?currency=RUB'
r = requests.get(url)
page = r.text
def get_table():
    
    data = []
    global page
    import unicodedata
    un = unicodedata.normalize
    soup = BeautifulSoup(page, "html.parser") 
    
    table = soup.find('table')
    table_body = table.find('tbody')

    rows = table_body.find_all('tr')

    for row in rows:
        cols = row.find_all('td')
        cols = [un("NFKD",ele.text.strip())+' ' for ele in cols]#
        data.append([ele for ele in cols if ele]) 
    return data

print(get_table())

我们得到.. 在此处输入图像描述

以及如何使用空格？

python
Martin Hope
tormozzzz
Asked: 2020-08-20 00:33:33 +0800 CST
  • 0

按类型输入数据

x_train = array([[1.3590000e+03, 1.3180000e+03, 1.7082020e+07, 1.2000000e+03], [4.0380000e+03, 4.6170000e+03, 1.7082020e+07, 1.2000000e+03], [2.6300000e+03, 3.9840000e+03, 1.7082020e+07, 1.0540000e+03], [3.4460000e+03, 4.5310000e+03, 1.8102014e+07, 2.1610000e+03], [9.1500000e+02, 4.5310000e+03, 1.8102014e+07, 2.1610000e+03], [3.4460000e+03, 4.4570000e+03, 1.8102014e+07, 2.1610000e+03]])
y_train = array([[ 1., 2., 2.], [ 1., 2., 2.], [ 1., 2., 2.], [16., 2., 1.], [16., 4., 1.], [16., 0., 1.]])
но результат [[1616849.4   1550158.4     54270.72 ]
 [1616645.    1549958.6     54247.965]
 [1530179.1   1467063.      51288.938]
 [1529552.4   1466477.8     51272.31 ]
 [1531608.9   1468432.5     51389.453]
 [1529679.2   1466598.      51271.816]
 [1529510.4   1466437.1     51267.277]
 [1531926.    1468735.9     51406.277]
 [1529213.1   1466153.1     51250.47 ]
 [1529175.5   1466111.1     51226.37 ]
 [1529424.9   1466356.1     51264.93 ]
 [1531726.    1468537.9     51373.17 ]
 [1531182.4   1468027.1     51378.06 ]
 [1532010.6   1468815.6     51411.83 ]
 [1531546.9   1468376.6     51398.44 ]
 [1531727.9   1468543.2     51385.023]
 [1447227.9   1387525.2     48541.914]
 [1447429.9   1387724.8     48575.203]
 [1447145.4   1387455.6     48571.164]
 [1444912.    1385329.5     48432.594]
 [1446700.2   1387032.      48556.8  ]
 [1446735.2   1387061.6     48540.684]
 [1445760.2   1386125.1     48466.57 ]]# как обучить нейроную сеть близку к результату y_train ?

编码：

x_train,y_train = np.load('datsx.npy'),np.load('datsy.npy')
wb = None
from keras.models import Sequential
from keras.layers import Dense
from keras.models import load_model
import h5py

# Среднее значение
mean = x_train.mean(axis=0)
# Стандартное отклонение
std = x_train.std(axis=0)
x_train -= mean
x_train /= std

model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(x_train.shape[1],)))#shape 1
model.add(Dense(100))
model.add(Dense(100))
model.add(Dense(3))
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
model.fit(x_train, y_train, epochs=100, batch_size=1, verbose=2)
x_test, y_test = importdata()
mse, mae = model.evaluate(x_test, y_test, verbose=0)
print("Средняя абсолютная ошибка (тысяч долларов):", mae)
pred = model.predict(x_test)

print("Предсказанная стоимость:", pred*-1, ", правильная стоимость:", y_test)
python
Martin Hope
tormozzzz
Asked: 2020-08-19 03:01:33 +0800 CST
  • 2
import numpy as np
x_train,y_train = np.load('datsx.npy'),np.load('datsy.npy')
wb = None
from keras.models import Sequential
from keras.layers import Dense

# Среднее значение
mean = x_train.mean(axis=0)
# Стандартное отклонение
std = x_train.std(axis=0)
x_train -= mean

x_train /= std
model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(x_train.shape[1],)))#shape 1
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
model.fit(x_train, y_train, epochs=100, batch_size=1, verbose=2)

检查目标时出错：预期 dense_2 的形状为 (1,) 但得到的数组的形状为 (3,)

如何修复这里的错误？

样本数据：

x_train = array(
    [[1.3590000e+03, 1.3180000e+03, 1.7082020e+07, 1.2000000e+03],
     [4.0380000e+03, 4.6170000e+03, 1.7082020e+07, 1.2000000e+03], [2.6300000e+03,
      3.9840000e+03, 1.7082020e+07, 1.0540000e+03], [3.4460000e+03, 4.5310000e+03, 
      1.8102014e+07, 2.1610000e+03], [9.1500000e+02, 4.5310000e+03, 1.8102014e+07,
      2.1610000e+03], [3.4460000e+03, 4.4570000e+03, 1.8102014e+07, 2.1610000e+03]]) 

y_train = array(
    [[ 1., 2., 2.], [ 1., 2., 2.], [ 1., 2., 2.], 
     [16., 2., 1.], [16., 4., 1.], [16., 0., 1.]])
python