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)
我们从https://www.sravni.ru/bank/gazprombank/debetovaja-karta/visagold/umnaya/?currency=RUB
拿一张桌子
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..
以及如何使用空格？
<td><div>текст1</div><div>текст2</div><div>текст3</div></td>
像//*[text()[contains(.,"текст2")]]/position()# 应该是 2 - 有没有办法返回元素的位置？
您需要选择带有“更多”字样的元素，其中没有“更多”字样，它们位于块 //*[@data-test="grid-row"] 上。应该写什么xpath？
带有“详细信息”和“更多”元素的元素代码：
带有“详细信息”元素且没有“更多”的元素代码：
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)
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.]])