activity_regularizer=l1_regularizer(0.01)在tensorflow2怎么改

在TensorFlow 2中，使用`tf.keras.regularizers`模块来设置正则化器。要使用L1正则化器，可以使用`tf.keras.regularizers.l1`，并将其作为参数传递给相应的层。例如，如果要在全连接层中应用L1正则化器，则可以按照以下方式编写代码：

from tensorflow.keras.layers import Dense
from tensorflow.keras.regularizers import l1

model = tf.keras.Sequential([
    Dense(64, activation='relu', kernel_regularizer=l1(0.01)),
    Dense(10, activation='softmax')
])

在这个例子中，我们在第一层中应用了一个L1正则化器，其强度为0.01。您可以根据需要调整正则化器的强度。

相关问题

代码：import os import pandas as pd folder_path = "D:/活动生成" file_name = "活动.xlsx" new_file_name = "一口价3.xlsx" activity_file_path = os.path.join(folder_path, file_name) activity_df = pd.read_excel(activity_file_path) discounts = {"6.9折": 0.69, "5折": 0.5, "3.9折": 0.39} new_price_df = pd.DataFrame(columns=["商品编码", "价格"]) for index, row in activity_df.iterrows(): if row["活动"] in discounts: standard_price = row["标准售价"] discount = discounts.get(row["活动"], 1) new_price = round(standard_price * discount, 2) # only save the 7-digit 商品编码 product_code = str(row["商品编码"]) if len(product_code) == 7: new_price_df.loc[index] = [product_code, new_price] new_file_path = os.path.join(folder_path, new_file_name) new_price_df.to_excel(new_file_path, index=False) print("已生成") 没法保存商品编码为0开头的商品编码，例如商品编码为：0311069的商品编码不能保存，我需要将0开头的商品编码能进行计算的同时也保存下来

可以将商品编码转换为字符串后，判断字符串长度是否为7位，如果不足7位，在前面添加相应数量的0，使其长度为7位。修改后的代码如下：

import os
import pandas as pd

folder_path = "D:/活动生成"
file_name = "活动.xlsx"
new_file_name = "一口价3.xlsx"

activity_file_path = os.path.join(folder_path, file_name)
activity_df = pd.read_excel(activity_file_path)

discounts = {"6.9折": 0.69, "5折": 0.5, "3.9折": 0.39}

new_price_df = pd.DataFrame(columns=["商品编码", "价格"])

for index, row in activity_df.iterrows():
    if row["活动"] in discounts:
        standard_price = row["标准售价"]
        discount = discounts.get(row["活动"], 1)
        new_price = round(standard_price * discount, 2)

        # convert product code to 7 digits
        product_code = str(row["商品编码"]).zfill(7)

        new_price_df.loc[index] = [product_code, new_price]

new_file_path = os.path.join(folder_path, new_file_name)
new_price_df.to_excel(new_file_path, index=False)

print("已生成")

这样就可以保存商品编码为0开头的商品编码了。

代码：import os import pandas as pd print("开始计算一口价") folder_path = "D:/一口价生成" file_name = "活动.xlsx" new_file_name = "一口价及升价3.xlsx" activity_file_path = os.path.join(folder_path, file_name) activity_df = pd.read_excel(activity_file_path) discounts = {"6.9折": 0.69, "5折": 0.5, "3.9折": 0.39} new_price_df = pd.DataFrame(columns=["商品编码", "价格"]) for index, row in activity_df.iterrows(): if row["活动"] in discounts: standard_price = row["标准售价"] discount = discounts.get(row["活动"], 1) new_price = round(standard_price * discount, 2) product_code = str(row["商品编码"]).zfill(7) new_price_df.loc[index] = [product_code, new_price] elif isinstance(row["活动"], int) or isinstance(row["活动"], float): product_code = str(row["商品编码"]).zfill(7) new_price = int(row["活动"]) new_price_df.loc[index] = [product_code, new_price] new_file_path = os.path.join(folder_path, new_file_name) new_price_df.to_excel(new_file_path, index=False) new_file_path = os.path.join(folder_path, new_file_name) new_price_df.to_excel(new_file_path, index=False) print("已生成") 将“活动”列中所有数值都以int类型输出了，请修改代码输出类型位：如果是int类型，则输出int类型。是float类型，就输出float类型。，

代码：

import os
import pandas as pd

print("开始计算一口价")

folder_path = "D:/一口价生成"
file_name = "活动.xlsx"
new_file_name = "一口价及升价3.xlsx"

activity_file_path = os.path.join(folder_path, file_name)
activity_df = pd.read_excel(activity_file_path)

discounts = {"6.9折": 0.69, "5折": 0.5, "3.9折": 0.39}

new_price_df = pd.DataFrame(columns=["商品编码", "价格"])

for index, row in activity_df.iterrows():
if row["活动"] in discounts:
standard_price = row["标准售价"]
discount = discounts.get(row["活动"], 1)
new_price = round(standard_price * discount, 2)
product_code = str(row["商品编码"]).zfill(7)
new_price_df.loc[index] = [product_code, new_price]
elif isinstance(row["活动"], int):
product_code = str(row["商品编码"]).zfill(7)
new_price = int(row["活动"])
new_price_df.loc[index] = [product_code, new_price]
elif isinstance(row["活动"], float):
product_code = str(row["商品编码"]).zfill(7)
new_price = float(row["活动"])
new_price_df.loc[index] = [product_code, new_price]

new_file_path = os.path.join(folder_path, new_file_name)
new_price_df.to_excel(new_file_path, index=False)

print("已生成")

修改后的代码中，增加了判断“活动”列中是否为float类型的判断条件，并针对不同类型输出对应类型的数据。