代码路径：https://github.com/lilihongjava/leeblog_python/tree/master/tensorflow_logistic_regression
数据集iris.csv，sklearn 鸢尾花数据集
二分类
这里用二元的交叉熵作为二分类的损失函数，激活函数为sigmoid
代码语言：javascript复制 layer0 = tf.keras.layers.Dense(1, input_shape=(x_data.shape[1],), activation='sigmoid')
model = tf.keras.Sequential([layer0])
model.compile(loss='binary_crossentropy', optimizer='adam') 多分类
categorical_crossentropy：多类的对数损失,它是一个多分类损失函数，可以配合着softmax一起使用。
代码语言：javascript复制 layer0 = tf.keras.layers.Dense(class_num, input_shape=(x_data.shape[1],), activation='softmax')
model = tf.keras.Sequential([layer0])
model.compile(loss='categorical_crossentropy', optimizer='adam')整体代码
代码语言：javascript复制# encoding: utf-8
"""
@author: lee
@time: 2020/6/24 16:35
@file: main.py
@desc:
"""
import pandas as pd
import tensorflow as tf
from tensorflow.python.keras.utils.np_utils import to_categorical
from util.common_util import arg_check_transformation, multiple_gpu_strategy
def model_builder(x_data, class_num):
if class_num == 2: # 逻辑回归二分类
layer0 = tf.keras.layers.Dense(1, input_shape=(x_data.shape[1],), activation='sigmoid')
model = tf.keras.Sequential([layer0])
model.compile(loss='binary_crossentropy', optimizer='adam') # 这里用二元的交叉熵作为二分类的损失函数
else: # 多分类
layer0 = tf.keras.layers.Dense(class_num, input_shape=(x_data.shape[1],), activation='softmax')
model = tf.keras.Sequential([layer0])
model.compile(loss='categorical_crossentropy', optimizer='adam')
return model
def tf_logistic_regression(feature_column=None, label_column=None, class_num=None, gpu=None, input1=None,
output1=None):
print("输入参数：", locals())
feature_column = arg_check_transformation("list_name_str", "feature_column", feature_column)
label_column = arg_check_transformation("list_name_str", "label_column", label_column)
class_num = arg_check_transformation("int", "class_num", class_num)
df = pd.read_csv(input1)
try:
x_data = df[feature_column]
y_data = df[label_column]
if class_num != 2 and y_data.shape[1] == 1:
y_data = to_categorical(y_data) # 一维的分类转成多列
y_data = pd.DataFrame(y_data)
if gpu:
dataset, BATCH_SIZE, strategy = multiple_gpu_strategy(x_data, y_data)
with strategy.scope():
model = model_builder(x_data, class_num)
model.fit(dataset.batch(BATCH_SIZE), verbose=False)
else:
model = model_builder(x_data, class_num)
model.fit(x_data, y_data, epochs=1000)
except Exception:
raise Exception("模型训练错误")
print("模型训练完成")
if output1:
model.save(output1)
if __name__ == '__main__':
# 二分类
tf_logistic_regression(feature_column="sepal length (cm),sepal width (cm),petal length (cm),petal width (cm)",
label_column="target", class_num=2,
gpu=False, input1="./data/iris_two.csv", output1="./data/output.h5")
# 多分类
tf_logistic_regression(feature_column="sepal length (cm),sepal width (cm),petal length (cm),petal width (cm)",
label_column="target", class_num=3,
gpu=False, input1="./data/iris.csv", output1="./data/output.h5")
