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
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。
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 # encoding: utf-8"""@author: lee@time: 2020/6/24 16:35@file: main.py@desc: """import pandas as pdimport tensorflow as tffrom tensorflow.python.keras.utils.np_utils import to_categoricalfrom util.common_util import arg_check_transformation, multiple_gpu_strategydef 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 modeldef 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")