文章目录
- 基础MLP网络
- 1.回归任务
- 2.分类任务
- mlp及深度学习常见技巧
- 1.基础模型
- 2.权重初始化
- 3.激活函数
- 4.优化器
- 5.批正则化
- 6.dropout
基础MLP网络
1.回归任务
import tensorflow as tf
import tensorflow.keras as keras
import tensorflow.keras.layers as layers# 导入数据
(x_train, y_train), (x_test, y_test) = keras.datasets.boston_housing.load_data()
print(x_train.shape, ' ', y_train.shape)
print(x_test.shape, ' ', y_test.shape)#%%# 构建模型model = keras.Sequential([layers.Dense(32, activation='sigmoid', input_shape=(13,)),la
概率的基本概念)
(单表与多表))
