哪个网站是用vue做的,荆州市城市建设投资开发有限公司网站,php仿博客园网站,推广兼职知识要点 fetch_california_housing#xff1a;加利福尼亚的房价数据#xff0c;总计20640个样本#xff0c;每个样本8个属性表示#xff0c;以及房价作为target 超参数搜索的方式: 网格搜索, 随机搜索, 遗传算法搜索, 启发式搜索 超参数训练后用#xff1a; gv.estimat…知识要点 fetch_california_housing加利福尼亚的房价数据总计20640个样本每个样本8个属性表示以及房价作为target 超参数搜索的方式: 网格搜索, 随机搜索, 遗传算法搜索, 启发式搜索 超参数训练后用 gv.estimator调取最佳模型 函数式添加神经网络: model.add(keras.layers.Dense(layer_size, activation relu)) model.compile(loss mse, optimizer optimizer) # optimizer keras.optimizers.SGD (learning_rate) sklearn_model KerasRegressor(build_fn build_model)
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor # 回归神经网络
# 搜索最佳学习率
def build_model(hidden_layers 1, layer_size 30, learning_rate 3e-3):model keras.models.Sequential()model.add(keras.layers.Dense(layer_size, activation relu, input_shape x_train.shape[1:]))for _ in range(hidden_layers - 1):model.add(keras.layers.Dense(layer_size, activation relu))model.add(keras.layers.Dense(1))optimizer keras.optimizers.SGD(learning_rate)model.compile(loss mse, optimizer optimizer)# model.summary()return model
sklearn_model KerasRegressor(build_fn build_model) callbacks [keras.callbacks.EarlyStopping(patience 5, min_delta 1e-3)] # 回调函数设置 gv GridSearchCV(sklearn_model, param_grid params, n_jobs 1, cv 5,verbose 1) # 找最佳参数 gv.fit(x_train_scaled, y_train) 1 导包
from tensorflow import keras
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
cputf.config.list_physical_devices(CPU)
tf.config.set_visible_devices(cpu)
print(tf.config.list_logical_devices())
2 导入数据
from sklearn.model_selection import train_test_split
from sklearn.datasets import fetch_california_housinghousing fetch_california_housing()
x_train_all, x_test, y_train_all, y_test train_test_split(housing.data,housing.target,random_state 7)
x_train, x_valid, y_train, y_valid train_test_split(x_train_all, y_train_all,random_state 11)
3 标准化处理数据
from sklearn.preprocessing import StandardScaler, MinMaxScalerscaler StandardScaler()
x_train_scaled scaler.fit_transform(x_train)
x_valid_scaled scaler.transform(x_valid)
x_test_scaled scaler.transform(x_test)
4 函数式定义模型
from tensorflow.keras.wrappers.scikit_learn import KerasRegressor # 回归神经网络
# 搜索最佳学习率
def build_model(hidden_layers 1, layer_size 30, learning_rate 3e-3):model keras.models.Sequential()model.add(keras.layers.Dense(layer_size, activation relu, input_shape x_train.shape[1:]))for _ in range(hidden_layers - 1):model.add(keras.layers.Dense(layer_size, activation relu))model.add(keras.layers.Dense(1))optimizer keras.optimizers.SGD(learning_rate)model.compile(loss mse, optimizer optimizer)# model.summary()return model
sklearn_model KerasRegressor(build_fn build_model) 5 模型训练
callbacks [keras.callbacks.EarlyStopping(patience 5, min_delta 1e-3)]
history sklearn_model.fit(x_train_scaled, y_train, epochs 10,validation_data (x_valid_scaled, y_valid), callbacks callbacks) 6 超参数搜索
超参数搜索的方式: 网格搜索 定义n维方格 每个方格对应一组超参数 一组一组参数尝试 随机搜索 遗传算法搜索 对自然界的模拟 A: 初始化候选参数集合 -- 训练--- 得到模型指标作为生存概率 B: 选择 -- 交叉-- 变异 -- 产生下一代集合 C: 重新到A, 循环. 启发式搜索 研究热点-- AutoML的一部分 使用循环神经网络来生成参数 使用强化学习来进行反馈, 使用模型来训练生成参数.
# 使用sklearn 的网格搜索, 或者随机搜索
from sklearn.model_selection import GridSearchCV, RandomizedSearchCVparams {learning_rate : [1e-4, 3e-4, 1e-3, 3e-3, 1e-2, 3e-2],hidden_layers: [2, 3, 4, 5], layer_size: [20, 60, 100]}gv GridSearchCV(sklearn_model, param_grid params, n_jobs 1, cv 5,verbose 1)
gv.fit(x_train_scaled, y_train)
输出最佳参数
# 最佳得分
print(gv.best_score_) # -0.47164334654808043
# 最佳参数
print(gv.best_params_) # {hidden_layers: 5,layer_size: 100,learning_rate:0.01}
# 最佳模型
print(gv.estimator)
keras.wrappers.scikit_learn.KerasRegressor object at 0x0000025F5BB12220
gv.score 7 最佳参数建模
model keras.models.Sequential()
model.add(keras.layers.Dense(100, activation relu, input_shape x_train.shape[1:]))
for _ in range(4):model.add(keras.layers.Dense(100, activation relu))
model.add(keras.layers.Dense(1))
optimizer keras.optimizers.SGD(0.01)
model.compile(loss mse, optimizer optimizer)
model.summary() callbacks [keras.callbacks.EarlyStopping(patience 5, min_delta 1e-3)]
history model.fit(x_train_scaled, y_train, epochs 10,validation_data (x_valid_scaled, y_valid), callbacks callbacks) 8 手动实现超参数搜索
根据参数进行多次模型的训练, 然后记录 loss
# 搜索最佳学习率
learning_rates [1e-4, 3e-4, 1e-3, 3e-3, 1e-2, 3e-2]
histories []
for lr in learning_rates:model keras.models.Sequential([keras.layers.Dense(30, activation relu, input_shape x_train.shape[1:]),keras.layers.Dense(1)])optimizer keras.optimizers.SGD(lr)model.compile(loss mse, optimizer optimizer, metrics [mse])callbacks [keras.callbacks.EarlyStopping(patience 5, min_delta 1e-2)]history model.fit(x_train_scaled, y_train, validation_data (x_valid_scaled, y_valid), epochs 100, callbacks callbacks)histories.append(history) # 画图
import pandas as pd
def plot_learning_curves(history):pd.DataFrame(history.history).plot(figsize (8, 5))plt.grid(True)plt.gca().set_ylim(0, 1)plt.show()for lr, history in zip(learning_rates, histories): print(lr)plot_learning_curves(history)
