16年发布，用于分类和回归
应用到了Google Play中的应用推荐
○
■ 离散值特征
■ One-hot表示
■ Eg：专业={计算机，人文，其他}.人文=[0,1,0]
■ 叉乘之后
● 稀疏特征做叉乘获取共现信息
● 实现记忆的效果
○ 稀疏特征——优缺点
○ 优点
■ 有效，广泛用于工业界
○ 缺点
■ 需要人工设计
■ 可能过拟合，所有特征都叉乘，相当于记住每一个样本
■ 泛化能力差，没出现就不会起效果
○ 密集特征
○ 向量表达
○ Word2vec工具
■ 男-女 = 国王 - 王后
○ 密集特征——优缺点
○ 优点
■ 带有语义信息，不同向量之间有相关性
■ 兼容出现过的特征组合
■ 更少人工参与
○ 缺点
■ 过度泛化推荐不怎么相关的产品
wide&deep模型的构建
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
print(module.__name__, module.__version__)
2.6.2 sys.version_info(major=3, minor=6, micro=8, releaselevel='final', serial=0) matplotlib 3.3.4 numpy 1.19.5 pandas 1.1.5 sklearn 0.24.2 tensorflow 2.6.2 keras.api._v2.keras 2.6.0
from sklearn.datasets import fetch_california_housing housing = fetch_california_housing() print(housing.DESCR) print(housing.data.shape) print(housing.target.shape)
.. _california_housing_dataset:
California Housing dataset
--------------------------
**Data Set Characteristics:**
:Number of Instances: 20640
:Number of Attributes: 8 numeric, predictive attributes and the target
:Attribute Information:
- MedInc median income in block
- HouseAge median house age in block
- AveRooms average number of rooms
- AveBedrms average number of bedrooms
- Population block population
- AveOccup average house occupancy
- Latitude house block latitude
- Longitude house block longitude
:Missing Attribute Values: None
This dataset was obtained from the StatLib repository.
http://lib.stat.cmu.edu/datasets/
The target variable is the median house value for California districts.
This dataset was derived from the 1990 U.S. census, using one row per census
block group. A block group is the smallest geographical unit for which the U.S.
Census Bureau publishes sample data (a block group typically has a population
of 600 to 3,000 people).
It can be downloaded/loaded using the
:func:`sklearn.datasets.fetch_california_housing` function.
.. topic:: References
- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
Statistics and Probability Letters, 33 (1997) 291-297
(20640, 8)
(20640,)
from sklearn.model_selection import train_test_split
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)
print(x_train.shape, y_train.shape)
print(x_valid.shape, y_valid.shape)
print(x_test.shape, y_test.shape)
(11610, 8) (11610,) (3870, 8) (3870,) (5160, 8) (5160,)
from sklearn.preprocessing import StandardScaler scaler = StandardScaler() x_train_scaled = scaler.fit_transform(x_train) x_valid_scaled = scaler.transform(x_valid) x_test_scaled = scaler.transform(x_test)
使用函数式API构建wide&deep模型
# 函数式API 功能API:
input = keras.layers.Input(shape=x_train.shape[1:])
hidden1 = keras.layers.Dense(30, activation='relu')(input)
hidden2 = keras.layers.Dense(30, activation='relu')(hidden1)
# 复合函数: f(x) = h(g(x))
concat = keras.layers.concatenate([input, hidden2])
output = keras.layers.Dense(1)(concat)
model = keras.models.Model(inputs = [input],
outputs = [output])
model.summary()
model.compile(loss="mean_squared_error",
optimizer = keras.optimizers.SGD(0.001))
callbacks = [keras.callbacks.EarlyStopping(
patience=5, min_delta=1e-2)]
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 8)] 0
__________________________________________________________________________________________________
dense_3 (Dense) (None, 30) 270 input_2[0][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 30) 930 dense_3[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 38) 0 input_2[0][0]
dense_4[0][0]
__________________________________________________________________________________________________
dense_5 (Dense) (None, 1) 39 concatenate_1[0][0]
==================================================================================================
Total params: 1,239
Trainable params: 1,239
Non-trainable params: 0
__________________________________________________________________________________________________
history = model.fit(x_train_scaled, y_train,
validation_data = (x_valid_scaled, y_valid),
epochs = 100,
callbacks = callbacks)
Epoch 1/100 363/363 [==============================] - 1s 2ms/step - loss: 1.5109 - val_loss: 0.7195 Epoch 2/100 363/363 [==============================] - 0s 1ms/step - loss: 0.6312 - val_loss: 0.6640 Epoch 3/100 363/363 [==============================] - 1s 2ms/step - loss: 0.5953 - val_loss: 0.6301 Epoch 4/100 363/363 [==============================] - 0s 1ms/step - loss: 0.5695 - val_loss: 0.6055 Epoch 5/100 363/363 [==============================] - 0s 1ms/step - loss: 0.5512 - val_loss: 0.5858 Epoch 6/100 363/363 [==============================] - 0s 1ms/step - loss: 0.5344 - val_loss: 0.5675 Epoch 7/100 363/363 [==============================] - 0s 1ms/step - loss: 0.5214 - val_loss: 0.5529 Epoch 8/100 363/363 [==============================] - 0s 1ms/step - loss: 0.5102 - val_loss: 0.5443 Epoch 9/100 363/363 [==============================] - 0s 1ms/step - loss: 0.5007 - val_loss: 0.5344 Epoch 10/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4927 - val_loss: 0.5248 Epoch 11/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4852 - val_loss: 0.5184 Epoch 12/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4787 - val_loss: 0.5112 Epoch 13/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4732 - val_loss: 0.5046 Epoch 14/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4683 - val_loss: 0.4985 Epoch 15/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4631 - val_loss: 0.4926 Epoch 16/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4582 - val_loss: 0.4878 Epoch 17/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4539 - val_loss: 0.4827 Epoch 18/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4493 - val_loss: 0.4786 Epoch 19/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4457 - val_loss: 0.4743 Epoch 20/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4413 - val_loss: 0.4718 Epoch 21/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4378 - val_loss: 0.4655 Epoch 22/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4337 - val_loss: 0.4631 Epoch 23/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4302 - val_loss: 0.4578 Epoch 24/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4268 - val_loss: 0.4562 Epoch 25/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4232 - val_loss: 0.4499 Epoch 26/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4203 - val_loss: 0.4481 Epoch 27/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4178 - val_loss: 0.4457 Epoch 28/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4147 - val_loss: 0.4424 Epoch 29/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4121 - val_loss: 0.4382 Epoch 30/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4090 - val_loss: 0.4349 Epoch 31/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4064 - val_loss: 0.4329 Epoch 32/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4043 - val_loss: 0.4284 Epoch 33/100 363/363 [==============================] - 0s 1ms/step - loss: 0.4015 - val_loss: 0.4267 Epoch 34/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3990 - val_loss: 0.4218 Epoch 35/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3970 - val_loss: 0.4205 Epoch 36/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3952 - val_loss: 0.4189 Epoch 37/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3931 - val_loss: 0.4177 Epoch 38/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3910 - val_loss: 0.4142 Epoch 39/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3884 - val_loss: 0.4104 Epoch 40/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3865 - val_loss: 0.4088 Epoch 41/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3849 - val_loss: 0.4073 Epoch 42/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3835 - val_loss: 0.4053 Epoch 43/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3815 - val_loss: 0.4023 Epoch 44/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3801 - val_loss: 0.4029 Epoch 45/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3777 - val_loss: 0.3984 Epoch 46/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3768 - val_loss: 0.3971 Epoch 47/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3750 - val_loss: 0.3959 Epoch 48/100 363/363 [==============================] - 0s 1ms/step - loss: 0.3732 - val_loss: 0.3932
def plot_learning_curves(history):
pd.DataFrame(history.history).plot(figsize=(8, 5))
plt.grid(True)
plt.gca().set_ylim(0, 1)
plt.show()
plot_learning_curves(history)
model.evaluate(x_test_scaled, y_test, verbose=0)
0.39927732944488525
使用子类API构建模型
# 子类API
class WideDeepModel(keras.models.Model):
def __init__(self):
super(WideDeepModel, self).__init__()
"""定义模型的层次"""
self.hidden1_layer = keras.layers.Dense(30, activation='relu')
self.hidden2_layer = keras.layers.Dense(30, activation='relu')
self.output_layer = keras.layers.Dense(1)
def call(self, input):
"""完成模型的正向计算"""
hidden1 = self.hidden1_layer(input)
hidden2 = self.hidden2_layer(hidden1)
concat = keras.layers.concatenate([input, hidden2])
output = self.output_layer(concat)
return output
# model = WideDeepModel()
model = keras.models.Sequential([
WideDeepModel(),
])
model.build(input_shape=(None, 8))
model.summary()
model.compile(loss="mean_squared_error",
optimizer = keras.optimizers.SGD(0.001))
callbacks = [keras.callbacks.EarlyStopping(
patience=5, min_delta=1e-2)]
使用Sequential构建会隐藏layer的具体方式，直接使用会将模型的各层打印出来
Model: "sequential_1" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= wide_deep_model_2 (WideDeepM (None, 1) 1239 ================================================================= Total params: 1,239 Trainable params: 1,239 Non-trainable params: 0 _________________________________________________________________
多输入的模型构建
# 多输入
input_wide = keras.layers.Input(shape=[5])
input_deep = keras.layers.Input(shape=[6])
hidden1 = keras.layers.Dense(30, activation='relu')(input_deep)
hidden2 = keras.layers.Dense(30, activation='relu')(hidden1)
concat = keras.layers.concatenate([input_wide, hidden2])
output = keras.layers.Dense(1)(concat)
model = keras.models.Model(inputs = [input_wide, input_deep],
outputs = [output])
model.compile(loss="mean_squared_error", optimizer="sgd")
callbacks = [keras.callbacks.EarlyStopping(
patience=5, min_delta=1e-2)]
model.summary()
多输出模型的构建
# 多输出
input_wide = keras.layers.Input(shape=[5])
input_deep = keras.layers.Input(shape=[6])
hidden1 = keras.layers.Dense(30, activation='relu')(input_deep)
hidden2 = keras.layers.Dense(30, activation='relu')(hidden1)
concat = keras.layers.concatenate([input_wide, hidden2])
output = keras.layers.Dense(1)(concat)
output2 = keras.layers.Dense(1)(hidden2)
model = keras.models.Model(inputs = [input_wide, input_deep],
outputs = [output, output2])
model.compile(loss="mean_squared_error", optimizer="sgd")
callbacks = [keras.callbacks.EarlyStopping(
patience=5, min_delta=1e-2)]
model.summary()
