Keras(五)wide_deep模型

其他 2021-03-03 01:55:18 阅读次数: 0

本文将介绍：

  • wide_deep模型
  • 函数API实现wide&deep模型
  • 子类API实现wide&deep模型
  • wide&deep模型的多输入
  • wide&deep模型的多输出

一，wide_deep模型简介

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详解 Wide&Deep 推荐框架

二，函数API实现wide&deep模型

1，函数API实现wide&deep代码如下
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))
2，总结代码如下
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras

# 1,打印使用的python库的版本信息
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)
    
# 2,下载并使用sklearn中的“fetch_california_housing”数据集
from sklearn.datasets import fetch_california_housing

housing = fetch_california_housing()
print(housing.DESCR)
print(housing.data.shape)
print(housing.target.shape)

# 3,拆分数据集中的数据为 训练数据、验证数据、测试数据
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)

# 4,在将数据带入到模型之前，先进行预处理-训练、验证、测试数据标准化
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)

# 5,构建wide_deep回归模型、模型层级图、编译模型（添加损失函数、优化器）、添加回调函数
# 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))

# 定义 callbacks 
logdir = './dnn-bn-callbacks'
if not os.path.exists(logdir):
    os.mkdir(logdir)
output_model_file = os.path.join(logdir,"fashion_mnist_model.h5")
callbacks = [
    keras.callbacks.TensorBoard(logdir),
    keras.callbacks.ModelCheckpoint(output_model_file,save_best_only = True),
    keras.callbacks.EarlyStopping(patience=5, min_delta=1e-3)]

# 6,训练构建的模型
history = model.fit(x_train_scaled, y_train,
                    validation_data = (x_valid_scaled, y_valid),
                    epochs = 200,
                    callbacks = callbacks) 

# 7,得到训练曲线图
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)


# 8,调用估计器
print(model.evaluate(x_test_scaled, y_test, verbose=0))

三，子类API实现wide&deep模型

1，子类API实现wide&deep代码如下
# 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)]
2，总结代码如下
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras

# 1,打印使用的python库的版本信息
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)
    
# 2,下载并使用sklearn中的“fetch_california_housing”数据集
from sklearn.datasets import fetch_california_housing

housing = fetch_california_housing()
print(housing.DESCR)
print(housing.data.shape)
print(housing.target.shape)

# 3,拆分数据集中的数据为 训练数据、验证数据、测试数据
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)

# 4,在将数据带入到模型之前，先进行预处理-训练、验证、测试数据标准化
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)

# 5,构建wide_deep 子类API 回归模型、模型层级图、编译模型（添加损失函数、优化器）、添加回调函数
# 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(0.002))
callbacks = [keras.callbacks.EarlyStopping(
    patience=5, min_delta=1e-4)]

# 6,训练构建的模型
history = model.fit(x_train_scaled, y_train,
                    validation_data = (x_valid_scaled, y_valid),
                    epochs = 200,
                    callbacks = callbacks)

# 7,得到训练曲线图
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)

# 8,调用估计器
model.evaluate(x_test_scaled, y_test, verbose=0)

四，wide&deep模型的多输入

1，实现多输入的代码如下
# 5,构建wide_deep 子类API 回归模型、模型层级图、编译模型（添加损失函数、优化器）、添加回调函数
# 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-3)]
model.summary()

# 6,训练构建的模型
x_train_scaled_wide = x_train_scaled[:, :5]
x_train_scaled_deep = x_train_scaled[:, 2:]
x_valid_scaled_wide = x_valid_scaled[:, :5]
x_valid_scaled_deep = x_valid_scaled[:, 2:]
x_test_scaled_wide = x_test_scaled[:, :5]
x_test_scaled_deep = x_test_scaled[:, 2:]

history = model.fit(x = [x_train_scaled_wide, x_train_scaled_deep],
                    y = y_train,
                    validation_data = (
                        [x_valid_scaled_wide, x_valid_scaled_deep],
                        y_valid),
                    epochs = 100,
                    callbacks = callbacks)
2，总结代码如下
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras

# 1,打印使用的python库的版本信息
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)
    
# 2,下载并使用sklearn中的“fetch_california_housing”数据集
from sklearn.datasets import fetch_california_housing

housing = fetch_california_housing()
print(housing.DESCR)
print(housing.data.shape)
print(housing.target.shape)

# 3,拆分数据集中的数据为 训练数据、验证数据、测试数据
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)

# 4,在将数据带入到模型之前，先进行预处理-训练、验证、测试数据标准化
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)

# 5,构建wide_deep 子类API 回归模型、模型层级图、编译模型（添加损失函数、优化器）、添加回调函数
# 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-3)]
model.summary()

# 6,训练构建的模型
x_train_scaled_wide = x_train_scaled[:, :5]
x_train_scaled_deep = x_train_scaled[:, 2:]
x_valid_scaled_wide = x_valid_scaled[:, :5]
x_valid_scaled_deep = x_valid_scaled[:, 2:]
x_test_scaled_wide = x_test_scaled[:, :5]
x_test_scaled_deep = x_test_scaled[:, 2:]

history = model.fit(x = [x_train_scaled_wide, x_train_scaled_deep],
                    y = y_train,
                    validation_data = (
                        [x_valid_scaled_wide, x_valid_scaled_deep],
                        y_valid),
                    epochs = 100,
                    callbacks = callbacks)

# 7,得到训练曲线图
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)

# 8,调用估计器
print(model.evaluate([x_test_scaled_wide, x_test_scaled_deep], y_test, verbose=0))

五，wide&deep模型的多输出

1，实现多输出的代码如下
# 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])
2，总结代码如下
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import sklearn
import pandas as pd
import os
import sys
import time
import tensorflow as tf
from tensorflow import keras

# 1,打印使用的python库的版本信息
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)
    
# 2,下载并使用sklearn中的“fetch_california_housing”数据集
from sklearn.datasets import fetch_california_housing

housing = fetch_california_housing()
print(housing.DESCR)
print(housing.data.shape)
print(housing.target.shape)

# 3,拆分数据集中的数据为 训练数据、验证数据、测试数据
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)

# 4,在将数据带入到模型之前，先进行预处理-训练、验证、测试数据标准化
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)


# 5,构建wide_deep 子类API 回归模型、模型层级图、编译模型（添加损失函数、优化器）、添加回调函数
# 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()


# 6,训练构建的模型
x_train_scaled_wide = x_train_scaled[:, :5]
x_train_scaled_deep = x_train_scaled[:, 2:]
x_valid_scaled_wide = x_valid_scaled[:, :5]
x_valid_scaled_deep = x_valid_scaled[:, 2:]
x_test_scaled_wide = x_test_scaled[:, :5]
x_test_scaled_deep = x_test_scaled[:, 2:]

history = model.fit([x_train_scaled_wide, x_train_scaled_deep],
                    [y_train, y_train],
                    validation_data = (
                        [x_valid_scaled_wide, x_valid_scaled_deep],
                        [y_valid, y_valid]),
                    epochs = 100,
                    callbacks = callbacks)


# 7,得到训练曲线图
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)


# 8,调用估计器
print(model.evaluate([x_test_scaled_wide, x_test_scaled_deep],
               [y_test, y_test], verbose=0))

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