模型结构
函数式
关键代码
# 函数式API 功能API
input = tf.keras.layers.Input(shape=x_train.shape[1:])
hidden1 = tf.keras.layers.Dense(30, activation='relu')(input)
hidden2 = tf.keras.layers.Dense(30, activation='relu')(hidden1)
concat = tf.keras.layers.concatenate([input, hidden2])
output = tf.keras.layers.Dense(1)(concat)
model = tf.keras.models.Model(inputs=[input],
outputs=[output])
print(model.layers)
model.summary()
完整代码
import pprint
import sys
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, keras, tf:
print(module.__name__, module.__version__)
from sklearn.datasets import fetch_california_housing
# 1.加载数据集 波士顿房价预测
housing = fetch_california_housing()
print(housing.DESCR)
print(housing.data.shape)
print(housing.target.shape)
pprint.pprint(housing.data[:5])
pprint.pprint(housing.target[:5])
from sklearn.model_selection import train_test_split
# 2.拆分数据集
# 训练集与测试集拆分
x_train_all, x_test, y_train_all, y_test = train_test_split(housing.data,
housing.target,
random_state=7,
test_size=0.20)
# 训练集与验证集的拆分
x_train, x_valid, y_train, y_valid = train_test_split(
x_train_all, y_train_all, random_state=11, test_size=0.20)
print(x_train.shape, y_train.shape)
print(x_valid.shape, y_valid.shape)
print(x_test.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
# 3、数据预处理 数据集的归一化
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)
# 4、网络模型的搭建
# 函数式API 功能API
input = tf.keras.layers.Input(shape=x_train.shape[1:])
hidden1 = tf.keras.layers.Dense(30, activation='relu')(input)
hidden2 = tf.keras.layers.Dense(30, activation='relu')(hidden1)
concat = tf.keras.layers.concatenate([input, hidden2])
output = tf.keras.layers.Dense(1)(concat)
model = tf.keras.models.Model(inputs=[input],
outputs=[output])
print(model.layers)
model.summary()
# 5、模型的编译 设置损失函数 优化器
model.compile(loss='mean_squared_error',
optimizer='adam')
# 6、设置回调函数
callbacks = [tf.keras.callbacks.EarlyStopping(patience=5, min_delta=1e-3)]
# 7、训练网络
history = model.fit(x_train_scaled, y_train,
validation_data=(x_valid_scaled, y_valid),
epochs=10,
callbacks=callbacks)
# 8、绘制训练过程数据
def plot_learning_curves(hst):
pd.DataFrame(hst.history).plot()
plt.grid(True)
plt.gca().set_ylim(0, 1)
plt.show()
plot_learning_curves(history)
# 9.验证数据
model.evaluate(x_test_scaled, y_test)
子类式
关键代码
# 子类API
class WideDeepModel(tf.keras.models.Model):
def __init__(self):
super(WideDeepModel, self).__init__()
self.hidden1_layer = tf.keras.layers.Dense(30, activation='relu')
self.hidden2_layer = tf.keras.layers.Dense(30, activation='relu')
self.output_layer = tf.keras.layers.Dense(1)
def call(self, inputs, training=None, mask=None):
"""完成模型的正向计算"""
hidden1 = self.hidden1_layer(inputs)
hidden2 = self.hidden2_layer(hidden1)
concat = tf.keras.layers.concatenate([inputs, hidden2])
output = self.output_layer(concat)
return output
model = WideDeepModel()
model.build(input_shape=(None, 8))
print(model.layers)
model.summary()
完整代码
import pprint
import sys
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn
import tensorflow as tf
from tensorflow import keras
print(tf.__version__)
print(sys.version_info)
for module in mpl, np, pd, sklearn, keras, tf:
print(module.__name__, module.__version__)
from sklearn.datasets import fetch_california_housing
# 1.加载数据集 波士顿房价预测
housing = fetch_california_housing()
print(housing.DESCR)
print(housing.data.shape)
print(housing.target.shape)
pprint.pprint(housing.data[:5])
pprint.pprint(housing.target[:5])
from sklearn.model_selection import train_test_split
# 2.拆分数据集
# 训练集与测试集拆分
x_train_all, x_test, y_train_all, y_test = train_test_split(housing.data,
housing.target,
random_state=7,
test_size=0.20)
# 训练集与验证集的拆分
x_train, x_valid, y_train, y_valid = train_test_split(
x_train_all, y_train_all, random_state=11, test_size=0.20)
print(x_train.shape, y_train.shape)
print(x_valid.shape, y_valid.shape)
print(x_test.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
# 3、数据预处理 数据集的归一化
x_train_scaled = scaler.fit_transform(x_train)
x_valid_scaled = scaler.transform(x_valid)
x_test_scaled = scaler.transform(x_test)
# 4、网络模型的搭建
# 子类API
class WideDeepModel(tf.keras.models.Model):
def __init__(self):
super(WideDeepModel, self).__init__()
self.hidden1_layer = tf.keras.layers.Dense(30, activation='relu')
self.hidden2_layer = tf.keras.layers.Dense(30, activation='relu')
self.output_layer = tf.keras.layers.Dense(1)
def call(self, inputs, training=None, mask=None):
"""完成模型的正向计算"""
hidden1 = self.hidden1_layer(inputs)
hidden2 = self.hidden2_layer(hidden1)
concat = tf.keras.layers.concatenate([inputs, hidden2])
output = self.output_layer(concat)
return output
model = WideDeepModel()
model.build(input_shape=(None, 8))
print(model.layers)
model.summary()
# 5、模型的编译 设置损失函数 优化器
model.compile(loss='mean_squared_error',
optimizer='adam')
# 6、设置回调函数
callbacks = [tf.keras.callbacks.EarlyStopping(patience=5, min_delta=1e-3)]
# 7、训练网络
history = model.fit(x_train_scaled, y_train,
validation_data=(x_valid_scaled, y_valid),
epochs=10,
callbacks=callbacks)
# 8、绘制训练过程数据
def plot_learning_curves(hst):
pd.DataFrame(hst.history).plot()
plt.grid(True)
plt.gca().set_ylim(0, 1)
plt.show()
plot_learning_curves(history)
# 9.验证数据
model.evaluate(x_test_scaled, y_test)
函数式
关键代码
# 函数式API 功能API
input = tf.keras.la
