09-10 Tensorflow2.x模型的输入和输出

1.多输入

此外，训练、验证和测试所有的输入都需要按照模型中的格式进行输入

'''2.输入数据划分'''
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:]
'''3.多输入模型建立'''
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])

完整的代码

#-*- coding:utf-8 _*-
import os
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

# 输出库的名字和版本
print(sys.version_info)
for module in tf, mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)
# 指定GPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=config)

'''1.数据引入及数据集分类'''
from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()
# print(housing.DESCR)
# print(housing.data.shape)
# print(housing.target.shape)
# import pprint
# pprint.pprint(housing.data[0:5])
# pprint.pprint(housing.target[0:5])

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)

'''2.数据归一化'''
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)

'''3.model建立'''
# 将数据进行划分
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:]


# 多输入模型建立
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])
'''4.模型编译'''
model.summary()
model.compile(loss="mean_squared_error", optimizer="sgd")
callbacks = [keras.callbacks.EarlyStopping(patience=5, min_delta=1e-2)]

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

'''6.绘制曲线图'''
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)

'''7.模型测试'''
model.evaluate([x_test_scaled_wide,x_test_scaled_deep], y_test)

2.多输出

训练、验证和测试的所有输出都需要按照模型中定义的格式进行输出

'''3.model建立'''
# 将数据进行划分
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:]

# 多输出模型建立
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])

完整的代码

#-*- coding:utf-8 _*-
import os
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

# 输出库的名字和版本
print(sys.version_info)
for module in tf, mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)
# 指定GPU
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '1'
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.compat.v1.Session(config=config)

'''1.数据引入及数据集分类'''
from sklearn.datasets import fetch_california_housing
housing = fetch_california_housing()
# print(housing.DESCR)
# print(housing.data.shape)
# print(housing.target.shape)
#
# import pprint
# pprint.pprint(housing.data[0:5])
# pprint.pprint(housing.target[0:5])

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)

'''2.数据归一化'''
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)

'''3.model建立'''
# 将数据进行划分
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:]

# 多输出模型建立
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])

'''4.模型编译'''
model.summary()
model.compile(loss="mean_squared_error", optimizer="sgd")
callbacks = [keras.callbacks.EarlyStopping(patience=5, min_delta=1e-2)]

'''5.模型训练'''
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)

'''6.绘制曲线图'''
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)

'''7.模型测试'''
model.evaluate([x_test_scaled_wide,x_test_scaled_deep], [y_test,y_test])