[tensorflow2.0]06.BatchNormallization_batchnormallization需要导入什么库-优快云博客

本文通过使用深度学习技术，构建了一个预测加州房价的模型。利用Keras库搭建神经网络，采用批量归一化和ReLU激活函数进行训练，通过EarlyStopping回调函数防止过拟合。展示了模型训练过程的学习曲线，并最终评估了模型在测试集上的表现。

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import matplotlib as mpl
import matplotlib.pyplot as plt

import numpy as np
import pandas as pd
import sklearn
import os
import sys
import time
import tensorflow as tf
import pprint

from tensorflow import keras

print('Tensorflows Version:{}'.format(tf.__version__))
# print('Is gpu available:{}'.format(tf.test.is_gpu_available()))
print(sys.version_info)
for module in mpl, np, pd, sklearn, tf, keras:
    print(module.__name__, module.__version__)


from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

housing = fetch_california_housing()
# print(housing.DESCR)
# print(housing.data.shape)
# print(housing.target.shape)
# pprint.pprint(housing.data[0:5])
# pprint.pprint(housing.target[0:5])

x_train_all, x_test, y_train_all, y_test = train_test_split(
    housing.data, housing.target, random_state=7, test_size=0.25)
x_train, x_vaild, y_train, y_vaild = train_test_split(
    x_train_all, y_train_all, random_state=7, test_size=0.25)

scaler = StandardScaler()
x_train_scaler = scaler.fit_transform(x_train)
x_vaild_scaler = scaler.transform(x_vaild)
x_test_scaler = scaler.transform(x_test)

model = keras.models.Sequential()
model.add(keras.layers.Dense(100, input_shape=x_train.shape[1:]))
for _ in range(20):
    model.add(keras.layers.Dense(100, activation='relu'))
    model.add(keras.layers.BatchNormalization())
    '''
    model.add(keras.layers.Dense(100))
    model.add(keras.layers.BatchNormalization())
    model.add(keras.layers.Activation('relu'))
    '''
model.add(keras.layers.Dense(1))

model.summary()
model.compile(optimizer='adam',
              loss=keras.losses.mean_absolute_error)

callbacks = [keras.callbacks.EarlyStopping(patience=5, min_delta=1e-3)]

history = model.fit(x_train_scaler, y_train,
                    epochs=100,
                    validation_data=(x_vaild_scaler, y_vaild),
                    callbacks=callbacks)

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)

print('model.evaluate==================')
model.evaluate(x_test_scaler, y_test)