网站建设作业做一个简单的网站,搜索排名影响因素,产品推广软文500字,wordpress博客数据库#x1f9e1;#x1f49b;#x1f49a;TensorFlow2实战-系列教程 总目录 有任何问题欢迎在下面留言 本篇文章的代码运行界面均在Jupyter Notebook中进行 本篇文章配套的代码资源已经上传 1、环境测试
import tensorflow as tf
import numpy as np
tf.__version__打印结果 ‘…TensorFlow2实战-系列教程 总目录 有任何问题欢迎在下面留言 本篇文章的代码运行界面均在Jupyter Notebook中进行 本篇文章配套的代码资源已经上传 1、环境测试
import tensorflow as tf
import numpy as np
tf.__version__打印结果 ‘2.10.0’ x1 [[1,9],[3,6]]
x2 tf.constant(x1)
print(x1)
print(x2)打印结果 [[1, 9], [3, 6]] tf.Tensor( [[1 9] [3 6]], shape(2, 2), dtypeint32) 2、导包读数据
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers
import tensorflow.keras
import warnings
warnings.filterwarnings(ignore)
%matplotlib inlinefeatures pd.read_csv(temps.csv)
features.head()打印结果 year,moth,day,week分别表示的具体的时间temp_2前天的最高温度值temp_1昨天的最高温度值average在历史中每年这一天的平均最高温度值actual这就是我们的标签值了当天的真实最高温度friend这一列可能是凑热闹的你的朋友猜测的可能值咱们不管它就好了
星期是个文本特性用onehot转换一下
features pd.get_dummies(features)
features.head(5)3、标签制作与数据预处理
# 标签
labels np.array(features[actual])# 在特征中去掉标签
features features.drop(actual, axis 1)# 名字单独保存一下以备后患
feature_list list(features.columns)# 转换成合适的格式
features np.array(features)打印结果 (348, 14) from sklearn import preprocessing
input_features preprocessing.StandardScaler().fit_transform(features)
input_features[0]打印结果 array([ 0. , -1.5678393 , -1.65682171, -1.48452388, -1.49443549, -1.3470703 , -1.98891668, 2.44131112, -0.40482045, -0.40961596, -0.40482045, -0.40482045, -0.41913682, -0.40482045]) 4、 基于Keras构建网络模型
常用参数
activation激活函数的选择一般常用relukernel_initializer,bias_initializer权重与偏置参数的初始化方法kernel_regularizerbias_regularizer要不要加入正则化inputs输入可以自己指定也可以让网络自动选 units神经元个数
按顺序构造网络模型
model tf.keras.Sequential()
model.add(layers.Dense(16))
model.add(layers.Dense(32))
model.add(layers.Dense(1))创建一个执行序列添加全连接层16个神经元添加全连接层32个神经元添加全连接层1个神经元作为最后的输出
定好优化器和损失函数然后训练
model.compile(optimizertf.keras.optimizers.SGD(0.001), lossmean_squared_error)
model.fit(input_features, labels, validation_split0.25, epochs10, batch_size64)指定SGD为优化器学习率为0.001MSE为损失函数指定数据和标签然后训练25%为验证集10个epochs
打印结果 Epoch 1/10 5/5 - 0s 33ms/step - loss: 4267.9907 val_loss: 3133.0610 Epoch 2/10 5/5 0s 4ms/step - loss: 1925.8059 - val_loss: 3318.1531 Epoch 3/10 5/5 - 0s 3ms/step - loss: 181.2731 val_loss: 2728.9922 Epoch 4/10 5/5 0s 3ms/step - loss: 104.3410 - val_loss: 2093.8855 Epoch 5/10 5/5 - 0s 3ms/step - loss: 77.6116 - val_loss: 1377.6144 Epoch 6/10 5/5 0s 3ms/step - loss: 73.3877 - val_loss: 1163.6123 Epoch 7/10 5/5 0s 3ms/step - loss: 60.4262 val_loss: 867.4617 Epoch 8/10 5/5 0s 3ms/step - loss: 73.3110 - val_loss: 654.7820 Epoch 9/10 5/5 0s 3ms/step - loss: 36.6109 val_loss: 581.9786 Epoch 10/10 5/5 0s 3ms/step - loss: 56.6764 - val_loss: 383.0244 keras.callbacks.History at 0x22634a22760 从打印结果来看训练集的损失和验证集的损失差距比较大可能出现过拟合的现象
输入数据
input_features.shape打印结果 (348, 14) 查看网络结构
model.summary()打印结果 Model: “sequential” Layer (type) Output Shape Param # dense (Dense) multiple 240 dense_1 (Dense) multiple 544 dense_2 (Dense) multiple 33 Total params: 817 Trainable params: 817 Non-trainable params: 0 5、改初始化方法
model tf.keras.Sequential()
model.add(layers.Dense(16,kernel_initializerrandom_normal))
model.add(layers.Dense(32,kernel_initializerrandom_normal))
model.add(layers.Dense(1,kernel_initializerrandom_normal))
model.compile(optimizertf.keras.optimizers.SGD(0.001), lossmean_squared_error)
model.fit(input_features, labels, validation_split0.25, epochs100, batch_size64)部分打印结果 Epoch 99/100 261/261 0s 42us/sample - loss: 27.9759 - val_loss: 41.2864 Epoch 100/100 261/261 0s 42us/sample - loss: 44.5327 - val_loss: 48.2574 很显然差距消失了
6、加入正则化惩罚项
model tf.keras.Sequential()
model.add(layers.Dense(16,kernel_initializerrandom_normal,kernel_regularizertf.keras.regularizers.l2(0.03)))
model.add(layers.Dense(32,kernel_initializerrandom_normal,kernel_regularizertf.keras.regularizers.l2(0.03)))
model.add(layers.Dense(1,kernel_initializerrandom_normal,kernel_regularizertf.keras.regularizers.l2(0.03)))
model.compile(optimizertf.keras.optimizers.SGD(0.001), lossmean_squared_error)
model.fit(input_features, labels, validation_split0.25, epochs100, batch_size64)部分打印结果 Epoch 99/100 261/261 0s 42us/sample - loss: 26.2268 - val_loss: 20.5562 Epoch 100/100 261/261 0s 42us/sample - loss: 24.3962 - val_loss: 21.1083 很显然结果更好了
7、展示测试结果
# 转换日期格式
dates [str(int(year)) - str(int(month)) - str(int(day)) for year, month, day in zip(years, months, days)]
dates [datetime.datetime.strptime(date, %Y-%m-%d) for date in dates]# 创建一个表格来存日期和其对应的标签数值
true_data pd.DataFrame(data {date: dates, actual: labels})# 同理再创建一个来存日期和其对应的模型预测值
months features[:, feature_list.index(month)]
days features[:, feature_list.index(day)]
years features[:, feature_list.index(year)]test_dates [str(int(year)) - str(int(month)) - str(int(day)) for year, month, day in zip(years, months, days)]test_dates [datetime.datetime.strptime(date, %Y-%m-%d) for date in test_dates]predictions_data pd.DataFrame(data {date: test_dates, prediction: predict.reshape(-1)}) # 真实值
plt.plot(true_data[date], true_data[actual], b-, label actual)# 预测值
plt.plot(predictions_data[date], predictions_data[prediction], ro, label prediction)
plt.xticks(rotation 60);
plt.legend()# 图名
plt.xlabel(Date); plt.ylabel(Maximum Temperature (F)); plt.title(Actual and Predicted Values);
