Update 03 code

03. predicting-boston-house-price/main.py

@@ -81,6 +81,17 @@ with tf.compat.v1.Session() as sess:
     # TypeError: 'Tensor' object is not callable
     optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate).minimize(loss_function)
     
+    # Tensorboard 可视化
+    logdir = "03. predicting-boston-house-price/log"
+    # 创建一个用于记录损失值的操作
+    sum_loss_op = tf.summary.scalar("loss", loss_function)
+    # 把所有日志文件中内容合并，方便写入
+    merged = tf.compat.v1.summary.merge_all()
+    # 创建 Tensorboard 的写入器
+    writer = tf.compat.v1.summary.FileWriter(logdir, sess.graph)
+
+    loss_list = []
+
     # 模型训练
     for epoch in range(train_epochs):
         loss_sum = 0.0
@@ -89,9 +100,9 @@ with tf.compat.v1.Session() as sess:
             xs = xs.reshape(1, 12)
             ys = ys.reshape(1, 1)
 
-            _, loss = sess.run([optimizer, loss_function], feed_dict = {x: xs, y: ys})
-
-            loss_sum = loss_sum + loss
+            _, summary_str, loss = sess.run([optimizer, sum_loss_op, loss_function], feed_dict = {x: xs, y: ys})
+            writer.add_summary(summary_str, epoch)
+            loss_sum += loss
         
         # 打乱数据顺序
         x_data, y_data = shuffle(x_data, y_data)
@@ -101,3 +112,16 @@ with tf.compat.v1.Session() as sess:
         loss_average = loss_sum / len(y_data)
 
         print("epoch = ", epoch + 1, "loss = ", loss_average, "b = ", b0temp, " w = ", w0temp)
+
+    # 模型预测
+    n = numpy.random.randint(len(x_data[:, 0]))
+    # print(n)
+    x_test = x_data[n]
+    x_test = x_test.reshape([1, 12])
+    prediction = sess.run(predict, feed_dict={x: x_test})
+    print("预测值：%f" %prediction)
+    target = y_data[n]
+    print("真实值：%f" %target)
+
+    # 可视化损失值
+    pyplot.plot(loss_list)
\ No newline at end of file

03. predicting-boston-house-price/main_v1.py

@@ -8,45 +8,43 @@ import numpy
 import pandas as panda
 from sklearn.utils import shuffle
 
-with tf.compat.v1.Session() as sess:
-
-    # 读取配置文件
-    read_data = panda.read_csv("03. predicting-boston-house-price/data/boston.csv", header = 0)
-
-    # 显示数据摘要描述信息
-    # print(read_data.describe())
-    # 获取配置文件的值
-    read_data = read_data.values
-    # 二维数组，13 列 506 行
-    # print(read_data)
-    # 转换为 numpy 的数组格式
-    read_data = numpy.array(read_data)
-
-    # 对特征数据（0 到 11 列）做（0 - 1）的归一化
-    for i in range(12):
+# 读取配置文件
+read_data = panda.read_csv("03. predicting-boston-house-price/data/boston.csv", header = 0)
+
+# 显示数据摘要描述信息
+# print(read_data.describe())
+# 获取配置文件的值
+# read_data = read_data.values
+# 二维数组，13 列 506 行
+# print(read_data)
+# 转换为 numpy 的数组格式
+read_data = numpy.array(read_data)
+
+# 对特征数据（0 到 11 列）做（0 - 1）的归一化
+for i in range(12):
     read_data[:, i] = read_data[:, i] / (read_data[:, i].max() - read_data[:, i].min())
 
-    # x_data 为前 12 列特征数据
-    # 前半部分是所有数据，右半部分是列（0 - 11）
-    x_data = read_data[:, :12]
-    # y_data 为最后 1 列标签数据
-    y_data = read_data[:, 12]
-    # 是一个二维数组，506 行，12 列
-    # print(x_data, "\n shape = ", x_data.shape)
-    # 是一个一维数组，有 506 个元素/单元
-    # print(y_data, "\n shape = ", y_data.shape)
-
-    # 定义特征数据和标签数据的占位符（placeholder）
-    # 具有 12 个特征，shape 要和实际上的特征数据相吻合
-    # 12 个特征数据
-    # 即行不管，列有 12 列
-    x = tf.compat.v1.placeholder(tf.float32, [None, 12], name = "x")
-    # 1 个标签数据
-    # 有 1 列
-    y = tf.compat.v1.placeholder(tf.float32, [None, 1], name = "y")
-
-    # 定义命名空间
-    with tf.name_scope("Model"):
+# x_data 为前 12 列特征数据
+# 前半部分是所有数据，右半部分是列（0 - 11）
+x_data = read_data[:, :12]
+# y_data 为最后 1 列标签数据
+y_data = read_data[:, 12]
+# 是一个二维数组，506 行，12 列
+# print(x_data, "\n shape = ", x_data.shape)
+# 是一个一维数组，有 506 个元素/单元
+# print(y_data, "\n shape = ", y_data.shape)
+
+# 定义特征数据和标签数据的占位符（placeholder）
+# 具有 12 个特征，shape 要和实际上的特征数据相吻合
+# 12 个特征数据
+# 即行不管，列有 12 列
+x = tf.compat.v1.placeholder(tf.float32, [None, 12], name = "x")
+# 1 个标签数据
+# 有 1 列
+y = tf.compat.v1.placeholder(tf.float32, [None, 1], name = "y")
+
+# 定义命名空间
+with tf.name_scope("Model"):
     # 12 行 1 列的列向量，w1、w2、w3...
     # w 初始化为 shape = (12, 1) 的随机数，标准差设置为 0.01
     w = tf.Variable(tf.random.normal([12, 1], stddev = 0.01, name = "w"))
@@ -62,43 +60,43 @@ with tf.compat.v1.Session() as sess:
     # 预测计算操作，前向计算节点
     predict = model(x, w, b)
 
-    # 模型训练
-    # 设置模型训练超参数
-    # 迭代轮次
-    train_epochs = 50
-    # 学习率
-    learning_rate = 0.0251
-    # 定义均方差损失函数
-    with tf.name_scope("LossFunction"):
+# 模型训练
+# 设置模型训练超参数
+# 迭代轮次
+train_epochs = 50
+# 学习率
+learning_rate = 0.0251
+# 定义均方差损失函数
+with tf.name_scope("LossFunction"):
     # 均方误差
     loss_function = tf.reduce_mean(tf.pow(y - predict, 2))
-    # 创建优化器
-    optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate).minimize(loss_function)
+# 创建优化器
+optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate).minimize(loss_function)
 
-    config = tf.compat.v1.ConfigProto(allow_soft_placement = True)
-    gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction = 0.7)
-    config.gpu_options.allow_growth = True
+config = tf.compat.v1.ConfigProto(allow_soft_placement = True)
+gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction = 0.7)
+config.gpu_options.allow_growth = True
 
-    # 声明会话
-    sess = tf.compat.v1.Session(config = config)
-    init = tf.compat.v1.global_variables_initializer()
+# 声明会话
+sess = tf.compat.v1.Session(config = config)
+init = tf.compat.v1.global_variables_initializer()
 
-    # Tensorboard 可视化
-    logdir = "03. predicting-boston-house-price/log"
-    # 创建一个用于记录损失值的操作
-    sum_loss_op = tf.summary.scalar("loss", loss_function)
-    # 把所有日志文件中内容合并，方便写入
-    merged = tf.compat.v1.summary.merge_all()
+# Tensorboard 可视化
+logdir = "03. predicting-boston-house-price/log"
+# 创建一个用于记录损失值的操作
+sum_loss_op = tf.summary.scalar("loss", loss_function)
+# 把所有日志文件中内容合并，方便写入
+merged = tf.compat.v1.summary.merge_all()
 
-    sess.run(init)
+sess.run(init)
 
-    # 创建 Tensorboard 的写入器
-    writer = tf.compat.v1.summary.FileWriter(logdir, sess.graph)
+# 创建 Tensorboard 的写入器
+writer = tf.compat.v1.summary.FileWriter(logdir, sess.graph)
 
-    loss_list = []
+loss_list = []
 
-    # 模型训练
-    for epoch in range(train_epochs):
+# 模型训练
+for epoch in range(train_epochs):
     loss_sum = 0.0
     for xs, ys in zip(x_data, y_data):
         # Feed 数据必须和 Placeholder 的 shape 一致