Update 02:

 - Finish (actually not) tensorflow v2 version
 - Add usable tensorflow v1 version

02. linear-regression-with-one-variable/main.py

@@ -3,6 +3,7 @@
 import tensorflow as tf
 import numpy
 import matplotlib.pyplot as pyplot
+from datetime import datetime
 
 with tf.compat.v1.Session() as sess:
 
@@ -43,7 +44,8 @@ with tf.compat.v1.Session() as sess:
     # 本处初始化梯度下降优化器
     # https://stackoverflow.com/questions/55682718/module-tensorflow-api-v2-train-has-no-attribute-gradientdescentoptimizer
     # https://stackoverflow.com/questions/58722591/typeerror-minimize-missing-1-required-positional-argument-var-list
-    optimizer = tf.optimizers.SGD(learning_rate).minimize(loss_function, var_list=[w, b])
+    # optimizer = tf.keras.optimizers.SGD(learning_rate).minimize(loss_function, var_list=[w, b])
+    optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate).minimize(loss_function)
 
     # 开始训练
     # 记录训练步数
@@ -51,18 +53,44 @@ with tf.compat.v1.Session() as sess:
     # 用于保存loss值的列表
     loss_list = []  
     # 设置迭代轮次
-    for epoch in range(train_rounds):
+    for rounds in range(train_rounds):
         for xs, ys in zip(x_data, y_data):
-            _, loss = sess.run([optimizer, loss_function], feed_dict = {x: xs, y: ys})
+            _, loss = sess.run([optimizer, loss_function], feed_dict = {x: tf.keras.layers.Reshape(xs), y: tf.keras.layers.Reshape(ys)})
             # 将每一次的损失值放入列表
             loss_list.append(loss)
             step = step + 1
             # 每训练20个样本输出一次损失值
             display_step = 20
             if step % display_step == 0:
-                print("Train Epoch: %02d" % (epoch + 1),"Step: %04d" % step, "loss = ", "{:.9f}".format(loss))
+                print("Train Epoch: %02d" % (rounds + 1),"Step: %04d" % step, "loss = ", "{:.9f}".format(loss))
         # 每一轮训练完，有一个w,b值
         b0temp = b.eval(session = sess)
         w0temp = w.eval(session = sess)
         # 画出训练模型
-        pyplot.plot(x_data, w0temp * x_data + b0temp)
+        # pyplot.plot(x_data, w0temp * x_data + b0temp)
+
+    pyplot.scatter(x_data, y_data, label="Original data")
+    # 显示训练好的模型
+    pyplot.plot(x_data, x_data * sess.run(w) + sess.run(b), label = "Fitted line", color="r", linewidth = 1)
+    # 通过参数loc指定图例位置
+    pyplot.legend(loc = 2)
+    pyplot.show()
+    # pyplot.plot(loss_list)
+
+    print('最后w和b的值为：')
+    print('w:', sess.run(w))
+    print('b:', sess.run(b))
+
+    # 第四步：利用模型进行预测
+    x_test = 5.79
+    predict = sess.run(predict, feed_dict = {x: x_test})
+    print('预测值：%f'% predict)
+    target = 3.1234 * x_test + 2.98
+    print('目标值：%f' % target)
+    # 生成一个写日记的writer
+    logdir = "logs" + datetime.now().strftime("%Y%m%d-%H%M%S")
+    writer = tf.summary.create_file_writer(logdir)
+    with writer.as_default():
+        # Still TensorFlow v1 here
+        writer = tf.compat.v1.summary.FileWriter(logdir, tf.compat.v1.get_default_graph())
+        writer.close()

02. linear-regression-with-one-variable/main_v1.py

+# %matplotlib inline
+
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior()
+
+# 清除default_graph和不断增加的节点
+tf.reset_default_graph()
+
+# 设置日志路径
+logdir="log"
+
+# 设置随机数
+np.random.seed(5)
+
+#  生成等差数列
+x_data = np.linspace(0, 100, 500)
+# y = 3.1234x+2.89+噪声
+y_data = 3.1234 * x_data + 2.98 + np.random.randn(*x_data.shape)*0.4
+
+# 画出随机生成数据的散点图
+plt.scatter(x_data, y_data)
+
+# 绘出正确的线性函数图 y= 3.1234x+2.98
+plt.plot(x_data, 3.1234 * x_data + 2.98, color='red', linewidth=3)
+# 定义占位符
+x = tf.placeholder("float", name="x")
+y = tf.placeholder("float", name="y")
+
+# 定义模型函数
+def model(x, w, b):
+    """返回模型"""
+    return tf.multiply(x, w) +b
+# 构建线性函数的斜率， 变量w
+w = tf.Variable(1.0, name="w0")
+# 构截距 变量b
+b = tf.Variable(0.0, name="b0")
+# pred 是预测值
+pred = model(x, w, b)
+# 设置超参数
+# 训练论数
+train_epoches = 10
+# 学习率
+learn_rate = 0.00001
+# 定义损失参数
+# 采用均方差作为损失函数
+loss_function = tf.reduce_mean(tf.square(y - pred))
+# 定义优化器
+#创建梯度优化器
+optimizer = tf.train.GradientDescentOptimizer(learn_rate).minimize(loss_function)
+# 声明会话
+sess = tf.Session()
+# 变量初始化
+init = tf.global_variables_initializer()
+sess.run(init)
+# 迭代训练
+# 开始训练
+step = 0# 记录步数
+loss_list =[]# 用于保存损失值的列表
+disp_step = 20 # 每20个输出一次
+for epoch in range(train_epoches):
+    for xs, ys in zip(x_data, y_data):
+        _, loss=sess.run([optimizer, loss_function], feed_dict={x:xs, y:ys}) 
+        loss_list.append(loss)
+        step += 1
+        if step % disp_step ==0:
+            print("Train Epoch:","%02d" %(epoch+1), "Step: %03d"%(step),"loss=",\
+                  "{:.9f}".format(loss))
+    b0temp = b.eval(session=sess)
+    w0temp = w.eval(session=sess)
+    plt.plot(x_data, w0temp*x_data + b0temp)
+writer = tf.summary.FileWriter(logdir, tf.get_default_graph())
+writer.close()
+plt.plot(loss_list)
+# 打印结果
+print("w:", sess.run(w))# 在3.1234附近
+print("b:", sess.run(b))# 在2.98附近
+# 结果可视化
+plt.scatter(x_data, y_data, label="Original data")
+plt.plot(x_data, x_data*sess.run(w)+sess.run(b),\
+       label="Fitted line", color="r", linewidth=3)
+plt.legend(loc=2)
+# 利用模型进行预测
+x_test = 5.79
+predict = sess.run(pred, feed_dict={x:x_test})
+print("预测值：%f"%predict)
+
+target = 3.1234*x_test + 2.98
+print("目标值：%f"%target)
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