python机器学习-线性回归

# Calculating the predictions     
predictions = weight * x + bias     
print("Training cost =", training_cost, "Weight =", weight, "bias =", bias, '\n')     
# Plotting the Results     
plt.plot(x, y, 'ro', label ='Original data')     
plt.plot(x, predictions, label ='Fitted line')     
plt.title('Linear Regression Result')     
plt.legend()     
plt.show()  

上面的代码演示了我们如何使用训练好的模型来预测值

LR_TensorFlow.py

import numpy as np     
import tensorflow as tf     
import matplotlib.pyplot as plt     
np.random.seed(101)     
tf.set_random_seed(101)     
# Genrating random linear data     
# There will be 50 data points ranging from 0 to 50     
x = np.linspace(0, 50, 50)     
y = np.linspace(0, 50, 50)     
    
# Adding noise to the random linear data     
x += np.random.uniform(-4, 4, 50)     
y += np.random.uniform(-4, 4, 50)     
    
n = len(x) # Number of data points     
# Plot of Training Data     
plt.scatter(x, y)     
plt.xlabel('x')     
plt.xlabel('y')     
plt.title("Training Data")     
plt.show()     
    
X = tf.placeholder("float")     
Y = tf.placeholder("float")     
W = tf.Variable(np.random.randn(), name = "W")     
b = tf.Variable(np.random.randn(), name = "b")     
learning_rate = 0.01    
training_epochs = 1000    
# Hypothesis     
y_pred = tf.add(tf.multiply(X, W), b)     
    
# Mean Squared Error Cost Function     
cost = tf.reduce_sum(tf.pow(y_pred-Y, 2)) / (2 * n)     
    
# Gradient Descent Optimizer     
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)     
    
# Global Variables Initializer     
init = tf.global_variables_initializer()     
# Starting the Tensorflow Session     
with tf.Session() as sess:     
        
    # Initializing the Variables     
    sess.run(init)     
        
    # Iterating through all the epochs     
    for epoch in range(training_epochs):     
            
        # Feeding each data point into the optimizer using Feed Dictionary     
        for (_x, _y) in zip(x, y):     
            sess.run(optimizer, feed_dict = {X : _x, Y : _y})     
            
        # Displaying the result after every 50 epochs     
        if (epoch + 1) % 50 == 0:     
            # Calculating the cost a every epoch     
            c = sess.run(cost, feed_dict = {X : x, Y : y})     
            print("Epoch", (epoch + 1), ": cost =", c, "W =", sess.run(W), "b =", sess.run(b))     
        
    # Storing necessary values to be used outside the Session     
    training_cost = sess.run(cost, feed_dict ={X: x, Y: y})     
    weight = sess.run(W)     
    bias = sess.run(b)     
    
# Calculating the predictions     
predictions = weight * x + bias     
print("Training cost =", training_cost, "Weight =", weight, "bias =", bias, '\n')     
# Plotting the Results     
plt.plot(x, y, 'ro', label ='Original data')     
plt.plot(x, predictions, label ='Fitted line')     
plt.title('Linear Regression Result')     
plt.legend()     
plt.show()    

输出