斯坦福tensorflow教程(三) 线性和逻辑回归1. 线性回归：根据出生率来预测平均寿命

# -*- coding: utf-8 -*-
# @Author: yanqiang
# @Date:   2018-05-10 22:31:37
# @Last Modified by:   yanqiang
# @Last Modified time: 2018-05-10 23:05:47
import tensorflow as tf
import utils
import matplotlib.pyplot as plt

DATA_FILE = 'data/birth_life_2010.txt'

# Step 1: read in data from the .txt file
# data is a numpy array of shape (190, 2), each row is a datapoint
data, n_samples = utils.read_birth_life_data(DATA_FILE)

# Step 2: create placeholders for X (birth rate) and Y (life expectancy)
X = tf.placeholder(tf.float32, name='X')
Y = tf.placeholder(tf.float32, name='Y')

# Step 3: create weight and bias, initialized to 0
w = tf.get_variable('weights', initializer=tf.constant(0.0))
b = tf.get_variable('bias', initializer=tf.constant(0.0))

# Step 4: construct model to predict Y (life expectancy from birth rate)
Y_predicted = w * X + b

# Step 5: use the square error as the loss function
loss = tf.square(Y - Y_predicted, name='loss')

# Step 6: using gradient descent with learning rate of 0.01 to minimize loss
optimizer = tf.train.GradientDescentOptimizer(
    learning_rate=0.001).minimize(loss)

with tf.Session() as sess:
    # Step 7: initialize the necessary variables, in this case, w and b
    sess.run(tf.global_variables_initializer())

    # Step 8: train the model
    for i in range(100):  # run 100 epochs
        for x, y in data:
            # Session runs train_op to minimize loss
            sess.run(optimizer, feed_dict={X: x, Y: y})
    # Step 9: output the values of w and b
    w_out, b_out = sess.run([w, b])


# uncomment the following lines to see the plot
plt.plot(data[:, 0], data[:, 1], 'bo', label='Real data')
plt.plot(data[:, 0], data[:, 0] * w_out + b_out, 'r', label='Predicted data')
plt.legend()
plt.show()