TensorFlow (二): 单变量线性回归

代码

import tensorflow as tf 
import numpy as np 
import matplotlib.pyplot as plt 

# generate data

data_size = 100
x_data = np.random.rand(data_size) * 0.6 + 0.2
noise = np.random.rand(data_size)
noise = (noise - noise.mean()) * 0.2
w = 1.5
b = 0.5
y_data = x_data * w + b + noise

train_test_boundary = int(0.7 * data_size)

x_train = x_data[:train_test_boundary]
y_train = y_data[:train_test_boundary]
x_test = x_data[train_test_boundary:]
y_test = y_data[train_test_boundary:]

# my model

train_loss_history = []
test_loss_history = []
nb_epoch = 100
my_w = 0
my_b = 0
learning_rate = 0.1

with tf.Graph().as_default():
	x = tf.placeholder(dtype = tf.float32, shape = [None])
	y_ = tf.placeholder(dtype = tf.float32, shape = [None])
	w = tf.Variable(0, dtype = tf.float32)
	b = tf.Variable(0, dtype = tf.float32)
	y = x * w + b
	loss = tf.reduce_mean((y - y_) ** 2)
	train = tf.train.AdamOptimizer(learning_rate).minimize(loss)

	with tf.Session() as sess:
		sess.run(tf.global_variables_initializer())
		for i in range(nb_epoch):
			sess.run(train, feed_dict = {x: x_train, y_: y_train})
			l = sess.run(loss, feed_dict = {x: x_train, y_:y_train})
			train_loss_history.append(l)
			l = sess.run(loss, feed_dict = {x: x_test, y_: y_test})
			test_loss_history.append(l)

		my_w = w.eval()
		my_b = b.eval()


# display

plt.figure(figsize=(8, 4))
plt.subplot(1, 2, 1)
plt.xlim([0, 1])
plt.ylim([0, 3])
plt.plot(np.linspace(0, 1, 100), my_w * np.linspace(0, 1, 100) + my_b, 'r--')
plt.scatter(x_train, y_train, 1.0, 'b', marker='o')
plt.title('dataset & hypothesis function')

plt.subplot(1, 2, 2)
plt.title('training & testing loss')
plt.plot(train_loss_history, 'b--')
plt.plot(test_loss_history, 'r--')
plt.legend(['trian', 'test'])

plt.show()

效果图