- 首页 > it技术 > >
Tensorflow实现一元线性回归模型
#coding=utf-8
'''
Created on Jan 28, 2019@author: zhongzhu
'''
import tensorflow as tf
import os
import numpy as np
import matplotlib.pyplot as pltos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'if __name__ == '__main__':# 产生训练数据集
train_X = np.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,
7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1])
train_Y = np.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221,
2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3])
n_train_samples = train_X.shape[0]
print('训练样本数量: ', n_train_samples)
# 产生测试样本
test_X = np.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
test_Y = np.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])
n_test_samples = test_X.shape[0]
print('测试样本数量: ', n_test_samples)with tf.Graph().as_default():
#输入占位符
with tf.name_scope('Input'):
X = tf.placeholder(tf.float32, name='X')
Y_true = tf.placeholder(tf.float32, name='Y_true')with tf.name_scope('Inference'):
#模型参数变量
W = tf.Variable(np.random.randn(), name='Weight')
b = tf.Variable(np.random.randn(), name='Bias')
#inference 前向模型： y = w*x+b
y_pred = tf.add(tf.multiply(X, W), b)with tf.name_scope('Loss'):
#计算损失
TrainLoss = tf.reduce_mean(tf.pow((y_pred-Y_true), 2))/2with tf.name_scope('Train'):
#训练节点
Optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.01)
TrainOp = Optimizer.minimize(TrainLoss)with tf.name_scope('Evaluate'):
#添加评估节点
EvalLoss = tf.reduce_mean(tf.pow((y_pred-Y_true), 2))/2# 添加所有变量类型的初始化节点
InitOp = tf.global_variables_initializer()print('开启会话，运行计算图')sess = tf.Session()
sess.run(InitOp)print('不断的迭代训练并测试模型')for step in range(1000):
_, train_loss, train_w, train_b = sess.run([TrainOp, TrainLoss, W, b],
feed_dict={X: train_X, Y_true: train_Y})# 训练几步之后输出当前模型损失
if (step+1) % 5 == 0:
print("Step:", '%04d' % (step + 1), "train_loss=", "{:.9f}".format(train_loss),
"W=", train_w, "b=", train_b)# 每隔几步对模型进行测试
if (step+1) % 5 == 0:
test_loss, test_w, test_b = sess.run([EvalLoss, W, b],
feed_dict={X: test_X, Y_true: test_Y})print("Step:", '%04d' % (step + 1), "test_loss=", "{:.9f}".format(test_loss),
"W=", test_w, "b=", test_b)print('训练结束')
W,b = sess.run([W,b])
print("得到的模型参数：", "W=", W, "b=", b,)training_loss = sess.run(TrainLoss, feed_dict={X: train_X, Y_true: train_Y})
print("训练集上的损失：", training_loss)
test_loss = sess.run(EvalLoss, feed_dict={X: test_X, Y_true: test_Y})
print("测试集上的损失：", test_loss)writer = tf.summary.FileWriter(logdir='logs', graph=tf.get_default_graph())
writer.close()# 展示拟合曲线
plt.plot(train_X, train_Y, 'ro', label='Original Train Points')
plt.plot(test_X, test_Y, 'b*', label='Original Test Points')
plt.plot(train_X, W * train_X + b, label='Fitted Line')
plt.legend()
plt.show()
推荐阅读
