使用TensorFlow v2庫實現線性回歸

使用tensorflow v2庫實現線性回歸

此示例使用簡單方法來更好地理解訓練過程背後的所有機制

from __future__ import absolute_import, division, print_function
import tensorflow as tf
import numpy as np
rng = np.random

# 引數
learning_rate =
0.01
training_steps =
1000
display_step =
50

# 訓練資料
x = np.array(
[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])
y = np.array(
[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_samples = x.shape[
0]

# 隨機初始化權重，偏置
w = tf.variable(rng.randn(
),name=
"weight"
)b = tf.variable(rng.randn(
),name=
"bias"
)# 線性回歸(wx b)
deflinear_regression
(x):
return w * x b
# 均方差
defmean_square
(y_pred,y_true)
:return tf.reduce_sum(tf.
pow(y_pred-y_true,2)
)/(2
* n_samples)
# 隨機梯度下降優化器
optimizer = tf.optimizers.sgd(learning_rate)

# 優化過程
defrun_optimization()
:# 將計算封裝在gradienttape中以實現自動微分
with tf.gradienttape(
)as g:
pred = linear_regression(x)
loss = mean_square(pred,y)
# 計算梯度
gradients = g.gradient(loss,
[w,b]
)# 按gradients更新 w 和 b
zip(gradients,
[w,b]
))

# 針對給定訓練步驟數開始訓練
for step in
range(1
,training_steps 1):
# 執行優化以更新w和b值
run_optimization(
)if step % display_step ==0:
pred = linear_regression(x)
loss = mean_square(pred, y)
print
("step: %i, loss: %f, w: %f, b: %f"
%(step, loss, w.numpy(
), b.numpy())
)

output:

step: 50, loss: 0.210631, w: 0.458940, b: -0.670898 step: 100, loss: 0.195340, w: 0.446725, b: -0.584301 step: 150, loss: 0.181797, w: 0.435230, b: -0.502807 step: 200, loss: 0.169803, w: 0.424413, b: -0.426115 step: 250, loss: 0.159181, w: 0.414232, b: -0.353942 step: 300, loss: 0.149774, w: 0.404652, b: -0.286021 step: 350, loss: 0.141443, w: 0.395636, b: -0.222102 step: 400, loss: 0.134064, w: 0.387151, b: -0.161949 step: 450, loss: 0.127530, w: 0.379167, b: -0.105341 step: 500, loss: 0.121742, w: 0.371652, b: -0.052068 step: 550, loss: 0.116617, w: 0.364581, b: -0.001933 step: 600, loss: 0.112078, w: 0.357926, b: 0.045247 step: 650, loss: 0.108058, w: 0.351663, b: 0.089647 step: 700, loss: 0.104498, w: 0.345769, b: 0.131431 step: 750, loss: 0.101345, w: 0.340223, b: 0.170753 step: 800, loss: 0.098552, w: 0.335003, b: 0.207759 step: 850, loss: 0.096079, w: 0.330091, b: 0.242583 step: 900, loss: 0.093889, w: 0.325468, b: 0.275356 step: 950, loss: 0.091949, w: 0.321118, b: 0.306198 step: 1000, loss: 0.090231, w: 0.317024, b: 0.335223

import matplotlib.pyplot as plt
# 繪製圖
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