Linear Regression
Linear Regression
Sung-ju Kim
Content
- Prepare pseudo data
- Make model & Design cost function & optimizer
- Train & Draw graph
1. Prepare pseudo data
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
import matplotlib.cm as cmx
%matplotlib inline
plt.style.use('ggplot')
def pseudo_function(x, x_i, n_data):
n_wave = 5
exp_max = 1
exp_min = -1
bias = 0.5
a = 1.5
radian_unit = (np.pi * n_wave) / n_data
exp_unit = (exp_max - exp_min) / n_data
y = np.sin(x_i*radian_unit)* np.exp(exp_max-(exp_unit*x_i)) + bias + a*x
return y
n_data = 100
x_range = (-1, 1)
X_train = np.linspace(-1, 1, n_data,dtype=np.float32)
Y_train = np.array([pseudo_function(x,x_i,n_data) for x_i, x in enumerate(list(X_train))], dtype=np.float32)
plt.cla()
plt.plot(X_train, Y_train, 'ro', alpha=0.4, color='black')
[<matplotlib.lines.Line2D at 0x10c0e0048>]
# reshape for model
X_train = np.reshape(X_train, newshape=[-1,1])
Y_train = np.reshape(Y_train, newshape=[-1,1])
print("X_train shape : {}\nY_train shape : {}".format(np.shape(X_train), np.shape(X_train)))
X_train shape : (100, 1)
Y_train shape : (100, 1)
2. Make model & Design cost function & optimizer
with tf.variable_scope('variable'):
X = tf.placeholder(dtype=tf.float32,
shape=[None,1],
name="X")
Y = tf.placeholder(dtype=tf.float32,
shape=[None,1],
name="Y")
learning_rate = tf.placeholder(dtype=tf.float32, name='learning_rate')
with tf.variable_scope('linear_model'):
# declaration of model
W = tf.get_variable(name="W",
shape=[1,1],
dtype=tf.float32,
initializer=tf.random_normal_initializer(mean=0.0, stddev=0.02))
b = tf.get_variable(name="b",
shape=[1],
dtype=tf.float32,
initializer=tf.constant_initializer())
Y_pred = tf.add(tf.matmul(X,W),b)
# optimization
cost = tf.reduce_mean(tf.squared_difference(Y_pred, Y))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
init = tf.global_variables_initializer()
3. Train & Draw graph
lr = 0.001
n_epoch = 3000
draw_interval = 200
plt.cla()
plt.plot(X_train, Y_train, 'ro', alpha=0.4, label="origin", color="black")
cmap = plt.get_cmap('coolwarm')
c_norm = colors.Normalize(vmin=0, vmax=n_epoch)
scalar_map = cmx.ScalarMappable(norm=c_norm, cmap=cmap)
sess = tf.Session()
sess.run(init)
for epoch_i in range(n_epoch):
_ = sess.run(optimizer, feed_dict={X:X_train,
Y:Y_train,
learning_rate:lr})
if epoch_i % draw_interval == 0 :
X_show = X_train.copy()
Y_show = sess.run(Y_pred,
feed_dict={X:X_show})
plt.plot(X_show, Y_show, alpha=epoch_i/n_epoch, color=scalar_map.to_rgba(epoch_i))