Setup¶
In [1]:
from __future__ import absolute_import, division, print_function
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
%matplotlib inline
print(tf.__version__)
1.12.0
Load and Pre-process data¶
In [2]:
data = pd.read_table('../data/lecture03/example_with_placeholder/birth_life_2010.txt')
data.head()
Out[2]:
| Country | Birth rate | Life expectancy | |
|---|---|---|---|
| 0 | Vietnam | 1.822 | 74.828244 |
| 1 | Vanuatu | 3.869 | 70.819488 |
| 2 | Tonga | 3.911 | 72.150659 |
| 3 | Timor-Leste | 5.578 | 61.999854 |
| 4 | Thailand | 1.579 | 73.927659 |
In [3]:
x = data.loc[:,'Birth rate'].values
y = data.loc[:,'Life expectancy'].values
print(x.shape, y.shape)
(190,) (190,)
In [4]:
# split train and validation
tr_indices = np.random.choice(range(x.shape[0]), size = int(x.shape[0] * .7), replace=False)
x_tr = x[tr_indices]
y_tr = y[tr_indices]
x_val = np.delete(x, tr_indices, axis = 0)
y_val = np.delete(y, tr_indices, axis = 0)
print(x_tr.shape, y_tr.shape)
print(x_val.shape, y_val.shape)
(133,) (133,) (57,) (57,)
Define the graph of Simple Linear Regression¶
In [5]:
# create placeholders for X (birth rate) and Y (life expectancy)
X = tf.placeholder(dtype = tf.float32, shape = [None])
Y = tf.placeholder(dtype = tf.float32, shape = [None])
# create weight and bias, initialized to 0
w = tf.get_variable(name = 'weight', initializer = tf.constant(.0))
b = tf.get_variable(name = 'bias', initializer = tf.constant(.0))
# construct model to predict Y
yhat = X * w + b
# use the square error as huber loss function
huber_loss = tf.losses.huber_loss(labels = Y, predictions = yhat, delta = 14.)
huber_loss_summ = tf.summary.scalar(name = 'huber_loss', tensor = huber_loss) # for tensorboard
# using gradient descent with learning rate of 0.01 to minimize loss
opt = tf.train.GradientDescentOptimizer(learning_rate=.01)
training_op = opt.minimize(huber_loss)
Training¶
In [6]:
epochs = 100
batch_size = 8
total_step = int(x.shape[0] / batch_size)
print(total_step)
23
In [7]:
train_writer = tf.summary.FileWriter(logdir = '../graphs/lecture03/linreg_huber_high_tf_placeholder/train',
graph = tf.get_default_graph())
val_writer = tf.summary.FileWriter(logdir = '../graphs/lecture03/linreg_huber_high_tf_placeholder/val',
graph = tf.get_default_graph())
In [8]:
sess_config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
sess = tf.Session(config = sess_config)
sess.run(tf.global_variables_initializer())
tr_loss_hist = []
val_loss_hist = []
for epoch in range(epochs):
avg_tr_loss = 0
avg_val_loss = 0
for step in range(total_step):
batch_indices = np.random.choice(range(x_tr.shape[0]),
size = batch_size, replace = False)
val_indices = np.random.choice(range(x_val.shape[0]),
size = batch_size, replace = False)
batch_xs = x_tr[batch_indices]
batch_ys = y_tr[batch_indices]
val_xs = x_val[val_indices]
val_ys = y_val[val_indices]
_, tr_loss = sess.run(fetches = [training_op, huber_loss],
feed_dict = {X : batch_xs, Y : batch_ys})
tr_loss_summ = sess.run(huber_loss_summ, feed_dict = {X : batch_xs, Y : batch_ys})
val_loss, val_loss_summ = sess.run(fetches = [huber_loss, huber_loss_summ],
feed_dict = {X : val_xs, Y: val_ys})
avg_tr_loss += tr_loss / total_step
avg_val_loss += val_loss / total_step
train_writer.add_summary(tr_loss_summ, global_step = epoch)
val_writer.add_summary(val_loss_summ, global_step = epoch)
tr_loss_hist.append(avg_tr_loss)
val_loss_hist.append(avg_val_loss)
if epoch % 10 == 0:
print('epoch : {:3}, tr_loss : {:.2f}, val_loss : {:.2f}'.format(epoch, avg_tr_loss, avg_val_loss))
train_writer.close()
val_writer.close()
epoch : 0, tr_loss : 692.88, val_loss : 663.27 epoch : 10, tr_loss : 277.13, val_loss : 293.82 epoch : 20, tr_loss : 248.73, val_loss : 251.21 epoch : 30, tr_loss : 192.79, val_loss : 185.59 epoch : 40, tr_loss : 128.23, val_loss : 122.80 epoch : 50, tr_loss : 93.26, val_loss : 74.94 epoch : 60, tr_loss : 49.64, val_loss : 40.08 epoch : 70, tr_loss : 30.26, val_loss : 19.89 epoch : 80, tr_loss : 20.98, val_loss : 19.67 epoch : 90, tr_loss : 19.37, val_loss : 11.75
Visualization¶
In [9]:
plt.plot(tr_loss_hist, label = 'train')
plt.plot(val_loss_hist, label = 'validation')
plt.legend()
Out[9]:
<matplotlib.legend.Legend at 0x121c081d0>
In [10]:
w_out, b_out = sess.run([w, b])
plt.plot(data.iloc[:,1], data.iloc[:,2], 'bo', label='Real data')
plt.plot(data.iloc[:,1], data.iloc[:,1] * w_out + b_out, 'r', label='Predicted data')
plt.legend()
Out[10]:
<matplotlib.legend.Legend at 0x123c7bda0>
