Setup¶
In [1]:
import os, sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from pprint import pprint
%matplotlib inline
print(tf.__version__)
1.12.0
Build input pipeline¶
In [2]:
train_dir = os.listdir('../data/lecture03/example_with_data/train_dir/')
train_dir = list(map(lambda path : '../data/lecture03/example_with_data/train_dir/' + path, train_dir))
pprint(train_dir, compact = True)
['../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_6.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_7.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_5.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_4.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_14.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_10.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_1.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_11.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_13.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_3.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_2.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_12.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_9.txt', '../data/lecture03/example_with_data/train_dir/birth_life_2010_tr_8.txt']
In [3]:
val_dir = '../data/lecture03/example_with_data/val_dir/birth_life_2010_val.txt'
pprint(val_dir)
'../data/lecture03/example_with_data/val_dir/birth_life_2010_val.txt'
In [4]:
# hyper parameters
epochs = 100
batch_size = 8
In [5]:
# datasets construction
# for training dataset
tr_dataset = tf.data.TextLineDataset(filenames = train_dir)
tr_dataset = tr_dataset.map(lambda record : tf.decode_csv(records = record,
record_defaults = [[''],[.0],[.0]],
field_delim = '\t')[1:])
tr_dataset = tr_dataset.shuffle(200)
tr_dataset = tr_dataset.batch(batch_size = batch_size)
tr_iterator = tr_dataset.make_initializable_iterator()
# for validation dataset
val_dataset = tf.data.TextLineDataset(filenames = val_dir)
val_dataset = val_dataset.map(lambda record : tf.decode_csv(records = record,
record_defaults = [[''],[.0],[.0]],
field_delim = '\t')[1:])
val_dataset = val_dataset.batch(batch_size = batch_size)
val_iterator = val_dataset.make_initializable_iterator()
In [6]:
# handle constructions. Handle allows us to feed data from different dataset by providing a parameter in feed_dict
handle = tf.placeholder(dtype = tf.string)
iterator = tf.data.Iterator.from_string_handle(string_handle = handle,
output_types = tr_iterator.output_types)
X, Y = iterator.get_next()
In [7]:
sess_config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True))
sess = tf.Session(config = sess_config)
sess.run(tf.global_variables_initializer())
Define the graph of Simple Linear Regression¶
In [8]:
# 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 loss function
mse_loss = tf.reduce_mean(tf.square(Y - yhat))
mse_loss_summ = tf.summary.scalar(name = 'mse_loss', tensor = mse_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(mse_loss)
Training¶
In [9]:
train_writer = tf.summary.FileWriter(logdir = '../graphs/lecture03/linreg_mse_with_tf_data/train',
graph = tf.get_default_graph())
val_writer = tf.summary.FileWriter(logdir = '../graphs/lecture03/linreg_mse_with_tf_data/val',
graph = tf.get_default_graph())
In [10]:
'''
# hyper parameters
epochs = 100
batch_size = 8
'''
tr_loss_hist = []
val_loss_hist = []
sess = tf.Session()
sess.run(tf.global_variables_initializer())
tr_handle, val_handle = sess.run(fetches = [tr_iterator.string_handle(), val_iterator.string_handle()])
for epoch in range(epochs):
avg_tr_loss = 0
avg_val_loss = 0
tr_step = 0
val_step = 0
# for mini-batch training
sess.run(tr_iterator.initializer)
try:
while True:
_, tr_loss, tr_loss_summ = sess.run(fetches = [training_op, mse_loss, mse_loss_summ], feed_dict = {handle : tr_handle})
avg_tr_loss += tr_loss
tr_step += 1
except tf.errors.OutOfRangeError:
pass
# for validation
sess.run(val_iterator.initializer)
try:
while True:
val_loss, val_loss_summ = sess.run(fetches = [mse_loss, mse_loss_summ], feed_dict = {handle : val_handle})
avg_val_loss += val_loss
val_step += 1
except tf.errors.OutOfRangeError:
pass
train_writer.add_summary(tr_loss_summ, global_step = epoch)
val_writer.add_summary(val_loss_summ, global_step = epoch)
avg_tr_loss /= tr_step
avg_val_loss /= val_step
tr_loss_hist.append(avg_tr_loss)
val_loss_hist.append(avg_val_loss)
if epoch % 10 == 0:
print('epoch : {:3}, tr_loss : {:.3f}, val_loss : {:.3f}'.format(epoch, avg_tr_loss, avg_val_loss))
train_writer.close()
val_writer.close()
epoch : 0, tr_loss : 1757.168, val_loss : 1228.869 epoch : 10, tr_loss : 376.397, val_loss : 347.431 epoch : 20, tr_loss : 119.488, val_loss : 131.623 epoch : 30, tr_loss : 57.805, val_loss : 56.236 epoch : 40, tr_loss : 40.083, val_loss : 41.398 epoch : 50, tr_loss : 35.828, val_loss : 37.847 epoch : 60, tr_loss : 33.837, val_loss : 38.415 epoch : 70, tr_loss : 33.972, val_loss : 37.400 epoch : 80, tr_loss : 33.842, val_loss : 37.901 epoch : 90, tr_loss : 33.386, val_loss : 38.766
Visualization¶
In [11]:
plt.plot(tr_loss_hist, label = 'train')
plt.plot(val_loss_hist, label = 'validation')
plt.legend()
Out[11]:
<matplotlib.legend.Legend at 0x11e0651d0>
In [12]:
data = pd.read_table('../data/lecture03/example_with_placeholder/birth_life_2010.txt') # loading data for Visualization
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[12]:
<matplotlib.legend.Legend at 0x11e0e7ef0>
