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train_six_zero.py
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train_six_zero.py
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import tensorflow as tf
import numpy as np
import os
import time
from utils import *
os.environ["CUDA_VISIBLE_DEVICES"]="0"
# Parameters
window = 6
phrase = 'six'
BATCH_SIZE = 5
LEARNING_RATE = 1e-5
TRAINING_FLAG = True
EPOCH = 5000
SNAPSHOT_DIR = './checkpoint/ck_{}_zero'.format(phrase)
# SNAPSHOT_DIR = './checkpoint/useful_zero_six'
LOG_DIR = './logs/{}_zero'.format(phrase)
TRAIN_ID_FILE = 'dataset/dataSets/train_zero_{}_id.txt'.format(window)
# Network Parameters
n_hidden_1 = 9 # 1st layer number of features
n_hidden_2 = 9 # 2nd layer number of features
n_hidden_3 = 18
n_input = 9
n_classes = 1
# Store layers weight & bias
weights = {
'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1], stddev=0.1)),
'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2], stddev=0.1)),
'h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3], stddev=0.1)),
'out': tf.Variable(tf.random_normal([n_hidden_3, n_classes], stddev=0.1))
}
biases = {
'b1': tf.Variable(tf.constant(0.0, shape=[n_hidden_1])),
'b2': tf.Variable(tf.constant(0.0, shape=[n_hidden_2])),
'b3': tf.Variable(tf.constant(0.0, shape=[n_hidden_3])),
'out': tf.Variable(tf.constant(0.0, shape=[n_classes]))
}
def network(data, name):
with tf.variable_scope(name) as scope:
layer_1 = tf.add(tf.matmul(data, weights['h1']), biases['b1'])
layer_1 = tf.nn.relu(layer_1)
layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
layer_2 = tf.nn.relu(layer_2)
layer_3 = tf.add(tf.matmul(layer_2, weights['h3']), biases['b3'])
layer_3 = tf.nn.relu(layer_3)
out_layer = tf.matmul(layer_3, weights['out']) + biases['out']
return out_layer
def main(sess):
data = tf.placeholder(tf.float32, [BATCH_SIZE, n_input], name='data')
label = tf.placeholder(tf.float32, [BATCH_SIZE, n_classes], name='label')
pred = network(data, 'nn')
# Define loss and optimizer
cost = tf.reduce_mean(tf.abs(pred - label))
optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE).minimize(cost)
# loss summary
loss_sum = tf.summary.scalar("loss", cost)
summary_writer = tf.summary.FileWriter(LOG_DIR, graph=tf.get_default_graph())
tf.global_variables_initializer().run()
# Restore variables
restore_var = tf.global_variables()
saver = tf.train.Saver(var_list=restore_var, max_to_keep=5000)
loader = tf.train.Saver(var_list=restore_var)
if load(loader, sess, SNAPSHOT_DIR):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
if TRAINING_FLAG:
# Iterate over training steps.
counter = 1
fi = open('record/{}_zero.txt'.format(phrase), 'w')
for ee in xrange(EPOCH):
with open(TRAIN_ID_FILE, 'r') as list_file:
data_list = list_file.readlines()
np.random.shuffle(data_list)
batch_idxs = len(data_list) // BATCH_SIZE
for idx in xrange(0, batch_idxs):
batch_files = data_list[idx*BATCH_SIZE:(idx+1)*BATCH_SIZE]
x_, y_ = load_train_zero_data(batch_files, window)
summary, _ = sess.run([loss_sum, optimizer], feed_dict={data: x_, label: y_})
summary_writer.add_summary(summary, counter)
counter += 1
save(saver, sess, SNAPSHOT_DIR, counter)
## Test...
error = []
for idx in xrange(0, batch_idxs):
batch_files = data_list[idx*BATCH_SIZE:(idx+1)*BATCH_SIZE]
x_, y_ = load_train_zero_data(batch_files, window)
y, res = sess.run([label, pred], feed_dict={data: x_, label: y_})
for bb in xrange(BATCH_SIZE):
error.append(np.abs(y[bb][0]-res[bb][0])/y[bb][0])
test_result = np.mean(error)
y, res = sess.run([label, pred], feed_dict={data: x_, label: y_})
print('test_result: {:f}, epoch {:f}, step {:d}, y = {:.3f}, res = {:.3f}, loss = {:.3f}'.format(test_result, ee, counter, y[0][0], res[0][0], np.abs(y[0][0]-res[0][0])/y[0][0]))
fi.write('test_result: {:f}, epoch {:f}, step {:d}\n'.format(test_result, ee, counter))
fi.close()
else:
error = []
with open(TRAIN_ID_FILE, 'r') as list_file:
data_list = list_file.readlines()
batch_idxs = len(data_list) // BATCH_SIZE
for idx in xrange(0, batch_idxs):
batch_files = data_list[idx*BATCH_SIZE:(idx+1)*BATCH_SIZE]
x_, y_ = load_train_zero_data(batch_files, window)
y, res = sess.run([label, pred], feed_dict={data: x_, label: y_})
for bb in xrange(BATCH_SIZE):
error.append(np.abs(y[bb][0]-res[bb][0])/y[bb][0])
print np.mean(error)
if __name__ == '__main__':
# Set up tf session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
main(sess)