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test_miss_six.py
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test_miss_six.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
BATCH_SIZE = 1
SNAPSHOT_DIR = './checkpoint/useful_six'
DATA_DIR = './dataset/dataSets/testing_six'
TEST_ONE = False
# 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')
pred = network(data, 'nn')
tf.global_variables_initializer().run()
# Saver for storing checkpoints of the model.
# Restore variables
restore_var = tf.global_variables()
loader = tf.train.Saver(var_list=restore_var)
if load(loader, sess, SNAPSHOT_DIR):
print(" [*] Load SUCCESS")
else:
print(" [!] Load failed...")
## test for one data
if TEST_ONE:
# input data
link = 165.0
# t1 = 102.83
# t2 = 106.34
# t3 = 141.43
# t4 = 73.72
# t5 = 80.27
# t6 = 130.12
hour = 115.0
day = 280
x_ = np.array([260.66,142.09237671,112.49593353,122.97939301,120.63757324,119.17584229,119.26552582,115.0,280])
res = sess.run(pred, feed_dict={data: [x_]})
print res
## test for pipeline
else:
for root, dirs, files in os.walk(DATA_DIR):
for fname in files:
test_file = os.path.join(root, fname)
x_ = []
y_ = []
with open(test_file, 'r') as fr:
line_ = fr.readline()
data_ = line_.split(' ')
item_x = [float(tt) for tt in data_]
x_.append(item_x)
for win in xrange(6):
res = sess.run(pred, feed_dict={data: x_})
y_.append(round(res[0][0], 2))
for jj in xrange(1, 6):
x_[0][jj] = x_[0][jj+1]
x_[0][6] = res[0][0]
fi = open('result_six/{}'.format(fname), 'w')
for ii in xrange(6):
fi.write(str(y_[ii]) + ' ')
fi.close()
# for root, dirs, files in os.walk(DATA_DIR):
# for fname in files:
# test_file = os.path.join(root, fname)
# x_ = []
# y_ = []
# with open(test_file, 'r') as fr:
# line_ = fr.readline()
# data_ = line_.split(' ')
# data_ = data_[:-1]
# item_x = [float(tt) for tt in data_]
# x_.append(item_x)
# res = sess.run(pred, feed_dict={data: x_})
# with open('result_combine/stage_6/six/{}'.format(fname), 'w') as fi:
# fi.write(str(res[0][0]) + ' ')
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)