kddcup2017/test_six.py at master · Engineering-Course/kddcup2017 · GitHub

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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
NUM_STEPS = 84
SNAPSHOT_DIR = './checkpoint/useful_six'

# 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...")

    for step in range(NUM_STEPS):
        x_ = load_test_travel_data(step)
        y_ = []
        # print x_[0]
        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/{}.txt'.format(step), 'w')
        for ii in xrange(6):
            fi.write(str(y_[ii]) + ' ')
        fi.close()
            # print x_
            # wait = raw_input()


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)