test_input_array.py

# coding: utf-8
from __future__ import print_function
import numpy as np
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.contrib.timeseries.python.timeseries import  NumpyReader


x = np.array(range(1000))
noise = np.random.uniform(-0.2, 0.2, 1000)
y = np.sin(np.pi * x / 100) + x / 200. + noise
plt.plot(x, y)
plt.savefig('timeseries_y.jpg')

data = {
    tf.contrib.timeseries.TrainEvalFeatures.TIMES: x,
    tf.contrib.timeseries.TrainEvalFeatures.VALUES: y,
}

reader = NumpyReader(data)

with tf.Session() as sess:
    full_data = reader.read_full()
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    print(sess.run(full_data))
    coord.request_stop()

train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(
    reader, batch_size=2, window_size=10)

with tf.Session() as sess:
    batch_data = train_input_fn.create_batch()
    coord = tf.train.Coordinator()
    threads = tf.train.start_queue_runners(sess=sess, coord=coord)
    one_batch = sess.run(batch_data[0])
    coord.request_stop()

print('one_batch_data:', one_batch)