Create an interactive session and initialize a variable:
In [1]:
import tensorflow as tf
sess = tf.InteractiveSession()
raw_data = [1., 2., 8., -1., 0., 5.5, 6., 13]
spikes = tf.Variable([False] * len(raw_data), name='spikes')
spikes.initializer.run()
The saver op will enable saving and restoring:
In [2]:
saver = tf.train.Saver()
Loop through the data and update the spike variable when there is a significant increase:
In [3]:
for i in range(1, len(raw_data)):
if raw_data[i] - raw_data[i-1] > 5:
spikes_val = spikes.eval()
spikes_val[i] = True
updater = tf.assign(spikes, spikes_val)
updater.eval()
Now, save your variable to disk!
In [4]:
save_path = saver.save(sess, "./spikes.ckpt")
print("spikes data saved in file: %s" % save_path)
Adieu:
In [5]:
sess.close()