accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
'''Model save'''
# Initialize the saver to save session.
saver = tf.train.Saver(write_version=tf.train.SaverDef.V1, max_to_keep=50)
saved_model_path = 'model/'
to_save_model_path = 'model/'
'''Start a session and run up.'''
with tf.Session(config=config) as sess:
logging.info("Session started!")
sess.run(tf.global_variables_initializer())
# Prepare data set.
dataSet = InputReader(dataFile, batchSize, timestepSize)
# Prepare result writer.
resultWriter = ResultWriter(resultFile)
for i in range(iteration):
(batchX, batchY) = dataSet.getBatch(i)
_, trainingCost, modelOutput = sess.run([train_op, cost, logits],
feed_dict={
X: batchX,
y: batchY,
keep_prob: 1.0
})
logging.info("Iteration:" + str(i) +
", \tbatch loss= {:.6f}".format(trainingCost))
logging.debug("batchX:" + str(batchX[0]))
logging.debug("batchY:" + str(batchY[0]))
logging.debug("modelOutput:" + str(modelOutput[0]))
# Save output result.
if (i) % saveIteration == 0:
# Save model
saver.save(sess, to_save_model_path, global_step=saveIteration)
