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rnn_autoencoder.py
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rnn_autoencoder.py
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#!/usr/bin/env python3
"""
Training RNN autoencoder
"""
import tensorflow as tf
import numpy as np
import random
import sys
from utils import nn_model
from utils.config import TrainConfig, DecodeConfig
from utils.data_parser import load_data_into_mem, padding
#Data config
scp_file = 'feature_scp/train.scp'
dev_scp_file = 'feature_scp/dev.scp'
max_len = 777
min_epoch = 20
def print_progress(progress, total, loss, output_msg):
sys.stdout.write('\b'*len(output_msg))
new_output_msg = "Progress: {}/{} loss:{:.4f}"\
.format(progress, total, loss)
sys.stdout.write(new_output_msg)
sys.stdout.flush()
return new_output_msg
if __name__ == '__main__':
tr_config = TrainConfig("info")
tr_config.max_len = max_len
tr_config.show_config()
sys.stdout.flush()
max_len = max_len
tr_dropout = tr_config.dropout_keep_prob
tr_zoneout = tr_config.zoneout_keep_prob
max_epoch = tr_config.max_epoch
tr_batch = tr_config.batch_size
print("=============================================================")
print(" Loading data ")
print("=============================================================")
sys.stdout.flush()
data_list = load_data_into_mem(scp_file)
dev_data_list = load_data_into_mem(dev_scp_file)
feature_dim = len(data_list[0][0])
train_utt_num = len(data_list)
dev_utt_num = len(dev_data_list)
print("feature dim: " + str(feature_dim))
print("utt_num: " + str(train_utt_num))
print("dev_utt_num: " + str(dev_utt_num))
tr_config.feature_dim = feature_dim
print("=============================================================")
print(" Set up models ")
print("=============================================================")
sys.stdout.flush()
sess = tf.Session()
#setup nn model's input tensor
x = tf.placeholder(tf.float32, [None, max_len, feature_dim])
y_ = tf.placeholder(tf.float32, [None, max_len, feature_dim])
batch_size = tf.placeholder(tf.int32)
add_noise = tf.placeholder(tf.bool)
dropout_keep_prob = tf.placeholder(tf.float32)
zoneout_keep_prob = tf.placeholder(tf.float32)
##pack the input tensors
input_tensors = {}
input_tensors["x"] = x
input_tensors["y_"] = y_
input_tensors["batch_size"] = batch_size
input_tensors["add_noise"] = add_noise
input_tensors["dropout_keep_prob"] = dropout_keep_prob
input_tensors["zoneout_keep_prob"] = zoneout_keep_prob
model = nn_model.NeuralNetwork(tr_config, input_tensors)
model.setup_train()
model.init_vars(sess)
output_msg = ''
best_dev_recon_loss = float('inf')
print("=============================================================")
print(" Start Training ")
print("=============================================================")
sys.stdout.flush()
for epoch in range(1, max_epoch + 1):
print("[ Epoch {} ]".format(epoch))
print("")
random.shuffle(data_list)
counter = 0
while counter < train_utt_num:
remain_utt_num = train_utt_num - counter
batch_size = min(tr_config.batch_size, remain_utt_num)
X = padding(data_list[counter:counter + batch_size], \
max_len, feature_dim)
counter += len(X)
model.train(sess, X, X, tr_dropout, tr_zoneout, True, len(X))
recon_loss = model.get_tensor_val('re_loss', sess, X, X, len(X))
output_msg = print_progress(counter, train_utt_num, recon_loss,\
output_msg)
print('')
if epoch >= min_epoch:
#dev eval
output_msg = ''
counter = 0
##precision/recall_set dimension: [th * utt]
recon_loss_list = []
while counter < dev_utt_num:
remain_utt_num = dev_utt_num - counter
batch_size = min(tr_config.batch_size, remain_utt_num)
X = padding(dev_data_list[counter:counter + batch_size], \
max_len, feature_dim)
counter += len(X)
recon_loss = model.get_tensor_val('re_loss', sess, X, X, len(X))
recon_loss_list.append(recon_loss)
output_msg = print_progress(counter, dev_utt_num, recon_loss,\
output_msg)
dev_recon_loss = sum(recon_loss_list) / len(recon_loss_list)
print('')
print('The dev loss is {:.4f}'.format(dev_recon_loss))
if epoch == min_epoch:
model.save_vars(sess, tr_config.model_loc)
best_dev_recon_loss = dev_recon_loss
continue
if dev_recon_loss < best_dev_recon_loss:
model.save_vars(sess, tr_config.model_loc)
best_dev_recon_loss = dev_recon_loss
else:
print('Performance get worse, stop training')
break
print('=============================================================')
print(' Training finished, show config info. again ')
print('=============================================================')
tr_config.show_config()