# Variable initialization
if (kaldi_model == None) and (tf_model == None):
sess.run(tf.global_variables_initializer())
start_time = time.time()
start_clock = time.clock()
log.info(
"Starting training at: " +
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime(start_time)))
train_nn(n_epoch,
n_batch,
lr_first,
lr_last,
train_batch_func,
check_dev=check_dev_class_loss_acc,
get_para_func=get_para,
set_para_func=set_para,
model_save_file=model_prefix,
patience=patience,
save_func=save_func,
patience_2=patience_2,
half_every_N_epochs=half_every_N_epochs,
save_every_epoch=False)
end_time = time.time()
end_clock = time.clock()
log.info(
" Started training at: " +
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime(start_time)))
log.info(
" Ended training at: " +
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime(end_time)))
# Variable initialization
if (kaldi_txt_model == None) and (tf_model == None):
sess.run(tf.global_variables_initializer())
start_time = time.time()
start_clock = time.clock()
log.info(
"Starting training at: " +
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime(start_time)))
train_nn(n_epoch,
n_batch,
lr_first,
lr_last,
train_batch_func,
check_dev=check_dev_multi_loss_acc,
get_para_func=get_para,
set_para_func=set_para,
model_save_file=model_prefix,
patience=patience,
save_func=save_func,
patience_2=patience_2)
end_time = time.time()
end_clock = time.clock()
log.info(
" Started training at: " +
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime(start_time)))
log.info(
" Ended training at: " +
time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.gmtime(end_time)))
log.info(" CPU time used: " + str(end_clock - start_clock) + " s.")