def train(train_id_data, num_vocabs, num_taget_class):
max_epoch = 200
model_dir = "E:\Pycharm Project\FYP\RNN\Trained_models\save_models.ckpt"
hps = RNN.get_default_hparams()
hps.update(batch_size=150,
num_steps=120,
emb_size=100,
enc_dim=150,
vocab_size=num_vocabs + 1,
num_target_class=num_taget_class)
with tf.variable_scope("model"):
model = RNN(hps, "train")
sv = tf.train.Supervisor(is_chief=True,
logdir=model_dir,
summary_op=None,
global_step=model.global_step)
# tf assign compatible operators for gpu and cpu
tf_config = tf.ConfigProto(allow_soft_placement=True)
with sv.managed_session(config=tf_config) as sess:
local_step = 0
prev_global_step = sess.run(model.global_step)
train_data_set = SentimentDataset(train_id_data, hps.batch_size,
hps.num_steps)
losses = []
while not sv.should_stop():
fetches = [model.global_step, model.loss, model.train_op]
a_batch_data = next(train_data_set.iterator)
y, x, w = a_batch_data
fetched = sess.run(fetches, {
model.x: x,
model.y: y,
model.w: w,
model.keep_prob: hps.keep_prob
})
local_step += 1
_global_step = fetched[0]
_loss = fetched[1]
losses.append(_loss)
if local_step < 10 or local_step % 10 == 0:
epoch = train_data_set.get_epoch_num()
print("Epoch = {:3d} Step = {:7d} loss = {:5.3f}".format(
epoch, _global_step, np.mean(losses)))
_loss = []
if epoch >= max_epoch: break
print("Training is done.")
sv.stop()
# model.out_pred, model.out_probs
freeze_graph(
model_dir, "model/out_pred,model/out_probs",
"Final_graph.tf.pb") ## freeze graph with params to probobuf format
