def train(self, train_data, dev_data=None):
hparams = self.hparams
sess = self.sess
assert len(train_data[0]) == len(
train_data[1]), "Size of features data must be equal to label"
for epoch in range(hparams.epoch):
info = {}
info['loss'] = []
info['norm'] = []
start_time = time.time()
for idx in range(len(train_data[0]) // hparams.batch_size + 3):
try:
if hparams.steps <= idx:
T = (time.time() - start_time)
self.eval(T, dev_data, hparams, sess)
break
except:
pass
if idx * hparams.batch_size >= len(train_data[0]):
T = (time.time() - start_time)
self.eval(T, dev_data, hparams, sess)
break
batch=train_data[0][idx*hparams.batch_size:\
min((idx+1)*hparams.batch_size,len(train_data[0]))]
batch = utils.hash_batch(batch, hparams)
label=train_data[1][idx*hparams.batch_size:\
min((idx+1)*hparams.batch_size,len(train_data[1]))]
loss,_,norm=sess.run([self.loss,self.update,self.grad_norm],\
feed_dict={self.features:batch,self.label:label})
info['loss'].append(loss)
info['norm'].append(norm)
if (idx + 1) % hparams.num_display_steps == 0:
info['learning_rate'] = hparams.learning_rate
info["train_ppl"] = np.mean(info['loss'])
info["avg_grad_norm"] = np.mean(info['norm'])
utils.print_step_info(" ", epoch, idx + 1, info)
del info
info = {}
info['loss'] = []
info['norm'] = []
if (idx + 1) % hparams.num_eval_steps == 0 and dev_data:
T = (time.time() - start_time)
self.eval(T, dev_data, hparams, sess)
self.saver.restore(sess, 'model_tmp/model')
T = (time.time() - start_time)
self.eval(T, dev_data, hparams, sess)
os.system("rm -r model_tmp")
def train(self,train,dev):
hparams=self.hparams
sess=self.sess
train_single_features=train[hparams.single_features].values
train_label=train[hparams.label].values
if hparams.multi_features is not None:
train_multi_features=train[hparams.multi_features].values
train_multi_weights=train[hparams.multi_weights].values
if hparams.dense_features is not None:
train_dense_features=train[hparams.dense_features].values
if hparams.kv_features is not None:
train_kv_features=train[hparams.kv_features].values
if hparams.cross_features is not None:
train_cross_features=train[hparams.cross_features].values
for epoch in range(hparams.epoch):
info={}
info['loss']=[]
info['norm']=[]
start_time = time.time()
for idx in range(len(train)//hparams.batch_size+3):
if idx*hparams.batch_size>=len(train):
T=(time.time()-start_time)
info['learning_rate']=hparams.learning_rate
info["train_ppl"]= np.mean(info['loss'])
info["avg_grad_norm"]=np.mean(info['norm'])
utils.print_step_info(" ", epoch,idx+1, info)
if dev is not None:
self.eval(T,dev,hparams,sess)
break
feed_dic={}
single_batch=train_single_features[idx*hparams.batch_size:min((idx+1)*hparams.batch_size,len(train))]
single_batch=utils.hash_single_batch(single_batch,hparams)
feed_dic[self.single_features]=single_batch
if hparams.multi_features is not None:
multi_batch=train_multi_features[idx*hparams.batch_size:min((idx+1)*hparams.batch_size,len(train))]
multi_weight=train_multi_weights[idx*hparams.batch_size:min((idx+1)*hparams.batch_size,len(train))]
multi_batch,multi_weights=utils.hash_multi_batch(multi_batch,multi_weight,hparams)
feed_dic[self.multi_features]=multi_batch
feed_dic[self.multi_weights]=multi_weights
if hparams.dense_features is not None:
feed_dic[self.dense_features]=train_dense_features[idx*hparams.batch_size:\
min((idx+1)*hparams.batch_size,len(train))]
if hparams.kv_features is not None:
feed_dic[self.kv_features]=train_kv_features[idx*hparams.batch_size:\
min((idx+1)*hparams.batch_size,len(train))]
if hparams.cross_features is not None:
cross_batch=train_cross_features[idx*hparams.batch_size:min((idx+1)*hparams.batch_size,len(train))]
cross_batch=utils.hash_single_batch(cross_batch,hparams)
feed_dic[self.cross_features]=cross_batch
label=train_label[idx*hparams.batch_size: min((idx+1)*hparams.batch_size,len(train))]
feed_dic[self.label]=label
feed_dic[self.use_norm]=True
loss,_,norm=sess.run([self.loss,self.update,self.grad_norm],feed_dict=feed_dic)
info['loss'].append(loss)
info['norm'].append(norm)
if (idx+1)%hparams.num_display_steps==0:
info['learning_rate']=hparams.learning_rate
info["train_ppl"]= np.mean(info['loss'])
info["avg_grad_norm"]=np.mean(info['norm'])
utils.print_step_info(" ", epoch,idx+1, info)
del info
info={}
info['loss']=[]
info['norm']=[]
T=(time.time()-start_time)
return self.best_score
