def train(self,data,sess,isTree=True):
from random import shuffle
shuffle(data)
losses=[]
for i in range(0,len(data),self.batch_size):
batch_size = min(i+self.batch_size,len(data))-i
batch_data=data[i:i+batch_size]
seqdata,seqlabels,seqlngths,max_len=extract_seq_data(batch_data
,self.internal,self.config.maxseqlen)
feed={self.input:seqdata,self.labels:seqlabels,
self.dropout:self.config.dropout,self.lngths:
seqlngths,self.batch_len:len(seqdata),self.max_time:max_len}
#loss,_=sess.run([self.loss,self.train_op],feed_dict=feed)
loss,_,_=sess.run([self.loss,self.train_op1,self.train_op2],feed_dict=feed)
#sess.run(self.train_op,feed_dict=feed)
losses.append(loss)
avg_loss=np.mean(losses)
sstr='avg loss %.2f at example %d of %d\r' % (avg_loss, i, len(data))
sys.stdout.write(sstr)
sys.stdout.flush()
#if i>100: break
return np.mean(losses)
def evaluate(self, data, sess):
num_correct = 0
total_data = 0
for i in range(0, len(data), self.batch_size):
batch_size = min(i + self.batch_size, len(data)) - i
batch_data = data[i:i + batch_size]
seqdata, seqlabels, seqlngths, max_len = extract_seq_data(
batch_data, 0, self.config.maxseqlen)
feed = {
self.input: seqdata,
self.labels: seqlabels,
self.dropout: 1.0,
self.lngths: seqlngths,
self.batch_len: len(seqdata),
self.max_time: max_len
}
pred = sess.run(self.pred, feed_dict=feed)
y = np.argmax(pred, axis=1)
#print y,seqlabels,pred
#print y,seqlabels,pred
for i, v in enumerate(y):
if seqlabels[i] == v:
num_correct += 1
total_data += 1
acc = float(num_correct) / float(total_data)
return acc
def train(self,data,sess,isTree=True):
from random import shuffle
shuffle(data)
losses=[]
for i in range(0,len(data),self.batch_size):
batch_size = min(i+self.batch_size,len(data))-i
batch_data=data[i:i+batch_size]
seqdata,seqlabels,seqlngths,max_len=extract_seq_data(batch_data
,self.internal,self.config.maxseqlen)
feed={self.input:seqdata,self.labels:seqlabels,
self.dropout:self.config.dropout,self.lngths:
seqlngths,self.batch_len:len(seqdata),self.max_time:max_len}
#loss,_=sess.run([self.loss,self.train_op],feed_dict=feed)
loss,_,_=sess.run([self.loss,self.train_op1,self.train_op2],feed_dict=feed)
#sess.run(self.train_op,feed_dict=feed)
losses.append(loss)
avg_loss=np.mean(losses)
sstr='avg loss %.2f at example %d of %d\r' % (avg_loss, i, len(data))
sys.stdout.write(sstr)
sys.stdout.flush()
#if i>100: break
return np.mean(losses)
def evaluate(self,data,sess):
num_correct=0
total_data=0
for i in range(0,len(data),self.batch_size):
batch_size = min(i+self.batch_size,len(data))-i
batch_data=data[i:i+batch_size]
seqdata,seqlabels,seqlngths,max_len=extract_seq_data(batch_data
,0,self.config.maxseqlen)
feed={self.input:seqdata,self.labels:seqlabels,
self.dropout:1.0,self.lngths:
seqlngths,self.batch_len:len(seqdata),self.max_time:max_len}
pred=sess.run(self.pred,feed_dict=feed)
y=np.argmax(pred,axis=1)
#print y,seqlabels,pred
#print y,seqlabels,pred
for i,v in enumerate(y):
if seqlabels[i]==v:
num_correct+=1
total_data+=1
acc=float(num_correct)/float(total_data)
return acc
