def RNN_predict(stringlist):
data_x=[]
seq_len=[]
for line in stringlist:
cur=helper_fun.paragraph_to_sentencelist(line, remove_stopwords=True)
data_x+=cur
seq_len.append(len(cur[0]))
outputs=[]
for i in range(len(data_x)):
seq=seq_len[i:i+1]
dat=data_x[i:i+1]
x_data=[]
for sen in dat:
x_data.append(helper_fun.sentence_to_vector(sen, seq[0]+1))
seq=np.array(seq)
ans=sess.run(myRnnModelpredict['output'], feed_dict={myRnnModelpredict['x']:x_data, myRnnModelpredict['seqlen']:seq})
outputs=outputs+list(ans)
return outputs
train_x = [train_x[i] for i in idx]
train_y = [train_y[i] for i in idx]
seq_len = [seq_len[i] for i in idx]
print("training epoch", i + 1, "of", RNN_model.parameters.num_epoch)
z = len(train_x)
j = 0
epoch_loss = 0
while j < z and j + RNN_model.parameters.num_batch < z:
x_train = train_x[j:j + RNN_model.parameters.num_batch]
y_train = train_y[j:j + RNN_model.parameters.num_batch]
seq = seq_len[j:j + RNN_model.parameters.num_batch]
max_len = max(seq) + 1
j += RNN_model.parameters.num_batch
data_x = []
for sen in x_train:
data_x.append(helper_fun.sentence_to_vector(sen, max_len))
seq = np.array(seq)
_, loss = sess.run(
[myRnnModel['minimizer'], myRnnModel['loss']],
feed_dict={
myRnnModel['x']: data_x,
myRnnModel['y']: y_train,
myRnnModel['seqlen']: seq
})
epoch_loss += loss
print("Epoch: ", i + 1, "completed. Loss: ", epoch_loss)
if i % 10 == 0:
myRnnModel['saver'].save(sess, "trainedmodels/rnn.model")
if epoch_loss <= 5.0:
break
myRnnModel['saver'].save(sess, "trainedmodels/rnn.model")
sess = tf.Session()
init = tf.initialize_all_variables()
#sess.run(init)
myCnnModelpredict['saver'].restore(sess, "trainedmodels/cnn.model")
outputs = []
for i in range(len(data_x)):
#seq=seq_len[i:i+1]
dat = data_x[i:i + 1]
if (len(dat[0]) < 5):
continue
x_data = []
seq = seq_len[i]
#print (dat)
for sen in dat:
x_data.append(helper_fun.sentence_to_vector(sen, seq))
ans = sess.run(myCnnModelpredict['output'],
feed_dict={
myCnnModelpredict['x']: x_data,
myCnnModelpredict['seqlen']: seq
})
outputs = outputs + list(ans)
#print (outputs)
filename = './output/outputCNN.txt'
dire = os.path.dirname(filename)
if not os.path.exists(dire):
os.makedirs(dire)
with open(filename, 'w') as f:
for i in outputs:
f.write(str(i) + '\n')