def __init__(self, train=False):
# load data from pickle and npy files
self.metadata, idx_q, idx_a = data.load_data(PATH='datasets/twitter/')
(trainX, trainY), (testX, testY), (validX,
validY) = data_utils.split_dataset(
idx_q, idx_a) # parameters
xseq_len = trainX.shape[-1]
yseq_len = trainY.shape[-1]
batch_size = 16
xvocab_size = len(self.metadata['idx2w'])
yvocab_size = xvocab_size
emb_dim = 1024
importlib.reload(seq2seq_wrapper)
self.model = seq2seq_wrapper.Seq2Seq(xseq_len=xseq_len,
yseq_len=yseq_len,
xvocab_size=xvocab_size,
yvocab_size=yvocab_size,
ckpt_path='ckpt/twitter/',
emb_dim=emb_dim,
num_layers=3)
if train:
val_batch_gen = data_utils.rand_batch_gen(validX, validY, 32)
train_batch_gen = data_utils.rand_batch_gen(
trainX, trainY, batch_size)
sess = self.model.train(train_batch_gen, val_batch_gen)
self.sess = self.model.restore_last_session()
# # Demonstrate Seq2Seq Wrapper with twitter chat log
# In[ ]:
import tensorflow as tf
import numpy as np
# preprocessed data
from datasets.twitter import data
import data_utils
# In[ ]:
# load data from pickle and npy files
metadata, idx_q, idx_a = data.load_data(PATH='datasets/twitter/')
(trainX, trainY), (testX,
testY), (validX,
validY) = data_utils.split_dataset(idx_q, idx_a)
# In[3]:
# parameters
xseq_len = trainX.shape[-1]
yseq_len = trainY.shape[-1]
batch_size = 16
xvocab_size = len(metadata['idx2w'])
yvocab_size = xvocab_size
emb_dim = 1024
# In[4]:
# In[1]:
import tensorflow as tf
import numpy as np
# preprocessed data
from datasets.twitter import data
import data_utils
tf.flags.DEFINE_boolean("restore", False, "restore the model from checkpoints")
FLAGS = tf.flags.FLAGS
# load data from pickle and npy files
metadata, idx_q, idx_a = data.load_data(PATH='datasets/opensubtitle/')
(trainX, trainY), (testX, testY), (validX, validY) = data_utils.split_dataset(idx_q, idx_a)
# parameters
xseq_len = trainX.shape[-1]
yseq_len = trainY.shape[-1]
batch_size = 128
xvocab_size = len(metadata['idx2w'])
yvocab_size = xvocab_size
emb_dim = 1024
import seq2seq_wrapper
# In[7]:
model = seq2seq_wrapper.Seq2Seq(xseq_len=xseq_len,
