import tensorflow as tf
import numpy as np
# preprocessed data
from datasets.cornell_corpus import data
import data_utils
# load data from pickle and npy files
metadata, idx_q, idx_a = data.load_data(PATH='datasets/cornell_corpus/')
(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 = 32
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,
yseq_len=yseq_len,
xvocab_size=xvocab_size,
yvocab_size=yvocab_size,
ckpt_path='ckpt/cornell_corpus/',
emb_dim=emb_dim,
num_layers=3
)
# preprocessed data
from datasets.cornell_corpus import data
import data_utils
import importlib
importlib.reload(data)
# load data from pickle and npy files
metadata, idx_q, idx_a = data.load_data(PATH='datasets/danny/')
(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(metadata['idx2w'])
yvocab_size = xvocab_size
emb_dim = 1024
import seq2seq_wrapper
model = seq2seq_wrapper.Seq2Seq(xseq_len=xseq_len,
yseq_len=yseq_len,
xvocab_size=xvocab_size,
yvocab_size=yvocab_size,
ckpt_path='ckpt/danny/',
emb_dim=emb_dim,
num_layers=3)
