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)