def get_model(): importlib.reload(d_data) importlib.reload(IE_data) d_metadata, d_idx_q, d_idx_a = d_data.load_data(PATH='../datasets/danny/') i_metadata, i_idx_q, i_idx_a = IE_data.load_data(PATH='../datasets/IE/') (d_trainX, d_trainY), (d_testX, d_testY), (d_validX, d_validY) = data_utils.split_dataset(d_idx_q, d_idx_a) (i_trainX, i_trainY), (i_testX, i_testY), (i_validX, i_validY) = data_utils.split_dataset(i_idx_q, i_idx_a) d_model = seq2seq_wrapper.Seq2Seq( xseq_len=d_trainX.shape[-1], yseq_len=d_trainY.shape[-1], xvocab_size=len(d_metadata['idx2w']), yvocab_size=len(d_metadata['idx2w']), ckpt_path='../ckpt/danny/', loss_path='', metadata=d_metadata, emb_dim=1024, num_layers=3 ) i_model = seq2seq_wrapper.Seq2Seq( xseq_len=i_trainX.shape[-1], yseq_len=i_trainY.shape[-1], xvocab_size=len(i_metadata['idx2w']), yvocab_size=len(i_metadata['idx2w']), ckpt_path='../ckpt/IE/', loss_path='', metadata=i_metadata, emb_dim=1024, num_layers=3 ) d_sess = d_model.restore_last_session() i_sess = i_model.restore_last_session() return d_model, i_model, d_sess, i_sess, d_metadata, i_metadata
# preprocessed data from datasets.danny import data import data_utils # 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 = 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/danny/', loss_path='ckpt/danny/preset/', metadata=metadata, emb_dim=emb_dim, num_layers=3,
# preprocessed data from datasets.danny import data import data_utils import numpy as np import importlib importlib.reload(data) # load data from pickle and npy files metadata, idx_q, idx_a = data.load_data(PATH='datasets/IE/') (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] 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/IE/', loss_path='', metadata=metadata, emb_dim=emb_dim,