def __init__(self, hyper_params=None, params=None):
graph_base.GraphBase.__init__(self, hyper_params, params)
# network design
tf.logging.info(
"============================================================")
tf.logging.info("BUILDING NETWORK...")
self.hyper_params["model_type"] = "Test HREDDECODER"
self.embedding = graph_base.get_params([
self.hyper_params["common_vocab"] + self.hyper_params["kb_vocab"],
self.hyper_params["emb_dim"]
])
self.encoder = encoder.Encoder(self.hyper_params["encoder_layer_num"],
self.hyper_params["emb_dim"],
self.hyper_params["encoder_h_dim"],
norm=True)
self.kb_scorer = KBscorer.BiKBScorer(
self.hyper_params["emb_dim"] + self.hyper_params["hred_h_dim"],
FLAGS.candidate_num)
self.hred = HRED.HRED(self.hyper_params["encoder_h_dim"],
self.hyper_params["hred_h_dim"],
self.hyper_params["emb_dim"],
norm=True)
self.decoder = decoder.Decoder([
self.hyper_params["decoder_gen_layer_num"],
self.hyper_params["emb_dim"],
self.hyper_params["decoder_gen_h_dim"],
self.hyper_params["hred_h_dim"] + FLAGS.candidate_num,
self.hyper_params["common_vocab"]
], [], [
self.hyper_params["decoder_mlp_layer_num"],
self.hyper_params["emb_dim"] + FLAGS.candidate_num * 2 +
self.hyper_params["hred_h_dim"] +
self.hyper_params["decoder_gen_h_dim"],
self.hyper_params["decoder_mlp_h_dim"], 2
],
d_type="MASK",
norm=True,
hyper_params=None,
params=None)
self.print_params()
self.encoder.print_params()
self.kb_scorer.print_params()
self.hred.print_params()
self.decoder.print_params()
self.params = [self.embedding] + self.encoder.params + self.hred.params + \
self.kb_scorer.params + self.decoder.params
params_dict = {}
for i in range(0, len(self.params)):
params_dict[str(i)] = self.params[i]
self.saver = tf.train.Saver(params_dict)
self.optimizer = self.get_optimizer()
self.params_simple = [
self.embedding
] + self.encoder.params + self.hred.params + self.decoder.params
def __init__(self, hyper_params=None, params=None):
graph_base.GraphBase.__init__(self, hyper_params, params)
# network design
tf.logging.info(
"============================================================")
tf.logging.info("BUILDING NETWORK...")
self.hyper_params["model_type"] = "Test Autoencoder"
self.embedding = graph_base.get_params([
self.hyper_params["common_vocab"] + self.hyper_params["kb_vocab"],
self.hyper_params["emb_dim"]
])
self.encoder = encoder.Encoder(self.hyper_params["encoder_layer_num"],
self.hyper_params["emb_dim"],
self.hyper_params["encoder_h_dim"])
self.aux_decoder = decoder.AuxDecoder([
self.hyper_params["decoder_gen_layer_num"],
self.hyper_params["emb_dim"],
self.hyper_params["decoder_gen_h_dim"],
self.hyper_params["encoder_h_dim"],
FLAGS.common_vocab + FLAGS.candidate_num
])
self.print_params()
self.encoder.print_params()
self.aux_decoder.print_params()
self.params = [self.embedding
] + self.encoder.params + self.aux_decoder.params
params_dict = {}
for i in range(0, len(self.params)):
params_dict[str(i)] = self.params[i]
self.saver = tf.train.Saver(params_dict)
self.optimizer = self.get_optimizer()
self.params_simple = [self.embedding
] + self.encoder.params + self.aux_decoder.params