def train(self):
start_time = time.time()
print('Training and evaluating...')
self.sess.run(tf.global_variables_initializer())
l = self.X_train["feature_emb"].shape[0]
qid_unique = np.unique(self.X_train["qid"])
num_qid_unique = len(qid_unique)
train_idx_shuffle = np.arange(l)
total_batch = 0
# evaluate before training
#loss_mean_valid, err_mean_valid, ndcg_mean_valid, ndcg_all_mean_valid = self.evaluate(self.X_valid)
# training model...
for epoch in range(2):
np.random.seed(epoch)
np.random.shuffle(train_idx_shuffle)
batches = get_batch_index(train_idx_shuffle, conf.batch_size)
for i, idx in enumerate(batches):
ind = idx
feed_dict = self._get_feed_dict(self.X_train, ind, training=True)
#Asess_out = self.sess.run({'loss': self.loss, 'train_op': self.train_op, 'learn_rate': self.learning_rate,
# 'debug_info': self.debug_info}, feed_dict=feed_dict)
loss, lr, opt = self.sess.run((self.loss, self.learning_rate, self.train_op), feed_dict=feed_dict)
total_batch += 1
if total_batch % conf.eval_every_num_update == 0:
loss_mean_valid, err_mean_valid, ndcg_mean_valid, ndcg_all_mean_valid = self.evaluate(self.X_valid)
print("[epoch-{}, batch-{}] -- Train Loss: {:5f} -- Valid Loss: {:5f} NDCG: {:5f} -- {:5f} s".format(
epoch + 1, total_batch, loss, loss_mean_valid, ndcg_mean_valid, time.time() - start_time))
a=1
self.save_session()
def get_batch_data(self, aspects, contexts, labels, context_lens, cost_ws, feature_eng, aspects_ch, contexts_ch,
aspects_ch_lens, contexts_ch_lens, batch_size, is_shuffle, keep_prob):
total = int(len(context_lens) / batch_size) + 1 if len(context_lens) % batch_size != 0 else int(
len(context_lens) / batch_size)
for index in tqdm_notebook(get_batch_index(len(aspects), batch_size, is_shuffle), total=total):
feed_dict = {
self.aspects: aspects[index],
self.contexts: contexts[index],
self.context_lens: context_lens[index],
self.feature_eng: feature_eng[index],
self.cost_ws: cost_ws[index],
self.dropout_keep_prob: keep_prob
}
if self.use_char_emb:
feed_dict[self.aspects_ch] = aspects_ch[index]
feed_dict[self.contexts_ch] = contexts_ch[index]
feed_dict[self.aspects_ch_lens] = aspects_ch_lens[index].reshape(-1)
feed_dict[self.contexts_ch_lens] = contexts_ch_lens[index]
if len(labels) > 0: feed_dict[self.labels] = labels[index]
if keep_prob < 1 and self.random_del_prob > 0: # train mode
cnt_per_row = int(self.random_del_prob * self.max_context_len)
zero_rows = np.concatenate([np.ones(cnt_per_row).astype(int) * i for i in range(len(index))])
zero_cols = np.random.choice(self.max_context_len, len(index) * cnt_per_row)
if self.use_char_emb:
feed_dict[self.contexts_ch_lens][zero_rows, zero_cols] = 0
# 这里不一定是0，可以试试别的
feed_dict[self.contexts][zero_rows, zero_cols] = 0
if self.use_char_emb:
feed_dict[self.contexts_ch_lens] = feed_dict[self.contexts_ch_lens].reshape(-1)
yield feed_dict, len(index)
def get_batch_data(self, aspects, contexts, labels, aspect_lens, context_lens, batch_size, is_shuffle, keep_prob):
for index in get_batch_index(len(aspects), batch_size, is_shuffle):
feed_dict = {
self.aspects: aspects[index],
self.contexts: contexts[index],
self.labels: labels[index],
self.aspect_lens: aspect_lens[index],
self.context_lens: context_lens[index],
self.dropout_keep_prob: keep_prob,
}
yield feed_dict, len(index)
def get_batch_data(self, sentences, aspects, sentence_lens, sentence_locs, labels, batch_size, is_shuffle, keep_prob):
for index in get_batch_index(len(sentences), batch_size, is_shuffle):
feed_dict = {
self.sentences: sentences[index],
self.aspects: aspects[index],
self.sentence_lens: sentence_lens[index],
self.sentence_locs: sentence_locs[index],
self.labels: labels[index],
self.dropout_keep_prob: keep_prob,
}
yield feed_dict, len(index)
def get_batch_data(self, sentences, aspects, sentence_lens, sentence_locs,
labels, batch_size, is_shuffle, keep_prob):
for index in get_batch_index(len(sentences), batch_size, is_shuffle):
feed_dict = {
self.sentences: sentences[index],
self.aspects: aspects[index],
self.sentence_lens: sentence_lens[index],
self.sentence_locs: sentence_locs[index],
self.labels: labels[index],
self.dropout_keep_prob: keep_prob,
}
yield feed_dict, len(index)
def get_batch(aspects, contexts, labels, aspect_lens, context_lens, aspect_lex, context_lex,
batch_size, is_shuffle, keep_prob):
aspects = np.array(aspects)
contexts = np.array(contexts)
labels = np.array(labels)
aspect_lens = np.array(aspect_lens)
context_lens = np.array(context_lens)
context_lex = np.array(context_lex)
aspect_lex = np.array(aspect_lex)
for index in get_batch_index(len(aspects), batch_size, is_shuffle):
feed_dict = {
input_aspects: aspects[index],
input_contexts: contexts[index],
input_labels: labels[index],
input_aspect_lens: aspect_lens[index],
input_context_lens: context_lens[index],
input_context_lex: context_lex[index],
input_aspect_lex: aspect_lex[index],
dropout_keep_prob: keep_prob
}
yield feed_dict, len(index)
def get_batch_data(self, aspects, contexts, labels, aspect_lens, context_lens, aspect_lex, context_lex, batch_size, is_shuffle,
keep_prob):
aspects = np.array(aspects)
contexts = np.array(contexts)
labels = np.array(labels)
aspect_lens = np.array(aspect_lens)
context_lens = np.array(context_lens)
context_lex = np.array(context_lex)
aspect_lex = np.array(aspect_lex)
for index in get_batch_index(len(aspects), batch_size, is_shuffle):
feed_dict = {
self.aspects: aspects[index],
self.contexts: contexts[index],
self.labels: labels[index],
self.aspect_lens: aspect_lens[index],
self.context_lens: context_lens[index],
self.context_lex_embedding: context_lex[index],
self.aspect_lex_embedding: aspect_lex[index],
self.dropout_keep_prob: keep_prob,
}
yield feed_dict, len(index)
def get_batch_data(self, aspects, contexts, labels, aspect_lens,
context_lens, batch_size, is_shuffle, keep_prob):
for index in tqdm_notebook(
get_batch_index(len(aspects), batch_size, is_shuffle),
total=int(len(context_lens) / batch_size) + 1):
if len(labels) <= 0:
feed_dict = {
self.aspects: aspects[index],
self.contexts: contexts[index],
self.aspect_lens: aspect_lens[index],
self.context_lens: context_lens[index],
self.dropout_keep_prob: keep_prob,
}
else:
feed_dict = {
self.aspects: aspects[index],
self.contexts: contexts[index],
self.labels: labels[index],
self.aspect_lens: aspect_lens[index],
self.context_lens: context_lens[index],
self.dropout_keep_prob: keep_prob,
}
yield feed_dict, len(index)
