def get_batch_data(x_f,
sen_len_f,
x_b,
sen_len_b,
n_asp_b,
yi,
y_sen_i,
target,
tl,
batch_size,
is_shuffle=True):
# for index in batch_index(len(yi), batch_size, 1, is_shuffle):
for index in batch_index(len(n_asp_b), batch_size, 1, is_shuffle):
selected_rows = itemgetter(*index)(list(n_asp_b.values()))
r_index = []
for idxs in selected_rows:
if idxs != []:
r_index.extend(idxs)
_n_asp = np.asarray(
[len(tup) for tup in list(selected_rows) if len(tup) != 0])
# print(f"length of _n_asp: {_n_asp.shape[0]}")
feed_dict = {
x: x_f[r_index],
x_bw: x_b[r_index],
y: yi[r_index],
y_sen: y_sen_i[index],
n_asp: _n_asp,
sen_len: sen_len_f[r_index],
sen_len_bw: sen_len_b[r_index],
target_words: target[r_index],
tar_len: tl[r_index]
}
yield feed_dict, len(r_index)
def train_get_batch_data(x_f,
sen_len_f,
x_b,
sen_len_b,
yi,
target,
tl,
batch_size,
kp1,
kp2,
learning,
moment,
is_shuffle=True):
for index in batch_index(len(yi), batch_size, 1, is_shuffle):
feed_dict = {
x: x_f[index],
x_bw: x_b[index],
y: yi[index],
sen_len: sen_len_f[index],
sen_len_bw: sen_len_b[index],
target_words: target[index],
tar_len: tl[index],
keep_prob1: kp1,
keep_prob2: kp2,
learning_rate: learning,
momentum: moment,
}
yield feed_dict, len(index)
def get_batches(self, x, y=None, batch_size=100, is_shuffle=True):
for index in batch_index(len(x), batch_size, is_shuffle=is_shuffle):
n = len(index)
feed_dict = {self.x: x[index]}
if y is not None:
feed_dict[self.y] = y[index]
yield feed_dict, n
def get_batch_data(self, x, y, batch_size, keep_prob):
for index in batch_index(len(y), batch_size, 1):
feed_dict = {
self.x: x[index],
self.y: y[index],
self.dropout_keep_prob: keep_prob,
}
yield feed_dict, len(index)
def get_batch_data(self, x, y, sen_len, batch_size, keep_prob):
for index in batch_index(len(y), batch_size, 1):
feed_dict = {
self.x: x[index],
self.y: y[index],
self.sen_len: sen_len[index],
self.dropout_keep_prob: keep_prob,
}
yield feed_dict, len(index)
def get_batch_data(self, x, sen_len, y, target_words, batch_size, keep_prob1, keep_prob2, is_shuffle=True):
for index in batch_index(len(y), batch_size, 1, is_shuffle):
feed_dict = {
self.x: x[index],
self.y: y[index],
self.sen_len: sen_len[index],
self.aspect_id: target_words[index],
self.keep_prob1: keep_prob1,
self.keep_prob2: keep_prob2,
}
yield feed_dict, len(index)
def get_batch_data(self, x_fw, len_fw, x_bw, len_bw, y, batch_size,
keep_prob):
for index in batch_index(len(y), batch_size, 1):
feed_dict = {
self.x_fw: x_fw[index],
self.x_bw: x_bw[index],
self.y: y[index],
self.len_fw: len_fw[index],
self.len_bw: len_bw[index],
self.dropout_keep_prob: keep_prob
}
yield feed_dict, len(index)
def get_batch_data(self, x, sen_len, x_bw, sen_len_bw, y, target_words, batch_size, keep_prob):
for index in batch_index(len(y), batch_size, 1):
feed_dict = {
self.x: x[index],
self.x_bw: x_bw[index],
self.y: y[index],
self.sen_len: sen_len[index],
self.sen_len_bw: sen_len_bw[index],
self.target_words: target_words[index],
self.dropout_keep_prob: keep_prob,
}
yield feed_dict, len(index)
def get_batch_data(self, x, sen_len, x_bw, sen_len_bw, y, batch_size,
keep_prob):
for index in batch_index(len(y), batch_size, 1, is_shuffle=False):
feed_dict = {
self.x: x[index],
self.x_bw: x_bw[index],
self.y: y[index],
self.sen_len: sen_len[index],
self.sen_len_bw: sen_len_bw[index],
self.dropout_keep_prob: keep_prob,
}
yield feed_dict, len(index)
def get_batch_data(self, x, sen_len, x_bw, sen_len_bw, y, target_words, batch_size, keep_prob):
for index in batch_index(len(y), batch_size, 1):
feed_dict = {
self.x: x[index],
self.x_bw: x_bw[index],
self.y: y[index],
self.sen_len: sen_len[index],
self.sen_len_bw: sen_len_bw[index],
self.target_words: target_words[index],
self.dropout_keep_prob: keep_prob,
}
yield feed_dict, len(index)
def get_batch_data(self, x, sen_len, y, target_words, batch_size, keep_prob1, keep_prob2, is_shuffle=True):
if y!=None:
for index in batch_index(len(y), batch_size, 1, is_shuffle):
feed_dict = {
self.x: x[index],
self.y: y[index],
self.sen_len: sen_len[index],
self.aspect: target_words[index],
self.keep_prob1: keep_prob1,
self.keep_prob2: keep_prob2,
}
yield feed_dict, len(index)
else:
for index in batch_index(len(sen_len), batch_size, 1, None):
feed_dict = {
self.x: x[index],
self.sen_len: sen_len[index],
self.aspect: target_words[index],
self.keep_prob1: keep_prob1,
self.keep_prob2: keep_prob2,
}
yield feed_dict, len(index)
def get_batch_data(self, x, sen_len, y, target_words, batch_size, keep_prob1, keep_prob2, is_shuffle=True):
for index in batch_index(len(y), batch_size, 1, is_shuffle):
#print ('第一个batch训练集下标',index)
feed_dict = {
self.x: x[index],
self.y: y[index],
self.sen_len: sen_len[index],
self.aspect_id: target_words[index],
self.keep_prob1: keep_prob1,
self.keep_prob2: keep_prob2,
}
yield feed_dict, len(index)
def get_batches(self, X, y=None, batch_size=100, shuffle=True):
x, sen_len, x_bw, sen_len_bw, target_words = X
for index in batch_index(len(x), batch_size, shuffle):
n = len(index)
feed_dict = {
self.x: x[index],
self.x_bw: x_bw[index],
self.sen_len: sen_len[index],
self.sen_len_bw: sen_len_bw[index],
self.target_words: target_words[index],
}
if y is not None:
feed_dict[self.y] = y[index]
yield feed_dict, n
def get_batch_data(self,
x,
y,
sen_len,
aspect,
position,
batch_size,
is_shuffle=True):
for index in batch_index(len(y), batch_size, 1, is_shuffle):
feed_dict = {
self.x: x[index],
self.y: y[index],
self.sen_len: sen_len[index],
self.aspect_id: aspect[index],
self.position: position[index]
}
yield feed_dict, len(index)
def get_batch_data(x_f, sen_len_f, x_b, sen_len_b, yi, target, tl, batch_size, kp1, kp2, senshort, sen, multMask, is_shuffle=True):
for index in batch_index(len(yi), batch_size, 1, is_shuffle):
feed_dict = {
x: x_f[index],
x_bw: x_b[index],
y: yi[index],
sen_len: sen_len_f[index],
sen_len_bw: sen_len_b[index],
target_words: target[index],
tar_len: tl[index],
keep_prob1: kp1,
keep_prob2: kp2,
sent_short: sen[index],
sent: sen[index],
mult_mask: multMask[index]
}
yield feed_dict, len(index)
def get_batch_data(self,
x,
pos,
sen_len,
y,
entity,
aspect,
batch_size,
keep_prob1,
keep_prob2,
is_shuffle=True):
for index in batch_index(len(y), batch_size, 1, is_shuffle):
feed_dict = {
self.x: x[index],
self.pos_x: pos[index],
self.y: y[index],
self.sen_len: sen_len[index],
self.entity_id: entity[index],
self.aspect_id: aspect[index],
self.keep_prob1: keep_prob1,
self.keep_prob2: keep_prob2,
}
yield feed_dict, len(index)
def get_batch_data(x_f,
sen_len_f,
x_b,
sen_len_b,
yi,
target,
tl,
batch_size,
kp1,
kp2,
is_shuffle=True):
for index in batch_index(len(yi), batch_size, 1, is_shuffle):
feed_dict = {
x_real: x_f[index],
x_bw: x_b[index],
y_real: yi[index],
sen_len: sen_len_f[index],
sen_len_bw: sen_len_b[index],
target_words: target[index],
tar_len: tl[index],
keep_prob1: kp1,
keep_prob2: kp2,
}
yield feed_dict, len(index)
