def train(self, data, labels, test_data, test_labels, sess, epoches):
best_val = 0.0
for e in range(epoches):
Loss = 0
results = []
batches = generator(zip(data, labels), self.batch_size)
for step, (sent_a, sent_b, label) in enumerate(batches):
loss, _, res = sess.run(
[self.loss, self.train_op, self.pred],
feed_dict={
self.input_a: np.array(sent_a),
self.input_b: np.array(sent_b),
self.labels: np.array(label),
self.dropout: 0.8
})
Loss += loss
for r in res:
results.append(r)
print('epoch: ' + str(e) + ' step: ' + str(step) + ' loss: ' +
str(loss))
if step % 100 == 0:
val = self.test(test_data, test_labels, sess)
if val > best_val:
best_val = val
print('Higher score, ' + str(val))
def test(self, data, labels, sess):
results = []
batches = generator(zip(data, labels), self.batch_size)
for step, (sent, label) in enumerate(batches):
res = sess.run(self.pred,
feed_dict={
self.inputs: np.array(sent),
self.labels: np.array(label),
self.dropout_: 0
})
for r in res:
results.append(r)
res = self.acc(results, labels)
print('test_acc: ' + str(res))
return res
