def eval(self, data_sentence, data_pos, data_label, batch_size=64):
"""
Evaluate data result
:param data_sentence:
:param data_pos:
:param data_label:
:param batch_size:
:return:
"""
pred_labels = []
nb_dev = int(len(data_label) / batch_size) + 1
for i in range(nb_dev):
sentence_feed = data_sentence[i * batch_size:(i + 1) * batch_size]
pos_feed = data_pos[i * batch_size:(i + 1) * batch_size]
label_feed = data_label[i * batch_size:(i + 1) * batch_size]
feed_dict = {
self.input_sentence_ph: sentence_feed,
self.input_pos_ph: pos_feed,
self.label_ph: label_feed,
self.keep_prob_ph: 1.0,
self.word_keep_prob_ph: 1.0,
self.pos_keep_prob_ph: 1.0
}
pred_temp = self.sess.run(self.pred_op, feed_dict=feed_dict)
pred_labels += list(pred_temp)
true_labels = data_label[:len(pred_labels)]
p, r, f = simple_evaluate(true_labels, pred_labels)
return p, r, f
def paint(self):
self.random_models = np.random.choice(self.style_models,
size=3,
replace=False)
for random_model in self.random_models[-3:]:
model_path = 'checkpoint/' + self.museum + '/' + random_model
print(model_path)
self.random_styled_images.append(
simple_evaluate(self.final_image, model_path))
def simple_evaluate(f_pred,prepare_data,test_set):
x,mask,y = prepare_data(test_set[0],test_set[1])
prediction = f_pred(x,mask)
precision,recall,f_score,micro_avg = evaluate.simple_evaluate(prediction,test_set)
return precision,recall,f_score,micro_avg
def simple_evaluate(f_pred,prepare_data,test_set):
x,mask,y = prepare_data(test_set[0],test_set[1])
x_1 = [x[t][::-1] for t in xrange(len(x))]
prediction = f_pred(x,x_1,mask)
precision,recall,f_score,micro_avg = evaluate.simple_evaluate(prediction,test_set)
return precision,recall,f_score,micro_avg
