def prepare_test_data(self):
# 读取词汇表
if self.train_mode == 'CHAR-RANDOM':
# 1.字符级
self.vocab = preprocess.read_vocab(os.path.join('data',preprocess.CHAR_VOCAB_PATH))
elif self.train_mode == 'WORD-NON-STATIC':
self.vocab = preprocess.read_vocab(os.path.join('data', preprocess.WORD_VOCAB_PATH))
# 测试集有标题，读取时注意跳过第一行
dataset = TextLineDataset(os.path.join('data',preprocess.TEST_PATH))
dataset = dataset.shuffle(preprocess.TOTAL_TEST_SIZE).batch(self.test_batch_size)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
return dataset, next_element
def draw_confusion_matrix(self):
# train_init_op, test_init_op, next_train_element, next_test_element = self.cnn.prepare_data()
test_dataset = TextLineDataset(
os.path.join('data', preprocess.FILTERED_TEST_PATH)).skip(1).batch(
self.cnn.test_batch_size)
# Create a reinitializable iterator
test_iterator = test_dataset.make_one_shot_iterator()
next_test_element = test_iterator.get_next()
y_true = []
y_pred = []
test_loss = 0.0
test_accuracy = 0.0
test_precision = 0.0
test_recall = 0.0
test_f1_score = 0.0
i = 0
while True:
try:
lines = self.sess.run(next_test_element)
batch_x, batch_y = self.cnn.convert_input(lines)
feed_dict = {
self.input_x: batch_x,
self.labels: batch_y,
self.dropout_keep_prob: 1.0,
self.training: False
}
# loss, pred, true = sess.run([self.cnn.loss, self.cnn.prediction, self.cnn.labels], feed_dict)
# 多次验证，取loss和score均值
mean_score = 0
for i in range(self.config.multi_test_num):
score = self.sess.run(self.score, feed_dict)
mean_score += score
mean_score /= self.config.multi_test_num
pred = self.sess.run(tf.argmax(mean_score, 1))
y_pred.extend(pred)
y_true.extend(batch_y)
i += 1
except tf.errors.OutOfRangeError:
# 遍历完验证集，计算评估
test_loss /= i
test_accuracy = metrics.accuracy_score(y_true=y_true,
y_pred=y_pred)
test_precision = metrics.precision_score(y_true=y_true,
y_pred=y_pred,
average='weighted')
test_recall = metrics.recall_score(y_true=y_true,
y_pred=y_pred,
average='weighted')
test_f1_score = metrics.f1_score(y_true=y_true,
y_pred=y_pred,
average='weighted')
log = ('precision: %0.6f, recall: %0.6f, f1_score: %0.6f' %
(test_precision, test_recall, test_f1_score))
print(log)
cm = confusion_matrix(y_true, y_pred)
print('Total samples:', np.sum(cm))
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] # 归一化
print('Confusion matrix:\n', cm)
# 绘制混淆矩阵
# ==============================================================
fig, ax = plt.subplots()
im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(
xticks=np.arange(cm.shape[1]),
yticks=np.arange(cm.shape[0]),
# ... and label them with the respective list entries
xticklabels=self.class_name,
yticklabels=self.class_name,
title="Normalized confusion matrix",
ylabel='True label',
xlabel='Predicted label')
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(),
rotation=45,
ha="right",
rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
fmt = '.2f'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
for j in range(cm.shape[1]):
ax.text(
j,
i,
format(cm[i, j], fmt),
ha="center",
va="center",
color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()
plt.savefig('./data/confusion_matrix.jpg')
plt.show()
# =====================================================================
break
