我正在尝试使用tensorflow实现多标签句子分类模型。大约有1500个标签。 该模型工作得很好，但我不确定它生成的指标。
这是生成指标的代码段：
with tf.name_scope('loss'):
losses = tf.nn.softmax_cross_entropy_with_logits(labels=self.input_y, logits=self.scores) # only named arguments accepted
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
with tf.name_scope('accuracy'):
correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name='accuracy')
with tf.name_scope('num_correct'):
correct = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
self.num_correct = tf.reduce_sum(tf.cast(correct, 'float'))
with tf.name_scope('fp'):
fp = tf.metrics.false_positives(labels=tf.argmax(self.input_y, 1), predictions=self.predictions)
self.fp = tf.reduce_sum(tf.cast(fp, 'float'), name='fp')
with tf.name_scope('fn'):
fn = tf.metrics.false_negatives(labels=tf.argmax(self.input_y, 1), predictions=self.predictions)
self.fn = tf.reduce_sum(tf.cast(fn, 'float'), name='fn')
with tf.name_scope('recall'):
self.recall = self.num_correct / (self.num_correct + self.fn)
with tf.name_scope('precision'):
self.precision = self.num_correct / (self.num_correct + self.fp)
with tf.name_scope('F1'):
self.F1 = (2 * self.precision * self.recall) / (self.precision + self.recall)
with tf.name_scope('merged_summary'):
tf.summary.scalar("loss", self.loss)
tf.summary.scalar("accuracy", self.accuracy)
tf.summary.scalar("recall", self.recall)
tf.summary.scalar("precision", self.precision)
tf.summary.scalar("f-measure", self.F1)
self.merged_summary = tf.summary.merge_all()
然后，在火车部分，我为Tensorboard创建了保护程序：
summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
最后，培训会将指标保存如下：
for train_batch in train_batches:
x_train_batch, y_train_batch = zip(*train_batch)
train_step(x_train_batch, y_train_batch)
current_step = tf.train.global_step(sess, global_step)
# Evaluate the model with x_dev and y_dev
if current_step % params['evaluate_every'] == 0:
dev_batches = data_helper.batch_iter(list(zip(x_dev, y_dev)), params['batch_size'], 1)
total_dev_correct = 0
for dev_batch in dev_batches:
x_dev_batch, y_dev_batch = zip(*dev_batch)
acc, loss, num_dev_correct, predictions, recall, precision, f1, summary = dev_step(x_dev_batch, y_dev_batch)
total_dev_correct += num_dev_correct
accuracy = float(total_dev_correct) / len(y_dev)
logging.info('Accuracy on dev set: {}'.format(accuracy))
# added loss
logging.info('Loss on dev set: {}'.format(loss))
# adding more measures
logging.info('Recall on dev set: {}'.format(recall))
logging.info('Precision on dev set: {}'.format(precision))
logging.info('F1 on dev set: {}'.format(f1))
summary_writer.add_summary(summary, current_step)
if accuracy >= best_accuracy:
best_accuracy, best_loss, best_at_step, best_recall, best_precision, best_f1 = accuracy, loss, current_step, recall, precision, f1
path = saver.save(sess, checkpoint_prefix, global_step=current_step)
logging.critical('Saved model {} at step {}'.format(path, best_at_step))
logging.critical('Best accuracy {} at step {}'.format(best_accuracy, best_at_step))
logging.critical('Best loss {} at step {}'.format(best_loss, best_at_step))
logging.critical('Best recall {} at step {}'.format(best_recall, best_at_step))
logging.critical('Best precision {} at step {}'.format(best_precision, best_at_step))
logging.critical('Best F1 {} at step {}'.format(best_f1, best_at_step))
logging.critical('Training is complete, testing the best model on x_test and y_test')
dev_step和train_step如下所示：
def train_step(x_batch, y_batch):
feed_dict = {
cnn_rnn.input_x: x_batch,
cnn_rnn.input_y: y_batch,
cnn_rnn.dropout_keep_prob: params['dropout_keep_prob'],
cnn_rnn.batch_size: len(x_batch),
cnn_rnn.pad: np.zeros([len(x_batch), 1, params['embedding_dim'], 1]),
cnn_rnn.real_len: real_len(x_batch),
}
_, step, loss, accuracy = sess.run([train_op, global_step, cnn_rnn.loss, cnn_rnn.accuracy], feed_dict)
def dev_step(x_batch, y_batch):
feed_dict = {
cnn_rnn.input_x: x_batch,
cnn_rnn.input_y: y_batch,
cnn_rnn.dropout_keep_prob: 1.0,
cnn_rnn.batch_size: len(x_batch),
cnn_rnn.pad: np.zeros([len(x_batch), 1, params['embedding_dim'], 1]),
cnn_rnn.real_len: real_len(x_batch),
}
step, loss, accuracy, num_correct, predictions, recall, precision, f1, summary = sess.run(
[global_step, cnn_rnn.loss, cnn_rnn.accuracy, cnn_rnn.num_correct, cnn_rnn.predictions, cnn_rnn.recall, cnn_rnn.precision, cnn_rnn.F1, cnn_rnn.merged_summary], feed_dict)
return accuracy, loss, num_correct, predictions, recall, precision, f1, summary
我的问题是，是否针对多标签分类问题正确生成了指标，还是应该通过混淆矩阵来实现？ 如果我应该使用混淆矩阵，我应该添加：
tf.confusion_matrix(labels=, predictions=)
在我声明指标的代码的第一部分？如果是，我接下来应该做些什么来获得精确度和召回。
编辑：我已添加此内容，但张量板中的图片只是一个黑屏。
batch_confusion = tf.confusion_matrix(labels=tf.argmax(self.input_y, 1), predictions=self.predictions, name='batch_confusion', num_classes=num_classes)
confusion = tf.Variable(tf.zeros([num_classes, num_classes], dtype=tf.int32), name='confusion')
confusion_image = tf.reshape(tf.cast(confusion, tf.float32), [1, num_classes, num_classes, 1])
tf.summary.image('confusion', confusion_image)
感谢您的帮助，