这是我用来执行二进制分类的线性分类器，下面是代码片段：

my_optimizer = tf.train.AdagradOptimizer(learning_rate = learning_rate)
my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer,5.0)
# Create a linear classifier object
linear_classifier = tf.estimator.LinearClassifier(
          feature_columns = feature_columns, 
          optimizer = my_optimizer 
          )
linear_classifier.train(input_fn = training_input_fn, steps = steps)

数据集是不平衡的，只有两个类是/否。NO类示例的数量为36548，而YES类示例的数量为4640。
我如何将平衡应用到这些数据上呢？我一直在四处搜索，我可以找到与类权重等相关的东西，但我找不到如何创建类权重以及如何应用到tensorflow 的训练方法。
下面是我计算损失的方法：

training_probabilities = linear_classifier.predict(input_fn = training_predict_input_fn)
training_probabilities = np.array([item['probabilities'] for item in training_probabilities])

validation_probabilities = linear_classifier.predict(input_fn=validation_predict_input_fn)
validation_probabilities = np.array([item['probabilities'] for item in validation_probabilities])

training_log_loss = metrics.log_loss(training_targets, training_probabilities)
validation_log_loss = metrics.log_loss(validation_targets, validation_probabilities)