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add ZILN loss for ltv prediction task (#498)
* add ZILN loss for ltv prediction task & add documents
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| # 自定义辅助损失函数组件 | ||
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| 可以使用如下方法添加多个辅助损失函数。 | ||
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| 在`easy_rec/python/layers/keras/auxiliary_loss.py`里添加一个新的loss函数。 | ||
| 如果计算逻辑比较复杂，建议在一个单独的python文件中实现，然后在`auxiliary_loss.py`里import并使用。 | ||
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| 注意：用来标记损失函数类型的`loss_type`参数需要全局唯一。 | ||
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| ## 配置方法 | ||
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| ```protobuf | ||
| blocks { | ||
| name: 'custom_loss' | ||
| inputs { | ||
| block_name: 'pred' | ||
| } | ||
| inputs { | ||
| block_name: 'logit' | ||
| } | ||
| merge_inputs_into_list: true | ||
| keras_layer { | ||
| class_name: 'AuxiliaryLoss' | ||
| st_params { | ||
| fields { | ||
| key: "loss_type" | ||
| value { string_value: "my_custom_loss" } | ||
| } | ||
| } | ||
| } | ||
| } | ||
| ``` | ||
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| st_params 参数列表下可以追加自定义参数。 | ||
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| 记得使用`concat_blocks`或者`output_blocks`配置输出的block列表（不包括当前`custom_loss`节点）。 |
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| # -*- encoding:utf-8 -*- | ||
| # Copyright (c) Alibaba, Inc. and its affiliates. | ||
| """Zero-inflated lognormal loss for lifetime value prediction.""" | ||
| import tensorflow as tf | ||
| import tensorflow_probability as tfp | ||
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| tfd = tfp.distributions | ||
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| if tf.__version__ >= '2.0': | ||
| tf = tf.compat.v1 | ||
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| def zero_inflated_lognormal_pred(logits): | ||
| """Calculates predicted mean of zero inflated lognormal logits. | ||
| Arguments: | ||
| logits: [batch_size, 3] tensor of logits. | ||
| Returns: | ||
| positive_probs: [batch_size, 1] tensor of positive probability. | ||
| preds: [batch_size, 1] tensor of predicted mean. | ||
| """ | ||
| logits = tf.convert_to_tensor(logits, dtype=tf.float32) | ||
| positive_probs = tf.keras.backend.sigmoid(logits[..., :1]) | ||
| loc = logits[..., 1:2] | ||
| scale = tf.keras.backend.softplus(logits[..., 2:]) | ||
| preds = ( | ||
| positive_probs * | ||
| tf.keras.backend.exp(loc + 0.5 * tf.keras.backend.square(scale))) | ||
| return positive_probs, preds | ||
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| def zero_inflated_lognormal_loss(labels, logits, name=''): | ||
| """Computes the zero inflated lognormal loss. | ||
| Usage with tf.keras API: | ||
| ```python | ||
| model = tf.keras.Model(inputs, outputs) | ||
| model.compile('sgd', loss=zero_inflated_lognormal) | ||
| ``` | ||
| Arguments: | ||
| labels: True targets, tensor of shape [batch_size, 1]. | ||
| logits: Logits of output layer, tensor of shape [batch_size, 3]. | ||
| name: the name of loss | ||
| Returns: | ||
| Zero inflated lognormal loss value. | ||
| """ | ||
| loss_name = name if name else 'ziln_loss' | ||
| labels = tf.cast(labels, dtype=tf.float32) | ||
| if labels.shape.ndims == 1: | ||
| labels = tf.expand_dims(labels, 1) # [B, 1] | ||
| positive = tf.cast(labels > 0, tf.float32) | ||
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| logits = tf.convert_to_tensor(logits, dtype=tf.float32) | ||
| logits.shape.assert_is_compatible_with( | ||
| tf.TensorShape(labels.shape[:-1].as_list() + [3])) | ||
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| positive_logits = logits[..., :1] | ||
| classification_loss = tf.keras.backend.binary_crossentropy( | ||
| positive, positive_logits, from_logits=True) | ||
| classification_loss = tf.keras.backend.mean(classification_loss) | ||
| tf.summary.scalar('loss/%s_classify' % loss_name, classification_loss) | ||
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| loc = logits[..., 1:2] | ||
| scale = tf.math.maximum( | ||
| tf.keras.backend.softplus(logits[..., 2:]), | ||
| tf.math.sqrt(tf.keras.backend.epsilon())) | ||
| safe_labels = positive * labels + ( | ||
| 1 - positive) * tf.keras.backend.ones_like(labels) | ||
| regression_loss = -tf.keras.backend.mean( | ||
| positive * tfd.LogNormal(loc=loc, scale=scale).log_prob(safe_labels)) | ||
| tf.summary.scalar('loss/%s_regression' % loss_name, regression_loss) | ||
| return classification_loss + regression_loss |
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