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gm_strategy_cvr
Commit 14de04d8 authored by 赵威's avatar 赵威
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update model

parent 70e96a9b
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Showing with 12 additions and 13 deletions
src/models/esmm/model.py
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import json
import timeit import timeit
import json
import tensorflow as tf import tensorflow as tf
from tensorflow import feature_column as fc from tensorflow import feature_column as fc
from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.estimator.canned import head as head_lib
...@@ -58,21 +58,20 @@ def esmm_model_fn(features, labels, mode, params): ...@@ -58,21 +58,20 @@ def esmm_model_fn(features, labels, mode, params):
ctcvr_loss = tf.reduce_sum(tf.compat.v1.losses.log_loss(labels=cvr_labels, predictions=ctcvr_preds)) ctcvr_loss = tf.reduce_sum(tf.compat.v1.losses.log_loss(labels=cvr_labels, predictions=ctcvr_preds))
loss = ctr_loss + ctcvr_loss loss = ctr_loss + ctcvr_loss
ctr_accuracy = tf.compat.v1.metrics.accuracy(labels=ctr_labels, predictions=tf.to_float(tf.greater_equal(ctr_preds, 0.5)))
ctcvr_accuracy = tf.compat.v1.metrics.accuracy(labels=cvr_labels,
predictions=tf.to_float(tf.greater_equal(ctcvr_preds, 0.5)))
ctr_auc = tf.compat.v1.metrics.auc(labels=ctr_labels, predictions=ctr_preds)
ctcvr_auc = tf.compat.v1.metrics.auc(labels=cvr_labels, predictions=ctcvr_preds)
metrics = {"ctcvr_accuracy": ctcvr_accuracy, "ctr_accuracy": ctr_accuracy, "ctr_auc": ctr_auc, "ctcvr_auc": ctcvr_auc}
tf.compat.v1.summary.scalar("ctr_accuracy", ctr_accuracy[1])
tf.compat.v1.summary.scalar("ctcvr_accuracy", ctcvr_accuracy[1])
tf.compat.v1.summary.scalar("ctr_auc", ctr_auc[1])
tf.compat.v1.summary.scalar("ctcvr_auc", ctcvr_auc[1])
if mode == tf.estimator.ModeKeys.EVAL: if mode == tf.estimator.ModeKeys.EVAL:
ctr_accuracy = tf.compat.v1.metrics.accuracy(labels=ctr_labels,
predictions=tf.to_float(tf.greater_equal(ctr_preds, 0.5)))
ctcvr_accuracy = tf.compat.v1.metrics.accuracy(labels=cvr_labels,
predictions=tf.to_float(tf.greater_equal(ctcvr_preds, 0.5)))
ctr_auc = tf.compat.v1.metrics.auc(labels=ctr_labels, predictions=ctr_preds)
ctcvr_auc = tf.compat.v1.metrics.auc(labels=cvr_labels, predictions=ctcvr_preds)
metrics = {"ctcvr_accuracy": ctcvr_accuracy, "ctr_accuracy": ctr_accuracy, "ctr_auc": ctr_auc, "ctcvr_auc": ctcvr_auc}
tf.compat.v1.summary.scalar("ctr_accuracy", ctr_accuracy[1])
tf.compat.v1.summary.scalar("ctcvr_accuracy", ctcvr_accuracy[1])
tf.compat.v1.summary.scalar("ctr_auc", ctr_auc[1])
tf.compat.v1.summary.scalar("ctcvr_auc", ctcvr_auc[1])
return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics) return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops=metrics)
train_op = optimizer.minimize(loss, global_step=tf.compat.v1.train.get_global_step()) train_op = optimizer.minimize(loss, global_step=tf.compat.v1.train.get_global_step())
res = tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op) res = tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op, eval_metric_ops=metrics)
return res return res
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