Commit 1342e3ec authored by 张彦钊's avatar 张彦钊

修改ctr_task_wgt参数

parent 044dc1f4
...@@ -332,10 +332,8 @@ object EsmmPredData { ...@@ -332,10 +332,8 @@ object EsmmPredData {
val native_data1 = sc.sql( val native_data1 = sc.sql(
s""" s"""
|select device_id,city_id as ucity_id, |select device_id,city_id as ucity_id,explode(split(native_queue,',')) as cid_id from native_data
|explode(native_queue) as cid_id """.stripMargin
|from native_data
""".stripMargin
).withColumn("label",lit(0)) ).withColumn("label",lit(0))
native_data1.createOrReplaceTempView("native_data1") native_data1.createOrReplaceTempView("native_data1")
println("native_explode_count",native_data1.count()) println("native_explode_count",native_data1.count())
......
...@@ -32,15 +32,15 @@ rm ${DATA_PATH}/nearby/nearby_* ...@@ -32,15 +32,15 @@ rm ${DATA_PATH}/nearby/nearby_*
echo "train..." echo "train..."
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.999 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=12 --feature_size=260100 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH} --task_type=train ${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.3 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=12 --feature_size=260100 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH} --task_type=train
echo "infer native..." echo "infer native..."
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.999 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=12 --feature_size=260100 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/native --task_type=infer > ${DATA_PATH}/infer.log ${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.3 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=12 --feature_size=260100 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/native --task_type=infer > ${DATA_PATH}/infer.log
echo "infer nearby..." echo "infer nearby..."
${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.999 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=12 --feature_size=260100 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/nearby --task_type=infer > ${DATA_PATH}/infer.log ${PYTHON_PATH} ${MODEL_PATH}/train.py --ctr_task_wgt=0.3 --learning_rate=0.0001 --deep_layers=256,128 --dropout=0.8,0.5 --optimizer=Adam --num_epochs=1 --embedding_size=16 --batch_size=1024 --field_size=12 --feature_size=260100 --l2_reg=0.005 --log_steps=100 --num_threads=36 --model_dir=${DATA_PATH}/model_ckpt/DeepCvrMTL/ --data_dir=${DATA_PATH}/nearby --task_type=infer > ${DATA_PATH}/infer.log
echo "sort and 2sql" echo "sort and 2sql"
${PYTHON_PATH} ${MODEL_PATH}/sort_to_sql.py ${PYTHON_PATH} ${MODEL_PATH}/sort_to_sql.py
......
...@@ -180,8 +180,7 @@ def model_fn(features, labels, mode, params): ...@@ -180,8 +180,7 @@ def model_fn(features, labels, mode, params):
ctr_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_ctr, labels=y)) ctr_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_ctr, labels=y))
#cvr_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_ctcvr, labels=z)) #cvr_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_ctcvr, labels=z))
cvr_loss = tf.reduce_mean(tf.losses.log_loss(predictions=pctcvr, labels=z)) cvr_loss = tf.reduce_mean(tf.losses.log_loss(predictions=pctcvr, labels=z))
# loss = ctr_task_wgt * ctr_loss + (1 -ctr_task_wgt) * cvr_loss + l2_reg * tf.nn.l2_loss(Feat_Emb) loss = ctr_task_wgt * ctr_loss + (1 -ctr_task_wgt) * cvr_loss + l2_reg * tf.nn.l2_loss(Feat_Emb)
loss = ctr_loss
tf.summary.scalar('ctr_loss', ctr_loss) tf.summary.scalar('ctr_loss', ctr_loss)
tf.summary.scalar('cvr_loss', cvr_loss) tf.summary.scalar('cvr_loss', cvr_loss)
......
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