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ffm-baseline
Commit 7f0b5dec authored by Your Name's avatar Your Name
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change train.py

parent 2bf956e8
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Showing with 3 additions and 4 deletions
eda/esmm/Model_pipline/train.py
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...@@ -100,7 +100,6 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False): ...@@ -100,7 +100,6 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
#print(batch_features,batch_labels) #print(batch_features,batch_labels)
return batch_features, batch_labels return batch_features, batch_labels
def model_fn(features, labels, mode, params): def model_fn(features, labels, mode, params):
"""Bulid Model function f(x) for Estimator.""" """Bulid Model function f(x) for Estimator."""
#------hyperparameters---- #------hyperparameters----
...@@ -161,9 +160,9 @@ def model_fn(features, labels, mode, params): ...@@ -161,9 +160,9 @@ def model_fn(features, labels, mode, params):
x_concat = tf.concat([tf.reshape(embedding_id, shape=[-1, common_dims]), app_id, level2, level3, tag1, x_concat = tf.concat([tf.reshape(embedding_id, shape=[-1, common_dims]), app_id, level2, level3, tag1,
tag2, tag3, tag4, tag5, tag6, tag7,search_tag2,search_tag3], axis=1) tag2, tag3, tag4, tag5, tag6, tag7,search_tag2,search_tag3], axis=1)
uid = features['uid'] uid = tf.sparse.to_dense(uid,default_value="")
city = features['city'] city = tf.sparse.to_dense(city,default_value="")
cid_id = features['cid_id'] cid_id = tf.sparse.to_dense(cid_id,default_value="")
with tf.name_scope("CVR_Task"): with tf.name_scope("CVR_Task"):
if mode == tf.estimator.ModeKeys.TRAIN: if mode == tf.estimator.ModeKeys.TRAIN:
......
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