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ffm-baseline
Commit 7cd9ae29 authored by Your Name's avatar Your Name
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test dist predict

parent 7cf73afa
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Showing with 2 additions and 2 deletions
eda/esmm/Model_pipline/dist_predict.py
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...@@ -31,7 +31,7 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False): ...@@ -31,7 +31,7 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
"number": tf.VarLenFeature(tf.int64), "number": tf.VarLenFeature(tf.int64),
"uid": tf.VarLenFeature(tf.string), "uid": tf.VarLenFeature(tf.string),
"city": tf.VarLenFeature(tf.string), "city": tf.VarLenFeature(tf.string),
"cid_id": tf.VarLenFeature(tf.int64) "cid_id": tf.VarLenFeature(tf.string)
} }
parsed = tf.parse_single_example(record, features) parsed = tf.parse_single_example(record, features)
y = parsed.pop('y') y = parsed.pop('y')
...@@ -113,7 +113,7 @@ def model_fn(features, labels, mode, params): ...@@ -113,7 +113,7 @@ def model_fn(features, labels, mode, params):
sample_id = tf.sparse.to_dense(number) sample_id = tf.sparse.to_dense(number)
uid = tf.sparse.to_dense(uid,default_value="") uid = tf.sparse.to_dense(uid,default_value="")
city = tf.sparse.to_dense(city,default_value="") city = tf.sparse.to_dense(city,default_value="")
cid_id = tf.sparse.to_dense(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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