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ffm-baseline
Commit a8abcd20 authored by Your Name's avatar Your Name
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test esmm predict

parent 58c55fa5
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Showing with 11 additions and 1 deletion
eda/esmm/Model_pipline/dist_predict.py
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from datetime import date, timedelta from datetime import date, timedelta
import tensorflow as tf import tensorflow as tf
import time
def model_fn(features, labels, mode, params): def model_fn(features, labels, mode, params):
...@@ -134,7 +135,7 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False): ...@@ -134,7 +135,7 @@ 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 esmm_predict(): def main():
dt_dir = (date.today() + timedelta(-1)).strftime('%Y%m%d') dt_dir = (date.today() + timedelta(-1)).strftime('%Y%m%d')
model_dir = "hdfs://172.16.32.4:8020/strategy/esmm/model_ckpt/DeepCvrMTL/" + dt_dir model_dir = "hdfs://172.16.32.4:8020/strategy/esmm/model_ckpt/DeepCvrMTL/" + dt_dir
te_files = ["hdfs://172.16.32.4:8020/strategy/esmm/part-r-00000"] te_files = ["hdfs://172.16.32.4:8020/strategy/esmm/part-r-00000"]
...@@ -156,3 +157,11 @@ def esmm_predict(): ...@@ -156,3 +157,11 @@ def esmm_predict():
with open("/home/gmuser/esmm/nearby" + "/pred.txt", "w") as fo: with open("/home/gmuser/esmm/nearby" + "/pred.txt", "w") as fo:
for prob in preds: for prob in preds:
fo.write("%f\t%f\t%f\n" % (prob['pctr'], prob['pcvr'], prob['pctcvr'])) fo.write("%f\t%f\t%f\n" % (prob['pctr'], prob['pcvr'], prob['pctcvr']))
if __name__ == "__main__":
b = time.time()
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run()
print("耗时(分钟)：")
print((time.time()-b)/60)
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