test

eda/esmm/Model_pipline/dist_predict.py

@@ -141,7 +141,7 @@ def input_fn(filenames, batch_size=32, num_epochs=1, perform_shuffle=False):
     #print(batch_features,batch_labels)
     return batch_features, batch_labels
 
-def esmm_predict(dist_data):
+def main(_):
     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
     te_files = ["hdfs://172.16.32.4:8020/strategy/esmm/nearby/part-r-00000"]
@@ -157,13 +157,17 @@ def esmm_predict(dist_data):
     }
     config = tf.estimator.RunConfig().replace(session_config = tf.ConfigProto(device_count={'GPU':0, 'CPU':36}),
             log_step_count_steps=100, save_summary_steps=100)
-    Estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir="hdfs://172.16.32.4:8020/strategy/esmm/model_ckpt/DeepCvrMTL/", params=model_params, config=config)
+    Estimator = tf.estimator.Estimator(model_fn=model_fn, model_dir=model_dir, params=model_params, config=config)
+
+    preds = Estimator.predict(input_fn=lambda: input_fn(te_files, num_epochs=1, batch_size=10000), predict_keys=["pctcvr","pctr","pcvr"])
+    # indices = []
+    # for prob in preds:
+    #     indices.append([prob['pctr'], prob['pcvr'], prob['pctcvr']])
+    # return indices
+    with open("/home/gmuser/esmm/nearby/pred.txt", "w") as fo:
+        for prob in preds:
+            fo.write("%f\t%f\t%f\n" % (prob['pctr'], prob['pcvr'], prob['pctcvr']))
 
-    preds = Estimator.predict(input_fn=lambda: input_fn(dist_data, num_epochs=1, batch_size=10000), predict_keys=["pctcvr","pctr","pcvr"])
-    indices = []
-    for prob in preds:
-        indices.append([prob['pctr'], prob['pcvr'], prob['pctcvr']])
-    return indices
 
 
 
@@ -184,5 +188,7 @@ if __name__ == "__main__":
     df.show()
 
     b = time.time()
+    tf.logging.set_verbosity(tf.logging.INFO)
+    tf.app.run()
     print("耗时(分钟)：")
-    print((time.time()-b)/60)
+    print((time.time()-b)/60)
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