模型上线

train/train_service_sk_tf2_distibute.py

@@ -241,7 +241,13 @@ train_dataset = input_fn(DATA_DIR + 'train_samples.csv', 1, True, 2048)
 
 eval_dataset = input_fn(DATA_DIR + 'eval_samples.csv', 1, False, 2 ** 15)
 
-train_spec = tf.estimator.TrainSpec(input_fn = lambda: train_dataset, hooks = hooks)
+def train_dataset_input_fn(train_dataset):
+    return train_dataset
+
+def eval_dataset_input_fn(eval_dataset):
+    return eval_dataset
+
+train_spec = tf.estimator.TrainSpec(input_fn = lambda: train_dataset_input_fn(train_dataset), hooks = hooks)
 
 serving_feature_spec = tf.feature_column.make_parse_example_spec(
   linear_feature_columns + dnn_feature_columns)
@@ -255,7 +261,7 @@ exporter = tf.estimator.BestExporter(
   serving_input_receiver_fn = serving_input_receiver_fn,
   exports_to_keep = 1)
 
-eval_spec = tf.estimator.EvalSpec(input_fn = lambda: eval_dataset, steps = None, throttle_secs = 120, exporters = exporter)
+eval_spec = tf.estimator.EvalSpec(input_fn = lambda: eval_dataset_input_fn(eval_dataset), steps = None, throttle_secs = 120, exporters = exporter)
 
 # def my_auc(labels, predictions):
 #     return {'auc_pr_careful_interpolation': tf.metrics.auc(labels, predictions['logistic'], curve='ROC',