模型上线

train/train_service_sk_tf2_distibute.py

@@ -236,7 +236,11 @@ early_stopping = tf.estimator.experimental.stop_if_no_increase_hook(wideAndDeepM
 
 hooks = [early_stopping]
 
-train_spec = tf.estimator.TrainSpec(input_fn = lambda: input_fn(DATA_DIR + 'train_samples.csv', 1, True, 2048), hooks = hooks)
+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)
 
 serving_feature_spec = tf.feature_column.make_parse_example_spec(
   linear_feature_columns + dnn_feature_columns)
@@ -250,7 +254,7 @@ exporter = tf.estimator.BestExporter(
   serving_input_receiver_fn = serving_input_receiver_fn,
   exports_to_keep = 1)
 
-eval_spec = tf.estimator.EvalSpec(input_fn = lambda: input_fn(DATA_DIR + 'eval_samples.csv', 1, False, 2 ** 15), steps = None, throttle_secs = 120, exporters = exporter)
+eval_spec = tf.estimator.EvalSpec(input_fn = lambda: 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',