模型调试

train/train_service_sk.py

 import tensorflow as tf
 tf.logging.set_verbosity(tf.logging.INFO)
 import sys
+import time
 import os
 os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
 
+start = time.time()
+
 BASE_DIR = '/data/files/wideAndDeep/'
 
 def input_fn(csv_path, epoch, shuffle, batch_size):
@@ -78,10 +81,10 @@ def input_fn(csv_path, epoch, shuffle, batch_size):
                        'ITEM_MULTI_CATEGORY_second_solutions': '-1', 'ITEM_MULTI_CATEGORY_second_positions': '-1',
                        'ITEM_MULTI_CATEGORY_projects': '-1', 'ITEM_NUMERIC_sku_price': 0.0}, 0.0)
 
-    dataset = dataset.map(parse_line, num_parallel_calls=8).cache()
+    dataset = dataset.map(parse_line, num_parallel_calls = 8).cache()
     dataset = dataset.padded_batch(batch_size, padded_shapes, padding_values=padding_values)
     if shuffle:
-        dataset = dataset.shuffle(1000).prefetch(512 * 100).repeat(epoch)
+        dataset = dataset.shuffle(2048).prefetch(512 * 100).repeat(epoch)
     else:
         dataset = dataset.prefetch(512 * 100).repeat(epoch)
 
@@ -216,7 +219,7 @@ session_config.gpu_options.allow_growth = True
 
 config = tf.estimator.RunConfig(save_checkpoints_steps = 3000, session_config = session_config)
 
-wideAndDeepModel = tf.estimator.DNNLinearCombinedClassifier(model_dir = BASE_DIR + 'model',
+wideAndDeepModel = tf.estimator.DNNLinearCombinedClassifier(model_dir = BASE_DIR + 'model_csv',
     linear_feature_columns = linear_feature_columns,
     dnn_feature_columns = dnn_feature_columns,
     dnn_hidden_units = [128, 32],
@@ -254,3 +257,5 @@ eval_spec = tf.estimator.EvalSpec(input_fn = lambda: input_fn(BASE_DIR + 'eval_s
 tf.estimator.train_and_evaluate(wideAndDeepModel, train_spec, eval_spec)
 
 wideAndDeepModel.evaluate(lambda: input_fn(BASE_DIR + 'eval_samples.csv', 1, False, 2 ** 15))
+
+print("训练耗时: {}s".format(time.time() - start))
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