update

deepctr_torch/inputs.py

@@ -42,28 +42,17 @@ def __new__(cls, name, dimension, maxlen, combiner="mean", use_hash=False, dtype
 
 
 def get_feature_names(feature_columns):
-    features = build_input_features(
-        feature_columns, include_varlen=True, include_fixlen=True)
+    features = build_input_features(feature_columns)
     return list(features.keys())
 
-
-# def get_varlen_feature_names(feature_columns):
-#     features = build_input_features(
-#         feature_columns, include_varlen=True, include_fixlen=False)
-#     return list(features.keys())
-
-
 def get_inputs_list(inputs):
     return list(chain(*list(map(lambda x: x.values(), filter(lambda x: x is not None, inputs)))))
 
 
-def build_input_features(feature_columns, include_varlen=True, mask_zero=True, prefix='', include_fixlen=True):
-    input_features = OrderedDict()
+def build_input_features(feature_columns):
     features = OrderedDict()
 
     start = 0
-
-    #if include_fixlen:
     for feat in feature_columns:
         feat_name = feat.name
         if feat_name in features:
@@ -79,15 +68,6 @@ def build_input_features(feature_columns, include_varlen=True, mask_zero=True, p
             start += feat.maxlen
         else:
             raise TypeError("Invalid feature column type,got",type(feat))
-    #if include_varlen:
-        # for feat in feature_columns:
-        #     feat_name = feat.name
-        #     if feat_name in features:
-        #         continue
-        #     if isinstance(feat, VarLenSparseFeat):
-        #         features[feat_name] = (start, start + feat.maxlen)
-        #         start += feat.maxlen
-
     return features
 
 

deepctr_torch/models/basemodel.py

@@ -135,7 +135,8 @@ def fit(self, x=None,
             shuffle=True, ):
         """
 
-        :param x: Numpy array of training data (if the model has a single input), or list of Numpy arrays (if the model has multiple inputs).
+        :param x: Numpy array of training data (if the model has a single input), or list of Numpy arrays (if the model has multiple inputs).If input layers in the model are named, you can also pass a
+            dictionary mapping input names to Numpy arrays.
         :param y: Numpy array of target (label) data (if the model has a single output), or list of Numpy arrays (if the model has multiple outputs).
         :param batch_size: Integer or `None`. Number of samples per gradient update. If unspecified, `batch_size` will default to 256.
         :param epochs: Integer. Number of epochs to train the model. An epoch is an iteration over the entire `x` and `y` data provided. Note that in conjunction with `initial_epoch`, `epochs` is to be understood as "final epoch". The model is not trained for a number of iterations given by `epochs`, but merely until the epoch of index `epochs` is reached.