diff --git a/deeptables/models/layers.py b/deeptables/models/layers.py
index eef76ef..285f946 100644
--- a/deeptables/models/layers.py
+++ b/deeptables/models/layers.py
@@ -923,9 +923,17 @@ def get_config(self):
 
 
 class VarLenColumnEmbedding(Layer):
-    def __init__(self, emb_vocab_size, emb_output_dim, dropout_rate=0. ,  **kwargs):
+    def __init__(self, emb_vocab_size, emb_output_dim,
+                 embeddings_initializer,
+                 embeddings_regularizer,
+                 activity_regularizer,
+                 dropout_rate=0.,
+                 **kwargs):
         self.emb_vocab_size = emb_vocab_size
         self.emb_output_dim = emb_output_dim
+        self.embeddings_initializer = embeddings_initializer
+        self.embeddings_regularizer = embeddings_regularizer
+        self.activity_regularizer = activity_regularizer
         self.dropout_rate = dropout_rate
         super(VarLenColumnEmbedding, self).__init__(**kwargs)
         self.dropout = None
@@ -937,7 +945,11 @@ def compute_output_shape(self, input_shape):
 
     def build(self, input_shape=None):
         super(VarLenColumnEmbedding, self).build(input_shape)
-        self.emb_layer = Embedding(input_dim=self.emb_vocab_size, output_dim=self.emb_output_dim)
+        self.emb_layer = Embedding(input_dim=self.emb_vocab_size,
+                                   output_dim=self.emb_output_dim,
+                                   embeddings_initializer=self.embeddings_initializer,
+                                   embeddings_regularizer=self.embeddings_regularizer,
+                                   activity_regularizer=self.activity_regularizer)
         if self.dropout_rate > 0:
             self.dropout = SpatialDropout1D(self.dropout_rate, name='var_len_emb_dropout')
         else:
@@ -945,20 +957,25 @@ def build(self, input_shape=None):
         self.built = True
 
     def call(self, inputs):
-        embedding_output = self.emb_layer.call(inputs)
-        embedding_output = embedding_output.reshape((embedding_output[0], 1, -1))
+        embedding_output = self.emb_layer(inputs)
+        embedding_output_reshape = tf.reshape(embedding_output, [embedding_output.shape[0], 1, -1])
         if self.dropout is not None:
-            dropout_output = self.dropout(embedding_output)
+            dropout_output = self.dropout(embedding_output_reshape)
         else:
-            dropout_output = embedding_output
+            dropout_output = embedding_output_reshape
         return dropout_output
 
     def compute_mask(self, inputs, mask=None):
         return None
 
     def get_config(self, ):
-        config = { 'dropout_rate': self.dropout_rate,
-                   'emb_layer': self.emb_layer.get_config()}
+        config = {  'dropout_rate': self.dropout_rate,
+                    'emb_layer': self.emb_layer.get_config(),
+                    'embeddings_initializer': self.embeddings_initializer,
+                    'embeddings_regularizer': self.embeddings_regularizer,
+                    'emb_vocab_size': self.emb_vocab_size,
+                    'emb_output_dim': self.emb_output_dim
+                   }
         base_config = super(VarLenColumnEmbedding, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))