Merge branch 'tf2' into tf2-entmax

Author: Ghostvv

rasa/nlu/selectors/response_selector.py

@@ -94,13 +94,13 @@ class ResponseSelector(DIETClassifier):
         # sizes of hidden layers before the embedding layer
         # for input words and responses
         # the number of hidden layers is thus equal to the length of this list
-        HIDDEN_LAYERS_SIZES: {TEXT: [], LABEL: []},
+        HIDDEN_LAYERS_SIZES: {TEXT: [256, 128], LABEL: [256, 128]},
         # Whether to share the hidden layer weights between input words and intent labels
         SHARE_HIDDEN_LAYERS: False,
         # number of units in transformer
-        TRANSFORMER_SIZE: 256,
+        TRANSFORMER_SIZE: None,
         # number of transformer layers
-        NUM_TRANSFORMER_LAYERS: 2,
+        NUM_TRANSFORMER_LAYERS: 0,
         # number of attention heads in transformer
         NUM_HEADS: 4,
         # max sequence length if pos_encoding='emb'
@@ -154,7 +154,7 @@ class ResponseSelector(DIETClassifier):
         # use a unidirectional or bidirectional encoder
         UNIDIRECTIONAL_ENCODER: False,
         # if true apply dropout to sparse tensors
-        SPARSE_INPUT_DROPOUT: True,
+        SPARSE_INPUT_DROPOUT: False,
         # visualization of accuracy
         # how often to calculate training accuracy
         EVAL_NUM_EPOCHS: 20,  # small values may hurt performance

rasa/utils/tensorflow/layers.py

@@ -77,7 +77,9 @@ def build(self, input_shape: tf.TensorShape) -> None:
         kernel_mask = tf.cast(
             tf.greater_equal(kernel_mask, self.sparsity), self.kernel.dtype
         )
-        self.kernel_mask = tf.Variable(initial_value=kernel_mask, trainable=False)
+        self.kernel_mask = tf.Variable(
+            initial_value=kernel_mask, trainable=False, name="kernel_mask"
+        )
 
     def call(self, inputs: tf.Tensor) -> tf.Tensor:
         # set some weights to 0 according to precomputed mask