decoder and encoder have the same TFmer blocks

model/transformer.py

@@ -74,27 +74,6 @@ def build_transformerblock(self, inputs, head_size, num_heads,
 
         return outputs
 
-    def build_decoderblock(self, inputs, head_size, num_heads, ff_dim,
-                           dropout):
-        """
-        Constructs the decoder block. This consists of masked multi-head
-        attention, dropout, layer normalization, a residual connection,
-        a feedforward neural network, and another residual connection, but in
-        the reverse order as the encoder block.
-        """
-
-        x = LayerNormalization(epsilon=1e-6)(inputs)
-        x = Conv1D(filters=ff_dim, kernel_size=1, activation="relu")(inputs)
-        x = Dropout(dropout)(x)
-        x = Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)
-        x = Dropout(dropout)(x)
-        res = x + inputs
-        outputs = MultiHeadAttention(
-                key_dim=head_size, num_heads=num_heads,
-                dropout=dropout)(res, res, use_causal_mask=True)
-
-        return outputs
-
     def call(self, inputs):
         """
         Calls the TimeSeriesTransformer model on a batch of inputs.