Working version of decoder added?? I think

model/model.py

@@ -8,8 +8,8 @@
 
 from keras import Input, Model
 from keras.layers import BatchNormalization, Conv1D, Dense, Dropout, Reshape, \
-        GlobalAveragePooling1D, LayerNormalization, Masking, \
-        MultiHeadAttention
+        GlobalAveragePooling1D, LayerNormalization, Masking, Conv2D, \
+        MultiHeadAttention, concatenate
 
 from model.transformer import TimeseriesTransformerBuilder as TSTFBuilder
 
@@ -146,7 +146,7 @@ def __init__(self, input_shape, head_size, num_heads, ff_dim,
         """
         self.tstfbuilder = TSTFBuilder()
 
-        super(CompoundModel, self).__init__()
+        super(DecoderModel, self).__init__()
         self.timeseriestransdecoder = self._modelstack(
                 input_shape, head_size, num_heads, ff_dim,
                 num_Transformer_blocks, mlp_units, n_classes,
@@ -176,26 +176,38 @@ def _modelstack(self, input_shape, head_size, num_heads, ff_dim,
             A Keras model.
         """
 
-        x1, x2 = Input(shape=input_shape)
-        x1 = Dense(n_classes[0], activation="relu")(x1)
-        x2 = Dense(n_classes[1], activation="relu")(x2)
-        x = (x1 + x2)
-        x = GlobalAveragePooling1D(data_format="channels_first")(x)
+        shape0 = n_classes[0]
+        shape1 = n_classes[1]
+        x0 = Input(shape=(shape0,))
+        x1 = Input(shape=(shape1,))
+        inputs = [x0, x1]
+        x0 = Dense(n_classes[0], activation="relu")(x0)
+        x1 = Dense(n_classes[1], activation="relu")(x1)
+        x = concatenate([x0, x1], axis=-1)
+        full_dimension = input_shape[0] * input_shape[1]
+        x = Dense(full_dimension, activation="relu")(x)
+        x = Reshape((input_shape[0], input_shape[1]))(x)
 
         for dim in mlp_units:
             x = Dense(dim, activation="relu")(x)
             x = Dropout(mlp_dropout)(x)
 
         for _ in range(num_Transformer_blocks):
-            x = self.tstfbuilder.build_decoderblock(
+            x = self.tstfbuilder.build_transformerblock(
                 x,
                 head_size,
                 num_heads,
                 ff_dim,
                 dropout
             )
 
-        return Model(inputs, z)
+        # final layer with corrected shape
+        x = Conv1D(filters=input_shape[1],
+                   kernel_size=1,
+                   padding="valid",
+                   activation="linear")(x)
+
+        return Model(inputs, x)
 
     def call(self, inputs):
         """
@@ -207,7 +219,7 @@ def call(self, inputs):
         Returns:
             Tensor, resulting output of the TimeSeriesTransformer model.
         """
-        return self.timeseriestransformer(inputs)
+        return self.timeseriestransdecoder(inputs)
 
     def summary(self):
         """
@@ -219,6 +231,6 @@ def summary(self):
         Returns:
             None.
         """
-        self.timeseriestransformer.summary()
+        self.timeseriestransdecoder.summary()
 
 # EOF