Added a Conv1D layer at the end of both decoder and next_sequence_predictor

deepof/models.py

@@ -341,7 +341,7 @@ class SEQ_2_SEQ_GMVAE:
             "bidirectional_merge": "concat",
             "clipvalue": 1.0,
             "dense_activation": "relu",
-            "dense_layers_per_branch": 1,
+            "dense_layers_per_branch": 3,
             "dropout_rate": 0.05,
             "learning_rate": 1e-3,
             "units_conv": 64,
@@ -362,7 +362,7 @@ class SEQ_2_SEQ_GMVAE:
             filters=self.CONV_filters,
             kernel_size=5,
             strides=1,
-            padding="causal",
+            padding="same",
             activation=self.dense_activation,
             kernel_initializer=he_uniform(),
             use_bias=True,
@@ -418,6 +418,8 @@ class SEQ_2_SEQ_GMVAE:
         Model_B1 = BatchNormalization()
         Model_B2 = BatchNormalization()
         Model_B3 = BatchNormalization()
+        Model_B4 = BatchNormalization()
+
         seq_D = [
             Dense(
                 self.DENSE_2,
@@ -463,6 +465,15 @@ class SEQ_2_SEQ_GMVAE:
             ),
             merge_mode=self.bidirectional_merge,
         )
+        Model_D6 = tf.keras.layers.Conv1D(
+            filters=self.CONV_filters,
+            kernel_size=5,
+            strides=1,
+            padding="same",
+            activation=self.dense_activation,
+            kernel_initializer=he_uniform(),
+            use_bias=True,
+        )
 
         # Predictor layers
         Model_P1 = Dense(
@@ -519,11 +530,13 @@ class SEQ_2_SEQ_GMVAE:
             Model_B1,
             Model_B2,
             Model_B3,
+            Model_B4,
             Model_D1,
             Model_D2,
             Model_D3,
             Model_D4,
             Model_D5,
+            Model_D6,
             Model_P1,
             Model_P2,
             Model_P3,
@@ -547,11 +560,13 @@ class SEQ_2_SEQ_GMVAE:
             Model_B1,
             Model_B2,
             Model_B3,
+            Model_B4,
             Model_D1,
             Model_D2,
             Model_D3,
             Model_D4,
             Model_D5,
+            Model_D6,
             Model_P1,
             Model_P2,
             Model_P3,
@@ -577,7 +592,7 @@ class SEQ_2_SEQ_GMVAE:
             self.number_of_components,
             name="cluster_assignment",
             activation="softmax",
-            kernel_regularizer=(
+            activity_regularizer=(
                 tf.keras.regularizers.l1_l2(l1=0.01, l2=0.01)
                 if self.reg_cat_clusters
                 else None
@@ -688,6 +703,8 @@ class SEQ_2_SEQ_GMVAE:
         generator = Model_B2(generator)
         generator = Model_D5(generator)
         generator = Model_B3(generator)
+        generator = Model_D6(generator)
+        generator = Model_B4(generator)
         x_decoded_mean = Dense(
             tfpl.IndependentNormal.params_size(input_shape[2:]) // 2
         )(generator)