large refactor

models/cond_encoder.py

@@ -0,0 +1,20 @@
+import tensorflow as tf
+from models.base import AEEncoder, Decoder
+
+
+class AutoEncoder(tf.keras.Model):
+    
+    def __init__(self, config):
+        super(AutoEncoder, self).__init__()
+
+        self.encoder : AEEncoder = AEEncoder(config['latent_dim'], config['encoder_depths'], config['encoder_dims'], config['bottleneck_size'], config['kernel_size'])
+        self.decoder : Decoder= Decoder(config['decoder_input'], config['decoder_depths'], config['decoder_dims'], kernel_size=config['kernel_size'], is_label=config['is_label'])
+
+
+    def call(self, inputs, training=None):
+        x = inputs
+
+        x = self.encoder(x, training=training)
+        x = self.decoder(x, training=training)
+
+        return x