trying to fix input dimensions:

model/autoencoder.py

@@ -1,27 +1,28 @@
 import keras
 
 class Encoder(keras.Model):
-    def __init__(self, input_size=(130, 26)):
+    def __init__(self, input_size=(130, 26), latent_dim=128):
         super(Encoder, self).__init__()
         self.encoder = keras.Sequential([
+            keras.layers.InputLayer(input_shape=input_size),
             keras.layers.Conv2D(8, (3, 3), activation="relu",
                                 input_shape=input_size),
             keras.layers.Conv2D(16, (3, 3), activation="relu",
                                 input_shape=input_size),
             keras.layers.Conv2D(32, (3, 3), activation="relu",
                                 input_shape=input_size),
             keras.layers.Flatten(),
-            keras.layers.Dense(128, activation="relu")
+            keras.layers.Dense(latent_dim, activation="relu")
         ])
 
     def call(self, x):
         return self.encoder(x)
 
 class Decoder(keras.Model):
-    def __init__(self):
+    def __init__(self, latent_dim=128):
         super(Decoder, self).__init__()
         self.decoder = keras.Sequential([
-            keras.layers.Dense(128, activation="relu"),
+            keras.layers.InputLayer(input_shape=(latent_dim,)),
             keras.layers.Reshape((4, 4, 8)),
             keras.layers.Conv2DTranspose(32, (3, 3), activation="relu"),
             keras.layers.Conv2DTranspose(16, (3, 3), activation="relu"),
@@ -33,10 +34,10 @@ def call(self, x):
         return self.decoder(x)
 
 class Autoencoder(keras.Model):
-    def __init__(self, input_size=(130, 26)):
+    def __init__(self, input_size=(130, 26), latent_dim=128, **kwargs):
         super(Autoencoder, self).__init__()
-        self.encoder = Encoder(input_size=input_size)
-        self.decoder = Decoder()
+        self.encoder = Encoder(input_size=input_size, latent_dim=latent_dim)
+        self.decoder = Decoder(latent_dim=latent_dim)
 
     def call(self, x):
         encoded = self.encoder(x)