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diff --git a/code/sunlab/sunflow/models/autoencoder.py b/code/sunlab/sunflow/models/autoencoder.py
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+class Autoencoder:
+    """# Autoencoder Model
+
+    Constructs an encoder-decoder model"""
+
+    def __init__(self, model_base_directory):
+        """# Autoencoder Model Initialization
+
+        - model_base_directory: The base folder directory where the model will
+        be saved/ loaded"""
+        self.model_base_directory = model_base_directory
+
+    def init(self, encoder, decoder):
+        """# Initialize an Autoencoder
+
+        - encoder: The encoder to use
+        - decoder: The decoder to use"""
+        from tensorflow import keras
+
+        self.load_parameters()
+        self.model = keras.models.Sequential()
+        self.model.add(encoder.model)
+        self.model.add(decoder.model)
+        self.model._name = "Autoencoder"
+        return self
+
+    def load(self):
+        """# Load an existing Autoencoder"""
+        from os import listdir
+
+        if "autoencoder.keras" not in listdir(f"{self.model_base_directory}/portable/"):
+            return None
+        import tensorflow as tf
+
+        self.model = tf.keras.models.load_model(
+            f"{self.model_base_directory}/portable/autoencoder.keras", compile=False
+        )
+        self.model._name = "Autoencoder"
+        return self
+
+    def save(self, overwrite=False):
+        """# Save the current Autoencoder
+
+        - Overwrite: overwrite any existing autoencoder that has been saved"""
+        from os import listdir
+
+        if overwrite:
+            self.model.save(f"{self.model_base_directory}/portable/autoencoder.keras")
+            return True
+        if "autoencoder.keras" in listdir(f"{self.model_base_directory}/portable/"):
+            return False
+        self.model.save(f"{self.model_base_directory}/portable/autoencoder.keras")
+        return True
+
+    def load_parameters(self):
+        """# Load Autoencoder Model Parameters from File
+        The file needs to have the following parameters defined:
+         - data_size: int
+         - autoencoder_layer_size: int
+         - latent_size: int
+         - autoencoder_depth: int
+         - dropout: float (set to 0. if you don't want a dropout layer)
+         - use_leaky_relu: boolean"""
+        from pickle import load
+
+        with open(
+            f"{self.model_base_directory}/portable/model_parameters.pkl", "rb"
+        ) as phandle:
+            parameters = load(phandle)
+        self.data_size = parameters["data_size"]
+        self.layer_size = parameters["autoencoder_layer_size"]
+        self.latent_size = parameters["latent_size"]
+        self.depth = parameters["autoencoder_depth"]
+        self.dropout = parameters["dropout"]
+        self.use_leaky_relu = parameters["use_leaky_relu"]
+
+    def summary(self):
+        """# Returns the summary of the Autoencoder model"""
+        return self.model.summary()
+
+    def __call__(self, *args, **kwargs):
+        """# Callable
+
+        When calling the autoencoder class, return the model's output"""
+        return self.model(*args, **kwargs)