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diff --git a/code/sunlab/sunflow/models/discriminator.py b/code/sunlab/sunflow/models/discriminator.py
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+class Discriminator:
+    """# Discriminator Model
+
+    Constructs a discriminator model with a certain depth of intermediate
+    layers of fixed size"""
+
+    def __init__(self, model_base_directory):
+        """# Discriminator 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):
+        """# Initialize a new Discriminator
+
+        Expects a model parameters file to already exist in the initialization
+        base directory when initializing the model"""
+        from tensorflow import keras
+        from tensorflow.keras import layers
+
+        self.load_parameters()
+        assert self.depth >= 0, "Depth must be non-negative"
+        self.model = keras.models.Sequential()
+        if self.depth == 0:
+            self.model.add(
+                layers.Dense(
+                    1,
+                    input_shape=(self.latent_size,),
+                    activation=None,
+                    name="discriminator_output_vector",
+                )
+            )
+        else:
+            self.model.add(
+                layers.Dense(
+                    self.layer_size,
+                    input_shape=(self.latent_size,),
+                    activation=None,
+                    name="discriminator_dense_1",
+                )
+            )
+            if self.use_leaky_relu:
+                self.model.add(layers.LeakyReLU())
+            else:
+                self.model.add(layers.ReLU())
+            if self.dropout > 0.0:
+                self.model.add(layers.Dropout(self.dropout))
+            for _d in range(1, self.depth):
+                self.model.add(
+                    layers.Dense(
+                        self.layer_size,
+                        activation=None,
+                        name=f"discriminator_dense_{_d+1}",
+                    )
+                )
+                if self.use_leaky_relu:
+                    self.model.add(layers.LeakyReLU())
+                else:
+                    self.model.add(layers.ReLU())
+                if self.dropout > 0.0:
+                    self.model.add(layers.Dropout(self.dropout))
+            self.model.add(
+                layers.Dense(
+                    1, activation="sigmoid", name="discriminator_output_vector"
+                )
+            )
+        self.model._name = "Discriminator"
+        return self
+
+    def load(self):
+        """# Load an existing Discriminator"""
+        from os import listdir
+
+        if "discriminator.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/discriminator.keras", compile=False
+        )
+        self.model._name = "Discriminator"
+        return self
+
+    def save(self, overwrite=False):
+        """# Save the current Discriminator
+
+        - Overwrite: overwrite any existing discriminator that has been
+        saved"""
+        from os import listdir
+
+        if overwrite:
+            self.model.save(f"{self.model_base_directory}/portable/discriminator.keras")
+            return True
+        if "discriminator.keras" in listdir(f"{self.model_base_directory}/portable/"):
+            return False
+        self.model.save(f"{self.model_base_directory}/portable/discriminator.keras")
+        return True
+
+    def load_parameters(self):
+        """# Load Discriminator Model Parameters from File
+        The file needs to have the following parameters defined:
+         - data_size: int
+         - adversary_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["adversary_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 Discriminator model"""
+        return self.model.summary()
+
+    def __call__(self, *args, **kwargs):
+        """# Callable
+
+        When calling the discriminator class, return the model's output"""
+        return self.model(*args, **kwargs)