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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)
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