blob: 5efb6afca0cc807e94e8e951a5db4578d0032905 (
plain)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
|
class EncoderDiscriminator:
"""# EncoderDiscriminator Model
Constructs an encoder-discriminator model"""
def __init__(self, model_base_directory):
"""# EncoderDiscriminator 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, discriminator):
"""# Initialize a EncoderDiscriminator
- encoder: The encoder to use
- discriminator: The discriminator to use"""
from tensorflow import keras
self.load_parameters()
self.model = keras.models.Sequential()
self.model.add(encoder.model)
self.model.add(discriminator.model)
self.model._name = "EncoderDiscriminator"
return self
def load(self):
"""# Load an existing EncoderDiscriminator"""
from os import listdir
if "encoder_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/encoder_discriminator" + ".keras",
compile=False,
)
self.model._name = "EncoderDiscriminator"
return self
def save(self, overwrite=False):
"""# Save the current EncoderDiscriminator
- Overwrite: overwrite any existing encoder_discriminator that has been
saved"""
from os import listdir
if overwrite:
self.model.save(
f"{self.model_base_directory}/portable/encoder_discriminator" + ".keras"
)
return True
if "encoder_discriminator.keras" in listdir(
f"{self.model_base_directory}/portable/"
):
return False
self.model.save(
f"{self.model_base_directory}/portable/encoder_discriminator" + ".keras"
)
return True
def load_parameters(self):
"""# Load EncoderDiscriminator 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 EncoderDiscriminator model"""
return self.model.summary()
def __call__(self, *args, **kwargs):
"""# Callable
When calling the encoder_discriminator class, return the model's
output"""
return self.model(*args, **kwargs)
|
