Commit a2246f5d authored by lucas_miranda's avatar lucas_miranda
Browse files

Restructured and Now all helper functions and custom...

Restructured and Now all helper functions and custom layers are imported from
parent ba91b083
# @author lucasmiranda42
from kerastuner import HyperModel
from keras import backend as K
from tensorflow.keras import Input, Model, Sequential
from tensorflow.keras.constraints import Constraint, UnitNorm
from tensorflow.keras.constraints import UnitNorm
from tensorflow.keras.layers import Bidirectional, Dense, Dropout
from tensorflow.keras.layers import Lambda, Layer, LSTM
from tensorflow.keras.layers import Lambda, LSTM
from tensorflow.keras.layers import RepeatVector, TimeDistributed
from tensorflow.keras.losses import Huber
from tensorflow.keras.optimizers import Adam
from source.model_utils import *
import tensorflow as tf
# Helper functions
def sampling(args, epsilon_std=1.0):
z_mean, z_log_sigma = args
epsilon = K.random_normal(shape=K.shape(z_mean), mean=0.0, stddev=epsilon_std)
return z_mean + K.exp(z_log_sigma) * epsilon
def compute_kernel(x, y):
x_size = K.shape(x)[0]
y_size = K.shape(y)[0]
dim = K.shape(x)[1]
tiled_x = K.tile(K.reshape(x, K.stack([x_size, 1, dim])), K.stack([1, y_size, 1]))
tiled_y = K.tile(K.reshape(y, K.stack([1, y_size, dim])), K.stack([x_size, 1, 1]))
return K.exp(
-tf.reduce_mean(K.square(tiled_x - tiled_y), axis=2) / K.cast(dim, tf.float32)
def compute_mmd(x, y):
x_kernel = compute_kernel(x, x)
y_kernel = compute_kernel(y, y)
xy_kernel = compute_kernel(x, y)
return (
+ tf.reduce_mean(y_kernel)
- 2 * tf.reduce_mean(xy_kernel)
# Custom layers for efficiency/losses
class DenseTranspose(Layer):
def __init__(self, dense, output_dim, activation=None, **kwargs):
self.dense = dense
self.output_dim = output_dim
self.activation = tf.keras.activations.get(activation)
def get_config(self):
config = super().get_config().copy()
"dense": self.dense,
"output_dim": self.output_dim,
"activation": self.activation,
return config
def build(self, batch_input_shape):
self.biases = self.add_weight(
name="bias", shape=[self.dense.input_shape[-1]], initializer="zeros"
def call(self, inputs, **kwargs):
z = tf.matmul(inputs, self.dense.weights[0], transpose_b=True)
return self.activation(z + self.biases)
def compute_output_shape(self, input_shape):
return input_shape[0], self.output_dim
class UncorrelatedFeaturesConstraint(Constraint):
def __init__(self, encoding_dim, weightage=1.0):
self.encoding_dim = encoding_dim
self.weightage = weightage
def get_config(self):
config = super().get_config().copy()
{"encoding_dim": self.encoding_dim, "weightage": self.weightage,}
return config
def get_covariance(self, x):
x_centered_list = []
for i in range(self.encoding_dim):
x_centered_list.append(x[:, i] - K.mean(x[:, i]))
x_centered = tf.stack(x_centered_list)
covariance =, K.transpose(x_centered)) / tf.cast(
x_centered.get_shape()[0], tf.float32
return covariance
# Constraint penalty
def uncorrelated_feature(self, x):
if self.encoding_dim <= 1:
return 0.0
output = K.sum(
- tf.math.multiply(self.covariance, K.eye(self.encoding_dim))
return output
def __call__(self, x):
self.covariance = self.get_covariance(x)
return self.weightage * self.uncorrelated_feature(x)
class KLDivergenceLayer(Layer):
""" Identity transform layer that adds KL divergence
to the final model loss.
def __init__(self, *args, **kwargs):
self.is_placeholder = True
super(KLDivergenceLayer, self).__init__(*args, **kwargs)
def call(self, inputs, **kwargs):
mu, log_var = inputs
kl_batch = -0.5 * K.sum(1 + log_var - K.square(mu) - K.exp(log_var), axis=-1)
self.add_loss(K.mean(kl_batch), inputs=inputs)
return inputs
class MMDiscrepancyLayer(Layer):
""" Identity transform layer that adds MM discrepancy
to the final model loss.
def __init__(self, *args, **kwargs):
self.is_placeholder = True
super(MMDiscrepancyLayer, self).__init__(*args, **kwargs)
def call(self, z, **kwargs):
true_samples = K.random_normal(
K.shape(z), mean=0.0, stddev=2.0 / K.cast_to_floatx(K.shape(z)[1])
mmd_batch = compute_mmd(z, true_samples)
self.add_loss(K.mean(mmd_batch), inputs=z)
return z
# Hypermodels
class SEQ_2_SEQ_AE(HyperModel):
def __init__(self, input_shape):
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