model_utils.py 7.78 KB
Newer Older
1
2
# @author lucasmiranda42

3
from itertools import combinations
4
from keras import backend as K
5
from sklearn.metrics import silhouette_score
6
7
8
from tensorflow.keras.constraints import Constraint
from tensorflow.keras.layers import Layer
import tensorflow as tf
9
import tensorflow_probability as tfp
10

11
tfd = tfp.distributions
12
tfpl = tfp.layers
13
14
15
16
17
18
19
20
21
22
23
24
25

# Helper functions
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)
    )


26
27
28
29
30
def compute_mmd(tensors):

    x = tensors[0]
    y = tensors[1]

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
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
    x_kernel = compute_kernel(x, x)
    y_kernel = compute_kernel(y, y)
    xy_kernel = compute_kernel(x, y)
    return (
        tf.reduce_mean(x_kernel)
        + 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)
        super().__init__(**kwargs)

    def get_config(self):
        config = super().get_config().copy()
        config.update(
            {
                "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"
        )
        super().build(batch_input_shape)

    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()
        config.update(
            {"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.dot(x_centered, 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
        else:
            output = K.sum(
                K.square(
                    self.covariance
                    - 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)


118
119
class KLDivergenceLayer(tfpl.KLDivergenceAddLoss):
    def __init__(self, *args, **kwargs):
120
121
122
        self.is_placeholder = True
        super(KLDivergenceLayer, self).__init__(*args, **kwargs)

123
124
125
126
127
    def call(self, distribution_a):
        kl_batch = self._regularizer(distribution_a)
        self.add_loss(kl_batch, inputs=[distribution_a])
        self.add_metric(
            kl_batch, aggregation="mean", name="kl_divergence",
128
        )
129
        self.add_metric(self._regularizer._weight, aggregation="mean", name="kl_rate")
130

131
        return distribution_a
132
133
134


class MMDiscrepancyLayer(Layer):
135
136
    """
    Identity transform layer that adds MM discrepancy
137
138
139
    to the final model loss.
    """

140
    def __init__(self, prior, beta=1.0, *args, **kwargs):
141
        self.is_placeholder = True
142
        self.beta = beta
143
        self.prior = prior
144
145
        super(MMDiscrepancyLayer, self).__init__(*args, **kwargs)

146
147
148
    def get_config(self):
        config = super().get_config().copy()
        config.update({"beta": self.beta})
149
        config.update({"prior": self.prior})
150
151
        return config

152
    def call(self, z, **kwargs):
153
        true_samples = self.prior.sample(1)
154
        mmd_batch = self.beta * compute_mmd([true_samples, z])
155
        self.add_loss(K.mean(mmd_batch), inputs=z)
156
        self.add_metric(mmd_batch, aggregation="mean", name="mmd")
157
        self.add_metric(self.beta, aggregation="mean", name="mmd_rate")
158
159

        return z
160
161


162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
class Gaussian_mixture_overlap(Layer):
    """
    Identity layer that measures the overlap between the components of the latent Gaussian Mixture
    using a specified metric (MMD, Wasserstein, Fischer-Rao)
    """

    def __init__(
        self,
        lat_dims,
        n_components,
        metric="mmd",
        loss=False,
        samples=100,
        *args,
        **kwargs
    ):
        self.lat_dims = lat_dims
        self.n_components = n_components
        self.metric = metric
        self.loss = loss
        self.samples = samples
        super(Gaussian_mixture_overlap, self).__init__(*args, **kwargs)

    def get_config(self):
        config = super().get_config().copy()
        config.update({"lat_dims": self.lat_dims})
        config.update({"n_components": self.n_components})
        config.update({"metric": self.metric})
        config.update({"loss": self.loss})
        config.update({"samples": self.samples})
        return config

    def call(self, target, loss=False):

        dists = []
        for k in range(self.n_components):
            locs = (target[..., : self.lat_dims, k],)
            scales = tf.keras.activations.softplus(target[..., self.lat_dims :, k])

201
202
203
            dists.append(
                tfd.BatchReshape(tfd.MultivariateNormalDiag(locs, scales), [-1])
            )
204
205
206
207
208
209
210
211
212
213
214
215
216
217

        dists = [tf.transpose(gauss.sample(self.samples), [1, 0, 2]) for gauss in dists]

        if self.metric == "mmd":

            intercomponent_mmd = K.mean(
                tf.convert_to_tensor(
                    [
                        tf.vectorized_map(compute_mmd, [dists[c[0]], dists[c[1]]])
                        for c in combinations(range(len(dists)), 2)
                    ],
                    dtype=tf.float32,
                )
            )
218

219
220
221
222
            self.add_metric(
                intercomponent_mmd, aggregation="mean", name="intercomponent_mmd"
            )

223
224
225
            if self.loss:
                self.add_loss(-intercomponent_mmd, inputs=[target])

226
227
228
229
230
231
        elif self.metric == "wasserstein":
            pass

        return target


232
class Latent_space_control(Layer):
233
234
235
236
    """
    Identity layer that adds latent space and clustering stats
    to the metrics compiled by the model
    """
237

238
239
    def __init__(self, loss=False, *args, **kwargs):
        self.loss = loss
240
241
        super(Latent_space_control, self).__init__(*args, **kwargs)

242
243
244
245
    def get_config(self):
        config = super().get_config().copy()
        config.update({"loss": self.loss})

246
247
248
249
250
251
252
253
254
255
256
257
    def call(self, z, z_gauss, z_cat, **kwargs):

        # Adds metric that monitors dead neurons in the latent space
        self.add_metric(
            tf.math.zero_fraction(z_gauss), aggregation="mean", name="dead_neurons"
        )

        # Adds Silhouette score controling overlap between clusters
        hard_labels = tf.math.argmax(z_cat, axis=1)
        silhouette = tf.numpy_function(silhouette_score, [z, hard_labels], tf.float32)
        self.add_metric(silhouette, aggregation="mean", name="silhouette")

258
259
260
        if self.loss:
            self.add_loss(-K.mean(silhouette), inputs=[z, hard_labels])

261
        return z