model_utils.py 12.4 KB
Newer Older
1
# @author lucasmiranda42
2
3
4
5
6
7
8
9
# encoding: utf-8
# module deepof

"""

Functions and general utilities for the deepof tensorflow models. See documentation for details

"""
10

11
from itertools import combinations
12
from tensorflow.keras import backend as K
13
14
15
from tensorflow.keras.constraints import Constraint
from tensorflow.keras.layers import Layer
import tensorflow as tf
16
import tensorflow_probability as tfp
17

18
tfd = tfp.distributions
19
tfpl = tfp.layers
20

lucas_miranda's avatar
lucas_miranda committed
21

22
# Helper functions
23
@tf.function
lucas_miranda's avatar
lucas_miranda committed
24
25
26
def far_away_uniform_initialiser(
    shape: tuple, minval: int = 0, maxval: int = 15, iters: int = 100000
) -> tf.Tensor:
27
28
    """
    Returns a uniformly initialised matrix in which the columns are as far as possible
lucas_miranda's avatar
lucas_miranda committed
29
30
31
32
33
34
35
36
37
38
39
40
41

        Parameters:
            - shape (tuple): shape of the object to generate.
            - minval (int): Minimum value of the uniform distribution from which to sample
            - maxval (int): Maximum value of the uniform distribution from which to sample
            - iters (int): the algorithm generates values at random and keeps those runs that
            are the farthest apart. Increasing this parameter will lead to more accurate,
            results while making the function run slowlier.

        Returns:
            - init (tf.Tensor): tensor of the specified shape in which the column vectors
             are as far as possible

42
    """
43
44
45
46
47
48
49
50

    init = tf.random.uniform(shape, minval, maxval)
    init_dist = tf.abs(tf.norm(tf.math.subtract(init[1:], init[:1])))
    i = 0

    while tf.less(i, iters):
        temp = tf.random.uniform(shape, minval, maxval)
        dist = tf.abs(tf.norm(tf.math.subtract(temp[1:], temp[:1])))
51
52
53
54
55

        if dist > init_dist:
            init_dist = dist
            init = temp

56
57
58
        i += 1

    return init
59
60


lucas_miranda's avatar
lucas_miranda committed
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
def compute_kernel(x: tf.Tensor, y: tf.Tensor) -> tf.Tensor:
    """

    Computes the MMD between the two specified vectors using a gaussian kernel.

        Parameters:
            - x (tf.Tensor): left tensor
            - y (tf.Tensor): right tensor

        Returns
            - kernel (tf.Tensor): returns the result of applying the kernel, for
            each training instance

    """

76
77
78
79
80
81
82
83
84
    x_size = tf.shape(x)[0]
    y_size = tf.shape(y)[0]
    dim = tf.shape(x)[1]
    tiled_x = tf.tile(
        tf.reshape(x, tf.stack([x_size, 1, dim])), tf.stack([1, y_size, 1])
    )
    tiled_y = tf.tile(
        tf.reshape(y, tf.stack([1, y_size, dim])), tf.stack([x_size, 1, 1])
    )
lucas_miranda's avatar
lucas_miranda committed
85
    kernel = tf.exp(
86
        -tf.reduce_mean(tf.square(tiled_x - tiled_y), axis=2) / tf.cast(dim, tf.float32)
87
    )
lucas_miranda's avatar
lucas_miranda committed
88
    return kernel
89
90


91
@tf.function
lucas_miranda's avatar
lucas_miranda committed
92
93
94
95
96
97
98
99
100
101
102
103
104
def compute_mmd(tensors: tuple) -> tf.Tensor:
    """

        Computes the MMD between the two specified vectors using a gaussian kernel.

            Parameters:
                - tensors (tuple): tuple containing two tf.Tensor objects

            Returns
                - mmd (tf.Tensor): returns the maximum mean discrepancy for each
                training instance

        """
105
106
107
108

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

109
110
111
    x_kernel = compute_kernel(x, x)
    y_kernel = compute_kernel(y, y)
    xy_kernel = compute_kernel(x, y)
lucas_miranda's avatar
lucas_miranda committed
112
    mmd = (
113
114
115
116
        tf.reduce_mean(x_kernel)
        + tf.reduce_mean(y_kernel)
        - 2 * tf.reduce_mean(xy_kernel)
    )
lucas_miranda's avatar
lucas_miranda committed
117
    return mmd
118
119


120
# Custom auxiliary classes
lucas_miranda's avatar
lucas_miranda committed
121
122
123
124
125
126
127
128
class one_cycle_scheduler(tf.keras.callbacks.Callback):
    """

    One cycle learning rate scheduler.
    Based on https://arxiv.org/pdf/1506.01186.pdf

    """

129
130
    def __init__(
        self,
lucas_miranda's avatar
lucas_miranda committed
131
132
133
134
135
        iterations: int,
        max_rate: float,
        start_rate: float = None,
        last_iterations: int = None,
        last_rate: float = None,
136
    ):
lucas_miranda's avatar
lucas_miranda committed
137
        super().__init__()
138
139
140
141
142
143
144
145
        self.iterations = iterations
        self.max_rate = max_rate
        self.start_rate = start_rate or max_rate / 10
        self.last_iterations = last_iterations or iterations // 10 + 1
        self.half_iteration = (iterations - self.last_iterations) // 2
        self.last_rate = last_rate or self.start_rate / 1000
        self.iteration = 0

lucas_miranda's avatar
lucas_miranda committed
146
    def _interpolate(self, iter1: int, iter2: int, rate1: float, rate2: float) -> float:
147
148
        return (rate2 - rate1) * (self.iteration - iter1) / (iter2 - iter1) + rate1

lucas_miranda's avatar
lucas_miranda committed
149
150
151
    # noinspection PyMethodOverriding,PyTypeChecker
    def on_batch_begin(self, batch: int, logs):
        """ Defines computations to perform for each batch """
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
        if self.iteration < self.half_iteration:
            rate = self._interpolate(
                0, self.half_iteration, self.start_rate, self.max_rate
            )
        elif self.iteration < 2 * self.half_iteration:
            rate = self._interpolate(
                self.half_iteration,
                2 * self.half_iteration,
                self.max_rate,
                self.start_rate,
            )
        else:
            rate = self._interpolate(
                2 * self.half_iteration,
                self.iterations,
                self.start_rate,
                self.last_rate,
            )
            rate = max(rate, self.last_rate)
        self.iteration += 1
        K.set_value(self.model.optimizer.lr, rate)
173
174


lucas_miranda's avatar
lucas_miranda committed
175
176
177
178
179
180
181
182
class uncorrelated_features_constraint(Constraint):
    """

    Tensorflow Constraint subclass that forces a layer to have uncorrelated features.
    Useful, among others, for auto encoder bottleneck layers

    """

183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
    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):
199
            x_centered_list.append(x[:, i] - K.mean(x[:, i]))
200
201

        x_centered = tf.stack(x_centered_list)
202
        covariance = K.dot(x_centered, K.transpose(x_centered)) / tf.cast(
203
204
205
206
207
208
            x_centered.get_shape()[0], tf.float32
        )

        return covariance

    # Constraint penalty
209
    def uncorrelated_feature(self, x):
210
211
212
        if self.encoding_dim <= 1:
            return 0.0
        else:
213
214
            output = K.sum(
                K.square(
215
                    self.covariance
216
                    - tf.math.multiply(self.covariance, tf.eye(self.encoding_dim))
217
218
219
220
221
222
223
224
225
                )
            )
            return output

    def __call__(self, x):
        self.covariance = self.get_covariance(x)
        return self.weightage * self.uncorrelated_feature(x)


226
227
228
229
230
231
# Custom Layers
class MCDropout(tf.keras.layers.Dropout):
    def call(self, inputs, **kwargs):
        return super().call(inputs, training=True)


232
233
class KLDivergenceLayer(tfpl.KLDivergenceAddLoss):
    def __init__(self, *args, **kwargs):
234
235
236
        self.is_placeholder = True
        super(KLDivergenceLayer, self).__init__(*args, **kwargs)

237
238
239
    def get_config(self):
        config = super().get_config().copy()
        config.update(
240
            {"is_placeholder": self.is_placeholder,}
241
242
243
        )
        return config

244
245
246
247
248
    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",
249
        )
250
        self.add_metric(self._regularizer._weight, aggregation="mean", name="kl_rate")
251

252
        return distribution_a
253
254


255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
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


287
class MMDiscrepancyLayer(Layer):
288
    """
289
    Identity transform layer that adds MM Discrepancy
290
291
292
    to the final model loss.
    """

293
    def __init__(self, batch_size, prior, beta=1.0, *args, **kwargs):
294
        self.is_placeholder = True
295
        self.batch_size = batch_size
296
        self.beta = beta
297
        self.prior = prior
298
299
        super(MMDiscrepancyLayer, self).__init__(*args, **kwargs)

300
301
    def get_config(self):
        config = super().get_config().copy()
302
        config.update({"batch_size": self.batch_size})
303
        config.update({"beta": self.beta})
304
        config.update({"prior": self.prior})
305
306
        return config

307
    def call(self, z, **kwargs):
308
        true_samples = self.prior.sample(self.batch_size)
309
        mmd_batch = self.beta * compute_mmd([true_samples, z])
310
        self.add_loss(K.mean(mmd_batch), inputs=z)
311
        self.add_metric(mmd_batch, aggregation="mean", name="mmd")
312
        self.add_metric(self.beta, aggregation="mean", name="mmd_rate")
313
314

        return z
315
316


317
318
319
320
321
322
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)
    """

323
    def __init__(self, lat_dims, n_components, loss=False, samples=10, *args, **kwargs):
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
        self.lat_dims = lat_dims
        self.n_components = n_components
        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({"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])

345
346
347
            dists.append(
                tfd.BatchReshape(tfd.MultivariateNormalDiag(locs, scales), [-1])
            )
348
349
350

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

351
        ### MMD-based overlap ###
352
        intercomponent_mmd = K.mean(
353
354
            tf.convert_to_tensor(
                [
355
                    tf.vectorized_map(compute_mmd, [dists[c[0]], dists[c[1]]])
356
357
358
                    for c in combinations(range(len(dists)), 2)
                ],
                dtype=tf.float32,
359
            )
360
        )
361

362
        self.add_metric(
363
            -intercomponent_mmd, aggregation="mean", name="intercomponent_mmd"
364
        )
365

366
367
        if self.loss:
            self.add_loss(-intercomponent_mmd, inputs=[target])
368
369
370
371

        return target


372
class Dead_neuron_control(Layer):
373
374
375
376
    """
    Identity layer that adds latent space and clustering stats
    to the metrics compiled by the model
    """
377

378
379
    def __init__(self, *args, **kwargs):
        super(Dead_neuron_control, self).__init__(*args, **kwargs)
380

381
382
383
384
385
386
387
388
    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"
        )

        return z
389
390
391
392
393
394
395


class Entropy_regulariser(Layer):
    """
    Identity layer that adds cluster weight entropy to the loss function
    """

396
    def __init__(self, weight=1.0, *args, **kwargs):
397
398
399
400
401
402
403
404
405
        self.weight = weight
        super(Entropy_regulariser, self).__init__(*args, **kwargs)

    def get_config(self):
        config = super().get_config().copy()
        config.update({"weight": self.weight})

    def call(self, z, **kwargs):

406
407
        # axis=1 increases the entropy of a cluster across instances
        # axis=0 increases the entropy of the assignment for a given instance
408
        entropy = K.sum(tf.multiply(z + 1e-5, tf.math.log(z) + 1e-5), axis=1)
409
410

        # Adds metric that monitors dead neurons in the latent space
411
        self.add_metric(entropy, aggregation="mean", name="-weight_entropy")
412

413
        self.add_loss(self.weight * K.sum(entropy), inputs=[z])
414
415

        return z