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b389c3d302
Pushservice is the main recommendation service we use to surface recommendations to our users via notifications. It fetches candidates from various sources, ranks them in order of relevance, and applies filters to determine the best one to send.
119 lines
4.0 KiB
Python
119 lines
4.0 KiB
Python
import numpy as np
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from tensorflow.keras import backend as K
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class VarianceScaling(object):
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"""Initializer capable of adapting its scale to the shape of weights.
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With `distribution="normal"`, samples are drawn from a truncated normal
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distribution centered on zero, with `stddev = sqrt(scale / n)` where n is:
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- number of input units in the weight tensor, if mode = "fan_in"
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- number of output units, if mode = "fan_out"
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- average of the numbers of input and output units, if mode = "fan_avg"
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With `distribution="uniform"`,
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samples are drawn from a uniform distribution
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within [-limit, limit], with `limit = sqrt(3 * scale / n)`.
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# Arguments
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scale: Scaling factor (positive float).
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mode: One of "fan_in", "fan_out", "fan_avg".
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distribution: Random distribution to use. One of "normal", "uniform".
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seed: A Python integer. Used to seed the random generator.
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# Raises
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ValueError: In case of an invalid value for the "scale", mode" or
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"distribution" arguments."""
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def __init__(
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self,
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scale=1.0,
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mode="fan_in",
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distribution="normal",
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seed=None,
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fan_in=None,
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fan_out=None,
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):
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self.fan_in = fan_in
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self.fan_out = fan_out
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if scale <= 0.0:
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raise ValueError("`scale` must be a positive float. Got:", scale)
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mode = mode.lower()
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if mode not in {"fan_in", "fan_out", "fan_avg"}:
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raise ValueError(
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"Invalid `mode` argument: " 'expected on of {"fan_in", "fan_out", "fan_avg"} ' "but got",
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mode,
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)
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distribution = distribution.lower()
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if distribution not in {"normal", "uniform"}:
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raise ValueError(
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"Invalid `distribution` argument: " 'expected one of {"normal", "uniform"} ' "but got",
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distribution,
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)
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self.scale = scale
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self.mode = mode
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self.distribution = distribution
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self.seed = seed
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def __call__(self, shape, dtype=None, partition_info=None):
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fan_in = shape[-2] if self.fan_in is None else self.fan_in
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fan_out = shape[-1] if self.fan_out is None else self.fan_out
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scale = self.scale
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if self.mode == "fan_in":
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scale /= max(1.0, fan_in)
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elif self.mode == "fan_out":
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scale /= max(1.0, fan_out)
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else:
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scale /= max(1.0, float(fan_in + fan_out) / 2)
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if self.distribution == "normal":
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stddev = np.sqrt(scale) / 0.87962566103423978
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return K.truncated_normal(shape, 0.0, stddev, dtype=dtype, seed=self.seed)
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else:
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limit = np.sqrt(3.0 * scale)
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return K.random_uniform(shape, -limit, limit, dtype=dtype, seed=self.seed)
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def get_config(self):
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return {
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"scale": self.scale,
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"mode": self.mode,
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"distribution": self.distribution,
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"seed": self.seed,
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}
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def customized_glorot_uniform(seed=None, fan_in=None, fan_out=None):
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"""Glorot uniform initializer, also called Xavier uniform initializer.
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It draws samples from a uniform distribution within [-limit, limit]
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where `limit` is `sqrt(6 / (fan_in + fan_out))`
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where `fan_in` is the number of input units in the weight tensor
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and `fan_out` is the number of output units in the weight tensor.
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# Arguments
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seed: A Python integer. Used to seed the random generator.
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# Returns
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An initializer."""
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return VarianceScaling(
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scale=1.0,
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mode="fan_avg",
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distribution="uniform",
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seed=seed,
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fan_in=fan_in,
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fan_out=fan_out,
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)
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def customized_glorot_norm(seed=None, fan_in=None, fan_out=None):
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"""Glorot norm initializer, also called Xavier uniform initializer.
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It draws samples from a uniform distribution within [-limit, limit]
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where `limit` is `sqrt(6 / (fan_in + fan_out))`
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where `fan_in` is the number of input units in the weight tensor
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and `fan_out` is the number of output units in the weight tensor.
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# Arguments
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seed: A Python integer. Used to seed the random generator.
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# Returns
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An initializer."""
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return VarianceScaling(
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scale=1.0,
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mode="fan_avg",
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distribution="normal",
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seed=seed,
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fan_in=fan_in,
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fan_out=fan_out,
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)
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