66 lines
1.8 KiB
Python
66 lines
1.8 KiB
Python
# -*- coding: utf-8 -*-
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"""Parameter initialisers.
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This module defines classes to initialise the parameters in a layer.
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"""
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import numpy as np
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from mlp import DEFAULT_SEED
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class ConstantInit(object):
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"""Constant parameter initialiser."""
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def __init__(self, value):
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"""Construct a constant parameter initialiser.
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Args:
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value: Value to initialise parameter to.
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"""
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self.value = value
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def __call__(self, shape):
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return np.ones(shape=shape) * self.value
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class UniformInit(object):
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"""Random uniform parameter initialiser."""
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def __init__(self, low, high, rng=None):
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"""Construct a random uniform parameter initialiser.
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Args:
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low: Lower bound of interval to sample from.
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high: Upper bound of interval to sample from.
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rng (RandomState): Seeded random number generator.
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"""
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self.low = low
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self.high = high
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if rng is None:
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rng = np.random.RandomState(DEFAULT_SEED)
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self.rng = rng
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def __call__(self, shape):
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return self.rng.uniform(low=self.low, high=self.high, size=shape)
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class NormalInit(object):
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"""Random normal parameter initialiser."""
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def __init__(self, mean, std, rng=None):
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"""Construct a random uniform parameter initialiser.
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Args:
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mean: Mean of distribution to sample from.
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std: Standard deviation of distribution to sample from.
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rng (RandomState): Seeded random number generator.
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"""
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self.mean = mean
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self.std = std
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if rng is None:
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rng = np.random.RandomState(DEFAULT_SEED)
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self.rng = rng
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def __call__(self, shape):
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return self.rng.normal(loc=self.mean, scale=self.std, size=shape)
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