wehub-resource-sync
99 lines
2.8 KiB
Python
99 lines
2.8 KiB
Python
# coding:utf-8
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import numpy as np
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from mla.neuralnet.initializations import get_initializer
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class Parameters(object):
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def __init__(
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self,
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init="glorot_uniform",
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scale=0.5,
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bias=1.0,
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regularizers=None,
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constraints=None,
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):
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"""A container for layer's parameters.
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Parameters
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----------
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init : str, default 'glorot_uniform'.
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The name of the weight initialization function.
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scale : float, default 0.5
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bias : float, default 1.0
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Initial values for bias.
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regularizers : dict
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Weight regularizers.
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>>> {'W' : L2()}
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constraints : dict
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Weight constraints.
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>>> {'b' : MaxNorm()}
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"""
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if constraints is None:
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self.constraints = {}
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else:
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self.constraints = constraints
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if regularizers is None:
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self.regularizers = {}
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else:
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self.regularizers = regularizers
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self.initial_bias = bias
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self.scale = scale
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self.init = get_initializer(init)
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self._params = {}
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self._grads = {}
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def setup_weights(self, W_shape, b_shape=None):
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if "W" not in self._params:
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self._params["W"] = self.init(shape=W_shape, scale=self.scale)
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if b_shape is None:
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self._params["b"] = np.full(W_shape[1], self.initial_bias)
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else:
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self._params["b"] = np.full(b_shape, self.initial_bias)
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self.init_grad()
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def init_grad(self):
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"""Init gradient arrays corresponding to each weight array."""
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for key in self._params.keys():
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if key not in self._grads:
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self._grads[key] = np.zeros_like(self._params[key])
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def step(self, name, step):
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"""Increase specific weight by amount of the step parameter."""
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self._params[name] += step
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if name in self.constraints:
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self._params[name] = self.constraints[name].clip(self._params[name])
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def update_grad(self, name, value):
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"""Update gradient values."""
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self._grads[name] = value
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if name in self.regularizers:
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self._grads[name] += self.regularizers[name](self._params[name])
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@property
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def n_params(self):
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"""Count the number of parameters in this layer."""
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return sum([np.prod(self._params[x].shape) for x in self._params.keys()])
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def keys(self):
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return self._params.keys()
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@property
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def grad(self):
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return self._grads
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# Allow access to the fields using dict syntax, e.g. parameters['W']
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def __getitem__(self, item):
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if item in self._params:
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return self._params[item]
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else:
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raise ValueError
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def __setitem__(self, key, value):
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self._params[key] = value
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