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rprop_gd.py
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rprop_gd.py
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# -*- coding: utf-8 -*-
"""
.. invisible:
_ _ _____ _ _____ _____
| | | | ___| | | ___/ ___|
| | | | |__ | | | |__ \ `--.
| | | | __|| | | __| `--. \
\ \_/ / |___| |___| |___/\__/ /
\___/\____/\_____|____/\____/
Created on Sep 16, 2014
RPROP for :class:`veles.znicz.All2All`
███████████████████████████████████████████████████████████████████████████████
Licensed to the Apache Software Foundation (ASF) under one
or more contributor license agreements. See the NOTICE file
distributed with this work for additional information
regarding copyright ownership. The ASF licenses this file
to you under the Apache License, Version 2.0 (the
"License"); you may not use this file except in compliance
with the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing,
software distributed under the License is distributed on an
"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
KIND, either express or implied. See the License for the
specific language governing permissions and limitations
under the License.
███████████████████████████████████████████████████████████████████████████████
"""
import numpy
from veles import memory
from veles.znicz.gd import GradientDescent
class GDRProp(GradientDescent):
MAPPING = {"rprop_gd"}
"""
Only CPU version is implemented
"""
def __init__(self, workflow, **kwargs):
super(GDRProp, self).__init__(workflow, **kwargs)
self.initial_learning_rate = 0.01
self.min_learning_rate = 10 ** -6
self.max_learning_rate = 1
self.increase = 1.05
self.decrease = 0.80
self.weight_lrs = memory.Array()
self.bias_lrs = memory.Array()
def initialize(self, device, **kwargs):
super(GDRProp, self).initialize(device=device, **kwargs)
self.weight_lrs.mem = numpy.zeros(
shape=self.weights.mem.shape, dtype=self.weights.mem.dtype)
self.bias_lrs.mem = numpy.zeros(
shape=self.bias.mem.shape, dtype=self.bias.mem.dtype)
self.weight_lrs.initialize(self.device)
self.bias_lrs.initialize(self.device)
def numpy_weights_update(self):
self.input.map_read()
self.err_output.map_read()
self.weights.map_write()
self.gradient_weights.map_write()
gradient = numpy.dot(
self.err_output.mem.swapaxes(0, 1),
self.input.mem.reshape((self.input.mem.shape[0],
numpy.prod(self.input.mem.shape[1:]))))
grad_sign = numpy.sign(gradient)
grad_delta_sign = numpy.sign(self.gradient_weights.mem * gradient)
increase_ratios = numpy.where(grad_delta_sign > 0, self.increase, 1)
decrease_ratios = numpy.where(grad_delta_sign < 0, self.decrease, 1)
self.weight_lrs.mem *= increase_ratios
self.weight_lrs.mem * decrease_ratios
self.weight_lrs.mem[:] = self.weight_lrs.mem.clip(
self.min_learning_rate, self.max_learning_rate)[:]
if self.weights_transposed:
self.weights.mem -= (grad_sign * self.weight_lrs.mem).transpose()
else:
self.weights.mem -= grad_sign * self.weight_lrs.mem
self.gradient_weights.mem[:] = gradient[:]
def numpy_bias_update(self):
if not self.include_bias:
return
self.err_output.map_read()
self.bias.map_write()
self.gradient_bias.map_write()
gradient = numpy.sum(self.err_output.mem, axis=0)
grad_sign = numpy.sign(gradient)
grad_delta_sign = numpy.sign(self.gradient_bias.mem * gradient)
increase_ratios = numpy.where(grad_delta_sign > 0, self.increase, 1)
decrease_ratios = numpy.where(grad_delta_sign < 0, self.decrease, 1)
self.bias_lrs.mem *= increase_ratios
self.bias_lrs.mem * decrease_ratios
self.bias_lrs.mem[:] = self.bias_lrs.mem.clip(
self.min_learning_rate, self.max_learning_rate)[:]
self.bias.mem -= grad_sign * self.bias_lrs.mem
self.gradient_bias.mem[:] = gradient[:]
def ocl_weights_update(self):
pass
def ocl_bias_update(self):
pass
def ocl_run(self):
# TODO(a.golovizin): implement OCL version
self.numpy_run()