def setup():
# Set up brain
# #########################################################################
brain = Brain(name='maxout-relu-zca-cifar10')
brain.attach(DropoutLayer(keep_prob=0.8, name='dropout1'))
brain.attach(
ConvolutionLayer([8, 8], [1, 1, 1, 1],
'SAME',
init_para={
"name": "uniform",
"range": 0.005
},
wd={
"type": "l2",
"scale": 0.0005
},
out_channel_num=192,
name='conv1'))
brain.attach(PoolingLayer([1, 4, 4, 1], [1, 2, 2, 1], 'SAME',
name='pool1'))
brain.attach(ReLULayer(name='relu1'))
brain.attach(DropoutLayer(keep_prob=0.5, name='dropout2'))
brain.attach(
ConvolutionLayer([8, 8], [1, 1, 1, 1],
'SAME',
init_para={
"name": "uniform",
"range": 0.005
},
wd={
"type": "l2",
"scale": 0.0005
},
out_channel_num=384,
name='conv2'))
brain.attach(PoolingLayer([1, 4, 4, 1], [1, 2, 2, 1], 'SAME',
name='pool2'))
brain.attach(ReLULayer(name='relu2'))
brain.attach(DropoutLayer(keep_prob=0.5, name='dropout3'))
brain.attach(
ConvolutionLayer([5, 5], [1, 1, 1, 1],
'SAME',
init_para={
"name": "uniform",
"range": 0.005
},
wd={
"type": "l2",
"scale": 0.0005
},
out_channel_num=384,
name='conv3'))
brain.attach(PoolingLayer([1, 2, 2, 1], [1, 2, 2, 1], 'SAME',
name='pool3'))
brain.attach(ReLULayer(name='relu3'))
brain.attach(DropoutLayer(keep_prob=0.5, name='dropout3'))
brain.attach(
InnerProductLayer(init_para={
"name": "uniform",
"range": 0.005
},
wd={
"type": "l2",
"scale": 0.004
},
out_channel_num=500,
name='ip1'))
brain.attach(ReLULayer(name='relu4'))
brain.attach(DropoutLayer(keep_prob=0.5, name='dropout3'))
brain.attach(
InnerProductLayer(init_para={
"name": "uniform",
"range": 0.005
},
wd={
"type": "l2",
"scale": 0
},
out_channel_num=10,
name='softmax_linear'))
brain.attach(SoftmaxWithLossLayer(class_num=10, name='loss'))
# Set up a sensor.
# #########################################################################
cifar_source = Cifar10FeedSource(
name="CIFAR10",
url='http://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz',
work_dir=AKID_DATA_PATH + '/cifar10',
use_zca=True,
num_train=50000,
num_val=10000)
sensor = FeedSensor(source_in=cifar_source, batch_size=128, name='data')
# Summon a survivor.
# #########################################################################
survivor = kids.Kid(sensor,
brain,
kongfus.MomentumKongFu(base_lr=0.025,
momentum=0.5,
decay_rate=0.1,
decay_epoch_num=50),
max_steps=200000)
survivor.setup()
return survivor
from __future__ import absolute_import
from akid import AKID_DATA_PATH
from akid.core import kids, kongfus
from akid import Cifar10TFSource
from akid import IntegratedSensor
from akid import WhitenJoker
from akid import Brain
from akid.layers import (ConvolutionLayer, PoolingLayer, ReLULayer,
InnerProductLayer, SoftmaxWithLossLayer,
BatchNormalizationLayer, DropoutLayer)
# Set up brain
# #########################################################################
brain = Brain(moving_average_decay=0.99, name='maxout-relu-cifar10')
brain.attach(
ConvolutionLayer([8, 8], [1, 1, 1, 1],
'SAME',
stddev=0.005,
weight_decay=0.0005,
out_channel_num=192,
name='conv1'))
brain.attach(PoolingLayer([1, 4, 4, 1], [1, 2, 2, 1], 'SAME', name='pool1'))
brain.attach(ReLULayer(name='relu1'))
brain.attach(DropoutLayer(keep_prob=0.8, name='dropout1'))
brain.attach(
ConvolutionLayer([8, 8], [1, 1, 1, 1],
'SAME',
stddev=0.005,
weight_decay=0.0005,