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April 11, 2016 18:53
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import os | |
from neon.util.argparser import NeonArgparser | |
from neon.layers import Conv, Pooling, MergeBroadcast, BranchNode, Affine, Tree, Dropout | |
from neon.layers import GeneralizedCost, Multicost | |
from neon.initializers import Constant, Xavier | |
from neon.optimizers import GradientDescentMomentum, MultiOptimizer | |
from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, TopKMisclassification | |
from neon.models import Model | |
from neon.data import ArrayIterator | |
import numpy as np | |
parser = NeonArgparser(__doc__) | |
parser.add_argument('--subset_pct', type=float, default=100, | |
help='subset of training dataset to use (percentage)') | |
parser.add_argument('--test_only', action='store_true', | |
help='skip fitting - evaluate metrics on trained model weights') | |
args = parser.parse_args() | |
test = ArrayIterator(np.random.rand(256,224,224,3)) | |
init1 = Xavier(local=False) | |
initx = Xavier(local=True) | |
bias = Constant(val=0.20) | |
relu = Rectlin() | |
common = dict(activation=relu, init=initx, bias=bias) | |
commonp1 = dict(activation=relu, init=initx, bias=bias, padding=1) | |
commonp2 = dict(activation=relu, init=initx, bias=bias, padding=2) | |
pool3s1p1 = dict(fshape=3, padding=1, strides=1) | |
pool3s2p1 = dict(fshape=3, padding=1, strides=2, op='max') | |
def inception(kvals): | |
(p1, p2, p3, p4) = kvals | |
branch1 = [Conv((1, 1, p1[0]), **common)] | |
branch2 = [Conv((1, 1, p2[0]), **common), | |
Conv((3, 3, p2[1]), **commonp1)] | |
branch3 = [Conv((1, 1, p3[0]), **common), | |
Conv((5, 5, p3[1]), **commonp2)] | |
branch4 = [Pooling(op="max", **pool3s1p1), | |
Conv((1, 1, p4[0]), **common)] | |
return MergeBroadcast(layers=[branch1, branch2, branch3, branch4], merge="depth") | |
def main_branch(branch_nodes): | |
return [Conv((7, 7, 64), padding=3, strides=2, **common), | |
Pooling(**pool3s2p1), | |
Conv((1, 1, 64), **common), | |
Conv((3, 3, 192), **commonp1), | |
Pooling(**pool3s2p1), | |
inception([(64, ), (96, 128), (16, 32), (32, )]), | |
inception([(128,), (128, 192), (32, 96), (64, )]), | |
Pooling(**pool3s2p1), | |
inception([(192,), (96, 208), (16, 48), (64, )]), | |
branch_nodes[0], | |
inception([(160,), (112, 224), (24, 64), (64, )]), | |
inception([(128,), (128, 256), (24, 64), (64, )]), | |
inception([(112,), (144, 288), (32, 64), (64, )]), | |
branch_nodes[1], | |
inception([(256,), (160, 320), (32, 128), (128,)]), | |
Pooling(**pool3s2p1), | |
inception([(256,), (160, 320), (32, 128), (128,)]), | |
inception([(384,), (192, 384), (48, 128), (128,)]), | |
Pooling(fshape=7, strides=1, op="avg"), | |
Affine(nout=1000, init=init1, activation=Softmax(), bias=Constant(0))] | |
def aux_branch(bnode): | |
return [bnode, | |
Pooling(fshape=5, strides=3, op="avg"), | |
Conv((1, 1, 128), **common), | |
Affine(nout=1024, init=init1, activation=relu, bias=bias), | |
Dropout(keep=0.3), | |
Affine(nout=1000, init=init1, activation=Softmax(), bias=Constant(0))] | |
# Now construct the model | |
branch_nodes = [BranchNode(name='branch' + str(i)) for i in range(2)] | |
main1 = main_branch(branch_nodes) | |
aux1 = aux_branch(branch_nodes[0]) | |
aux2 = aux_branch(branch_nodes[1]) | |
model = Model(layers=Tree([main1, aux1, aux2], alphas=[1.0, 0.3, 0.3])) | |
valmetric = TopKMisclassification(k=5) | |
# dummy optimizer for benchmarking | |
# training implementation coming soon | |
opt_gdm = GradientDescentMomentum(0.0, 0.0) | |
opt_biases = GradientDescentMomentum(0.0, 0.0) | |
opt = MultiOptimizer({'default': opt_gdm, 'Bias': opt_biases}) | |
# setup cost function as CrossEntropy | |
cost = Multicost(costs=[GeneralizedCost(costfunc=CrossEntropyMulti()), | |
GeneralizedCost(costfunc=CrossEntropyMulti()), | |
GeneralizedCost(costfunc=CrossEntropyMulti())], | |
weights=[1, 0., 0.]) # We only want to consider the CE of the main path | |
model.load_params("/home/iaroslav/temp/googlenet.p") | |
model.initialize(test, cost) |
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