#概要
kerasのモデルをtensorflowに、変換して、convnet.jsで、使ってみた。
手順を記載する。
#環境
windows 7 sp1 64bit
anaconda3
tensorflow 1.2
#kerasのモデルをtensorflowに、変換したpbファイルから、convnetjsのモデルjsonをつくるコード。
import os
import os.path
import tensorflow as tf
from tensorflow.python.platform import gfile
import string
import json
import struct
with gfile.FastGFile("keras16.pb", 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name = '')
layers = []
flt = []
nx = 0
for n0 in graph_def.node:
if n0.op == 'Placeholder':
shape = n0.attr['shape'].shape
layers += [{
'layer_type': 'input',
'out_depth': shape.dim[1].size,
'out_sx': 1,
'out_sy': 1
}]
elif n0.op == 'Const':
tensor = n0.attr['value'].tensor
if n0.name.endswith("/kernel"):
nx = tensor.tensor_shape.dim[0].size
n = tensor.tensor_shape.dim[1].size
k = 0;
for i in range(n):
k = i * 4
json_arr = {
'sx': 1,
'sy': 1,
'depth': nx,
'w': {}
}
for j in range(nx):
json_arr['w'][str(j)] = struct.unpack('<f', tensor.tensor_content[k : k + 4])[0]
k += 4 * n;
flt += [json_arr]
elif n0.name.endswith("/bias"):
n = tensor.tensor_shape.dim[0].size
json_arr = {
'sx': 1,
'sy': 1,
'depth': n,
'w': {}
}
if n == 1:
json_arr['w'][str(0)] = tensor.float_val[0]
else:
for i in range(n):
j = i * 4
json_arr['w'][str(i)] = struct.unpack('<f', tensor.tensor_content[j : j + 4])[0]
fc_array = {
'layer_type': 'fc',
'num_inputs': nx,
'out_depth': n,
'l1_decay_mul': 0,
'l2_decay_mul': 1,
'out_sx': 1,
'out_sy': 1,
'filters': flt,
'biases': json_arr
}
layers += [fc_array]
nx = tensor.tensor_shape.dim[0].size
flt = []
elif n0.op == 'Sigmoid':
layers += [{
'layer_type': 'sigmoid',
'out_depth': nx,
'out_sx': 1,
'out_sy': 1
}]
elif n0.op == 'StridedSlice':
layers += [{
'layer_type': 'regression',
'out_depth': 1,
'out_sx': 1,
'out_sy': 1,
'num_inputs': 1
}]
print ('Done.')
net = {
'layers': layers
}
#print (net)
f = open("./keras16net2.json", 'w')
f.write(json.dumps(net))
#写真
#サンプルコード
var net = new convnetjs.Net();
var canvas = document.getElementById("canvas");
var ctx = canvas.getContext("2d");
function draw(data, n) {
var hc = n * 100 + 100;
ctx.strokeStyle = "#f00";
ctx.lineWidth = 1;
ctx.moveTo(0, hc);
for (var i = 1; i < canvas.width; i++)
{
ctx.lineTo(i, hc - data[i] * 30);
}
ctx.stroke();
}
function test() {
var s = 200;
var ss = new Float32Array(s);
for (var t = 0; t < s; t++)
{
var i = t / 30.0;
var netx = new convnetjs.Vol(1, 1, 1);
netx.w[0] = i;
var ans = net.forward(netx);
ss[t] = ans.w[0];
}
draw(ss, 0);
}
$.getJSON("http://jsrun.it/assets/4/W/g/z/4Wgzx", function(json) {
net.fromJSON(json);
test();
});
#jsonデータ
{"layers":[{"out_depth":1,"out_sx":1,"out_sy":1,"layer_type":"input"},{"out_depth":30,"out_sx":1,"out_sy":1,"layer_type":"fc","num_inputs":1,"l1_decay_mul":0,"l2_decay_mul":1,"filters":[{"sx":1,"sy":1,"depth":1,"w":{"0":-0.252198725938797}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.24867171049118042}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.2970523238182068}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.2834603190422058}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.23915059864521027}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.23399460315704346}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.2548165023326874}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.21055857837200165}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.258003830909729}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.26047882437705994}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.21398022770881653}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.24368037283420563}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.24630802869796753}},{"sx":1,"sy":1,"depth":1,"w":{"0":-1.1452668905258179}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.1726691722869873}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.22082951664924622}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.32592421770095825}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.2452477663755417}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.24593061208724976}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.27058935165405273}},{"sx":1,"sy":1,"depth":1,"w":{"0":1.917065978050232}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.20580442249774933}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.17408236861228943}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.22818773984909058}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.2116508185863495}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.2744907736778259}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.27791154384613037}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.24978920817375183}},{"sx":1,"sy":1,"depth":1,"w":{"0":-0.2303035408258438}},{"sx":1,"sy":1,"depth":1,"w":{"0":0.2241906076669693}}],"biases":{"sx":1,"sy":1,"depth":30,"w":{"0":0.5794162750244141,"1":-0.38121646642684937,"2":1.3126007318496704,"3":-0.1879628300666809,"4":0.8406424522399902,"5":-0.746708333492279,"6":0.6560930609703064,"7":-0.12273017317056656,"8":0.6356635093688965,"9":-0.8339259028434753,"10":0.2332572042942047,"11":0.7938699722290039,"12":-0.8718310594558716,"13":0.3273533880710602,"14":-0.3774126470088959,"15":0.1903451830148697,"16":1.4930496215820312,"17":-0.4182469844818115,"18":-0.47124648094177246,"19":-0.4390168786048889,"20":-0.3601115047931671,"21":-0.09782565385103226,"22":0.09459342807531357,"23":0.38465195894241333,"24":0.39213815331459045,"25":1.0765173435211182,"26":0.21930475533008575,"27":-0.2090425044298172,"28":0.36953285336494446,"29":-0.317187637090683}}},{"out_depth":30,"out_sx":1,"out_sy":1,"layer_type":"sigmoid"},{"out_depth":10,"out_sx":1,"out_sy":1,"layer_type":"fc","num_inputs":30,"l1_decay_mul":0,"l2_decay_mul":1,"filters":[{"sx":1,"sy":1,"depth":30,"w":{"0":-0.11694007366895676,"1":0.37920019030570984,"2":-0.31143859028816223,"3":0.21690942347049713,"4":0.21245482563972473,"5":0.040130775421857834,"6":-0.0712108165025711,"7":-0.24941010773181915,"8":-0.3375298082828522,"9":0.288911372423172,"10":0.036419909447431564,"11":0.0995960459113121,"12":0.26822954416275024,"13":0.031109165400266647,"14":0.48464444279670715,"15":0.011000392958521843,"16":-0.2879745662212372,"17":0.43085888028144836,"18":0.22948773205280304,"19":0.3408307135105133,"20":0.9299984574317932,"21":0.49288585782051086,"22":0.15011066198349,"23":-0.33704644441604614,"24":-0.4416848421096802,"25":-0.122994564473629,"26":-0.27103403210639954,"27":-0.035270288586616516,"28":-0.1102561354637146,"29":0.42089706659317017}},{"sx":1,"sy":1,"depth":30,"w":{"0":0.6975963115692139,"1":-0.08670920133590698,"2":0.8197983503341675,"3":0.27173277735710144,"4":0.6513783931732178,"5":-0.08944439142942429,"6":0.3588143587112427,"7":-0.39308491349220276,"8":0.41725537180900574,"9":-0.24301253259181976,"10":0.22235877811908722,"11":0.5672311186790466,"12":-0.5162369608879089,"13":0.030823810026049614,"14":-0.6165170669555664,"15":0.17722119390964508,"16":0.6780178546905518,"17":-0.4753911793231964,"18":-0.32568663358688354,"19":-0.5428140163421631,"20":0.35451674461364746,"21":-0.11865320056676865,"22":0.5179699659347534,"23":0.6901814937591553,"24":0.5304526090621948,"25":0.3977537751197815,"26":0.18804974853992462,"27":0.13274644315242767,"28":0.7279859185218811,"29":-0.04368359595537186}},{"sx":1,"sy":1,"depth":30,"w":{"0":0.24401862919330597,"1":-0.4399615526199341,"2":0.2723301351070404,"3":-0.5774849057197571,"4":0.14514033496379852,"5":-0.15964382886886597,"6":0.07850401103496552,"7":-0.6695399284362793,"8":0.1253729909658432,"9":-0.3858738839626312,"10":0.1731533408164978,"11":0.43012315034866333,"12":-0.5791959762573242,"13":0.5741389989852905,"14":-0.018604161217808723,"15":0.026524832472205162,"16":0.3858923316001892,"17":0.008528056554496288,"18":-0.4759930670261383,"19":-0.6186662912368774,"20":-0.9774520397186279,"21":-0.23367896676063538,"22":0.0670010969042778,"23":-0.322417289018631,"24":-0.2190790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#成果物
http://jsdo.it/ohisama1/iVcWV
以上。