35 lines
879 B
JavaScript
35 lines
879 B
JavaScript
|
var data = csv.read('test-cnn-DT.csv'),
|
||
|
|
x,
|
||
|
|
y;
|
||
|
|
|
||
|
|
data.shift();
|
||
|
|
x=select(data,0,data[0].length-2);
|
||
|
|
y=select(data,data[0].length-1);
|
||
|
|
|
||
|
|
|
||
|
|
var model = ml.learn({
|
||
|
|
algorithm:ml.ML.CNN,
|
||
|
|
x:x, // [row1=[col1=[z1,z2,..],col2,..],row2,..]
|
||
|
|
y:y,
|
||
|
|
width:28,
|
||
|
|
height:28,
|
||
|
|
depth:1,
|
||
|
|
normalize:[-.5,.5],
|
||
|
|
iterations:100,
|
||
|
|
layers:[
|
||
|
|
// output Vol is of size 3x4x2 here
|
||
|
|
{type:'input', out_sx:28, out_sy:28, out_depth:1},
|
||
|
|
{type:'conv', sx:5, filters:8, stride:1, pad:2, activation:'relu'},
|
||
|
|
{type:'pool', sx:2, stride:2},
|
||
|
|
{type:'conv', sx:5, filters:16, stride:1, pad:2, activation:'relu'},
|
||
|
|
{type:'pool', sx:3, stride:3},
|
||
|
|
{type:'softmax', num_classes:10}
|
||
|
|
],
|
||
|
|
trainer : {method: 'adadelta',
|
||
|
|
l2_decay: 0.001,
|
||
|
|
batch_size: 10}
|
||
|
|
});
|
||
|
|
// print(model)
|
||
|
|
for(var i=0;i<100;i++)
|
||
|
|
print(y[i],ml.stats.utils.best(ml.classify(model,x[i]).w))
|