"Mxnet"의 두 판 사이의 차이

2017년 7월 4일 (화) 19:06 기준 최신판

뭘 이렇게들 만들어 대는지. tf가 맘에 안들기는 하지만.

etc

batch normalization example

def ConvFactory(data, num_filter, kernel, 
                stride=(1,1), pad=(0, 0),name=None, suffix=''):
    conv = mx.sym.Convolution(data=data,
                  num_filter=num_filter,
                  kernel=kernel,
                  stride=stride,
                  pad=pad,
                  name='conv_%s%s' %(name, suffix))
    bn = mx.sym.BatchNorm(data=conv, name='bn_%s%s' %(name, suffix))
    act = mx.sym.Activation(data=bn, act_type='relu', name='relu_%s%s'
                  %(name, suffix))
    return act

feature extraction
- Predict with pre-trained models
- How do I fine-tune pre-trained models to a new dataset?

@@ 9번째 줄: / 9번째 줄: @@
 ==etc==
 * batch normalization example
-<pre>def ConvFactory(data, num_filter, kernel, stride=(1,1), pad=(0, 0),name=None, suffix=''):
+<pre>def ConvFactory(data, num_filter, kernel,
-     conv = mx.sym.Convolution(data=data, num_filter=num_filter, kernel=kernel,
+                stride=(1,1), pad=(0, 0),name=None, suffix=''):
-                   stride=stride, pad=pad, name='conv_%s%s' %(name, suffix))
+     conv = mx.sym.Convolution(data=data,
+                  num_filter=num_filter,
+                  kernel=kernel,
+                   stride=stride,
+                  pad=pad,
+                  name='conv_%s%s' %(name, suffix))
      bn = mx.sym.BatchNorm(data=conv, name='bn_%s%s' %(name, suffix))
      act = mx.sym.Activation(data=bn, act_type='relu', name='relu_%s%s'
                    %(name, suffix))
      return act</pre>
-* feature extraction [https://github.com/dmlc/mxnet/blob/master/docs/tutorials/python/predict_image.md Predict with pre-trained models]<br>
+* feature extraction
-[http://mxnet.io/how_to/finetune.html#train How do I fine-tune pre-trained models to a new dataset?]
+**[https://github.com/dmlc/mxnet/blob/master/docs/tutorials/python/predict_image.md Predict with pre-trained models]
+** [http://mxnet.io/how_to/finetune.html#train How do I fine-tune pre-trained models to a new dataset?]