Tensorflow weight initialization | 台灣精品獎-歷屆得獎名單
![Tensorflow weight initialization](https://i.imgur.com/DERULla.jpg)
Weightinitializationstrategiescanbeanimportantandoftenoverlookedstepinimprovingyourmodel,andsincethisisnowthetopresult ...
![Tensorflow weight initialization](https://i.imgur.com/DERULla.jpg)
Regarding the MNIST tutorial[1] on the TensorFlow website, I ran an experiment (gist[2]) to see what the effect of different weight initializations would be on learning. I noticed that, against what I read in the popular [Xavier, Glorot 2010] paper[3], learning is just fine regardless of weight initialization.
The different curves represent different values for w for initializing the weights of the convolutional and fully connected layers. Note that all values for w work fine, even though 0.3 and 1.0 end up at lower performance and some values train faster - in particular, 0.03 and 0.1 are fastest. Nevertheless, the plot shows a rather large range of w which works, suggesting robustness w.r.t. weight initialization.
def weight_variable(shape, w=0.1): initial = tf.truncated_normal(shape, stddev=w) return tf.Variable(initial) def bias_variable(shape, w=0.1): initial ...Tensorflow weight initialization | 台灣精品獎-歷屆得獎名單
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![](https://i.imgur.com/DERULla.jpg)
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