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Word2vec Tutorial

I wrote a blog post to explain the detail.

Experiment

Parameter From Scratch Tensorflow (CPU)
batch size 128 128
embedding size 30 30
num sampled 10 10
num steps 70001 1500001
learning rate 0.025 1
# from scratch               # tensorflow      
# spend: 53.94 min           # spend: 38.09 min 
                             
雲 1.0                       雲 1.0
嵐 0.818097894953            嵐 0.731922132211
緲 0.807170161919            霞 0.710187307407
烽 0.806751349354            烟 0.693668808384
烟 0.791932317029            雪 0.684637639979
靄 0.790464066718            虹 0.683235227787
-----                        -----
峰 1.0                       峰 1.0
峯 0.96521154438             峯 0.942029995583
層 0.869375215503            嶽 0.73387296403
巒 0.847521841138            嵋 0.732944525
巖 0.842055300736            巒 0.716149847575
巔 0.834164942036            巔 0.714281751101
-----                        -----
風 1.0                       風 1.0
飆 0.839413385589            吹 0.820511746298
涼 0.812897226871            飆 0.809179019451
凜 0.790959089145            逆 0.67986909613
颸 0.786966264664            颸 0.663089281948
暄 0.771490669881            涼 0.659044072466
-----                        -----
女 + 父 - 男                 女 + 父 - 男
母 0.765840473955            母 0.735594336365
婦 0.758031202523            子 0.729155945201
子 0.724152991944            伴 0.696736003898
伴 0.707958812532            彿 0.645417693955
阿 0.702062120972            阿 0.629788529922

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Note when I learn word2vec algorithm

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