This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).
Matrix multiplication is a mathematical operation that defines the product of
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There are a lot of Topic Models. 18/02/23 # of TM is 24
A list of useful commands for the FFmpeg command line tool.
Download FFmpeg: https://www.ffmpeg.org/download.html
Full documentation: https://www.ffmpeg.org/ffmpeg.html
| def conv_forward(X, W): | |
| ''' | |
| The forward computation for a convolution function | |
| Arguments: | |
| X -- output activations of the previous layer, numpy array of shape (n_H_prev, n_W_prev) assuming input channels = 1 | |
| W -- Weights, numpy array of size (f, f) assuming number of filters = 1 | |
| Returns: | |
| H -- conv output, numpy array of size (n_H, n_W) |
| def conv_backward(dH, cache): | |
| ''' | |
| The backward computation for a convolution function | |
| Arguments: | |
| dH -- gradient of the cost with respect to output of the conv layer (H), numpy array of shape (n_H, n_W) assuming channels = 1 | |
| cache -- cache of values needed for the conv_backward(), output of conv_forward() | |
| Returns: | |
| dX -- gradient of the cost with respect to input of the conv layer (X), numpy array of shape (n_H_prev, n_W_prev) assuming channels = 1 |
| /* | |
| This snippet is esssentially the same as being in the Twitter longer tweets test, for tweetdeck. | |
| The Tweet length counter is fixed by tricking TweetDeck into counting up to 140 characters, twice, so you'll see 140 | |
| instead of 280 in the counter but going over 140 will give you another set of 140 charactrs. | |
| */ | |
| TD.services.TwitterClient.prototype.makeTwitterCall=function(b,e,f,g,c,d,h){c=c||function(){};d=d||function(){};b=this.request(b,{method:f,params:Object.assign(e,{weighted_character_count:!0}),processor:g,feedType:h});return b.addCallbacks(function(a){c(a.data)},function(a){d(a.req,"",a.msg,a.req.errors)}),b}; | |
| twttrTxt=Object.assign({},twttr.txt,{isInvalidTweet:function(){return!1},getTweetLength:function(x){return x=twttr.txt.getTweetLength.apply(this,arguments),x<140||x/140>2?x:x%140}}); |
| import numpy as np | |
| from keras.preprocessing import sequence | |
| from keras.models import Sequential | |
| from keras.layers import Dense, Embedding | |
| from keras.layers import LSTM | |
| from keras.datasets import imdb | |
| def batch_iter(data, labels, batch_size, shuffle=True): | |
| num_batches_per_epoch = int((len(data) - 1) / batch_size) + 1 |
