This firmware is a system software program for your DDJ-SX. When you install driver software or firmware downloaded from Pioneer Support Web page. Pioneer ddj sx firmware. Developed specifically for Serato DJ Pro, the DDJ-SX controller and standalone. This hardware unlocks Serato DJ Pro for free when plugged into the software. Serato DJ don't recognize Pioneer DDJ SX in Mac os 10.8.5. Internal mac device to see if that allows the software to grab the Pioneer driver. Do you own a CDJ-2000NXS, CDJ-2000NXS2, DDJ-SR2, DDJ-XP1, DJM-S9, XDJ-RX2, DDJ-WEGO4, DDJ-1000, TORAIZ AS-1 or XDJ-RR? Give us your.

Simple Neural Text Classification(NTC) This repository contains implementation of naive and simple text classification using recurrent neural network (LSTM) and convolutional neural network (from ). You need to specify architecture to train, and you can select both.

If you choose both arthictecture to classify sentences, inference will be done by ensemble (just simple average). In addition, this repo is for and, what I conduct. Please, refer those site for further information. Pre-requisite. Python 3.6 or higher.

PyTorch 0.4 or higher. TorchText 0.3 or higher (You may need to install via.).

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Tokenized corpus (e.g., Mecab, ) Usage Preparation Format The input file would have a format with two columns, class and sentence. Those columns are delimited by tab. Class does not need to be a number, but a word (without white-space). Below is example corpus to explain. $ python train.py -h usage: train.py -h -model MODEL -train TRAIN -valid VALID -gpuid GPUID -verbose VERBOSE -minvocabfreq MINVOCABFREQ -maxvocabsize MAXVOCABSIZE -batchsize BATCHSIZE -nepochs NEPOCHS -earlystop EARLYSTOP -dropout DROPOUT -wordvecdim WORDVECDIM -hiddensize HIDDENSIZE -rnn -nlayers NLAYERS -cnn -windowsizes WINDOWSIZES -nfilters NFILTERS or you can check default hyper-parameter from train.py. Inference You can feed standard input as input for inference, like as below. Prediction result consists of two columns(top-k classes and input sentence) with tab delimiter.

The result will be shown as standard output. $ python classify.py -h usage: classify.py -h -model MODEL -gpuid GPUID -batchsize BATCHSIZE -topk TOPK Evaluation I took an evaluation with my own corpus, which is crawled from. The task is classify the correct category of the sentence. There are 15 categories, like as below.

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CNNClassifier( (emb): Embedding(35532, 128) (cnn-3-100): Conv2d(1, 100, kernelsize=(3, 128), stride=(1, 1)) (cnn-4-100): Conv2d(1, 100, kernelsize=(4, 128), stride=(1, 1)) (cnn-5-100): Conv2d(1, 100, kernelsize=(5, 128), stride=(1, 1)) (relu): ReLU (dropout): Dropout(p=0.3) (generator): Linear(infeatures=300, outfeatures=15, bias=True) (activation): LogSoftmax ) Following table shows that the evaluation result of each architecture. The size of validation set is 16,664. You can see that the ensemble is slightly better than others. Architecture Valid Loss Valid Accuracy Bi-LSTM 7.9818e-01 0.7666 CNN 8.4225e-01 0.7497 Bi-LSTM + CNN 0.7679 Below is confusion matrix of validation-set.