If this is the first time to use python and you have not built any python development environment, setting up IDE (Integrated Development Environment) might be a one good choice to start coding quite easily. I will introduce how to setup PyCharm, one of the major python development tool, which I am also using […]
Continue reading →If you are familiar with machine learning before deep learning becomes popular, you might have been using sklearn (scikit-learn), which is very popular machine learning library in python. Its interface is used for a long time, and I thought it is better to support this interface with python to allow users to try deep learning more […]
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Once you have setup python environment, now we can install chainer. Nowadays it is important for deep learning library to use GPU to enhance its calculation speed, and Chainer provides several levels of GPU support. The packages which you need to install depends on your PC envitonment, please follow this figure to understand what category you […]
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※ This post is mainly just a summary/translation of the Japanese blog, データサイエンティストを目指す人のpython環境構築 2016 TL;DR; I recommend to install “anaconda” instead of using “official python package”. If you just want to proceed environment setup, jump to “Environment setup for each OS”. At first, I will explain little bit about the background […]
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This tutorial corresponds to 03_custom_dataset_mlp folder in the source code. We have trained the model with own dataset, MyDataset, in previous post, let’s write predict code. Source code: predict_custom_dataset1.py predict_custom_dataset2.py Prepare test data It is not difficult for the model to fit to the train data, so we will check how the model is […]
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This tutorial corresponds to 03_custom_dataset_mlp folder in the source code. We have prepared your own dataset, MyDataset, in previous post. Training procedure for this dataset is now almost same with MNIST traning. Differences from MNIST dataset are, This task is regression task (estimate final “value”), instead of classification task (estimate the probability of category) Training […]
Continue reading →This tutorial corresponds to 03_custom_dataset_mlp folder in the source code. In previous chapter we have learned how to train deep neural network using MNIST handwritten digits dataset. However, MNIST dataset has prepared by chainer utility library and you might now wonder how to prepare dataset when you want to use your own data for […]
Continue reading →Predict code is easy, implemented in predict_simple_sequence.py. First, construct the model and load the trained model parameters,
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# Model Setup model = archs[args.arch](n_vocab=N_VOCABULARY, n_units=args.unit) if args.gpu >= 0: chainer.cuda.get_device(args.gpu).use() # Make a specified GPU current model.to_gpu() # Copy the model to the GPU xp = np if args.gpu < 0 else cuda.cupy serializers.load_npz(args.modelpath, model) |
Then we only specify the first index (corresponds to word id), primeindex, and generate next index. We can generate next index repeatedly based on the generated index.
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# Predict predicted_sequence = [prev_index] for i in range(args.length): prev = chainer.Variable(xp.array([prev_index], dtype=xp.int32)) current = model(prev) current_index = np.argmax(cuda.to_cpu(current.data)) predicted_sequence.append(current_index) prev_index = current_index print('Predicted sequence: ', predicted_sequence) |
The result is the following, successfully generate […]
Continue reading →Chainer version 2 has been released on 2017 June 1, #Chainer v2.0.0 has been released! Memory reduction (33% in ResNet), API clean up, and CuPy as a separate package. https://t.co/xRrmZAlJWT — Chainer (@ChainerOfficial) June 1, 2017 This post is a summary of what you need to change in your code for your chainer development. Detail […]
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We have learned in previous post that RNN is expected to have an ability to remember the sequence information. Let’s do a easy experiment to check it before trying actual NLP application. Simple sequence dataset I just prepared a simple script to generate simple integer sequence as follows, Source code: simple_sequence_dataset.py
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import numpy as np N_VOCABULARY = 10 def get_simple_sequence(n_vocab, repeat=100): data = [] for i in range(repeat): for j in range(n_vocab): for k in range(j): data.append(j) return np.asarray(data, dtype=np.int32) if __name__ == '__main__': data = get_simple_sequence(N_VOCABULARY) print(data) |
Its output is, […]
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