| Révision | 34ae2927eba2d754bb53a93158e23a320eca49ca (tree) |
|---|---|
| l'heure | 2018-06-10 17:11:31 |
| Auteur | unknown <shupeluter@hotm...> |
| Commiter | unknown |
numpyデータで返すメソッドを追加
src/main/Python/DataGenerator.py src/main/Python/DataReader.py (diff) src/main/Python/Lern.py src/main/Python/TestFileUtil.py src/main/Python/sample1.py src/test/Python/HelloChainar.py| @@ -1,13 +0,0 @@ | ||
| 1 | -from Data import Data | |
| 2 | -from typing import List | |
| 3 | -""" | |
| 4 | -学習データファイルの読み込みデータから、学習データ形式に変換するモジュール | |
| 5 | - | |
| 6 | -学習データx：array[総データファイル][日][価格データ]を想定 | |
| 7 | -学習データy: array[総データファイル][正解ラベル] | |
| 8 | - | |
| 9 | -となるように生成する。 | |
| 10 | - | |
| 11 | -""" | |
| 12 | - | |
| 13 | -__whole_data: List[Data] |
| @@ -18,15 +18,24 @@ class DataReader: | ||
| 18 | 18 | self.DATA_SOURCE = confdata['dataPath'] |
| 19 | 19 | |
| 20 | 20 | def get_learning_data(self): |
| 21 | + ''' | |
| 22 | + | |
| 23 | + :return: トレーニングデータ、トレーニングラベル、テストデータ、テストラベル | |
| 24 | + ''' | |
| 25 | + | |
| 21 | 26 | target_data = self.parse_data_files() |
| 22 | 27 | datasets: List(numpy.ndarray) = [] |
| 23 | - labels: List(str) =[] | |
| 28 | + labels: List(str) = [] | |
| 24 | 29 | |
| 25 | 30 | for cdata in target_data: |
| 26 | 31 | datasets.append(cdata.get_array_data()) |
| 27 | 32 | labels.append(cdata.getLable()) |
| 28 | 33 | |
| 29 | - return numpy.array(datasets), numpy.array(labels) | |
| 34 | + center = len(datasets)//2 | |
| 35 | + offset = len(datasets) % 2 | |
| 36 | + center = center + offset | |
| 37 | + | |
| 38 | + return numpy.array(datasets[:center]), numpy.array(labels[:center]), numpy.array(datasets[center:]), numpy.array(labels[center:]) | |
| 30 | 39 | |
| 31 | 40 | def __check_data(self, data: Data): |
| 32 | 41 |
| @@ -1,78 +0,0 @@ | ||
| 1 | -from chainer import Function, gradient_check, report, training, utils, Variable | |
| 2 | -from chainer import datasets, iterators, optimizers, serializers | |
| 3 | -from chainer import Link, Chain, ChainList | |
| 4 | -import chainer.functions as F | |
| 5 | -import chainer.links as L | |
| 6 | -from chainer.training import extensions | |
| 7 | -import math | |
| 8 | -from DataReader import DataReader | |
| 9 | -from DatasetGenerator import DatasetGenerator | |
| 10 | - | |
| 11 | -from Data import Data | |
| 12 | - | |
| 13 | -class MyChain(Chain): | |
| 14 | - def __init__(self): | |
| 15 | - super(MyChain,self).__init__( | |
| 16 | - l1=L.Linear(500,100), | |
| 17 | - l2=L.Linear(100,100), | |
| 18 | - l3=L.Linear(100,10), | |
| 19 | - ) | |
| 20 | - | |
| 21 | - def __call__(self,x,t): | |
| 22 | - return F.softmax_cross_entropy(self.fwd(x),t) | |
| 23 | - | |
| 24 | - def fwd(self,x): | |
| 25 | - h1=F.relu(self.l1(x)) | |
| 26 | - h2=F.relu(self.l2(h1)) | |
| 27 | - return self.l3(h2) | |
| 28 | - | |
| 29 | - | |
| 30 | -class MyClassifer(Chain): | |
| 31 | - def __init__(self,predictor): | |
| 32 | - super(MyClassifer,self).__init__() | |
| 33 | - with self.init_scope(): | |
| 34 | - self.predictor = predictor | |
| 35 | - def __call__(self,x,t): | |
| 36 | - y = self.predictor(x) | |
| 37 | - loss = F.softmax_cross_entropy(y,t) | |
| 38 | - accuracy = F.accuracy(y,t) | |
| 39 | - report({'loss': loss, 'accuracy': accuracy}, self) | |
| 40 | - return loss; | |
| 41 | - | |
| 42 | -def main(): | |
| 43 | -# try: | |
| 44 | - #モデルを準備 | |
| 45 | - model = MyChain() | |
| 46 | - | |
| 47 | - #オプティマイザを準備 | |
| 48 | - optimizer = optimizers.Adam() | |
| 49 | - optimizer.setup(model) | |
| 50 | - | |
| 51 | - #データを準備 | |
| 52 | - train,test = dataPreparation() | |
| 53 | - | |
| 54 | - updater = training.StandardUpdater(train,optimizer) | |
| 55 | - trainer = training.Trainer(updater,(10,'epoch')) | |
| 56 | - trainer.extend(extensions.progress_bar) | |
| 57 | - trainer.run() | |
| 58 | - | |
| 59 | -def dataPreparation(): | |
| 60 | - return "hoge" | |
| 61 | - | |
| 62 | -def dataPreparation_back(): | |
| 63 | - # データ用意 | |
| 64 | - train_data = [] | |
| 65 | - train_label = [] | |
| 66 | - # 元データ生成 | |
| 67 | - reader = DataReader() # type DataReader | |
| 68 | - dgene = DatasetGenerator() # type DataSetGenerator | |
| 69 | - dataist = [] | |
| 70 | - dataList = reader.createLearningData() | |
| 71 | - train_data, train_label = dgene.generateDataset(dataList) | |
| 72 | - dust, batchsize = math.modf(len(dataList) / 2) | |
| 73 | - batchsize = int(batchsize) | |
| 74 | - train = datasets.tuple_dataset.TupleDataset(train_data[0:batchsize], train_label[0:batchsize]) | |
| 75 | - test = datasets.tuple_dataset.TupleDataset(train_data[batchsize:], train_label[batchsize:]) | |
| 76 | - return train,test | |
| 77 | - | |
| 78 | -main() | |
| \ No newline at end of file |
| @@ -1,14 +0,0 @@ | ||
| 1 | -import unittest | |
| 2 | - | |
| 3 | -class TestFileUtil (unittest.TestCase): | |
| 4 | - """test class of hoge""" | |
| 5 | - | |
| 6 | - def test_1(self): | |
| 7 | - print(1) | |
| 8 | - def test_2(self): | |
| 9 | - print(2) | |
| 10 | - | |
| 11 | -if __name__ == "__main__": | |
| 12 | - unittest.main() | |
| 13 | - | |
| 14 | - |
| @@ -1,18 +0,0 @@ | ||
| 1 | -import os | |
| 2 | -import yaml | |
| 3 | - | |
| 4 | -DATA_SOURCE = '' | |
| 5 | - | |
| 6 | -with open('config\\toolconf.yml' , 'r') as yml: | |
| 7 | - data = yaml.load(yml) | |
| 8 | - # 設定値の読み込み | |
| 9 | - DATA_SOURCE = data['dataPath'] | |
| 10 | - | |
| 11 | -def find_all_files(directory): | |
| 12 | - for root, dirs, files in os.walk(directory): | |
| 13 | - yield root | |
| 14 | - for file in files: | |
| 15 | - yield os.path.join(root,file) | |
| 16 | - | |
| 17 | -for file in find_all_files(DATA_SOURCE): | |
| 18 | - print(file) | |
| \ No newline at end of file |
| @@ -1 +0,0 @@ | ||
| 1 | -from chainer.datasets import mnist |
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