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与えられた値を2倍にする by Python3.5 & Chainer1.12
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| #!/usr/bin/env/ python | |
| # coding:utf-8 | |
| import numpy as np | |
| import chainer.links as L | |
| from chainer import functions as F | |
| from chainer import Variable, optimizers, Chain | |
| class Model(Chain): | |
| def __init__(self): | |
| super(Model, self).__init__( | |
| l1=L.Linear(1, 1), | |
| ) | |
| def __call__(self, x): | |
| h = self.l1(x) | |
| return h | |
| # モデル定義 | |
| model = Model() | |
| optimizer = optimizers.SGD() | |
| optimizer.setup(model) | |
| # 学習させる回数 | |
| times = 50 | |
| # 学習用データ | |
| x = Variable(np.atleast_2d(np.array([[1], [2], [7]], dtype=np.float32))) | |
| # 正解データ(入力されたデータを2倍にしたい) | |
| t = Variable(np.atleast_2d(np.array([[2], [4], [14]], dtype=np.float32))) | |
| # 学習ループ | |
| for i in range(0, times): | |
| # 勾配を初期化 | |
| optimizer.zero_grads() | |
| # ここでモデルに予測させている | |
| y = model(x) | |
| # モデルが出した答えを表示 | |
| print(y.data) | |
| # 損失を計算する | |
| loss = F.MeanSquaredError()(y, t) | |
| # 逆伝播する | |
| loss.backward() | |
| # optimizerを更新する | |
| optimizer.update() | |
| # 学習済みモデルに対してデータを入力 | |
| print("result") | |
| x = Variable(np.atleast_2d(np.array([[3], [4], [5]], dtype=np.float32))) | |
| y = model(x) | |
| print(y.data) |
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