Created
January 9, 2016 16:29
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| import numpy as np | |
| import chainer | |
| from chainer import Chain, optimizers, serializers, Variable, cuda | |
| import chainer.functions as F | |
| import chainer.links as L | |
| class Model(Chain): | |
| def __init__(self, n_units=10): | |
| super(Model, self).__init__( | |
| l1=L.Linear(3, n_units), | |
| l2=L.Linear(n_units, n_units), | |
| l3=L.Linear(n_units, n_units), | |
| l4=L.Linear(n_units, 3), | |
| ) | |
| def __call__(self, x): | |
| h = F.relu(self.l1(x)) | |
| h = F.relu(self.l2(h)) | |
| h = F.relu(self.l3(h)) | |
| return self.l4(h) | |
| def train(model, optimizer, x_data, y_data): | |
| batch_size = 100 | |
| N = x_data.shape[0] | |
| indices = np.random.permutation(N) | |
| sum_loss = 0.0 | |
| for i in range(0, N, batch_size): | |
| xs = Variable(cuda.to_gpu(x_data[indices[i:i + batch_size]])) | |
| ts = Variable(cuda.to_gpu(y_data[indices[i:i + batch_size]])) | |
| ys = model(xs) | |
| model.zerograds() | |
| loss = F.mean_squared_error(ys, ts) | |
| sum_loss += loss.data * len(ts.data) | |
| loss.backward() | |
| optimizer.update() | |
| return sum_loss / N |
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