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How to hyperparameter of deep learning by hyperopt.
try:
import matplotlib
matplotlib.use('Agg')
except ImportError:
pass
import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions
from hyperopt import fmin, tpe, hp
from hyperopt.pyll import scope
import numpy as np
# Network definition
class MLP(chainer.Chain):
def __init__(self, n_units1, n_units2, n_units3, n_units4, n_out, layer_num, activate):
super(MLP, self).__init__(
l1=L.Linear(None, n_units1),
l2=L.Linear(None, n_units2),
l3=L.Linear(None, n_units3),
l4=L.Linear(None, n_units4),
lfinal=L.Linear(None, n_out),
)
self.layer_num = layer_num
if activate == 'relu':
self.act = F.relu
else:
self.act = F.sigmoid
def __call__(self, x):
h1 = self.act(self.l1(x))
h2 = self.act(self.l2(h1))
if self.layer_num == 3:
return self.lfinal(h2)
h3 = self.act(self.l3(h2))
if self.layer_num == 4:
return self.lfinal(h3)
h4 = self.act(self.l4(h3))
if self.layer_num == 5:
return self.lfinal(h4)
def main(params):
epoch = 40
gpu = 0
n_out = 10
batchsize = 100
n_units1 = params['n_units1']
n_units2 = params['n_units2']
n_units3 = params['n_units3']
n_units4 = params['n_units4']
layer_num = params['layer_num']
activate = params['activate']
optimizer_name = params['optimizer_name']
lr = params['lr']
model = L.Classifier(MLP(n_units1, n_units2, n_units3, n_units4, n_out, layer_num,
activate))
if gpu >= 0:
chainer.cuda.get_device(gpu).use()
model.to_gpu()
# Setup an optimizer
if optimizer_name == 'Adam':
optimizer = chainer.optimizers.Adam()
elif optimizer_name == 'AdaDelta':
optimizer = chainer.optimizers.AdaDelta()
else:
optimizer = chainer.optimizers.MomentumSGD(lr=lr)
optimizer.setup(model)
# optimizer.add_hook(chainer.optimizer.WeightDecay(weight_decay))
# Load the MNIST dataset
train, test = chainer.datasets.get_mnist()
train_iter = chainer.iterators.SerialIterator(train, batchsize)
test_iter = chainer.iterators.SerialIterator(test, batchsize,
repeat=False, shuffle=False)
# Set up a trainer
updater = training.StandardUpdater(train_iter, optimizer, device=gpu)
trainer = training.Trainer(updater, (epoch, 'epoch'), out="result")
# Evaluate the model with the test dataset for each epoch
trainer.extend(extensions.Evaluator(test_iter, model, device=gpu))
# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport())
# Save two plot images to the result dir
trainer.extend(
extensions.PlotReport(['main/loss', 'validation/main/loss'],
'epoch', file_name='loss.png'))
trainer.extend(
extensions.PlotReport(
['main/accuracy', 'validation/main/accuracy'],
'epoch', file_name='accuracy.png'))
# Write report
trainer.extend(extensions.PrintReport(
['epoch', 'main/loss', 'validation/main/loss',
'main/accuracy', 'validation/main/accuracy', 'elapsed_time']))
# Print a progress bar to stdout
trainer.extend(extensions.ProgressBar())
# Run the training
trainer.run()
valid_data = trainer._extensions['PlotReport'].extension._data
loss_data = [data for i, data in valid_data['validation/main/loss']]
best10_loss = sorted(loss_data)[:10]
return sum(best10_loss)
if __name__ == '__main__':
# params = {'n_units1': 200,
# 'n_units2': 200,
# 'n_units3': 200,
# 'n_units4': 200,
# 'layer_num': 3,
# 'activate': 'relu',
# 'optimizer_name': 'Adam',
# 'lr': 0.01,
# }
# main(params)
space = {'n_units1': scope.int(hp.quniform('n_units1', 100, 300, 50)),
'n_units2': scope.int(hp.quniform('n_units2', 100, 300, 50)),
'n_units3': scope.int(hp.quniform('n_units3', 100, 300, 50)),
'n_units4': scope.int(hp.quniform('n_units4', 100, 300, 50)),
'layer_num': scope.int(hp.quniform('layer_num', 3, 5, 1)),
'activate': hp.choice('activate',
('relu', 'sigmoid')),
'optimizer_name': hp.choice('optimizer_name',
('Adam', 'AdaDelta', 'MomentumSGD')),
'lr': hp.uniform('lr', 0.005, 0.02),
}
best = fmin(main, space, algo=tpe.suggest, max_evals=20)
print("best parameters", best)
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