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multilayer_perceptron.py
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multilayer_perceptron.py
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# coding: utf-8
import os
import sys
import numpy as np
import theano
import theano.tensor as T
import timeit
import logistic_regression
class HiddenLayer(object):
## rmp: numpy.random.RandomState. a random number generator used to initilize weights
## input: theano.tensor.dmatrix. a symbolic tenso of shape(n_examples, n_in)
## n_in: int dim of input
## n_out: int num of hidden units
## activation: theano.Op or function activation.
def __init__(self, rng, input, n_in, n_out, W=None, b=None, activation=T.tanh):
if W is None:
#Wの初期化
W_values = np.asarray(
rng.uniform(
low=-np.sqrt(6./(n_in + n_out)),
high=np.sqrt(6./(n_in + n_out)),
size=(n_in, n_out)),
dtype=theano.config.floatX
)
if activation == theano.tensor.nnet.sigmoid:
# sigmoidの場合は4倍する
W_values *= 4
# 学習する変数を共有型にする
W = theano.shared(value=W_values, name='W', borrow=True)
if b is None:
# bの初期化(n_out行)
b_values = np.zeros((n_out,), dtype=theano.config.floatX)
# 学習する変数を共有型にする
b = theano.shared(value=b_values, name='b', borrow=True)
self.W = W
self.b = b
self.input = input
#隠れ層からの出力を計算
#入力と重みの内積とバイアス
lin_output = T.dot(input, self.W) + self.b
#出力を計算
#activationがNoneの場合(activationを挟まない線形変換の場合)
if activation is None:
self.output = lin_output
else:
self.output = activation(lin_output)
# parameters
self.params = [self.W, self.b]
class MLP(object):
def __init__(self, rng, input, n_in, n_hidden, n_out):
# 隠れ層
self.hiddenLayer = HiddenLayer(rng=rng, input=input, n_in=n_in, n_out=n_hidden, activation=T.tanh)
# 出力層
self.logRegressionLayer = logistic_regression.LogisticRegression(input=self.hiddenLayer.output, n_in=n_hidden, n_out=n_out)
# L1正則化項
self.L1 = abs(self.hiddenLayer.W).sum() + abs(self.logRegressionLayer.W).sum()
# L2正則化
self.L2_sqr = ((self.hiddenLayer.W) ** 2).sum() + ((self.logRegressionLayer.W) ** 2).sum()
# loss（出力層にのみ依存するのでロジスティック回帰と同じで良い）
self.negative_log_likelihood = self.logRegressionLayer.negative_log_likelihood
# 誤差計算シンボル
self.errors = self.logRegressionLayer.errors
# パラメータ
self.params = self.hiddenLayer.params + self.logRegressionLayer.params
# self tracking input
self.input = input
def test_mlp(learning_rate=0.01, L1_reg=0.00, L2_reg=0.0001, n_epochs=1000, dataset='data/mnist.pkl.gz', batch_size=20, n_hidden=500):
datasets = logistic_regression.load_data(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
n_train_batches = train_set_x.get_value(borrow=True).shape[0] / batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] / batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] / batch_size
# ミニバッチのindex
index = T.lscalar()
# 事例ベクトルx
x = T.matrix('x')
# int型の1次元ベクトル
y = T.ivector('y')
# ランダム変数
rng = np.random.RandomState(1234)
# MLPの構築
classifier = MLP(rng=rng, input=x, n_in=28*28, n_hidden=n_hidden, n_out=10)
# cost関数のシンボル 対数尤度と正則化項
cost = classifier.negative_log_likelihood(y) + L1_reg * classifier.L1 + L2_reg * classifier.L2_sqr
# ミニバッチごとのエラー率を計算するシンボル(test)
test_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: test_set_x[index * batch_size: (index+1)*batch_size],
y: test_set_y[index * batch_size: (index+1)*batch_size]
})
# ミニバッチごとのエラー率を計算するシンボル(validation)
validate_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: valid_set_x[index * batch_size: (index+1)*batch_size],
y: valid_set_y[index * batch_size: (index+1)*batch_size]
})
# 勾配の計算 back propagation
# gparamsに格納した変数でコストを偏微分する
gparams = [T.grad(cost, param) for param in classifier.params]
# パラメータの更新式のシンボル(複数の更新式を定義するときは配列にする)
# classifierのparamとgparamsを同時にループ、paramsとその微分gparamsを使ったパラメータの更新式
updates = [(param, param - learning_rate * gparam) for param, gparam in zip(classifier.params, gparams)]
# 学習モデルでは、updatesに更新シンボルを入れてやれば良い
train_model = theano.function(
inputs=[index],
outputs=cost,
updates=updates,
givens={
x: train_set_x[index * batch_size: (index+1)*batch_size],
y: train_set_y[index * batch_size: (index+1)*batch_size]
})
print '... training'
patience = 10000
patience_increase = 2
improvement_threashold = 0.995
validation_frequency = min(n_train_batches, patience / 2)
best_validation_loss = np.inf
best_iter = 0
test_score = 0.
start_time = timeit.default_timer()
epoch = 0
done_looping = False
while(epoch < n_epochs) and (not done_looping):
epoch = epoch + 1
for minibatch_index in xrange(n_train_batches):
minibatch_avg_cost = train_model(minibatch_index)
iter = (epoch - 1) * n_train_batches + minibatch_index
if (iter + 1) % validation_frequency == 0:
## validationのindexをvalidationのエラー率を計算するfunctionに渡し、配列としてかえす
validation_losses = [validate_model(i) for i in xrange(n_valid_batches)]
# 平均してscoreにする
this_validation_loss = np.mean(validation_losses)
print('epoch %i, minibatch %i/%i, validation error %f ' % (epoch, minibatch_index+1, n_train_batches, this_validation_loss*100.))
if this_validation_loss < best_validation_loss:
if(this_validation_loss < best_validation_loss * improvement_threashold):
patience = max(patience, iter*patience_increase)
best_validation_loss = this_validation_loss
best_iter = iter
## testのindex をtestのエラー率を計算するfunctionに渡し、配列として渡す
test_losses = [test_model(i) for i in xrange(n_test_batches)]
## 平均してscoreにする
test_score = np.mean(test_losses)
##
print('epoch %i, minibatch %i/%i, test error %f ' % (epoch, minibatch_index+1, n_train_batches, test_score*100.))
if patience <= iter:
done_looping = True
break
end_time = timeit.default_timer()
print(('optimization complete. Best validation score of %f obtained at iteration %i, with test performance %f') % (best_validation_loss * 100., best_iter + 1, test_score * 100.))
print >> sys.stderr,('This code for file ' + os.path.split(__file__)[1] + ' ran for %.2fm' % ((end_time - start_time)/60.))
if __name__ == '__main__':
test_mlp()