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View Code? Open in Web Editor NEWA deep generative model library in Theano and Lasagne
A deep generative model library in Theano and Lasagne
https://github.com/jych/cle/blob/master/cle/cost/__init__.py
これに書かれている分布は全て実装したい.
具体的には
・GMM
・BiGMM
・BiGauss(実装済みだが,より汎用的にしたい)
Hi,
Thank you for sharing.
I have a problem. I want to add convolutional operation to the model. However, it performs poorly. Below is my generator part code:
z = InputLayer((None,z_dim))
network = DenseLayer(z,num_units=1024,nonlinearity=activation)
network = DenseLayer(network,num_units=128 * 7 * 7,nonlinearity=activation)
network = lasagne.layers.ReshapeLayer(network, ((-1,128,7,7)))
network=lasagne.layers.TransposedConv2DLayer(network,num_filters=64,\ filter_size=(3,3),stride=(2,2),output_size=(14,14),crop='same',nonlinearity=rectify)
p_mean=lasagne.layers.TransposedConv2DLayer(network,1,\ filter_size=(3,3),stride=(2,2),output_size=(28,28),crop='same',nonlinearity=sigmoid)
p = Deterministic(p_mean,given=[z]) #p(x|z)
Other codes are not changed except for some minor changes to avoid error.
However, the generation is poor.
So, how can I add conv operation, is there something wrong?
Thank you!
DRAW [Gregor+ 2015]を実装したい.
・https://github.com/skaae/lasagne-draw を参考にすれば実装できそう.
すべてのメソッドにdocstringを書く
Currently abstract methods in Distribution
are not implemented in its child classes, but in the derived classes of Distribution_sample
even though it calls them in other methods such as sample_given_x
and log_likelihood_given_x
, assuming its child classes inherit Distribution_sample
.
class Distribution(object):
__metaclass__ = ABCMeta
...
def sample_given_x(self, x, repeat=1, **kwargs):
...
return [x, self.sample(*tolist(mean))]
def log_likelihood_given_x(self, samples, **kwargs):
...
return self.log_likelihood(sample, *tolist(mean))
...
@abstractmethod
def sample(self):
pass
@abstractmethod
def log_likelihood(self):
pass
class Gaussian_sample(Distribution_sample):
"""
Gaussian distribution
p(x) = \frac{1}{\sqrt{2*\pi*var}} * exp{-\frac{{x-mean}^2}{2*var}}
"""
def sample(self, mean, var):
"""
Paramaters
----------
mean : Theano variable, the output of a fully connected layer (Linear)
var : Theano variable, the output of a fully connected layer (Softplus)
"""
eps = self.srng.normal(mean.shape, dtype=mean.dtype)
return mean + T.sqrt(var) * eps
def log_likelihood(self, samples, mean, var):
"""
Paramaters
--------
sample : Theano variable
mean : Theano variable, the output of a fully connected layer (Linear)
var : Theano variable, the output of a fully connected layer (Softplus)
"""
loglike = self._gaussian_like(samples, mean, var)
return mean_sum_samples(loglike)
def _gaussian_like(self, x, mean, var):
c = - 0.5 * math.log(2 * math.pi)
_var = var + epsilon() # avoid NaN
return c - T.log(_var) / 2 - (x - mean)**2 / (2 * _var)
class Gaussian(Gaussian_sample, Distribution_double):
def __init__(self, mean_network, var_network, given, seed=1):
Distribution_double.__init__(self, mean_network, var_network, given)
super(Gaussian, self).__init__(seed=seed)
self._set_theano_func()
This is a bit confusing, and it might be better to change the class inheritance structure to resolve this. For example, we could let Distribution
have a variable of Gaussian_sample
in which sample
and log_likelihood
methods are defined, and call them in sample_given_x
and log_likelihood_given_x
methods. Also, we should use camelcase for class names.
Currently temperature is cast to numpy.float32 in the attempt to fix #59:
class ConcreteSample(GumbelSample):
...
def __init__(self, temp=0.1, seed=1):
super(ConcreteSample, self).__init__(seed=seed)
self.temp = np.float32(temp)
It would be more preferable if we could use floatX such as lasagne.utils.floatX(temp)
while making sure all test cases pass on different machines.
かなりごちゃごちゃしてきたので,思い切って整理したい
def test_consistency(self):
# Ensure that returned values stay the same when setting a fixed seed.
mu, beta = 0, 1
gumbel_sample = Gumbel_sample(temp=0.01, seed=1234567890)
actual = TestGumbelSample.get_sample(mu, beta, gumbel_sample, 5)
desired = [
1.7462246417999268,
0.2874840497970581,
0.9974965453147888,
0.3290553689002991,
-1.0215276479721069
]
assert_array_almost_equal(actual, desired, decimal=15)
Added test cases ensuring the consistency of sampling methods, and they all passed on the local environment.
However, if we set the decimals more than 6 or 7, test cases other than Gaussian and Gumbel distributions will fail when run on other machines including servers.
Example log on travis ci:
_____________________ TestBernoulliSample.test_consistency _____________________
self = <Tars.tests.test_distribution_samples.TestBernoulliSample testMethod=test_consistency>
def test_consistency(self):
# Ensure that returned values stay the same when setting a fixed seed.
mean = 0.5
bernoulli_sample = Bernoulli_sample(temp=0.1, seed=1234567890)
actual = TestBernoulliSample.get_sample(mean, bernoulli_sample, 5)
desired = [
0.9999971508356551,
0.9101269246280252,
0.0248156363467501,
0.3538078165724645,
0.1615775890919983
]
> assert_array_almost_equal(actual, desired, decimal=15)
E AssertionError:
E Arrays are not almost equal to 15 decimals
E
E (mismatch 20.0%)
E x: array([ 0.999997150839051, 0.910126924628025, 0.02481563634675 ,
E 0.353807816572465, 0.161577589091998])
E y: array([ 0.999997150835655, 0.910126924628025, 0.02481563634675 ,
E 0.353807816572465, 0.161577589091998])
Tars/tests/test_distribution_samples.py:97: AssertionError
I've implemented the distribution class "Multilayer" which enable to model multi-latent layers.
But it can't be used in the current version, so we should fix it.
I've implemented the JMVAE models on v0.0.2.
https://arxiv.org/abs/1611.01891
I'm going to implement them on v0.0.3. It is necessary to be aware that some specifications have changed from v0.0.2 to v.0.0.3.
VAEやGANの親クラスを作成した方がいいかどうか検討中(現在はobjectを継承).
distributionのBernoulliやGaussianはDistributionを継承しているので,そちらに合わせてModelクラスを作ってもいいかもしれない.
We would like to implement generative adversarial nets as "implicit distribution" as follows:
z = InputLayer((None, z_dim))
g = batch_norm(DenseLayer(z,num_units=512,nonlinearity=activation))
g = batch_norm(DenseLayer(g,num_units=512,nonlinearity=activation))
g_mean = DenseLayer(g,num_units=x_dim,nonlinearity=sigmoid)
g = Deterministic(g_mean,given=[z]) #p(x|z)
x = InputLayer((None, x_dim))
d_0 = DenseLayer(x,num_units=512,nonlinearity=leaky_rectify)
d_1 = DenseLayer(d_0,num_units=512,nonlinearity=leaky_rectify)
d_mean = DenseLayer(d_1,num_units=1,nonlinearity=sigmoid)
d = Bernoulli(d_mean,given=[x])
p = ImplicitDistribution(mean=g, loss=d)
KL距離をアニーリングできるようにしたい.
今の実装だと,KLに重み付けできないので,theano変数でパラメータを設定する必要がある.
スケジューリングをVAEクラス外部で行うか内部で行うかは要検討.
公開日をいつにしようか問題.
・個人的には来月頭,早ければ今月末には公開したい.
・公開に向けてやることリストとスケジュールを考える必要がある.
・何人かに手伝ってもらう予定
VRNNを実装したが,現在うまく動作しない.
おそらく,学習部分か推論部分にバグがあると思われる.
In the current implementation, we implement prior distributions as the Distribution_sample class but I believe that it is better to implement them as the Distribution_model class.
This issue is quite related to #54 .
今のVAEクラスで問題ないが,入力が3次元なので,次の問題がある.
・t_repeatが2次元or4次元しか対応していない.
・distributionのmean_sum_samplesが2次元or4次元しか対応していない.
Such as Distribution
, Bernoulli
, and Gaussian
.
To make sure that the sampling/log_likelihood methods return proper values.
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