class PyRBMFeatureExtractor(BaseEstimator, TransformerMixin):
def __init__(self,
n_hidden=500,
binary=True,
learning_rate=0.1,
momentum=0.2,
l2_weight=0.001,
sparsity=0.1,
n_training_epochs=10,
scale=0.001,
reconstruction=True,
n_gibbs=10,
batch_size=20):
self.n_hidden = n_hidden
self.binary = binary
self.momentum = momentum
self.learning_rate = learning_rate
self.l2_weight = l2_weight
self.sparsity = sparsity
self.batch_size = batch_size
self.scale = scale
self.n_training_epochs = n_training_epochs
self.reconstruction = reconstruction
self.n_gibbs = n_gibbs
super(PyRBMFeatureExtractor, self).__init__()
def fit(self, X, y=None):
n_features = X.shape[1]
self.rbm = RBM(n_features, self.n_hidden, self.binary, self.scale)
trainer = Trainer(self.rbm,
l2=self.l2_weight,
momentum=self.momentum,
target_sparsity=self.sparsity)
for i in range(self.n_training_epochs):
for j, batch in enumerate(chunks(X, self.batch_size)):
#log.debug("Training rbm on epoch %s batch %s" % (i, j))
trainer.learn(batch, learning_rate=self.learning_rate)
return self
def transform(self, X, y=None):
if self.reconstruction:
return self.rbm.reconstruct(X, self.n_gibbs)
else:
return self.rbm.hidden_expectation(X)
class PyRBMFeatureExtractor(BaseEstimator, TransformerMixin):
def __init__(
self,
n_hidden=500, binary=True,
learning_rate=0.1, momentum=0.2,
l2_weight=0.001, sparsity=0.1,
n_training_epochs=10,
scale=0.001,
reconstruction=True,
n_gibbs=10,
batch_size=20):
self.n_hidden = n_hidden
self.binary = binary
self.momentum = momentum
self.learning_rate = learning_rate
self.l2_weight = l2_weight
self.sparsity = sparsity
self.batch_size = batch_size
self.scale = scale
self.n_training_epochs = n_training_epochs
self.reconstruction = reconstruction
self.n_gibbs = n_gibbs
super(PyRBMFeatureExtractor, self).__init__()
def fit(self, X, y=None):
n_features = X.shape[1]
self.rbm = RBM(
n_features, self.n_hidden, self.binary, self.scale)
trainer = Trainer(
self.rbm, l2=self.l2_weight, momentum=self.momentum,
target_sparsity=self.sparsity)
for i in range(self.n_training_epochs):
for j, batch in enumerate(chunks(X, self.batch_size)):
#log.debug("Training rbm on epoch %s batch %s" % (i, j))
trainer.learn(batch, learning_rate=self.learning_rate)
return self
def transform(self, X, y=None):
if self.reconstruction:
return self.rbm.reconstruct(X, self.n_gibbs)
else:
return self.rbm.hidden_expectation(X)
def fit(self, X, y=None):
n_features = X.shape[1]
self.rbm = RBM(n_features, self.n_hidden, self.binary, self.scale)
trainer = Trainer(self.rbm,
l2=self.l2_weight,
momentum=self.momentum,
target_sparsity=self.sparsity)
for i in range(self.n_training_epochs):
for j, batch in enumerate(chunks(X, self.batch_size)):
#log.debug("Training rbm on epoch %s batch %s" % (i, j))
trainer.learn(batch, learning_rate=self.learning_rate)
return self
def fit(self, X, y=None):
n_features = X.shape[1]
self.rbm = RBM(
n_features, self.n_hidden, self.binary, self.scale)
trainer = Trainer(
self.rbm, l2=self.l2_weight, momentum=self.momentum,
target_sparsity=self.sparsity)
for i in range(self.n_training_epochs):
for j, batch in enumerate(chunks(X, self.batch_size)):
#log.debug("Training rbm on epoch %s batch %s" % (i, j))
trainer.learn(batch, learning_rate=self.learning_rate)
return self
