def output(self, input_value):
if not self.training_state:
return input_value
theano_random = theano_random_stream()
noise = theano_random.normal(size=input_value.shape, avg=self.mean, std=self.std)
return input_value + noise
def output(self, input_value):
if not self.training_state:
return input_value
theano_random = theano_random_stream()
noise = theano_random.normal(size=input_value.shape,
avg=self.mean, std=self.std)
return input_value + noise
def output(self, input_value):
if not self.training_state:
return input_value
theano_random = theano_random_stream()
proba = 1.0 - self.proba
mask = theano_random.binomial(n=1, p=proba, size=input_value.shape, dtype=input_value.dtype)
return (mask * input_value) / proba
def output(self, input_value):
if not self.training_state:
return input_value
theano_random = theano_random_stream()
proba = (1.0 - self.proba)
mask = theano_random.binomial(n=1, p=proba,
size=input_value.shape,
dtype=input_value.dtype)
return (mask * input_value) / proba
def __init__(self, n_visible, n_hidden, **options):
self.theano_random = theano_random_stream()
super(ConfigurableABC, self).__init__(n_hidden=n_hidden,
n_visible=n_visible,
**options)
self.weight = create_shared_parameter(value=self.weight,
name='algo:rbm/matrix:weight',
shape=(n_visible, n_hidden))
self.hidden_bias = create_shared_parameter(
value=self.hidden_bias,
name='algo:rbm/vector:hidden-bias',
shape=(n_hidden, ),
)
self.visible_bias = create_shared_parameter(
value=self.visible_bias,
name='algo:rbm/vector:visible-bias',
shape=(n_visible, ),
)
super(RBM, self).__init__(**options)
def __init__(self, n_visible, n_hidden, **options):
self.theano_random = theano_random_stream()
super(ConfigurableABC, self).__init__(n_hidden=n_hidden,
n_visible=n_visible, **options)
self.weight = create_shared_parameter(
value=self.weight,
name='algo:rbm/matrix:weight',
shape=(n_visible, n_hidden)
)
self.hidden_bias = create_shared_parameter(
value=self.hidden_bias,
name='algo:rbm/vector:hidden-bias',
shape=(n_hidden,),
)
self.visible_bias = create_shared_parameter(
value=self.visible_bias,
name='algo:rbm/vector:visible-bias',
shape=(n_visible,),
)
super(RBM, self).__init__(**options)
def output(self, *input_values):
mu, sigma = input_values
random = theano_random_stream()
return mu + T.exp(sigma) * random.normal(mu.shape)
def output(self, *input_values):
mu, sigma = input_values
random = theano_random_stream()
return mu + T.exp(sigma) * random.normal(mu.shape)
def __init__(self, n_hidden, **options):
self.theano_random = theano_random_stream()
super(RBM, self).__init__(n_hidden=n_hidden, **options)
