def fprop(self,
X,
corruption_level=None,
noise_type="binomial",
epoch=None,
decay_rate=1.):
"""Forward pass of convolutional auto-encoder
Parameters
----------
X : 4D tensor
data in (batch size, channel, height, width)
corruption_level : float
corruption_level on data
noise_type : string
type of noise: "binomial" or "gaussian"
Returns
-------
out : 4-D tensor
output list for each layer
"""
out=[];
if epoch is not None:
self.corruption_level=corruption_level*(epoch**(-decay_rate));
else:
self.corruption_level=corruption_level;
if self.corruption_level is None:
level_out=X;
else:
level_out=corrupt_input(X, self.corruption_level, noise_type);
for k, layer in enumerate(self.layers):
level_out=layer.apply(level_out);
out.append(level_out);
return out;
def fprop(self,
X,
corruption_level=None,
noise_type="binomial",
epoch=None,
decay_rate=1.):
"""Forward pass of convolutional auto-encoder
Parameters
----------
X : 4D tensor
data in (batch size, channel, height, width)
corruption_level : float
corruption_level on data
noise_type : string
type of noise: "binomial" or "gaussian"
Returns
-------
out : 4-D tensor
output list for each layer
"""
out = []
if epoch is not None:
self.corruption_level = corruption_level * (epoch**(-decay_rate))
else:
self.corruption_level = corruption_level
if self.corruption_level is None:
level_out = X
else:
level_out = corrupt_input(X, self.corruption_level, noise_type)
for k, layer in enumerate(self.layers):
level_out = layer.apply(level_out)
out.append(level_out)
return out
def fprop(self,
X,
corruption_level=None,
noise_type="binomial",
epoch=None,
decay_rate=1.):
"""Forward pass of auto-encoder
Parameters
----------
X : matrix
number of samples in (number of samples, dim of sample)
corruption_level : float
corruption_level on data
noise_type : string
type of noise: "binomial" or "gaussian"
Returns
-------
out : matrix
output list for each layer
"""
out=[];
if epoch is not None:
self.corruption_level=corruption_level*(epoch**(-decay_rate));
else:
self.corruption_level=corruption_level;
if self.corruption_level == None:
level_out=X;
else:
level_out=corrupt_input(X, self.corruption_level, noise_type);
for k, layer in enumerate(self.layers):
level_out=layer.apply(level_out);
out.append(level_out);
return out;
def fprop(self,
X,
corruption_level=None,
noise_type="binomial",
epoch=None,
decay_rate=1.):
"""Forward pass of auto-encoder
Parameters
----------
X : matrix
number of samples in (number of samples, dim of sample)
corruption_level : float
corruption_level on data
noise_type : string
type of noise: "binomial" or "gaussian"
Returns
-------
out : matrix
output list for each layer
"""
out = []
if epoch is not None:
self.corruption_level = corruption_level * (epoch**(-decay_rate))
else:
self.corruption_level = corruption_level
if self.corruption_level == None:
level_out = X
else:
level_out = corrupt_input(X, self.corruption_level, noise_type)
for k, layer in enumerate(self.layers):
level_out = layer.apply(level_out)
out.append(level_out)
return out