def drop_output(self, input, drop=0, rng=None, p=0.5):
###--- Unpool
if self.poolsize[0] == 1 and self.poolsize[1] == 1:
unpool_out = input
else:
unpool_out = Textra.repeat(Textra.repeat(
input, self.poolsize[0], axis=2),
self.poolsize[1],
axis=3) * self.mask
image_shape = list(self.image_shape)
if n_batch is not None:
image_shape[0] = n_batch
if self.border_mode == 'same':
conv_out = dnn.dnn_conv(
img=unpool_out,
kerns=self.W,
subsample=(1, 1),
border_mode=self.border,
#conv_mode='cross'
)
else:
raise Exception('Unknown conv type')
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
lin_output = conv_out + self.b.dimshuffle('x', 0, 'x', 'x')
output = (lin_output
if self.activation is None else self.activation(lin_output))
droppedOutput = nonlinearity.dropout(rng, output, p)
return T.switch(T.neq(drop, 0), droppedOutput, output)
def drop_output(self, input, drop=0, rng=None, p=0.5):
###--- Unpool
if self.poolsize[0] == 1 and self.poolsize[1] == 1:
unpool_out = input
else:
unpool_out = Textra.repeat(Textra.repeat(input, self.poolsize[0], axis = 2), self.poolsize[1], axis = 3) * self.mask
image_shape = list(self.image_shape)
if n_batch is not None:
image_shape[0] = n_batch
if self.border_mode == 'same':
conv_out = dnn.dnn_conv(
img=unpool_out,
kerns=self.W,
subsample=(1,1),
border_mode=self.border,
#conv_mode='cross'
)
else:
raise Exception('Unknown conv type')
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
lin_output = conv_out + self.b.dimshuffle('x', 0, 'x', 'x')
output= (
lin_output if self.activation is None
else self.activation(lin_output)
)
droppedOutput = nonlinearity.dropout(rng, output, p)
return T.switch(T.neq(drop, 0), droppedOutput, output)
def drop_output(self, input, drop=0, rng=None, p=0.5):
###--- Unpool
if self.poolsize[0] == 1 and self.poolsize[1] == 1:
unpool_out = input
else:
unpool_out = Textra.repeat(Textra.repeat(input, self.poolsize[0], axis = 2), self.poolsize[1], axis = 3) * self.mask
image_shape = list(self.image_shape)
if n_batch is not None:
image_shape[0] = n_batch
###--- Unpool + conv
# convolve input feature maps with filters
if self.border_mode == 'valid':
conv_out = conv.conv2d(
input=unpool_out,
filters=self.W,
filter_shape=self.filter_shape,
image_shape=image_shape,
border_mode='valid'
)
elif self.border_mode == 'same':
conv_out = conv.conv2d(
input=unpool_out,
filters=self.W,
filter_shape=self.filter_shape,
image_shape=image_shape,
border_mode='full'
)
padding_w = theano.shared((self.filter_shape[2] - 1) / 2)
padding_h = theano.shared((self.filter_shape[3] - 1) / 2)
conv_out = conv_out[:,:,padding_w:-padding_w,padding_h:-padding_h]
elif self.border_mode == 'full':
conv_out = conv.conv2d(
input=unpool_out,
filters=self.W,
filter_shape=self.filter_shape,
image_shape=image_shape,
border_mode='full'
)
else:
raise Exception('Unknown conv type')
# downsample each feature map individually, using maxpooling
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
lin_output = conv_out + self.b.dimshuffle('x', 0, 'x', 'x')
output= (
lin_output if self.activation is None
else self.activation(lin_output)
)
droppedOutput = nonlinearity.dropout(rng, output, p)
return T.switch(T.neq(drop, 0), droppedOutput, output)
def drop_output(self, input, drop=0, rng=None, p=0.5):
lin_output = T.dot(input, self.W) + self.b
output = (
lin_output if self.activation is None
else self.activation(lin_output)
)
droppedOutput = nonlinearity.dropout(rng, output, p)
return T.switch(T.neq(drop, 0), droppedOutput, output)
def drop_output(self, input, drop=0, rng=None, p=0.5):
# convolve input feature maps with filters
if self.border_mode == 'valid':
conv_out = conv.conv2d(
input=input,
filters=self.W,
filter_shape=self.filter_shape,
image_shape=self.image_shape,
border_mode='valid'
)
elif self.border_mode == 'same':
conv_out = conv.conv2d(
input=input,
filters=self.W,
filter_shape=self.filter_shape,
image_shape=self.image_shape,
border_mode='full'
)
padding_w = theano.shared((self.filter_shape[2] - 1) / 2)
padding_h = theano.shared((self.filter_shape[3] - 1) / 2)
conv_out = conv_out[:,:,padding_w:-padding_w,padding_h:-padding_h]
elif self.border_mode == 'full':
conv_out = conv.conv2d(
input=input,
filters=self.W,
filter_shape=self.filter_shape,
image_shape=self.image_shape,
border_mode='full'
)
else:
raise Exception('Unknown conv type')
# downsample each feature map individually, using maxpooling
if self.poolsize[0] == 1 and self.poolsize[1] == 1:
pooled_out = conv_out
else:
pooled_out = downsample.max_pool_2d(
input=conv_out,
ds=self.poolsize,
ignore_border=True
)
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
lin_output = pooled_out + self.b.dimshuffle('x', 0, 'x', 'x')
output = (
lin_output if self.activation is None
else self.activation(lin_output)
)
droppedOutput = nonlinearity.dropout(rng, output, p)
return T.switch(T.neq(drop, 0), droppedOutput, output)
def drop_output(self, input, drop=0, rng=None, p=0.5):
# convolve input feature maps with filters
'''
dnn
'''
if self.border_mode == 'same':
conv_out = dnn.dnn_conv(
img=input,
kerns=self.W,
subsample=(1,1),
border_mode=self.border,
#conv_mode='cross'
)
else:
raise Exception('Unknown conv type')
# downsample each feature map individually, using maxpooling
if self.poolsize[0] == 1 and self.poolsize[1] == 1:
pooled_out = conv_out
else:
pooled_out = downsample.max_pool_2d(
input=conv_out,
ds=self.poolsize,
ignore_border=True
)
if self.cnorm:
print 'cnorm size', self.filter_shape[0]/8+1
pooled_out=ContrastCrossChannels.ContrastCrossChannels(input=pooled_out, n=self.filter_shape[0]/8+1)
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
lin_output = pooled_out + self.b.dimshuffle('x', 0, 'x', 'x')
output = (
lin_output if self.activation is None
else self.activation(lin_output)
)
droppedOutput = nonlinearity.dropout(rng, output, p)
return T.switch(T.neq(drop, 0), droppedOutput, output)
def drop_output(self, input, drop=0, rng=None, p=0.5):
# convolve input feature maps with filters
'''
dnn
'''
if self.border_mode == 'same':
conv_out = dnn.dnn_conv(
img=input,
kerns=self.W,
subsample=(1,1),
border_mode=self.border,
#conv_mode='cross'
)
else:
raise Exception('Unknown conv type')
# downsample each feature map individually, using maxpooling
if self.poolsize[0] == 1 and self.poolsize[1] == 1:
pooled_out = conv_out
else:
pooled_out = dnn.dnn_pool(
img=conv_out,
ws=self.poolsize,
stride=self.poolsize,
mode='max',
pad=(0,0)
)
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
lin_output = pooled_out + self.b.dimshuffle('x', 0, 'x', 'x')
output = (
lin_output if self.activation is None
else self.activation(lin_output)
)
droppedOutput = nonlinearity.dropout(rng, output, p)
return T.switch(T.neq(drop, 0), droppedOutput, output)
def drop_output(self, input, drop=0, rng=None, p=0.5):
lin_output = T.dot(input, self.W) + self.b
output = (lin_output
if self.activation is None else self.activation(lin_output))
droppedOutput = nonlinearity.dropout(rng, output, p)
return T.switch(T.neq(drop, 0), droppedOutput, output)
