merge
- merge layer classes¶
The file yann.layers.merge.py
contains the definition for the merge layers.
-
class
yann.layers.merge.
dropout_merge_layer
(x, input_shape, id=-1, error='rmse', rng=None, dropout_rate=0.5, verbose=2)[source]¶ This is a dropotu merge layer. This takes two layer inputs and produces an error between them if the
type
argument supplied was'error'
. It does other things too accordingly.Parameters: - x – a list of inputs (lenght must be two basically)
- input_shape – List of the shapes of all inputs.
- type –
'error'
creates an error layer. other options are'sum'
and'concatenate'
- error – If the type was
'error'
, then this variable is used. options include,'rmse'
,'l2'
,'l1'
,``’cross_entropy’``.
-
class
yann.layers.merge.
merge_layer
(x, input_shape, id=-1, type='error', error='rmse', input_type='layer', verbose=2)[source]¶ This is a merge layer. This takes two layer inputs and produces an error between them if the
type
argument supplied was'error'
. It does other things too accordingly.Parameters: - x – a list of inputs (lenght must be two basically)
- input_shape – List of the shapes of all inputs.
- type –
'error'
creates an error layer. other options are'sum'
,'batch'
and'concatenate'
- error – If the type was
'error'
, then this variable is used. options include,'rmse'
,'l2'
,'l1'
,``’cross_entropy’``. - input_type – If this argument was
'tensor'
, we simply merge the ouptus, if this was not provided or was'layer'
, this merges the outputs of the two layers.
Notes
'concatenate'
concatenates the outputs on the channels where as'batch'
concatenates across the batches. It will increase the batchsize.-
loss
(type=None)[source]¶ This method will return the cost function of the merge layer. This can be used by the optimizer module for instance to acquire a symbolic loss function that tries to minimize the distance between the two layers.
Parameters: - y – symbolic
theano.ivector
variable of labels to calculate loss from. - type – options None - Simple error ‘log’ - log loss
Returns: loss value.
Return type: theano symbolic variable
- y – symbolic