elektronn.net package¶
elektronn.net.convlayer2d module¶

elektronn.net.convlayer2d.
getOutputShape
(insh, fsh, pool, mfp, r=1)[source]¶ Returns shape of convolution result from (bs, ch, x, y) * (nof, ch, xf, yf)

elektronn.net.convlayer2d.
getProbShape
(output_shape, mfp_strides)[source]¶ Given outputshape (bs, ch, x, y) and mfp_stride (sx, sy) returns shape of Class Prob output

class
elektronn.net.convlayer2d.
ConvLayer2d
(input, input_shape, filter_shape, pool, activation_func, enable_dropout, use_fragment_pooling, reshape_output, mfp_offsets, mfp_strides, input_layer=None, W=None, b=None, pooling_mode='max')[source]¶ Bases:
object
ConvPool Layer of a CNN
Parameters:  input (theano.tensor.dtensor4 ('batch', 'channel', x, y)) – symbolic image tensor, of shape input_shape
 input_shape (tuple or list of length 4) – (batch size, num input feature maps, image height, image width)
 filter_shape (tuple or list of length 4) – (number of filters, input_channels, filter height,filter width)
 pool (int 2tuple) – the downsampling (maxpooling) factor
 activation_func (string) – Options: tanh, relu, sig, abs, linear, maxout <i>
 enable_dropout (Bool) – whether to enable dropout in this layer. The default rate is 0.5 but it can be changed with self.activation_noise.set_value(set_value(np.float32(p)) or using cnn.setDropoutRates
 use_fragment_pooling (Bool) – whether to use max fragment pooling in this layer (MFP)
 reshape_output (Bool) – whether to reshape class_probabilities to (bs, cls, x, y) and reassemble fragments to dense images if MFP was enabled. Use this in for the last layer.
 mfp_offsets (list of list of ints) – this lists specifies the offsets that the MFPfragments have w.r.t to the original patch. Only needed if MFP is enabled.
 mfp_strides (list of int) – the strides of the output in each dimension
 input_layer (layer object) – just for keeping track of unusual input layers
 W (np.ndarray or T.TensorVariable) – weight matrix. If array, the values are used to initialise a shared variable for this layer. If TensorVariable, than this variable is directly used (weight sharing with the layer from which this variable comes from)
 b (np.ndarray or T.TensorVariable) – bias vector. If array, the values are used to initialise a shared variable for this layer. If TensorVariable, than this variable is directly used (weight sharing with the layer from which this variable comes from)
 pooling_mode (str) – ‘max’ or ‘maxabs’ where the first is normal maxpooling and the second also retains sign of large negative values

NLL
(y, class_weights=None, mask_class_labeled=None, mask_class_not_present=None, label_prop_thresh=None)[source]¶ Returns the symbolic mean and instancewise negative loglikelihood of the prediction of this model under a given target distribution.
 y: theano.tensor.TensorType
 corresponds to a vector that gives for each example the correct label. Labels < 0 are ignored (e.g. can be used for label propagation)
 class_weights: theano.tensor.TensorType
 weight vector of float32 of length
n_lab
. Values:1.0
(default),w < 1.0
(less important),w > 1.0
(more important class)
The following refers to lazy labels, the masks are always on a per patch basis, depending on the origin cube of the patch. The masks are properties of the individual image cubes and must be loaded into CNNData.
 mask_class_labeled: theano.tensor.TensorType
 shape = (batchsize, num_classes).
Binary masks indicating whether a class is properly labeled in
y
. If a classk
is (in general) present in the image patches andmask_class_labeled[k]==1
, then the labels must obeyy==k
for all pixels where the class is present. If a classk
is present in the image, but was not labeled (> cheaper labels), setmask_class_labeled[k]=0
. Then all pixels for which they==k
will be ignored. Alternative: sety=1
to ignore those pixels. Limit case:mask_class_labeled[:]==1
will result in the ordinary NLL.  mask_class_not_present: theano.tensor.TensorType
 shape = (batchsize, num_classes).
Binary mask indicating whether a class is present in the image patches.
mask_class_not_present[k]==1
means that the image does not contain examples of classk
. Then for all pixels in the patch, classk
predictive probabilities are trained towards0
. Limit case:mask_class_not_present[:]==0
will result in the ordinary NLL.  label_prop_thresh: float (0.5,1)
 This threshold allows unsupervised label propagation (only for examples with negative/ignore labels). If the predictive probability of the most likely class exceeds the threshold, this class is assumed to be the correct label and the training is pushed in this direction. Should only be used with pretrained networks, and values <= 0.5 are disabled.
Examples:
 A cube contains no class
k
. Instead of labelling the remaining classes they can be marked as unlabelled by the first mask (mask_class_labeled[:]==0
, whethermask_class_labeled[k]
is0
or1
is actually indifferent because the labels should not bey==k
anyway in this case). Additionallymask_class_not_present[k]==1
(otherwise0
) to suppress predictions ofk
in in this patch. The actual value of the labels is indifferent, it can either be1
or it could be the background class, if the background is marked as unlabelled (i.e. then those labels are ignored).  Only part of the cube is densely labelled. Set
mask_class_labeled[:]=1
for all classes, but set the label values in the unlabelled part to1
to ignore this part.  Only a particular class
k
is labelled in the cube. Either set all other label pixels to1
or the corresponding flags inmask_class_labeled
for the unlabelled classes.
Note
Using
1
labels or telling that a class is not labelled, is somewhat redundant and just supported for convenience.

NLL_weak
(y, class_weights=None, mask_class_labeled=None, mask_class_not_present=None, label_prop_thresh=None)[source]¶ NLL that mixes the current cnn output and the hard labels as target
elektronn.net.convlayer3d module¶

elektronn.net.convlayer3d.
getOutputShape
(insh, fsh, pool, mfp, r=1)[source]¶ Returns shape of convolution result from (bs, z, ch, x, y) * (nof, z, ch, xf, yf)

elektronn.net.convlayer3d.
getProbShape
(output_shape, mfp_strides)[source]¶ Given outputshape (bs, z, ch, x, y) and mfp_stride (sx, sy) returns shape of Class Prob output

class
elektronn.net.convlayer3d.
ConvLayer3d
(input, input_shape, filter_shape, pool, activation_func, enable_dropout, use_fragment_pooling, reshape_output, mfp_offsets, mfp_strides, input_layer=None, W=None, b=None, pooling_mode='max', affinity=False)[source]¶ Bases:
object
ConvPool Layer of a CNN
Parameters:  input (theano.tensor.dtensor5 ('batch', z, 'channel', x, y)) – symbolic image tensor, of shape input_shape
 input_shape (tuple or list of length 5) – (batch size, z, num input feature maps, y, x)
 filter_shape (tuple or list of length 5) – (number of filters, filter z, num input feature maps, filter y,filter x)
 pool (int 3tuple) – the downsampling (maxpooling) factor
 activation_func (string) – Options: tanh, relu, sig, abs, linear, maxout <i>
 enable_dropout (Bool) – whether to enable dropout in this layer. The default rate is 0.5 but it can be changed with self.activation_noise.set_value(set_value(np.float32(p)) or using cnn.setDropoutRates
 use_fragment_pooling (Bool) – whether to use max fragment pooling in this layer (MFP)
 reshape_output (Bool) – whether to reshape class_probabilities to (bs, cls, x, y) and reassemble fragments to dense images if MFP was enabled. Use this in for the last layer.
 mfp_offsets (list of list of ints) – this lists specifies the offsets that the MFPfragments have w.r.t to the original patch. Only needed if MFP is enabled.
 mfp_strides (list of int) – the strides of the output in each dimension
 input_layer (layer object) – just for keeping track of unusual input layers
 W (np.ndarray or T.TensorVariable) – weight matrix. If array, the values are used to initialise a shared variable for this layer. If TensorVariable, than this variable is directly used (weight sharing with the layer from which this variable comes from)
 b (np.ndarray or T.TensorVariable) – bias vector. If array, the values are used to initialise a shared variable for this layer. If TensorVariable, than this variable is directly used (weight sharing with the layer from which this variable comes from)
 pooling_mode (str) – ‘max’ or ‘maxabs’ where the first is normal maxpooling and the second also retains sign of large negative values

NLL
(y, class_weights=None, example_weights=None, mask_class_labeled=None, mask_class_not_present=None, label_prop_thresh=None)[source]¶ Returns the symbolic mean and instancewise negative loglikelihood of the prediction of this model under a given target distribution.
 y: theano.tensor.TensorType
 corresponds to a vector that gives for each example the correct label. Labels < 0 are ignored (e.g. can be used for label propagation)
 class_weights: theano.tensor.TensorType
 weight vector of float32 of length
n_lab
. Values:1.0
(default),w < 1.0
(less important),w > 1.0
(more important class)  example_weights: theano.tensor.TensorType
 weight vector of float32 of shape
(bs, z, x, y) that can give the individual examples (i.e. labels for output pixels) different weights. Values: ``1.0
(default),w < 1.0
(less important),w > 1.0
(more important example). Note: if this is not normalised/bounded it may result in a effectively modified learning rate!
The following refers to lazy labels, the masks are always on a per patch basis, depending on the origin cube of the patch. The masks are properties of the individual image cubes and must be loaded into CNNData.
 mask_class_labeled: theano.tensor.TensorType
 shape = (batchsize, num_classes).
Binary masks indicating whether a class is properly labeled in
y
. If a classk
is (in general) present in the image patches andmask_class_labeled[k]==1
, then the labels must obeyy==k
for all pixels where the class is present. If a classk
is present in the image, but was not labeled (> cheaper labels), setmask_class_labeled[k]=0
. Then all pixels for which they==k
will be ignored. Alternative: sety=1
to ignore those pixels. Limit case:mask_class_labeled[:]==1
will result in the ordinary NLL.  mask_class_not_present: theano.tensor.TensorType
 shape = (batchsize, num_classes).
Binary mask indicating whether a class is present in the image patches.
mask_class_not_present[k]==1
means that the image does not contain examples of classk
. Then for all pixels in the patch, classk
predictive probabilities are trained towards0
. Limit case:mask_class_not_present[:]==0
will result in the ordinary NLL.  label_prop_thresh: float (0.5,1)
 This threshold allows unsupervised label propagation (only for examples with negative/ignore labels). If the predictive probability of the most likely class exceeds the threshold, this class is assumed to be the correct label and the training is pushed in this direction. Should only be used with pretrained networks, and values <= 0.5 are disabled.
Examples:
 A cube contains no class
k
. Instead of labelling the remaining classes they can be marked as unlabelled by the first mask (mask_class_labeled[:]==0
, whethermask_class_labeled[k]
is0
or1
is actually indifferent because the labels should not bey==k
anyway in this case). Additionallymask_class_not_present[k]==1
(otherwise0
) to suppress predictions ofk
in in this patch. The actual value of the labels is indifferent, it can either be1
or it could be the background class, if the background is marked as unlabelled (i.e. then those labels are ignored).  Only part of the cube is densely labelled. Set
mask_class_labeled[:]=1
for all classes, but set the label values in the unlabelled part to1
to ignore this part.  Only a particular class
k
is labelled in the cube. Either set all other label pixels to1
or the corresponding flags inmask_class_labeled
for the unlabelled classes.
Note
Using
1
labels or telling that a class is not labelled, is somewhat redundant and just supported for convenience.

NLL_weak
(y, class_weights=None, mask_class_labeled=None, mask_class_not_present=None, label_prop_thresh=None)[source]¶ NLL that mixes the current cnn output and the hard labels as target

NLL_affinity
(y, class_weights=None, mask_class_labeled=None, mask_class_not_present=None, label_prop_thresh=None)[source]¶ TODO

class
elektronn.net.convlayer3d.
AffinityLayer3d
(input, input_shape, filter_shape, pool, activation_func, enable_dropout, use_fragment_pooling, reshape_output, mfp_offsets, mfp_strides, input_layer=None, W=None, b=None, pooling_mode='max')[source]¶ Bases:
object

class
elektronn.net.convlayer3d.
MalisLayer
(input, input_shape, filter_shape, pool, activation_func, enable_dropout, use_fragment_pooling, reshape_output, mfp_offsets, mfp_strides, input_layer=None, W=None, b=None, pooling_mode='max')[source]¶ Bases:
elektronn.net.convlayer3d.AffinityLayer3d

NLL_Malis
(aff_gt, seg_gt, unrestrict_neg=True)[source]¶ Parameters:  aff_gt: 4d, (bs, #edges, x, y, z) int16
 seg_gt: (bs, x, y, z) int16
Returns:  pos_count: for every edge number of pixelpairs that should be connected by this edge
(excluding background/ECS pixels and only edges considered within the same object, such that paths that go out from an object and back to the same object are irgnored)
 neg_count: for every edge number of pixelpairs that should be separated by this edge
(excluding background/ECS pixels and only edges considered between objects, such that minimal edges inside an object are not consideres to play a role for separating objects)
 unrestrict_neg: Bool
Use this to relax the restriction on neg_counts. The restriction modifies the edge weights for before calculating the negative counts as:
edge_weights_neg = np.maximum(affinity_pred, affinity_gt)
If unrestricted the predictions are used directly.

elektronn.net.convnet module¶

class
elektronn.net.convnet.
MixedConvNN
(input_size=None, input_depth=None, batch_size=None, enable_dropout=False, recurrent=False, dimension_calc=None)[source]¶ Bases:
object
Parameters:  input_size: tuple
 Data shapes, excluding batch and channel (used to infer the dimensionality)
 input_depth: int/None
 Is None by default this means nonimage data (no conv layers allowed). Change to 1 for b/w, 3 for RGB and 4 for RGBD images etc. For RNN this is the length of the time series.
 batch_size: int/None
 None for variable batch size
 enable_dropout: Bool
 Turn on or off dropout
 recurrent: Bool
 Support recurrent iterations along input depth/time
 dimension_calc: dimension calculator object
Examples
Note that image data must have at least 1 channel, e.g. a 2d image (1,x,y). 3d requires data in the format (z,ch,x,y). E.g. to create an isotropic 3d CNN with 5 channels (total input shape is (1,30,5,30,30)):
>>> MixedConvNN((30,30,30), input_depth=5, batch_size=1)
A nonconvolutional MLP can be created as:
>>> MixedConvNN((100,), input_depth=None, batch_size=2000)

addPerceptronLayer
(n_outputs=10, activation_func='tanh', enable_input_noise=False, add_in_output_layers=False, force_no_dropout=False, W=None, b=None)[source]¶ Adds a Perceptron layer to the CNN.
Normally the each layer creates its own set of randomly initialised neuron weights. To reuse the weights of another layer (weight sharing) use the arguments
W
andb
an passT.TensorVariable
. IfW
andb
are numpy arrays own weights are initialised with these values.Parameters:  n_outputs: int
The size of this layer
 activation_func: string
{tanh, relu, sigmoid, abs, linear, maxout <i>} Activation function
 enable_input_noise: Bool
If True set 20% of input to 0 randomly (similar to dropout)
 force_no_dropout: Bool
Set True for last/output layer

addConvLayer
(nof_filters=None, filter_size=None, pool_shape=2, activation_func='tanh', add_in_output_layers=False, force_no_dropout=False, use_fragment_pooling=False, reshape=False, is_last_layer=False, layer_input_shape=None, layer_input=None, W=None, b=None, pooling_mode='max', affinity=False)[source]¶ Adds a convolutional layer to the CNN. The dimensionality is automatically inferred.
Normally the inputs are automatically connected the the outputs of the last added layer. To connect to a different layer use
layer_input_shape
andlayer_input
arguments.Normally the each layer creates its own set of randomly initialised neuron weights. To reuse the weights of another layer (weight sharing) use the arguments
W
andb
an passT.TensorVariable
. IfW
andb
are numpy arrays own weights are initialised with these values.Parameters:  nof_filters: int
Number of feature maps
 filter_size: int/tuple
Size/shape of convolutional filters, xy/zxy, (scalars are automatically extended to the 2d or 3d)
 pool_shape: int/tuple
Size/shape of pool, xy/zxy, (scalars are automatically extended to the 2d or 3d)
 activation_func: string
{tanh, relu, sigmoid, abs, linear, maxout <i>} Activation function
 force_no_dropout: Bool
Set True for last/output layer
 use_fragment_pooling: Bool
Set to True for predicting dense images efficiently. Requires batch_size==1.
 reshape: Bool
Set to True to get 2d/3d output instead of flattened class_probabilities in the last layer
 is_last_layer: Bool
Shorthand for reshape=True, force_no_dropout=True and reconstruction of pooling fragments (if mfp was active)
 layer_input_shape: tuple of int
Only needed if layer_input is not not None
 layer_input: T.TensorVariable
Symbolic input if you do not want to use the previous layer of the cnn. This requires specification of the shape of that input with
layer_input_shape
. W: np.ndarray
 weight matrix. If array, the values are used to initialise a shared variable for this layer.
If TensorVariable, than this variable is directly used (weight sharing with the layer from which this variable comes from)
 b: np.ndarray or T.TensorVariable
 bias vector. If array, the values are used to initialise a shared variable for this layer.
If TensorVariable, than this variable is directly used (weight sharing with the layer from which this variable comes from)
 pooling_mode: str
‘max’ or ‘maxabs’ where the first is normal maxpooling and the second also retains sign of large negative values

addRecurrentLayer
(n_hid=None, activation_func='tanh', iterations=None)[source]¶ Adds a recurrent layer (only possible for nonimage input of format (batch, time, features))
Parameters:  n_hid: int
Number of hidden units
 activation_func: string
{tanh, relu, sigmoid, abs, linear}
 iterations: int
If layer input is not timelike (iterable on axis 1) it can be broadcasted and iterated over for a fixed number of iterations

addTiedAutoencoderChain
(n_layers=None, force_no_dropout=False, activation_func='tanh', input_noise=0.3, tie_W=True)[source]¶ Creates connected layers to invert Perceptron layers. Input is assumed to come from the first layer.
Parameters:  n_layers: int
Number of layers that will be added/inverted, (input < 0 means all)
 activation_func: string
{tanh, relu, sigmoid, abs, linear} Activation function
 force_no_dropout: Bool
set True for last/output layer
 input_noise: Bool
Noise rate that will be applied to the input of the first reconstructor
 tie_W: Bool
Whether to share weight of dual layer pairs

compileDebugFunctions
(gradients=True)[source]¶ Compiles the debug_functions which return the network activations / output. To use them compile them with this function. They by accessible as cnn.debug_functions (normal output), cnn.debug_conv_output, cnn.debug_gradients_function (if True).

compileOutputFunctions
(target='nll', use_class_weights=False, use_example_weights=False, use_lazy_labels=False, use_label_prop=False, only_forward=False)[source]¶ Compiles the output functions
get_loss
,get_error
,class_probabilities
and defines the gradient (which is not compiled)Parameters:  target: string
 ‘nll’/’regression’, regression has squared error and nll_masked allows training with
lazy labels; this requires the auxiliary (*aux) masks.
 use_class_weights: Bool
whether to use class weights for the error
 use_example_weights: Bool
whether to use example weights for the error
 use_lazy_labels: Bool
whether to use lazy labels; this requires the auxiliary (*aux) masks
 use_label_prop: Bool
whether to activate label propagation on unlabelled (1) examples
 only_forward: Bool
This exlcudes the building of the gradient (faster)
 Defined functions:
 (They are accessible as methods of ``MixedConvNN``)
 get_loss: theanofunction
[data, labels(, *aux)] –> [loss, loss_instance]
 get_error: theanofunction
[data, labels(, *aux)] –> [loss, (error,) prediction] no error for regression
 class_probabilities: theanofunction
[data] –> [prediction]

randomizeWeights
(reset_momenta=True)[source]¶ Resets weights to random values (calls randomize_weights() on each layer)

trainingStep
(*args, **kwargs)[source]¶ Perform one optimiser iteration. Optimizers can be chosen by the kwarg
mode
. They are complied on demand (which may take a while) and cachedSignature: cnn.trainingStep(data, label(, *aux)(,**kwargs))
Parameters:  data: float32 array
input [bs, ch (, x, y)] or [bs, z, ch, x, y]
 labels: int16 array
[bs,((z,)y,x)] if output is not flattened
 aux: int16 arrays
(optional) auxiliary weights/masks/etc. Should be unpacked list
 kwargs:
 mode: string
[‘SGD’]: (default) Good if data set is big and redundant
‘RPROP’: which does neither uses a fix learning rate nor the momentumvalue. It is faster than SGD if you do fullbatch Training and use NO dropout. Any source of noise leads to failure of convergence (at all).
‘CG’: Good generalisation but requires large batches. Returns current loss always
‘LBFGS’: http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin_l_bfgs_b.html
 update_loss: Bool
 determine current loss after update step (e.g. needed for queue, but
get_loss
can also be called explicitly)
Returns:  loss: float32
loss (nll or squared error)
 loss_instance: float32 array
loss for individual batch examples/pixels
 time_per_step: float
Time spent on the GPU per step

setOptimizerParams
(SGD={}, CG={}, RPROP={}, LBFGS={}, Adam={}, weight_decay=0.0)[source]¶ Initialise optimiser hyperparameters prior to compilation. SGD, CG and LBFGS this can also be done during Training.
weight_decay
is global to all optimisers and is identical to a L2penalty on the weights with the coefficient given byweight_decay

setDropoutRates
(rates)[source]¶ Assumes a vector/list/array as input, first entry <–> first layer (etc.)

predictDense
(raw_img, show_progress=True, offset=None, as_uint8=False, pad_raw=False)[source]¶ Core function that performs the inference
 raw_img : np.ndarray
 raw data in the format (ch, x, y(, z))
 show_progress: Bool
 Whether to print progress state
 offset: 2/3tuple
 If the cnn has no dimension calculator object, this specifies the cnn offset.
 as_uint8: Bool
 Return class proabilites as uint8 image (scaled between 0 and 255!)
 pad_raw: Bool
 Whether to apply padding (by mirroring) to the raw input image in order to get predictions on the full imgae domain.

saveParameters
(path='CNN.save', layers=None, show=True)[source]¶ Saves parameters to file, that can be loaded by
loadParameters

loadParameters
(myfile='CNN.save', strict=False, n_layers_to_load=1)[source]¶ Loads parameters from file created by
saveParameters
. The parameter shapes do not need to fit the CNN architecture, they “squeezed” or “padded” to fit.Additionally the momenta of the gradients are reset
Parameters:  myfile: string
Path to file
 strict: bool
If true, parameter shapes must fit exactly, this the only way to load RNN parameters
 n_layers_to_load: int
Only the first x layers are initialised if this is not at its default value (1)
elektronn.net.gaborfilters module¶
Supplementary functions to initialise CNNparams with gabor filters

elektronn.net.gaborfilters.
makeGabor
(filter_angle, n_modes, size, offset)[source]¶ Parameters:  filter_angle: in degree: 0 to 180
 n_modes = 1,2,3 etc.
 size: filter size
 offset: 0 to 180
elektronn.net.introspection module¶
Supplementary functions to plot various CNN states

elektronn.net.introspection.
plotFilters
(cnn, layer=0, channel=None, normalize=False, savename='filters_layer0.png')[source]¶

elektronn.net.introspection.
showActivations
(cnn, data, show_first_class_prob=False, no_show=False)[source]¶ Plots activation maps given data. It requires that cnn.debug_functions contains a list of functions that return the activations (i.e. cnn.compileDebugFunctions must have been called
Parameters:  cnn:
instance of MixedConvNN
 data:
input to cnn for which activations should be shown
 show_first_class_prob:
True/False whether to additionally show the probability map for the first class
 no_show:
True/False whether to pop up plots or silently return a list of image arrays

elektronn.net.introspection.
showParamHistogram
(cnn, no_show=False, onlyW=True)[source]¶ Plots histograms of parameter/weight values.
Parameters:  :type no_show: object
 cnn:
instance of MixedConvNN
 onlyW:
True/False whether to ignore the biases b
 no_show:
True/False whether to pop up plots or silently return a list of image arrays

elektronn.net.introspection.
showActivityHistogram
(cnn, data, no_show=False)[source]¶ Plots histograms of activation maps given data. It requires that cnn.debug_functions contains a list of functions that return the activations (i.e. cnn.compileDebugFunctions must have been called
Parameters:  cnn:
instance of MixedConvNN
 data:
input to cnn for which activations should be shown
 no_show:
True/False whether to pop up plots or silently return a list of image arrays
elektronn.net.netcreation module¶

elektronn.net.netcreation.
createNet
(config, input_size, n_ch, n_lab, dimension_calc)[source]¶ Creates CNN according to config
Parameters:  n_ch: int
Number of input channels in data
 n_lab: int
Number of labels/classes/output_neurons
 param_file: string/path
Optional file to initialise parameters of CNN from
Returns:  CNNObject

elektronn.net.netcreation.
createNetfromParams
(param_file, patch_size, batch_size=1, activation_func='tanh', poolings=None, MFP=None, only_prediction=False)[source]¶ Convenience function to create CNN without
config
directly from a saved parameter file. Therefore this function only allows restricted configuration and does not initialise the training optimisers.Parameters:  param_file: string/path
File to initialise parameters of CNN from. The file must contain a list of shapes of the Wparameters as first entry and should ideally contain a list of pooling factors as last entry, alternatively the can be given as optional argument
 patch_size: tuple of int
Patch size for input data
 batch_size: int
Number of input patches
 activation_func: string
Activation function to use for all layers
 poolings: list of int
Pooling factors per layer (if not included in the parameter file)
 MFP: list of bool/{0,1}
Whether to use MFP in the respective layers
 only_prediction: Bool
This excludes the building of the gradient (faster)
Returns:  CNNObject
elektronn.net.netutils module¶

elektronn.net.netutils.
CNNCalculator
(filters, poolings, desired_input=None, MFP=False, force_center=False, desired_output=None, n_dim=1)[source]¶ Helper to calculate CNN architectures
This is a function, but it returns an object that has various architecture values as attributes. Useful is also to simply print ‘d’ as in the example.
Parameters:  filters: list
Filter shapes (for anisotropic filters the shapes are again a list)
 poolings: list
Pooling factors
 desired_input: int or list of int
Desired input size(s). If
None
a range of suggestions can be found in the attributevalid_inputs
 MFP: list of int/{0,1}
Whether to apply MaxFragmentPooling in this layer and check compliance with maxfragmentpooling (requires other input sizes than normal pooling)
 force_center: Bool
Check if output neurons/pixel lie at center of input neurons/pixel (and not in between)
 desired_output: int or list of int
Alternative to
desired_input
 n_dim: int
Dimensionality of CNN
Examples
Calculation for anisotropic “flat” 3d CNN with MFP in the first layers only:
>>> desired_input = [211, 211, 20] >>> filters = [[6,6,1], [4,4,4], [2,2,2], [1,1,1]] >>> pool = [[2,2,1], [2,2,2], [2,2,2], [1,1,1]] >>> MFP = [1, 1, 0, 0, ] >>> n_dim=3 >>> d = CNNCalculator(filters, pool, desired_input, MFP=MFP, force_center=True, desired_output=None, n_dim=n_dim) Info: input (211) changed to (210) (size not possible) Info: input (211) changed to (210) (size not possible) Info: input (20) changed to (22) (size too small) >>> print d Input: [210, 210, 22] Layer/Fragment sizes: [[102, 49, 24, 24], [102, 49, 24, 24], [22, 9, 4, 4]] Unpooled Layer sizes: [[205, 99, 48, 24], [205, 99, 48, 24], [22, 19, 8, 4]] Receptive fields: [[7, 15, 23, 23], [7, 15, 23, 23], [1, 5, 9, 9]] Strides: [[2, 4, 8, 8], [2, 4, 8, 8], [1, 2, 4, 4]] Overlap: [[5, 11, 15, 15], [5, 11, 15, 15], [0, 3, 5, 5]] Offset: [11.5, 11.5, 4.5]. If offset is nonint: floor(offset). Select labels from within img[offsetx:offset+x] (nonint means, output neurons lie centered on input neurons, i.e. they have an odd field of view)
elektronn.net.optimizer module¶

class
elektronn.net.optimizer.
Optimizer
(model_obj=None, X=None, Y=None, Y_aux=[], top_loss=None, params=None)[source]¶ Bases:
object
Optimizer Base Object, initialises generic optimizer variables
Parameters:  model_obj: cnnobject
Encapsulation of theano model (instead of giving X,Y etc. manually), all other arguments are retrieved from this object if they are
None
. If an argument is notNone
it will override the value from the model X: symbolic input variable
Data
 Y: symbolic output variable
Target
 Y_aux: symbolic output variable
Auxiliary masks/weights/etc. for the loss, type: list!
 top_loss: symbolic loss function:
Requires (X, Y (,*Y_aux)) for compilation
 params: list of shared variables
List of parameter arrays against which the loss is optimised
Returns:  Callable optimizer object: loss = Optimizer(X, Y (,*Y_aux)) performs one iteration

class
elektronn.net.optimizer.
compileSGD
(optimizer_params, model_obj=None, X=None, Y=None, Y_aux=[], top_loss=None, params=None)[source]¶ Bases:
elektronn.net.optimizer.Optimizer
Stochastic Gradient Descent

class
elektronn.net.optimizer.
compileAdam
(optimizer_params, model_obj=None, X=None, Y=None, Y_aux=[], top_loss=None, params=None)[source]¶ Bases:
elektronn.net.optimizer.Optimizer
Stochastic Gradient Descent

class
elektronn.net.optimizer.
compileRPROP
(optimizer_params, model_obj=None, X=None, Y=None, Y_aux=[], top_loss=None, params=None)[source]¶ Bases:
elektronn.net.optimizer.Optimizer
Resilient backPROPagation

class
elektronn.net.optimizer.
compileCG
(optimizer_params, model_obj=None, X=None, Y=None, Y_aux=[], top_loss=None, params=None)[source]¶ Bases:
elektronn.net.optimizer.Optimizer
Conjugate Gradient

class
elektronn.net.optimizer.
compileLBFGS
(optimizer_params, model_obj=None, X=None, Y=None, Y_aux=[], top_loss=None, params=None, debug=False)[source]¶ Bases:
elektronn.net.optimizer.Optimizer
LBFGS (fast, fullbatch method)
 References (cite one):
R. H. Byrd, P. Lu and J. Nocedal. A Limited Memory Algorithm for Bound Constrained Optimization, (1995), SIAM Journal on Scientific and Statistical Computing, 16, 5, pp. 11901208.
C. Zhu, R. H. Byrd and J. Nocedal. LBFGSB: Algorithm 778: LBFGSB, FORTRAN routines for large scale bound constrained optimization (1997), ACM Transactions on Mathematical Software, 23, 4, pp. 550  560.
J.L. Morales and J. Nocedal. LBFGSB: Remark on Algorithm 778: LBFGSB, FORTRAN routines for large scale bound constrained optimization (2011), ACM Transactions on Mathematical Software, 38, 1.
elektronn.net.perceptronlayer module¶

class
elektronn.net.perceptronlayer.
PerceptronLayer
(input, n_in, n_out, batch_size, enable_dropout, activation_func='tanh', input_noise=None, input_layer=None, W=None, b=None)[source]¶ Bases:
object
Typical hidden layer of a MLP: units are fullyconnected. Weight matrix W is of shape (n_in,n_out), the bias vector b is of shape (n_out,).
Parameters:  input (theano.tensor.dmatrix) – a symbolic tensor of shape (n_examples, n_in)
 n_in (int) – dimensionality of input
 n_out (int) – number of hidden units
 batch_size (int) – batch_size
 enable_dropout (Bool) – whether to enable dropout in this layer. The default rate is 0.5 but it can be changed with self.activation_noise.set_value(set_value(np.float32(p)) or using cnn.setDropoutRates
 activation_func (string) – {‘relu’,’sigmoid’,’tanh’,’abs’, ‘maxout <i>’}
 input_noise (theano.shared float32) – std of gaussian (centered) input noise. 0 or None –> no noise
 input_layer (layer object) – just for keeping track of unusual input layers
 W (np.ndarray or T.TensorVariable) – weight matrix. If array, the values are used to initialise a shared variable for this layer. If TensorVariable, than this variable is directly used (weight sharing with the layer from which this variable comes from)
 b (np.ndarray or T.TensorVariable) – bias vector. If array, the values are used to initialise a shared variable for this layer. If TensorVariable, than this variable is directly used (weight sharing with the layer from which this variable comes from)

NLL
(y, class_weights=None, example_weights=None, label_prop_thresh=None)[source]¶ Returns the symbolic mean and instancewise negative loglikelihood of the prediction of this model under a given target distribution.
 y: theano.tensor.TensorType
 corresponds to a vector that gives for each example the correct label. Labels < 0 are ignored (e.g. can be used for label propagation)
 class_weights: theano.tensor.TensorType
 weight vector of float32 of length
n_lab
. Values:1.0
(default),w < 1.0
(less important),w > 1.0
(more important class)  label_prop_thresh: float (0.5,1)
 This threshold allows unsupervised label propagation (only for examples with negative/ignore labels). If the predictive probability of the most likely class exceeds the threshold, this class is assumed to be the correct label and the training is pushed in this direction. Should only be used with pretrained networks, and values <= 0.5 are disabled.

NLL_weak
(y, class_weights=None, example_weights=None, label_prop_thresh=None)[source]¶ Returns the symbolic mean and instancewise negative loglikelihood of the prediction of this model under a given target distribution.
 y: theano.tensor.TensorType
 corresponds to a vector that gives for each example the correct label. Labels < 0 are ignored (e.g. can be used for label propagation)
 class_weights: theano.tensor.TensorType
 weight vector of float32 of length
n_lab
. Values:1.0
(default),w < 1.0
(less important),w > 1.0
(more important class)  label_prop_thresh: float (0.5,1)
 This threshold allows unsupervised label propagation (only for examples with negative/ignore labels). If the predictive probability of the most likely class exceeds the threshold, this class is assumed to be the correct label and the training is pushed in this direction. Should only be used with pretrained networks, and values <= 0.5 are disabled.

nll_mutiple_binary
(y, class_weights=None)[source]¶ Returns the mean and instancewise negative loglikelihood of the prediction of this model under a given target distribution.
Parameters: y (theano.tensor.TensorType) – corresponds to a vector that gives for each example the correct label  Note: we use the mean instead of the sum so that
 the learning rate is less dependent on the batch size

squared_distance
(Target, Mask=None, return_instancewise=True)[source]¶ Target is the TARGET image (vectorized), > shape(x) = (batchsize, n_target) output: scalar float32 mask: vectorized, 1==hole, 0==no_hole (== DOES NOT TRAIN ON NONHOLES)

errors
(y)[source]¶ Returns classification accuracy
Parameters: y (theano.tensor.TensorType) – corresponds to a vector that gives for each example the correct label

class
elektronn.net.perceptronlayer.
RecurrentLayer
(input, n_in, n_hid, batch_size, activation_func='tanh')[source]¶ Bases:
object
Parameters:  input (symbolic input carrying [time, batch, feat]) – theano.tensor.ftensor3
 n_in (int) – dimensionality of input
 n_hid (int) – number of hidden units
 activation_func (string) – {‘relu’,’sigmoid’,’tanh’,’abs’}
elektronn.net.pooling module¶

elektronn.net.pooling.
my_max_pool_3d
(sym_input, pool_shape=(2, 2, 2))[source]¶ this one is pure theano. Hence all gradientrelated stuff is working! No dimshuffling

elektronn.net.pooling.
maxout
(conv_out, factor=2, mode='max', axis=1)[source]¶ Pools axis 1 (the channels) of
conv_out
byfactor
. I.e. the number of channels is decreased by this factor. The pooling can either be done asmax
ormaxabs
. Spatial dimensions are unchanged