elektronn2.neuromancer.various module

class elektronn2.neuromancer.various.GaussianRV(**kwargs)[source]

Bases: elektronn2.neuromancer.node_basic.Node

Parameters:
  • mu (node) – Mean of the Gaussian density
  • sig (node) – Sigma of the Gaussian density
  • n_samples (int) – Number of samples to be drawn per instance. Special case ‘0’: draw 1 sample but don’t’ increase rank of tensor!
  • output is a sample from separable Gaussians of given mean and (The) –
  • (but this operation is still differentiable, due to the (sigma) –
  • trick") ("re-parameterisation) –
  • output dimension mu.ndim+1 because the samples are accumulated along (The) –
  • new axis right of 'b' (batch) (a) –
make_priorlayer()[source]

Creates a new Layer that calculates the Auto-Encoding-Variation-Bayes (AEVB) prior corresponding to this Layer.

elektronn2.neuromancer.various.SkelLoss(pred, loss_kwargs, skel=None, name='skel_loss', print_repr=True)[source]
class elektronn2.neuromancer.various.SkelPrior(**kwargs)[source]

Bases: elektronn2.neuromancer.node_basic.Node

pred must be a vector of shape [(1,b),(3,f)] or [(3,f)] i.e. only batch_size=1 is supported.

Parameters:
  • pred
  • target_length
  • prior_n
  • prior_posz
  • prior_z
  • prior_xy
  • name
  • print_repr
elektronn2.neuromancer.various.Scan(step_result, in_memory, out_memory=None, in_iterate=None, in_iterate_0=None, n_steps=None, unroll_scan=True, last_only=False, name='scan', print_repr=True)[source]
Parameters:
  • step_result (node/list(nodes)) – nodes that represent results of step function
  • in_memory (node/list(nodes)) – nodes that indicate at which place in the computational graph the memory is feed back into the step function. If out_memory is not specified this must contain a node for every node in step_result because then the whole result will be fed back.
  • out_memory (node/list(nodes)) – (optional) must be subset of step_result and of same length as in_memory, tells which nodes of the result are fed back to in_memory. If None, all are fed back.
  • in_iterate (node/list(nodes)) – nodes with a leading 'r' axis to be iterated over (e.g. time series of shape [(30,r),(100,b),(50,f)]). In every step a slice from the first axis is consumed.
  • in_iterate_0 (node/list(nodes)) – nodes that consume a single slice of the in_iterate nodes. Part of “the inner function” of the scan loop in contrast to in_iterate
  • n_steps (int) –
  • unroll_scan (bool) –
  • last_only (bool) –
  • name (str) –
  • print_repr (bool) –
Returns:

  • A node for every node in step_result which either contains the last
  • state or the series of states - then it has a leading 'r' axis.

elektronn2.neuromancer.various.SkelGetBatch(skel, aux, img_sh, t_img_sh, t_grid_sh, t_node_sh, get_batch_kwargs, scale_strenght=None, name='skel_batch')[source]
class elektronn2.neuromancer.various.SkelLossRec(**kwargs)[source]

Bases: elektronn2.neuromancer.node_basic.Node

pred must be a vector of shape [(1,b),(3,f)] or [(3,f)] i.e. only batch_size=1 is supported.

Parameters:
  • pred
  • skel
  • loss_kwargs
  • name
  • print_repr
class elektronn2.neuromancer.various.Reshape(**kwargs)[source]

Bases: elektronn2.neuromancer.node_basic.Node

Reshape node.

Parameters:
  • parent
  • shape
  • tags
  • strides
  • fov
  • name
  • print_repr
elektronn2.neuromancer.various.SkelGridUpdate(grid, skel, radius, bio, name='skelgridupdate', print_repr=True)[source]