elektronn2.neuromancer.graphutils module

class elektronn2.neuromancer.graphutils.TaggedShape(shape, tags, strides=None, mfp_offsets=None, fov=None)

Bases: object

Object to manage shape and associated tags uniformly. The []-operator can be used get shape values by either index (int) or tag (str)

Parameters:

• shape (list/tuple of int) – shape of array, unspecified shapes are None
• tags (list/tuple of strings or comma-separated string) – tags indicate which purpose the dimensions of the tensor serve. They are sometimes used to decide about reshapes. The maximal tensor has tags: “r, b, s, f, z, y, x, s” which denote: * r: perform recurrence along this axis * b: batch size * s: samples of the same instance (over which expectations are calculated) * f: features, filters, channels * z: convolution no. 3 (slower than 1,2) * y: convolution no. 1 * x: convolution no. 2
• strides – list of strides, only for spatial dimensions, so it is 1-3 long
• mfp_offsets –

addaxis(axis, size, tag)

Create new TaggedShape with new axis inserted at axis of size size tagged tag. If axis is a tag, the new axis is right of that tag

copy()

delaxis(axis)

Create new TaggedShape with new axis inserted at axis of size size tagged tag. If axis is a tag, the new axis is right of that tag

ext_repr

fov

fov_all_centered

hastag(tag)

mfp_offsets

ndim

offsets

shape

spatial_axes

spatial_shape

spatial_size

strides

stripbatch_prod

Calculate product excluding batch dimension

stripnone

Return the shape but with all None elements removed (e.g. if batch size is unspecified)

stripnone_prod

Return the product of the shape but with all None elements removed (e.g. if batch size is unspecified)

tag2index(target_tag)

Finds the index of the desired tag

tags

updatefov(axis, new_fov)

Create new TaggedShape with new_fov on axis. Axis is given as index of the spatial axes (not matching the absolute index of sh).

updatemfp_offsets(mfp_offsets)

updateshape(axis, new_size, mode=None)

Create new TaggedShape with new_size on axis . Modes for updating: None (override), 'add', 'mult'

updatestrides(strides)

elektronn2.neuromancer.graphutils.as_floatX(x)

class elektronn2.neuromancer.graphutils.make_func(tt_input, tt_output, updates=None, name='Unnamed Function', borrow_inp=False, borrow_out=False, profile_execution=False)

Bases: object

Wrapper for compiled theano functions. Features:

• The function is compiled on demand (i.e. no wait at initialisation)
• Singleton return values are returned directly, multiple values as list
• The last execution time can inspected in the attribute last_exec_time
• Functions can be timed: profile_execution is an int that specifies the number of runs to average. The average time is printed then.
• In/Out values can have a borrow flag which might overwrite the numpy arrays but might speed up execution (see theano doc)

compile(profile=False)

elektronn2.neuromancer.graphutils.getinput_for_multioutput(outputs)

For list of several output layers return a list of required input tensors (without duplicates) to compute these outputs.