# neon.layers.layer.LRN¶

class neon.layers.layer.LRN(depth, alpha=1.0, beta=0.0, ascale=1.0, bpower=1.0, name=None)[source]

Local Response Normalization layer. This layer normalizes the output of each pixel/element across channels using the formula:

$output(h,w)_j = \frac{output(h,w)_j}{(1 + (ascale/N) \sum{x(h,w)_i^2})^{bpower}}$

$$x(h,w)_i$$ is the input element at coordinate $$(h,w)$$ of the i-th feature map, $$output(h,w)_j$$ is the corresponding normalized output and the sum is taken over $$i$$ in the range $$[j - (depth-1)/2, j + (depth-1)/2]$$

Parameters: depth (int) – the number of neighboring feature maps to include in the normalization, depth must be odd and (depth-1)/2 neighbors are included from each side, zeros are added as needed ascale (float) – the normalization scaling factor (see equation above) bpower (float) – the normalization exponent (see equation above) name (str) – layer name
__init__(depth, alpha=1.0, beta=0.0, ascale=1.0, bpower=1.0, name=None)[source]

Methods

 __init__(depth[, alpha, beta, ascale, …]) accumulates(f) Higher order decorator function that enables accumulation functionality for that function. allocate([shared_outputs]) Allocate output buffer to store activations from fprop. allocate_deltas(global_deltas) bprop(error) Apply the backward pass transformation to the input data. configure(in_obj) Sets shape based parameters of this layer given an input tuple or int or input layer. fprop(inputs[, inference]) Apply the forward pass transformation to the input data. gen_class(pdict) get_description(**kwargs) get_is_mklop() is_mklop true means this op is on mkl backend get_param_attrs() get_terminal() Used for recursively getting final nodes from layer containers. load_weights(pdict[, load_states]) Load weights. nested_str([level]) Utility function for displaying layer info with a given indentation level. recursive_gen(pdict, key) helper method to check whether the definition serialize() Get state parameters for this layer. set_acc_on(acc_on) Set the acc_on flag according to bool argument. set_batch_size(N) Set minibatch size. set_deltas(delta_buffers) Use pre-allocated (by layer containers) list of buffers for backpropagated error. set_is_mklop() set_next(layer) Set next_layer to provided layer. set_not_mklop() set_params(pdict) set_seq_len(S) Set sequence length. set_states(pdict)
accumulates(f)

Higher order decorator function that enables accumulation functionality for that function. Object that use this decorator are required to have an acc_param attribute. This attribute tuple declares the names for existing temp parameter and real parameter buffers. The temp parameter buffer copies the value of the parameter buffer before f is called, and after f is called the temp and normal buffers are summed. This decorator could be used to wrap any function that may want to accumulate parameters instead of overwriting.

allocate(shared_outputs=None)[source]

Allocate output buffer to store activations from fprop.

Parameters: shared_outputs (Tensor, optional) – pre-allocated tensor for activations to be computed into
allocate_deltas(global_deltas)
be = None
bprop(error)[source]

Apply the backward pass transformation to the input data.

Parameters: error (Tensor) – deltas back propagated from the adjacent higher layer deltas to propagate to the adjacent lower layer Tensor
classnm

Returns the class name.

configure(in_obj)[source]

Sets shape based parameters of this layer given an input tuple or int or input layer.

Parameters: in_obj (int, tuple, Layer or Tensor) – object that provides shape information for layer shape of output data (tuple)
fprop(inputs, inference=False)[source]

Apply the forward pass transformation to the input data.

Parameters: inputs (Tensor) – input data inference (bool) – is inference only output data Tensor
gen_class(pdict)
get_description(**kwargs)
get_is_mklop()

is_mklop true means this op is on mkl backend and may require convert when from non-mkl op

get_param_attrs()
get_terminal()

Used for recursively getting final nodes from layer containers.

load_weights(pdict, load_states=True)

Parameters: pdict – load_states – (Default value = True)
modulenm

Returns the full module path.

nested_str(level=0)

Utility function for displaying layer info with a given indentation level.

Parameters: level (int, optional) – indentation level layer info at the given indentation level str
recursive_gen(pdict, key)

helper method to check whether the definition dictionary is defining a NervanaObject child, if so it will instantiate that object and replace the dictionary element with an instance of that object

serialize()

Get state parameters for this layer.

Returns: whatever data this model wants to receive in order to restore state varies
set_acc_on(acc_on)

Set the acc_on flag according to bool argument. If set to true, the layer will accumulate some (preset) parameters on calls to functions that are decorated with the accumulates decorator. In order to use this feature, accumulate_updates=True must have been passed to the layer’s allocate function

This currently only works for a few hard coded parameters in select layers

Parameters: acc_on (bool) – Value to set the acc_on flag to.
set_batch_size(N)

Set minibatch size.

Parameters: N (int) – minibatch size
set_deltas(delta_buffers)

Use pre-allocated (by layer containers) list of buffers for backpropagated error. Only set deltas for layers that own their own deltas Only allocate space if layer owns its own deltas (e.g., bias and activation work in-place, so do not own their deltas).

Parameters: delta_buffers (DeltasTree) – list of pre-allocated tensors (provided by layer container)
set_is_mklop()
set_next(layer)

Set next_layer to provided layer.

Parameters: layer (layer) – Next layer
set_not_mklop()
set_params(pdict)
set_seq_len(S)

Set sequence length.

Parameters: S (int) – sequence length
set_states(pdict)