Caffe2 - Python API
A deep learning, cross platform ML framework
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caffe2.python.layers.random_fourier_features.RandomFourierFeatures Class Reference
Inheritance diagram for caffe2.python.layers.random_fourier_features.RandomFourierFeatures:
caffe2.python.layers.layers.ModelLayer

Public Member Functions

def __init__ (self, model, input_record, output_dims, sigma, w_init=None, b_init=None, name='random_fourier_features', kwargs)
 
def add_ops (self, net)
 
- Public Member Functions inherited from caffe2.python.layers.layers.ModelLayer
def __init__ (self, model, prefix, input_record, predict_input_record_fields=None, tags=None, kwargs)
 
def get_type (self)
 
def predict_input_record (self)
 
def input_record (self)
 
def predict_output_schema (self)
 
def predict_output_schema (self, output_schema)
 
def output_schema (self)
 
def output_schema (self, output_schema)
 
def get_parameters (self)
 
def get_fp16_compatible_parameters (self)
 
def get_memory_usage (self)
 
def add_init_params (self, init_net)
 
def create_param (self, param_name, shape, initializer, optimizer, ps_param=None, regularizer=None)
 
def get_next_blob_reference (self, name)
 
def add_operators (self, net, init_net=None, context=InstantiationContext.TRAINING)
 
def add_ops (self, net)
 
def add_eval_ops (self, net)
 
def add_train_ops (self, net)
 
def add_ops_to_accumulate_pred (self, net)
 
def add_param_copy_operators (self, net)
 
def export_output_for_metrics (self)
 
def export_params_for_metrics (self)
 

Public Attributes

 output_dims
 
 output_schema
 
 w
 
 b
 
- Public Attributes inherited from caffe2.python.layers.layers.ModelLayer
 name
 
 model
 
 kwargs
 
 request_only
 
 eval_output_schema
 
 tags
 
 params
 

Detailed Description

Implementation of random fourier feature map for feature processing.

Applies sqrt(2 / output_dims) * cos(wx+b), where:
    output_dims is the output feature dimensions, and
    wx + b applies FC using randomized, fixed weight and bias parameters

For more information, see the original paper:
    https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf

Inputs:
    output_dims -- output feature dimensions
    sigma -- bandwidth for the Gaussian kernel estimator
    w_init -- initalization options for weight parameter
    b_init -- initalization options for bias parameter

Definition at line 27 of file random_fourier_features.py.


The documentation for this class was generated from the following file: