from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class RandomFourierFeatures(ModelLayer):
"""
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 -- initialization options for weight parameter
b_init -- initialization options for bias parameter
"""
def __init__(
self,
model,
input_record,
output_dims,
sigma, # bandwidth
w_init=None,
b_init=None,
name='random_fourier_features',
**kwargs):
super(RandomFourierFeatures, self).__init__(model, name, input_record,
**kwargs)
assert isinstance(input_record, schema.Scalar), "Incorrect input type"
input_dims = input_record.field_type().shape[0]
assert input_dims >= 1, "Expected input dimensions >= 1, got %s" \
% input_dims
self.output_dims = output_dims
assert self.output_dims >= 1, "Expected output dimensions >= 1, got %s" \
% self.output_dims
self.output_schema = schema.Scalar(
(np.float32, (self.output_dims, )),
self.get_next_blob_reference('output')
)
assert sigma > 0.0, "Expected bandwidth > 0, got %s" % sigma
# Initialize train_init_net parameters
w_init = w_init if w_init else (
'GaussianFill', {'mean': 0.0, 'std': 1.0 / sigma}
)
b_init = b_init if b_init else (
'UniformFill', {'min': 0.0, 'max': 2 * np.pi}
)
self.w = self.create_param(param_name='w',
shape=[self.output_dims, input_dims],
initializer=w_init,
optimizer=model.NoOptim)
self.b = self.create_param(param_name='b',
shape=[self.output_dims],
initializer=b_init,
optimizer=model.NoOptim)
def add_ops(self, net):
# Random features: wx + b
cosine_arg = net.FC(self.input_record.field_blobs() + [self.w, self.b],
net.NextScopedBlob("cosine_arg"))
# Apply cosine to new vectors
new_feature_vec = net.Cos([cosine_arg],
net.NextScopedBlob('new_feature_vec'))
# Multiply each element in vector by sqrt(2/D)
scale = np.sqrt(2.0 / self.output_dims)
net.Scale([new_feature_vec],
self.output_schema.field_blobs(),
scale=scale)