47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
|
# -*- coding: utf-8 -*-
|
||
|
|
"""Error functions.
|
||
|
|
|
||
|
|
This module defines error functions, with the aim of model training being to
|
||
|
|
minimise the error function given a set of inputs and target outputs.
|
||
|
|
|
||
|
|
The error functions will typically measure some concept of distance between the
|
||
|
|
model outputs and target outputs, averaged over all data points in the data set
|
||
|
|
or batch.
|
||
|
|
"""
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
|
||
|
|
class SumOfSquaredDiffsError(object):
|
||
|
|
"""Sum of squared differences (squared Euclidean distance) error."""
|
||
|
|
|
||
|
|
def __call__(self, outputs, targets):
|
||
|
|
"""Calculates error function given a batch of outputs and targets.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
outputs: Array of model outputs of shape (batch_size, output_dim).
|
||
|
|
targets: Array of target outputs of shape (batch_size, output_dim).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Scalar error function value.
|
||
|
|
"""
|
||
|
|
#TODO write your code here
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def grad(self, outputs, targets):
|
||
|
|
"""Calculates gradient of error function with respect to outputs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
outputs: Array of model outputs of shape (batch_size, output_dim).
|
||
|
|
targets: Array of target outputs of shape (batch_size, output_dim).
|
||
|
|
|
||
|
|
Returns:
|
||
|
|
Gradient of error function with respect to outputs. This should be
|
||
|
|
an array of shape (batch_size, output_dim).
|
||
|
|
"""
|
||
|
|
#TODO write your code here
|
||
|
|
raise NotImplementedError()
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
return 'SumOfSquaredDiffsError'
|