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#!/usr/bin/env python
# -*- coding: utf-8 -*-
from theano import tensor as T
import deepy.tensor as DT
from . import NeuralNetwork
from deepy.utils import dim_to_var
class NeuralRegressor(NeuralNetwork):
"""
A class of defining stacked neural network regressors.
def __init__(self, input_dim, target_tensor=2, clip_value=None, input_tensor=None):
self.target_tensor = dim_to_var(target_tensor, "k") if type(target_tensor) == int else target_tensor
self.clip_value = clip_value
super(NeuralRegressor, self).__init__(input_dim, input_tensor=input_tensor)
def setup_variables(self):
super(NeuralRegressor, self).setup_variables()
self.k = self.target_tensor
self.target_variables.append(self.k)
def _cost_func(self, y):
if self.clip_value:
y = T.clip(y, -self.clip_value, self.clip_value)
return DT.costs.least_squares(y, self.k)
@property
def cost(self):
return self._cost_func(self.output)
def test_cost(self):
return self._cost_func(self.test_output)
zomux /
deepy
