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vardelay_utils.py
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vardelay_utils.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 2 11:19:34 2017 @author: emin
"""
import numpy as np
import generators, models
import torch
def build_generators(t_ind):
if t_ind==0:
generator = generators.VarDelayedEstimationTask(max_iter=25001, batch_size=50, n_loc=1, n_in=50, n_out=1,
stim_dur=25, max_delay=100, resp_dur=25, kappa=2.0,
spon_rate=0.1, tr_cond='all_gains')
test_generator = generators.VarDelayedEstimationTask(max_iter=2501, batch_size=50, n_loc=1, n_in=50, n_out=1,
stim_dur=25, max_delay=100, resp_dur=25, kappa=2.0,
spon_rate=0.1, tr_cond='all_gains')
elif t_ind==1:
generator = generators.VarDelayedEstimationTask(max_iter=25001, batch_size=50, n_loc=2, n_in=50, n_out=2,
stim_dur=25, max_delay=100, resp_dur=25, kappa=2.0,
spon_rate=0.1, tr_cond='all_gains')
test_generator = generators.VarDelayedEstimationTask(max_iter=2501, batch_size=50, n_loc=2, n_in=50, n_out=2,
stim_dur=25, max_delay=100, resp_dur=25, kappa=2.0,
spon_rate=0.1, tr_cond='all_gains')
elif t_ind==2:
generator = generators.VarChangeDetectionTask(max_iter=25001, batch_size=50, n_loc=1, n_in=50, n_out=1,
stim_dur=25, max_delay=100, resp_dur=25, kappa=2.0,
spon_rate=0.1, tr_cond='all_gains')
test_generator = generators.VarChangeDetectionTask(max_iter=2501, batch_size=50, n_loc=1, n_in=50, n_out=1,
stim_dur=25, max_delay=100, resp_dur=25, kappa=2.0,
spon_rate=0.1, tr_cond='all_gains')
elif t_ind==4:
generator = generators.VarGatedDelayedEstimationTask(max_iter=25001, batch_size=50, n_loc=2, n_in=50, n_out=1,
stim_dur=25, max_delay=100, resp_dur=25, kappa=2.0,
spon_rate=0.1, tr_cond='all_gains')
test_generator = generators.VarGatedDelayedEstimationTask(max_iter=2501, batch_size=50, n_loc=2, n_in=50, n_out=1,
stim_dur=25, max_delay=100, resp_dur=25, kappa=2.0,
spon_rate=0.1, tr_cond='all_gains')
elif t_ind==6:
generator = generators.VarHarvey2012(max_iter=25001, batch_size=50, n_in=50, n_out=1, stim_dur=25, max_delay=100,
resp_dur=25, sigtc=15.0, stim_rate=1.0, spon_rate=0.1)
test_generator = generators.VarHarvey2012(max_iter=2501, batch_size=50, n_in=50, n_out=1, stim_dur=25, max_delay=100,
resp_dur=25, sigtc=15.0, stim_rate=1.0, spon_rate=0.1)
elif t_ind==8:
generator = generators.VarComparisonTask(max_iter=25001, batch_size=50, n_loc=1, n_in=50, n_out=1, stim_dur=25,
max_delay=100, resp_dur=25, sig_tc=10.0, spon_rate=0.1, tr_cond='all_gains')
test_generator = generators.VarComparisonTask(max_iter=2501, batch_size=50, n_loc=1, n_in=50, n_out=1, stim_dur=25,
max_delay=100, resp_dur=25, sig_tc=10.0, spon_rate=0.1, tr_cond='all_gains')
return generator, test_generator
def build_loss(pred_var, target_var, resp_dur, t_ind):
if t_ind==0 or t_ind==1 or t_ind==4:
loss = torch.fmod(torch.abs(pred_var[:,-resp_dur:,:] - target_var[:,-resp_dur:,:]), np.pi).mean()
elif t_ind==2 or t_ind==6 or t_ind==8:
loss = torch.nn.functional.F.binary_cross_entropy(pred_var[:, -resp_dur:, -1], target_var[:, -resp_dur:, -1]).mean()
return loss
def build_performance(s_vec, opt_vec, net_vec, t_ind):
if t_ind==0 or t_ind==1 or t_ind==4:
rmse_opt = np.nanmean(np.mod(np.abs(s_vec - opt_s_vec), np.pi))
rmse_net = np.nanmean(np.mod(np.abs(np.squeeze(s_vec) - np.squeeze(ex_pred_vec)), np.pi))
performance = (rmse_net - rmse_opt) / rmse_opt
elif t_ind==2 or t_ind==6 or t_ind==8:
performance = np.nanmean(opt_vec * np.log(opt_vec/net_vec) + (1.0 - opt_vec) * np.log((1.0 - opt_vec)/(1.0 - net_vec)))\
/ np.nanmean(opt_vec * np.log(2.0*opt_vec) + (1.0-opt_vec) * np.log(2.0*(1.0-opt_vec)))
return performance