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dynamic_input_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 theano.tensor as T
import numpy as np
import lasagne.layers
import lasagne.nonlinearities
import lasagne.updates
import lasagne.objectives
import lasagne.init
import dynamic_input_generators, models
def build_generators(ExptDict):
# Unpack common variables
task = ExptDict["task"]["task_id"]
n_loc = ExptDict["task"]["n_loc"]
n_out = ExptDict["task"]["n_out"]
tr_cond = ExptDict["tr_cond"]
test_cond = ExptDict["test_cond"]
n_in = ExptDict["n_in"]
batch_size = ExptDict["batch_size"]
stim_dur = ExptDict["stim_dur"]
delay_dur = ExptDict["delay_dur"]
resp_dur = ExptDict["resp_dur"]
kappa = ExptDict["kappa"]
spon_rate = ExptDict["spon_rate"]
tr_max_iter = ExptDict["tr_max_iter"]
test_max_iter = ExptDict["test_max_iter"]
if task == 'DE1':
generator = dynamic_input_generators.DelayedEstimationTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur, kappa=kappa,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = dynamic_input_generators.DelayedEstimationTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'DE2':
generator = dynamic_input_generators.DelayedEstimationTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur, kappa=kappa,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = dynamic_input_generators.DelayedEstimationTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'CD1':
generator = dynamic_input_generators.ChangeDetectionTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur, kappa=kappa,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = dynamic_input_generators.ChangeDetectionTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'COMP':
generator = dynamic_input_generators.ComparisonTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur, sig_tc=10.0,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = dynamic_input_generators.ComparisonTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
sig_tc=10.0,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'CD2':
generator = dynamic_input_generators.ChangeDetectionTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur, kappa=kappa,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = dynamic_input_generators.ChangeDetectionTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'GDE2':
generator = dynamic_input_generators.GatedDelayedEstimationTask(max_iter=tr_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=tr_cond)
test_generator = dynamic_input_generators.GatedDelayedEstimationTask(max_iter=test_max_iter,
batch_size=batch_size,
n_loc=n_loc, n_in=n_in,
n_out=n_out,
stim_dur=stim_dur,
delay_dur=delay_dur,
resp_dur=resp_dur,
kappa=kappa,
spon_rate=spon_rate,
tr_cond=test_cond)
elif task == 'Harvey2012':
sigtc = ExptDict["task"]["sigtc"]
stim_rate = ExptDict["task"]["stim_rate"]
generator = dynamic_input_generators.Harvey2012(max_iter=tr_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur, resp_dur=resp_dur,
sigtc=sigtc, stim_rate=stim_rate, spon_rate=spon_rate)
test_generator = dynamic_input_generators.Harvey2012(max_iter=test_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur, resp_dur=resp_dur,
sigtc=sigtc, stim_rate=stim_rate, spon_rate=spon_rate)
elif task == 'Harvey2016':
sigtc = ExptDict["task"]["sigtc"]
stim_rate = ExptDict["task"]["stim_rate"]
epoch_dur = ExptDict["task"]["epoch_dur"]
n_epochs = ExptDict["task"]["n_epochs"]
generator = dynamic_input_generators.Harvey2016(max_iter=tr_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, n_epochs=n_epochs,
epoch_dur=epoch_dur, sigtc=sigtc,
stim_rate=stim_rate, spon_rate=spon_rate)
test_generator = dynamic_input_generators.Harvey2016(max_iter=test_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, n_epochs=n_epochs,
epoch_dur=epoch_dur, sigtc=sigtc,
stim_rate=stim_rate, spon_rate=spon_rate)
elif task == 'SINE':
alpha = ExptDict["task"]["alpha"]
generator = dynamic_input_generators.SineTask(max_iter=tr_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur, resp_dur=resp_dur, alpha=alpha)
test_generator = dynamic_input_generators.SineTask(max_iter=test_max_iter, batch_size=batch_size,
n_in=n_in, n_out=n_out, stim_dur=stim_dur,
delay_dur=delay_dur, resp_dur=resp_dur, alpha=alpha)
return generator, test_generator
def build_model(input_var,ExptDict):
# Unpack necessary variables
model = ExptDict["model"]["model_id"]
n_loc = ExptDict["task"]["n_loc"]
n_out = ExptDict["task"]["n_out"]
batch_size = ExptDict["batch_size"]
n_in = ExptDict["n_in"]
n_hid = ExptDict["n_hid"]
out_nonlin = ExptDict["task"]["out_nonlin"]
if model == 'LeInitRecurrent':
diag_val = ExptDict["model"]["diag_val"]
offdiag_val = ExptDict["model"]["offdiag_val"]
l_out, l_rec = models.LeInitRecurrent(input_var, batch_size=batch_size,
n_in=(n_loc+1)*n_in, n_out=n_out,
n_hid=n_hid, diag_val=diag_val,
offdiag_val=offdiag_val,
out_nlin=out_nonlin)
elif model == 'OrthoInitRecurrent':
init_val = ExptDict["model"]["init_val"]
l_out, l_rec = models.OrthoInitRecurrent(input_var, batch_size=batch_size,
n_in=(n_loc+1)*n_in, n_out=n_out,
n_hid=n_hid, init_val=init_val,
out_nlin=out_nonlin)
elif model == 'GRURecurrent':
diag_val = ExptDict["model"]["diag_val"]
offdiag_val = ExptDict["model"]["offdiag_val"]
l_out, l_rec = models.GRURecurrent(input_var, batch_size=batch_size,
n_in=(n_loc+1)*n_in, n_out=n_out, n_hid=n_hid,
diag_val=diag_val, offdiag_val=offdiag_val,
out_nlin=out_nonlin)
return l_out, l_rec
def build_loss(pred_var,target_var,ExptDict):
# Unpack necessary variables
task = ExptDict["task"]["task_id"]
resp_dur = ExptDict["resp_dur"]
if task in ['DE1','DE2','GDE2']:
loss = T.mean(T.mod(T.abs_(pred_var[:,-resp_dur:,:] - target_var[:,-resp_dur:,:]), np.pi))
elif task in ['CD1','CD2','Harvey2012','Harvey2016','COMP']:
loss = T.mean(lasagne.objectives.binary_crossentropy(pred_var[:,-resp_dur:,-1], target_var[:,-resp_dur:,-1]))
elif task in ['SINE']:
loss = T.mean(T.abs_(pred_var[:,-resp_dur:,:] - target_var[:,-resp_dur:,:]))
return loss
def build_performance(s_vec,opt_vec,net_vec,ExptDict):
# Unpack necessary variables
task = ExptDict["task"]["task_id"]
if task in ['DE1','DE2','GDE2']:
rmse_opt = np.nanmean(np.mod(np.abs(np.squeeze(s_vec) - np.squeeze(opt_vec)), np.pi))
rmse_net = np.nanmean(np.mod(np.abs(np.squeeze(s_vec) - np.squeeze(net_vec)), np.pi))
infloss = (rmse_net - rmse_opt) / rmse_opt
elif task in ['CD1','CD2','Harvey2012','Harvey2016','COMP']:
infloss = 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)) )
elif task in ['SINE']:
infloss = np.mean(np.abs(np.squeeze(opt_vec) - np.squeeze(net_vec)))
return infloss