def main():
parser = NeonArgparser(__doc__)
args = parser.parse_args(gen_be=False)
#mat_data = sio.loadmat('../data/timeseries/02_timeseries.mat')
#ts = V1TimeSeries(mat_data['timeseries'], mat_data['stim'], binning=10)
seq_len = 30
hidden = 20
be = gen_backend(**extract_valid_args(args, gen_backend))
kohn = KohnV1Dataset(path='../tmp/')
kohn.gen_iterators(seq_len)
import pdb; pdb.set_trace()
train_spike_set = V1IteratorSequence(ts.train, seq_len, return_sequences=False)
valid_spike_set = V1IteratorSequence(ts.test, seq_len, return_sequences=False)
init = GlorotUniform()
# dataset = MNIST(path=args.data_dir)
# (X_train, y_train), (X_test, y_test), nclass = dataset.load_data()
# train_set = ArrayIterator([X_train, X_train], y_train, nclass=nclass, lshape=(1, 28, 28))
# valid_set = ArrayIterator([X_test, X_test], y_test, nclass=nclass, lshape=(1, 28, 28))
# # weight initialization
# init_norm = Gaussian(loc=0.0, scale=0.01)
# # initialize model
# path1 = Sequential(layers=[Affine(nout=100, init=init_norm, activation=Rectlin()),
# Affine(nout=100, init=init_norm, activation=Rectlin())])
# path2 = Sequential(layers=[Affine(nout=100, init=init_norm, activation=Rectlin()),
# Affine(nout=100, init=init_norm, activation=Rectlin())])
# layers = [MergeMultistream(layers=[path1, path2], merge="stack"),
# Affine(nout=10, init=init_norm, activation=Logistic(shortcut=True))]
spike_rnn_path = Sequential( layers = [
LSTM(hidden, init, activation=Logistic(),
gate_activation=Logistic(), reset_cells=False),
Dropout(keep=0.5),
LSTM(hidden, init, activation=Logistic(),
gate_activation=Logistic(), reset_cells=False),
#Dropout(keep=0.85),
RecurrentLast(),
Affine(train_set.nfeatures, init, bias=init, activation=Identity(), name='spike_in')])
stim_rnn_path = Sequential( layers = [
LSTM(hidden, init, activation=Logistic(),
gate_activation=Logistic(), reset_cells=False),
Dropout(keep=0.5),
RecurrentLast(),
Affine(1, init, bias=init, activation=Identity(), name='stim')])
layers = [
MergeMultiStream(
layers = [
spike_rnn_path,
stim_rnn_path],
merge="stack"),
Affine(train_set.nfeatures, init, bias=init, activation=Identity(), name='spike_out'),
Round()
]
model = Model(layers=layers)
sched = ExpSchedule(decay=0.7)
# cost = GeneralizedCost(SumSquared())
cost = GeneralizedCost(MeanSquared())
optimizer_two = RMSProp(stochastic_round=args.rounding)
optimizer_one = GradientDescentMomentum(learning_rate=0.1, momentum_coef=0.9, schedule=sched)
opt = MultiOptimizer({'default': optimizer_one,
'Bias': optimizer_two,
'special_linear': optimizer_two})
callbacks = Callbacks(model, eval_set=valid_set, **args.callback_args)
callbacks.add_hist_callback(filter_key = ['W'])
#callbacks.add_callback(MetricCallback(eval_set=valid_set, metric=FractionExplainedVariance(), epoch_freq=args.eval_freq))
#callbacks.add_callback(MetricCallback(eval_set=valid_set,metric=Accuracy(), epoch_freq=args.eval_freq))
model.fit(train_set,
optimizer=opt,
num_epochs=args.epochs,
cost=cost,
callbacks=callbacks)
train_output = model.get_outputs(
train_set).reshape(-1, train_set.nfeatures)
valid_output = model.get_outputs(
valid_set).reshape(-1, valid_set.nfeatures)
train_target = train_set.y_series
valid_target = valid_set.y_series
tfev = fev(train_output, train_target, train_set.mean)
vfev = fev(valid_output, valid_target, valid_set.mean)
neon_logger.display('Train FEV: %g, Valid FEV: %g' % (tfev, vfev))
# neon_logger.display('Train Mean: %g, Valid Mean: %g' % (train_set.mean, valid_set.mean))
plt.figure()
plt.plot(train_output[:, 0], train_output[
:, 1], 'bo', label='prediction')
plt.plot(train_target[:, 0], train_target[:, 1], 'r.', label='target')
plt.legend()
plt.title('Neon on training set')
plt.savefig('neon_series_training_output.png')
plt.figure()
plt.plot(valid_output[:, 0], valid_output[
:, 1], 'bo', label='prediction')
plt.plot(valid_target[:, 0], valid_target[:, 1], 'r.', label='target')
plt.legend()
plt.title('Neon on validation set')
plt.savefig('neon_series_validation_output.png')
