def __init__(self, backend, dataset, subj):
ad = {
'type': 'adadelta',
'lr_params': {
'rho': 0.9,
'epsilon': 0.000000001
}
}
self.layers = []
self.add(
DataLayer(is_local=True,
nofm=dataset.nchannels,
ofmshape=[1, dataset.nsamples]))
self.add(
ConvLayer(nofm=64,
fshape=[1, 3],
activation=RectLin(),
lrule_init=ad))
self.add(PoolingLayer(op='max', fshape=[1, 2], stride=2))
if subj != 2:
self.add(FCLayer(nout=128, activation=RectLin(), lrule_init=ad))
self.add(
FCLayer(nout=dataset.nclasses,
activation=Logistic(),
lrule_init=ad))
self.add(CostLayer(cost=CrossEntropy()))
self.model = MLP(num_epochs=1, batch_size=128, layers=self.layers)
self.dataset = dataset
def create_model(nin):
layers = []
layers.append(DataLayer(nout=nin))
layers.append(FCLayer(nout=100, activation=RectLin()))
layers.append(FCLayer(nout=10, activation=Logistic()))
layers.append(CostLayer(cost=CrossEntropy()))
model = MLP(num_epochs=10, batch_size=128, layers=layers)
return model
def create_layer(self, backend):
weight_init = IdentityValGen()
layer = FCLayer(nin=nin,
nout=nout,
batch_size=batch_size,
weight_init=weight_init,
backend=backend)
layer.set_weight_shape()
layer.initialize([])
return layer
def create_layer(self, backend):
weight_init = IdentityValGen()
layer = FCLayer(nin=nin,
nout=nout,
batch_size=batch_size,
weight_init=weight_init,
backend=backend)
layer.set_weight_shape()
layer.initialize([])
return layer