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after extensive testing
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@EliasVansteenkiste
EliasVansteenkiste committed Nov 18, 2016
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1 change: 0 additions & 1 deletion batch_iterators.py
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Original file line number Diff line number Diff line change
Expand Up @@ -177,7 +177,6 @@ def __call__(self, X, y=None):
self.y = y
else:
self.y = y[0]
print X
self.X = X
return self

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272 changes: 272 additions & 0 deletions convnets/models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2057,6 +2057,278 @@ def net50(n_channels,width,height,n_output=2,nonlinearity=nonlinearities.very_le
layer = GlobalPoolLayer(layer,pool_function=Tmean4)
layer = NonlinearityLayer(layer,nonlinearity=nonlinearities.softmax)

net = NeuralNet(
layer,
update=adam,
update_learning_rate=0.001,
#update_momentum=0.9,
regression=False,
max_epochs=100,
verbose=1,
on_epoch_finished=[EarlyStopping(patience=10),],
batch_iterator_train = batch_iterator_train,
batch_iterator_test = batch_iterator_test,
train_split = train_split,
)
return net

def net51(n_channels,width,height,n_output=2,nonlinearity=nonlinearities.very_leaky_rectify,
train_split=TrainSplit(0.2),
batch_iterator_train=BatchIterator(batch_size=256),batch_iterator_test=BatchIterator(batch_size=256)):
# net13 with henorm
layer = InputLayer(shape=(None, n_channels, width, height))
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(64,1), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(1,64), pad=1, num_filters=32)
layer = dropout(layer,p=0.5)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, stride=(2,2), filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = dropout(layer,p=0.5)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, stride=(2,2), filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=None, W=he_norm, filter_size=(3,3), pad=1, num_filters=n_output)
layer = GlobalPoolLayer(layer)
layer = NonlinearityLayer(layer,nonlinearity=nonlinearities.softmax)

net = NeuralNet(
layer,
update=adam,
update_learning_rate=0.001,
#update_momentum=0.9,
regression=False,
max_epochs=100,
verbose=1,
on_epoch_finished=[EarlyStopping(patience=10),],
batch_iterator_train = batch_iterator_train,
batch_iterator_test = batch_iterator_test,
train_split = train_split,
)
return net

def net52(n_channels,width,height,n_output=2,nonlinearity=nonlinearities.very_leaky_rectify,
train_split=TrainSplit(0.2),
batch_iterator_train=BatchIterator(batch_size=256),batch_iterator_test=BatchIterator(batch_size=256)):
# net13 with henorm
layer = InputLayer(shape=(None, n_channels, width, height))
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(64,1), pad=1, num_filters=4)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(1,64), pad=1, num_filters=8)
layer = dropout(layer,p=0.5)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, stride=(2,2), filter_size=(3,3), pad=1, num_filters=16)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = dropout(layer,p=0.5)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, stride=(2,2), filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=None, W=he_norm, filter_size=(3,3), pad=1, num_filters=n_output)
layer = GlobalPoolLayer(layer)
layer = NonlinearityLayer(layer,nonlinearity=nonlinearities.softmax)

net = NeuralNet(
layer,
update=adam,
update_learning_rate=0.001,
#update_momentum=0.9,
regression=False,
max_epochs=100,
verbose=1,
on_epoch_finished=[EarlyStopping(patience=10),],
batch_iterator_train = batch_iterator_train,
batch_iterator_test = batch_iterator_test,
train_split = train_split,
)
return net


def net53(n_channels,width,height,n_output=2,nonlinearity=nonlinearities.very_leaky_rectify,
train_split=TrainSplit(0.2),
batch_iterator_train=BatchIterator(batch_size=256),batch_iterator_test=BatchIterator(batch_size=256)):
#from net41 with more filters in 1x1 layers
layer = InputLayer(shape=(None, n_channels, width, height))
# channel reduction == feature map reduction
layer = Conv2DLayer(layer, nonlinearity=None, filter_size=(1,1), num_filters=8)

layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = dropout(layer,p=0.5)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, stride=(2,2), filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = dropout(layer,p=0.5)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, stride=(2,2), filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=None, W=he_norm, filter_size=(3,3), pad=1, num_filters=n_output)
layer = GlobalPoolLayer(layer)
layer = NonlinearityLayer(layer,nonlinearity=nonlinearities.softmax)

net = NeuralNet(
layer,
update=adam,
update_learning_rate=0.001,
#update_momentum=0.9,
regression=False,
max_epochs=100,
verbose=1,
on_epoch_finished=[EarlyStopping(patience=10),],
batch_iterator_train = batch_iterator_train,
batch_iterator_test = batch_iterator_test,
train_split = train_split,
)
return net

def net54(n_channels,width,height,n_output=2,nonlinearity=nonlinearities.very_leaky_rectify,
train_split=TrainSplit(0.2),
batch_iterator_train=BatchIterator(batch_size=256),batch_iterator_test=BatchIterator(batch_size=256)):
#from net29 seperate input layers for each channel
layer = InputLayer(shape=(None, n_channels, width, height))
# channel reduction == feature map reduction
layer = Conv2DLayer(layer, nonlinearity=None, filter_size=(1,1), num_filters=4)
# reduction in freq components
layer = Conv2DLayer(layer, nonlinearity=None, filter_size=(1,64), stride=(1,1), num_filters=16)
#reshape to a 2D image with only one channel
layer = ReshapeLayer(layer, ([0], 1, [2], -1))

layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = dropout(layer,p=0.5)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, stride=(2,2), filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = dropout(layer,p=0.5)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, stride=(2,2), filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=None, W=he_norm, filter_size=(3,3), pad=1, num_filters=n_output)
layer = GlobalPoolLayer(layer)
layer = NonlinearityLayer(layer,nonlinearity=nonlinearities.softmax)

net = NeuralNet(
layer,
update=adam,
update_learning_rate=0.001,
#update_momentum=0.9,
regression=False,
max_epochs=100,
verbose=1,
on_epoch_finished=[EarlyStopping(patience=10),],
batch_iterator_train = batch_iterator_train,
batch_iterator_test = batch_iterator_test,
train_split = train_split,
)
return net

def net55(n_channels,width,height,n_output=2,nonlinearity=nonlinearities.very_leaky_rectify,
train_split=TrainSplit(0.2),
batch_iterator_train=BatchIterator(batch_size=256),batch_iterator_test=BatchIterator(batch_size=256)):
#from net29 seperate input layers for each channel
layer = InputLayer(shape=(None, n_channels, width, height))
# channel reduction == feature map reduction
layer = Conv2DLayer(layer, nonlinearity=None, filter_size=(1,1), num_filters=4)
# reduction in freq components
layer = Conv2DLayer(layer, nonlinearity=None, filter_size=(1,64), stride=(1,1), num_filters=16)
#reshape to a 2D image with only one channel
layer = ReshapeLayer(layer, ([0], 1, [2], -1))

layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = MaxPool2DLayer(layer, pool_size=(2,2))
layer = dropout(layer,p=0.5)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = MaxPool2DLayer(layer, pool_size=(2,2))
layer = dropout(layer,p=0.5)
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(3,3), pad=1, num_filters=32)
layer = Conv2DLayer(layer, nonlinearity=None, W=he_norm, filter_size=(3,3), pad=1, num_filters=n_output)
layer = GlobalPoolLayer(layer)
layer = NonlinearityLayer(layer,nonlinearity=nonlinearities.softmax)

net = NeuralNet(
layer,
update=adam,
update_learning_rate=0.001,
#update_momentum=0.9,
regression=False,
max_epochs=100,
verbose=1,
on_epoch_finished=[EarlyStopping(patience=10),],
batch_iterator_train = batch_iterator_train,
batch_iterator_test = batch_iterator_test,
train_split = train_split,
)
return net

def net56(n_channels,width,height,n_output=2,nonlinearity=nonlinearities.very_leaky_rectify,
train_split=TrainSplit(0.2),
batch_iterator_train=BatchIterator(batch_size=256),batch_iterator_test=BatchIterator(batch_size=256)):
#from net29 seperate input layers for each channel
layer = InputLayer(shape=(None, n_channels, width, height))
# channel reduction == feature map reduction
layer = Conv2DLayer(layer, nonlinearity=None, filter_size=(1,1), num_filters=4)

# reduction in freq components #reshape to a 2D image with only one channel
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(1,height), stride=(1,1), num_filters=32)
layer = ReshapeLayer(layer, ([0], 1, [2], -1))
layer = dropout(layer,p=0.5)

layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(width,1), stride=(1,1), num_filters=32)
layer = ReshapeLayer(layer, ([0], 1, -1, [3]))
layer = dropout(layer,p=0.5)

layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(1,32), stride=(1,1), num_filters=16)
layer = ReshapeLayer(layer, ([0], 1, [2], -1))
layer = dropout(layer,p=0.5)

layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(32,1), stride=(1,1), num_filters=16)
layer = ReshapeLayer(layer, ([0], 1, -1, [3]))
layer = dropout(layer,p=0.5)

layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(1,16), stride=(1,1), num_filters=8)
layer = ReshapeLayer(layer, ([0], 1, [2], -1))
layer = dropout(layer,p=0.5)

layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(16,1), stride=(1,1), num_filters=8)
layer = ReshapeLayer(layer, ([0], 1, -1, [3]))
layer = dropout(layer,p=0.5)

layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(8,8), pad=1, num_filters=16)
layer = Conv2DLayer(layer, nonlinearity=None, W=he_norm, filter_size=(1,1), pad=1, num_filters=n_output)
layer = GlobalPoolLayer(layer)
layer = NonlinearityLayer(layer,nonlinearity=nonlinearities.softmax)

net = NeuralNet(
layer,
update=adam,
update_learning_rate=0.001,
#update_momentum=0.9,
regression=False,
max_epochs=100,
verbose=1,
on_epoch_finished=[EarlyStopping(patience=10),],
batch_iterator_train = batch_iterator_train,
batch_iterator_test = batch_iterator_test,
train_split = train_split,
)
return net

def net57(n_channels,width,height,n_output=2,nonlinearity=nonlinearities.very_leaky_rectify,
train_split=TrainSplit(0.2),
batch_iterator_train=BatchIterator(batch_size=256),batch_iterator_test=BatchIterator(batch_size=256)):
#from net29 seperate input layers for each channel
layer = InputLayer(shape=(None, n_channels, width, height))
# channel reduction == feature map reduction
layer = Conv2DLayer(layer, nonlinearity=None, filter_size=(1,1), num_filters=4)

# reduction in freq components #reshape to a 2D image with only one channel
layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(1,height), stride=(1,1), num_filters=32)
layer = ReshapeLayer(layer, ([0], 1, [2], -1))
layer = dropout(layer,p=0.5)

layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(1,32), stride=(1,1), num_filters=32)
layer = ReshapeLayer(layer, ([0], 1, [2], -1))
layer = dropout(layer,p=0.5)

layer = Conv2DLayer(layer, nonlinearity=nonlinearity, W=he_norm, filter_size=(1,32), stride=(1,1), num_filters=32)
layer = dropout(layer,p=0.5)

gp_mean = GlobalPoolLayer(layer, pool_function=T.mean, name="gp_mean")
gp_max = GlobalPoolLayer(layer, pool_function=T.max, name="gp_max")
gp_min = GlobalPoolLayer(layer, pool_function=T.min, name="gp_min")
gp_var = GlobalPoolLayer(layer, pool_function=T.var, name="gp_var")
gp = ConcatLayer([gp_mean, gp_max, gp_min, gp_var], name="gp_concat")


layer = DenseLayer(gp, nonlinearity=nonlinearities.softmax, num_units=n_output, name="dense_softmax")

net = NeuralNet(
layer,
update=adam,
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