def __init__(self, variables, filter_shape, input_shape, out_shape, name,
**kwargs):
assert kwargs[
"convolutional"] == True, "Set 'convolutional = True' in configuration."
kwargs['variables'] = create_in_trg_conv(kwargs['name'])
Notifier.__init__(self)
Plotter.__init__(self)
UnitsCNNReLU.__init__(self, **kwargs)
assert not -1 in out_shape, "For de-convolutional layers, you need to " + \
"define the 'out_shape' in the configuration file."
filt_size = (input_shape[1], out_shape[1], filter_shape[2],
out_shape[3])
LOGGER.debug("out_size {0}".format(out_shape))
LOGGER.debug("filt_size {0}".format(filt_size))
DCNN.__init__(self, variables, name, input_shape, filt_size, out_shape,
**kwargs)
CostCrossEntropy.__init__(self, **kwargs)
WeightRegular.__init__(self, **kwargs)
SparsityLeeConv.__init__(self, **kwargs)
MaxNormRegular.__init__(self, **kwargs)
SerializeLayer.__init__(self)
Monitor.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
kwargs['variables'] = create_in_trg(kwargs['name'])
Notifier.__init__(self)
UnitsNNSoftmax.__init__(self)
NN_BN.__init__(self, **kwargs)
CostCategoricCrossEntropy.__init__(self, **kwargs)
WeightRegular.__init__(self, **kwargs)
MaxNormRegular.__init__(self, **kwargs)
SerializeLayer.__init__(self)
Monitor.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
kwargs['variables'] = create_in(kwargs['name'])
Notifier.__init__(self)
UnitsRBMSigmoid.__init__(self)
SparsityGOH.__init__(self, **kwargs)
RBM.__init__(self, **kwargs)
CostCD.__init__(self, **kwargs)
WeightRegular.__init__(self, **kwargs)
SerializeLayer.__init__(self)
Monitor.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
assert kwargs[
"convolutional"] == True, "Set 'convolutional = True' in configuration."
kwargs['variables'] = create_in_conv(kwargs['name'])
Notifier.__init__(self)
UnitsCRBMGauss.__init__(self)
CRBM.__init__(self, **kwargs)
CostCD.__init__(self, **kwargs)
WeightRegular.__init__(self, **kwargs)
SerializeLayer.__init__(self)
Monitor.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
kwargs['variables'] = create_in_trg(kwargs['name'])
Notifier.__init__(self)
UnitsNNReLU.__init__(self)
NN_BN.__init__(self, **kwargs)
CostSquaredError.__init__(self, **kwargs)
WeightRegular.__init__(self, **kwargs)
MaxNormRegular.__init__(self, **kwargs)
SparsityLee.__init__(self, **kwargs)
SerializeLayer.__init__(self)
Monitor.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
kwargs['variables'] = create_in_trg(kwargs['name'])
Notifier.__init__(self)
UnitsNNTanh.__init__(self)
LSTM.__init__(self, act_fun_out=lambda x: T.nnet.sigmoid(x), **kwargs)
CostLogLikelihoodBinomial.__init__(self, **kwargs)
SparsityLee.__init__(self, **kwargs)
WeightRegularRNN.__init__(self, **kwargs)
SerializeLayer.__init__(self)
Monitor.__init__(self)
Plotter.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
kwargs['variables'] = create_in(kwargs['name'])
Notifier.__init__(self)
UnitsNNLinear.__init__(self)
UnitsDropOut.__init__(self, **kwargs)
NonNegative.__init__(self, **kwargs)
NNAuto.__init__(self, **kwargs)
CostSquaredError.__init__(self, **kwargs)
SparsityLee.__init__(self, **kwargs)
WeightRegular.__init__(self, **kwargs)
SerializeLayer.__init__(self)
Monitor.__init__(self)
Plotter.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
kwargs['variables'] = create_in(kwargs['name'])
Notifier.__init__(self)
UnitsRBMReLU.__init__(self)
UnitsDropOut.__init__(self, **kwargs)
SparsityLee.__init__(self, **kwargs)
RBM.__init__(self, **kwargs)
CostCD.__init__(self, **kwargs)
WeightRegular.__init__(self, **kwargs)
MaxNormRegular.__init__(self, **kwargs)
SerializeLayer.__init__(self)
ActivationCrop.__init__(self, **kwargs)
Monitor.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
assert kwargs[
"convolutional"] == True, "Set 'convolutional = True' in configuration."
kwargs['variables'] = create_in_conv(kwargs['name'])
Notifier.__init__(self)
UnitsCRBMSigmoid.__init__(self)
CRBM.__init__(self, **kwargs)
CostPCD.__init__(self, **kwargs)
SparsityLeeConv.__init__(self, **kwargs)
MaxNormRegular.__init__(self, **kwargs)
WeightRegular.__init__(self, **kwargs)
ActivationCrop.__init__(self, **kwargs)
Approximator.__init__(self, **kwargs)
Monitor.__init__(self)
SerializeLayer.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
assert kwargs[
"convolutional"] == True, "Set 'convolutional = True' in configuration."
kwargs['variables'] = create_in_trg_conv(kwargs['name'])
Notifier.__init__(self)
Plotter.__init__(self)
UnitsCNNSigmoid.__init__(self, **kwargs)
UnitsDropOut.__init__(self, **kwargs)
CNN_BN.__init__(self, **kwargs)
CostCrossEntropy.__init__(self, **kwargs)
WeightRegular.__init__(self, **kwargs)
MaxNormRegular.__init__(self, **kwargs)
SparsityLeeConv.__init__(self, **kwargs)
SerializeLayer.__init__(self)
Monitor.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
kwargs['variables'] = create_in_trg(kwargs['name'])
Notifier.__init__(self)
UnitsNNTanh.__init__(self)
RNN_Gated.__init__(self,
act_fun_out=lambda x: T.nnet.sigmoid(x),
**kwargs)
CostCrossEntropy.__init__(self, **kwargs)
SparsityLee.__init__(self, **kwargs)
weight_params = [
self.Wxh, self.Wxr, self.Wxu, self.Whh, self.Why, self.Whr,
self.Whu
]
WeightRegular.__init__(self, wl_targets=weight_params, **kwargs)
SerializeLayer.__init__(self)
Monitor.__init__(self)
Plotter.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
assert kwargs[
"convolutional"] == True, "Set 'convolutional = True' in configuration."
kwargs['variables'] = create_in_trg_conv(kwargs['name'])
Notifier.__init__(self)
Plotter.__init__(self)
try:
UnitsCNNReLU.__init__(self,
downsample_out=kwargs['downsample_out'])
except KeyError:
raise ValueError("Entry 'downsample_out' in kwargs needed.")
UnitsDropOut.__init__(self, **kwargs)
CNN_BN.__init__(self, **kwargs)
CostCrossEntropy.__init__(self)
WeightRegular.__init__(self, **kwargs)
SparsityLeeConv.__init__(self, **kwargs)
MaxNormRegular.__init__(self, **kwargs)
SerializeLayer.__init__(self)
Monitor.__init__(self)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
