def __init__(self, **kwargs):
kwargs['variables'] = create_in(kwargs['name'])
Notifier.__init__(self)
ToDense.__init__(self, **kwargs)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
kwargs['variables'] = create_in_trg_conv(kwargs['name'])
Notifier.__init__(self)
ConvDShaping.__init__(self, **kwargs)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
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, layers, name, noise_level = 0.5, **kwargs):
Notifier.__init__(self)
NNStack.__init__(self, layers, layers[0].input, name, **kwargs)
CostReconErrDenoise.__init__(self, noise_level, **kwargs)
SerializeStack.__init__(self)
Monitor.__init__(self)
Plotter.__init__(self)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, layers, name, **kwargs):
Notifier.__init__(self)
NNStack.__init__(self, layers, layers[0].input, name)
CostCrossEntropy.__init__(self, **kwargs)
SerializeStack.__init__(self)
Monitor.__init__(self)
Plotter.__init__(self)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, layers, name, **kwargs):
Notifier.__init__(self)
NNStack.__init__(self, layers, layers[0].input, name, **kwargs)
CostCategoricCrossEntropy.__init__(self, self.activation_h(self.input),
self.target, **kwargs)
SerializeStack.__init__(self)
Monitor.__init__(self)
Plotter.__init__(self)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self,
input_sym,
in_size,
name="MaxPoolerOverlapping",
**kwargs):
kwargs['variables'] = create_in_trg_conv(kwargs['name'])
Notifier.__init__(self)
MaxPoolerOverlapping.__init__(self, **kwargs)
self.notify(Notifier.MAKE_FINISHED)
self.notify(Notifier.COMPILE_FUNCTIONS)
self.notify(Notifier.REGISTER_PLOTTING)
def __init__(self, **kwargs):
# Define symbolic variables
trans_in = T.matrix("transform_input", dtype=fx)
input_sym = T.matrix(name='input' + kwargs['name'], dtype=fx)
kwargs['variables'] = {'input': input_sym, 'trans_in': trans_in}
# initialize mix-in classes
Notifier.__init__(self)
UnitsNNLinear.__init__(self)
TransformingLayer.__init__(self, **kwargs)
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):
if kwargs['convolutional']:
kwargs['variables'] = create_in_trg_conv(kwargs['name'])
else:
kwargs['variables'] = create_in_trg(kwargs['name'])
try:
_ = kwargs['trans_func']
except KeyError:
raise ValueError("Please pass the parameter 'trans_func'")
Notifier.__init__(self)
TransitionFunc.__init__(self, **kwargs)
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)
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):
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, layers, name, lr, momentum = 0.0, n_samples = 1, **kwargs):
Notifier.__init__(self)
self.momentum = momentum
self.sol_shape = list(layers[0].input_shape)
self.sol_shape[0] = n_samples
self.solution = theano.shared(np.random.uniform(0, 1,
size = self.sol_shape).astype(fx),
name="solution")
self.reset_solution()
NNStackLess.__init__(self, layers, layers[0].input, name, **kwargs)
DeepDreamer.__init__(self, self.solution, lr, **kwargs)
NNFeatures.__init__(self)
ParamsBinder.__init__(self, layers)
NotifierForwarder.__init__(self, layers)
SerializeStack.__init__(self)
Monitor.__init__(self)
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)
