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)
LSTM.__init__(self, act_fun_out=lambda x: T.nnet.sigmoid(x), **kwargs)
CostLogLikelihoodBinomial.__init__(self, **kwargs)
SparsityLee.__init__(self, **kwargs)
weight_params = [
self.Wxc, self.Wxi, self.Wxf, self.Why, self.Whmo, self.Who,
self.Whc, self.Wxo, self.Whi, self.Whf
]
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):
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)
def __init__(self, cost, params, variables, data, batch_size, lr,
momentum = 0., grad_clip = None,
nan_protection = True, notifier = None):
self.params = params
LOGGER.debug("Optimizing parameter(s): {0}".format(params))
param_count = np.sum(np.asarray([np.prod(p.get_value().shape) for p in params]))
LOGGER.debug("Number parameter(s): {0}".format(param_count))
LOGGER.debug("Given variable(s): {0}".format(variables))
if data is not None:
LOGGER.debug("Given data: {0}".format([[k, shape(data[k])] for k in data.keys()]))
self.gparams = [theano.shared(x.get_value() *
np.cast[x.get_value().dtype](0.0),
name = "%s_grad" % x.name)
for x in self.params]
self.cost = cost
if variables is None:
self.variables = {}
else:
self.variables = variables
self.data = data
self.lr = theano.shared(np.cast[fx](lr))
self.mom = theano.shared(np.cast[fx](momentum))
self.grad_clip = grad_clip
print "grad_clip", grad_clip
self.nan_protection = nan_protection
self.batch_size = batch_size
if data is not None:
assert len(data) == len(self.variables), \
"You assigned {0} symbolic variable(s), but you provide {1} data set(s): \nVariables: {2}\nDatasets: {3}" \
.format(len(self.variables),
len(data), self.variables,
self.data.keys())
if notifier is None:
notifier = Notifier()
notifier.callback_del(Notifier.GET_DATA)
for key in variables.keys():
notifier.callback_add(partial(self.get_data_callback, key=key),
Notifier.GET_DATA)
self.notifier = notifier
self.train_model = self.init_updates()
