def __init__(
self,
config_string,
num_layers,
head_size,
activation='tanh',
use_bias=True,
weight_initializer='xavier_normal',
bias_initializer='zeros',
gutter=False,
gutter_bias=None,
**kwargs):
# Call parent's constructor
LayerWithNeurons.__init__(self, activation, weight_initializer, use_bias,
bias_initializer, **kwargs)
HardDriver.__init__(self, config_string, head_size, gutter=gutter,
gutter_bias=gutter_bias)
self._num_layers = checker.check_positive_integer(num_layers)
self._activation_string = activation
self.output_scale = [self.total_size]
self.write_heads = None
def __init__(self,
output_dim=None,
spatial_configs=None,
reverse=False,
activation='relu',
use_bias=True,
weight_initializer='xavier_normal',
bias_initializer='zeros',
**kwargs):
assert isinstance(activation, str)
self.activation_string = activation
# Call parent's constructor
LayerWithNeurons.__init__(self, activation, weight_initializer,
use_bias, bias_initializer, **kwargs)
assert not (output_dim is None and spatial_configs is None)
self._spatial_groups = []
if spatial_configs is not None:
self._spatial_groups = self._get_groups(spatial_configs)
total_size = self._get_total_size(self._spatial_groups)
if output_dim is None: output_dim = total_size
assert output_dim == total_size
self._output_dim = checker.check_positive_integer(output_dim)
self._reverse = checker.check_type(reverse, bool)
self.neuron_scale = [output_dim]
def __init__(self,
num_classes,
heads=1,
sum_method=None,
normalize=False,
use_bias=True,
weight_initializer='xavier_normal',
bias_initializer='zeros',
**kwargs):
# Call parent's constructor
LayerWithNeurons.__init__(self, None, weight_initializer, use_bias,
bias_initializer, **kwargs)
self.num_classes = checker.check_positive_integer(num_classes)
self._heads = checker.check_positive_integer(heads)
self._sum_method = sum_method
self._normalize = checker.check_type(normalize, bool)
self.neuron_scale = [num_classes]
def __init__(self,
layer_width,
num_layers,
activation='relu',
use_bias=True,
weight_initializer='xavier_normal',
bias_initializer='zeros',
t_bias_initializer=-1,
**kwargs):
# Call parent's constructor
LayerWithNeurons.__init__(self, activation, weight_initializer,
use_bias, bias_initializer, **kwargs)
self._layer_width = checker.check_positive_integer(layer_width)
self._num_layers = checker.check_positive_integer(num_layers)
assert isinstance(activation, str)
self._activation_string = activation
self._t_bias_initializer = initializers.get(t_bias_initializer)
def __init__(self,
num_neurons,
activation=None,
use_bias=True,
weight_initializer='xavier_normal',
bias_initializer='zeros',
prune_frac=0,
**kwargs):
# Call parent's constructor
LayerWithNeurons.__init__(self,
activation,
weight_initializer,
use_bias,
bias_initializer,
prune_frac=prune_frac,
**kwargs)
self.num_neurons = checker.check_positive_integer(num_neurons)
self.neuron_scale = [num_neurons]