def build(self, input_shape):
Conv2D.build(self, input_shape)
if self.needs_drop:
self.kernel = K.in_train_phase(K.dropout(self.kernel, self.prob, self.drop_noise_shape), self.kernel)
if self.drop_bias:
self.bias = K.in_train_phase(K.dropout(self.bias, self.prob, self.drop_noise_shape), self.bias)
def build(self, input_shape):
"""Creates the layer weights.
Must be implemented on all layers that have weights.
Parameters
----------
input_shape: Union[list, tuple, Any]
Keras tensor (future input to layer) or list/tuple of Keras tensors
to reference for weight shape computations.
"""
Conv2D.build(self, input_shape)
self.init_neurons(input_shape)
def build(self, input_shape):
"""Creates the layer weights.
Must be implemented on all layers that have weights.
Parameters
----------
input_shape: Union[list, tuple, Any]
Keras tensor (future input to layer) or list/tuple of Keras tensors
to reference for weight shape computations.
"""
Conv2D.build(self, input_shape)
self.init_neurons(input_shape)
if self.config.getboolean('cell', 'bias_relaxation'):
self.b0 = k.variable(k.get_value(self.bias))
self.add_update([(self.bias, self.update_b())])
