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())])