def train_function(inputs): # 重新定义训练函数 grads = embedding_gradients(inputs)[0] # Embedding梯度 delta = epsilon * grads / (np.sqrt((grads**2).sum()) + 1e-8) # 计算扰动 K.set_value(embeddings, K.eval(embeddings) + delta) # 注入扰动 outputs = old_train_function(inputs) # 梯度下降 K.set_value(embeddings, K.eval(embeddings) - delta) # 删除扰动 return outputs
def build(self, input_shape): output_dim = input_shape[-1] if not isinstance(output_dim, int): output_dim = output_dim.value self.trans = self.add_weight(name='trans', shape=(output_dim, output_dim), initializer='glorot_uniform', trainable=True) if self.lr_multiplier != 1: K.set_value(self.trans, K.eval(self.trans) / self.lr_multiplier) self.trans = self.lr_multiplier * self.trans