paddle 动态图自定义 layer

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class DNN(paddle.nn.Layer):
    #DNN层，负责抽取high-order特征
    def __init__(self, sparse_feature_number, sparse_feature_dim,
                 dense_feature_dim, num_field, layer_sizes):
        super(DNN, self).__init__()
        self.sparse_feature_number = sparse_feature_number
        self.sparse_feature_dim = sparse_feature_dim
        self.dense_feature_dim = dense_feature_dim
        self.num_field = num_field
        self.layer_sizes = layer_sizes
    #利用FM模型的隐特征向量作为网络权重初始化来获得子网络输出向量
        sizes = [sparse_feature_dim * num_field] + self.layer_sizes + [1]
        acts = ["relu" for _ in range(len(self.layer_sizes))] + [None]
        self._mlp_layers = []
        for i in range(len(layer_sizes) + 1):
            linear = paddle.nn.Linear(
                in_features=sizes[i],
                out_features=sizes[i + 1],
                weight_attr=paddle.ParamAttr(
                    initializer=paddle.nn.initializer.Normal(
                        std=1.0 / math.sqrt(sizes[i]))))
            self.add_sublayer('linear_%d' % i, linear)
            self._mlp_layers.append(linear)
            if acts[i] == 'relu':
                act = paddle.nn.ReLU()
                self.add_sublayer('act_%d' % i, act)
    #得到输入层到embedding层该神经元相连的五条线的权重
    #前向传播反馈
    def forward(self, feat_embeddings):
        y_dnn = paddle.reshape(feat_embeddings,
                               [-1, self.num_field * self.sparse_feature_dim])
        for n_layer in self._mlp_layers:
            y_dnn = n_layer(y_dnn)
        return y_dnn

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