コード例 #1
0
def p_builder(p_input):
    # CANDO: change activation
    p_layer1 = keras.layers.Dense(dim_l1, use_bias=use_bias, activation='relu',
                                  kernel_regularizer='l2', bias_regularizer='l2',
                                  name='p_layer1')(p_input)
    p_adj = layers.Bilinear(0, use_bias=use_bias,
                            kernel_regularizer='l2', bias_regularizer='l2',
                            name='p_adj')([p_layer1, p_layer1])
    p_v = keras.layers.Dense(dim_data, use_bias=use_bias,
                             kernel_regularizer='l2', bias_regularizer='l2',
                             name='p_v')(p_layer1)
    return ([p_adj, p_v], ('SigmoidBernoulliScaledAdjacency', 'SigmoidBernoulli'))
コード例 #2
0
ファイル: ae.py プロジェクト: tyut2018/an2vec
    def p_builder(p_input):
        # Get slices of the embeddings for each prediction
        p_input_adj = layers.InnerSlice(adj_embedding_slice)(p_input)
        p_input_v = layers.InnerSlice(v_embedding_slice)(p_input)

        # Build layer1 for adj and features, if requested, and have
        # it shared between adjacency and features if requested.
        if share_l1:
            # Shared intermediate layer.
            assert with_l1, "Can't share l1 if l1 is not requested"
            p_layer1 = keras.layers.Dense(dim_l1,
                                          use_bias=use_bias,
                                          activation='relu',
                                          kernel_regularizer='l2',
                                          bias_regularizer='l2',
                                          name='p_layer1')
            p_penultimate_adj = p_layer1(p_input_adj)
            p_penultimate_v = p_layer1(p_input_v)
        else:
            if with_l1:
                # Unshared intermediate layer.
                p_penultimate_adj = keras.layers.Dense(
                    dim_l1,
                    use_bias=use_bias,
                    activation='relu',
                    kernel_regularizer='l2',
                    bias_regularizer='l2',
                    name='p_layer1_adj')(p_input_adj)
                p_penultimate_v = keras.layers.Dense(
                    dim_l1,
                    use_bias=use_bias,
                    activation='relu',
                    kernel_regularizer='l2',
                    bias_regularizer='l2',
                    name='p_layer1_v')(p_input_v)
            else:
                # No intermediate layer.
                p_penultimate_adj = p_input_adj
                p_penultimate_v = p_input_v

        # Prepare kwargs for the Bilinear adj decoder, then build it.
        adj_kwargs = {}
        if adj_kernel is not None:
            adj_kwargs['fixed_kernel'] = adj_kernel
        else:
            adj_kwargs['kernel_regularizer'] = 'l2'
        p_adj = layers.Bilinear(
            0,
            use_bias=use_bias,
            name='p_adj',
            bias_regularizer='l2',
            **adj_kwargs)([p_penultimate_adj, p_penultimate_adj])

        # Finally build the feature decoder according to the requested codec.
        if feature_codec in ['SigmoidBernoulli', 'SoftmaxMultinomial']:
            p_v = keras.layers.Dense(dim_data,
                                     use_bias=use_bias,
                                     kernel_regularizer='l2',
                                     bias_regularizer='l2',
                                     name='p_v')(p_penultimate_v)
        else:
            assert feature_codec == 'OrthogonalGaussian'
            p_v_μ_flat = keras.layers.Dense(
                dim_data,
                use_bias=use_bias,
                kernel_regularizer='l2',
                bias_regularizer='l2',
                name='p_v_mu_flat')(p_penultimate_v)
            p_v_logS_flat = keras.layers.Dense(
                dim_data,
                use_bias=use_bias,
                kernel_regularizer='l2',
                bias_regularizer='l2',
                name='p_v_logS_flat')(p_penultimate_v)
            p_v = keras.layers.Concatenate(name='p_v_mulogS_flat')(
                [p_v_μ_flat, p_v_logS_flat])

        return ([p_adj,
                 p_v], ('SigmoidBernoulliScaledAdjacency', feature_codec))