b787cdf9d9d1c7f500f91ccdeb87aa6d7725ae14,deepctr/layers/interaction.py,FGCNNLayer,call,#FGCNNLayer#Any#,790

Before Change

                "Unexpected inputs dimensions %d, expect to be 3 dimensions" % (K.ndim(inputs)))

        embedding_size = inputs.shape[-1].value
        pooling_result = tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=3))(inputs)

        new_feature_list = []

        for i in range(1, len(self.filters) + 1):
            filters = self.filters[i - 1]
            width = self.kernel_width[i - 1]
            new_filters = self.new_maps[i - 1]
            pooling_width = self.pooling_width[i - 1]
            conv_result = tf.keras.layers.Conv2D(filters=filters, kernel_size=(width, 1), strides=(1, 1),
                                                 padding="same",
                                                 activation="tanh", use_bias=True, )(pooling_result)
            pooling_result = tf.keras.layers.MaxPooling2D(pool_size=(pooling_width, 1))(conv_result)
            flatten_result = tf.keras.layers.Flatten()(pooling_result)
            new_result = tf.keras.layers.Dense(pooling_result.shape[1].value * embedding_size * new_filters,
                                               activation="tanh", use_bias=True)(flatten_result)
            new_feature_list.append(

After Change

            new_result = self.dense_layers[i - 1](flatten_result)

            new_feature_list.append(
                tf.reshape(new_result, (-1, pooling_result.shape[1].value * new_filters, embedding_size)))

        new_features = concat_fun(new_feature_list, axis=1)
        return new_features

Instances