import paddle.fluid as fluid import math dense_feature_dim = 13 def ctr_deepfm_model(factor_size, sparse_feature_dim, dense_feature_dim, sparse_input): def dense_fm_layer(input, emb_dict_size, factor_size, fm_param_attr): """ dense_fm_layer """ first_order = fluid.layers.fc(input=input, size=1) emb_table = fluid.layers.create_parameter(shape=[emb_dict_size, factor_size], dtype='float32', attr=fm_param_attr) input_mul_factor = fluid.layers.matmul(input, emb_table) input_mul_factor_square = fluid.layers.square(input_mul_factor) input_square = fluid.layers.square(input) factor_square = fluid.layers.square(emb_table) input_square_mul_factor_square = fluid.layers.matmul(input_square, factor_square) second_order = 0.5 * (input_mul_factor_square - input_square_mul_factor_square) return first_order, second_order def sparse_fm_layer(input, emb_dict_size, factor_size, fm_param_attr): """ sparse_fm_layer """ first_embeddings = fluid.layers.embedding( input=input, dtype='float32', size=[emb_dict_size, 1], is_sparse=True) first_order = fluid.layers.sequence_pool(input=first_embeddings, pool_type='sum') nonzero_embeddings = fluid.layers.embedding( input=input, dtype='float32', size=[emb_dict_size, factor_size], param_attr=fm_param_attr, is_sparse=True) summed_features_emb = fluid.layers.sequence_pool(input=nonzero_embeddings, pool_type='sum') summed_features_emb_square = fluid.layers.square(summed_features_emb) squared_features_emb = fluid.layers.square(nonzero_embeddings) squared_sum_features_emb = fluid.layers.sequence_pool( input=squared_features_emb, pool_type='sum') second_order = 0.5 * (summed_features_emb_square - squared_sum_features_emb) return first_order, second_order dense_input = fluid.layers.data(name="dense_input", shape=[dense_feature_dim], dtype='float32') sparse_input_ids = [ fluid.layers.data(name="C" + str(i), shape=[1], lod_level=1, dtype='int64') for i in range(1, 27)] label = fluid.layers.data(name='label', shape=[1], dtype='int64') datas = [dense_input] + sparse_input_ids + [label] py_reader = fluid.layers.create_py_reader_by_data(capacity=64, feed_list=datas, name='py_reader', use_double_buffer=True) words = fluid.layers.read_file(py_reader) sparse_fm_param_attr = fluid.param_attr.ParamAttr(name="SparseFeatFactors", initializer=fluid.initializer.Normal( scale=1 / math.sqrt(sparse_feature_dim))) dense_fm_param_attr = fluid.param_attr.ParamAttr(name="DenseFeatFactors", initializer=fluid.initializer.Normal( scale=1 / math.sqrt(dense_feature_dim))) sparse_fm_first, sparse_fm_second = sparse_fm_layer( sparse_input, sparse_feature_dim, factor_size, sparse_fm_param_attr) dense_fm_first, dense_fm_second = dense_fm_layer( dense_input, dense_feature_dim, factor_size, dense_fm_param_attr) def embedding_layer(input): """embedding_layer""" emb = fluid.layers.embedding( input=input, dtype='float32', size=[sparse_feature_dim, factor_size], param_attr=sparse_fm_param_attr, is_sparse=True) return fluid.layers.sequence_pool(input=emb, pool_type='average') sparse_embed_seq = list(map(embedding_layer, sparse_input_ids)) concated = fluid.layers.concat(sparse_embed_seq + [dense_input], axis=1) fc1 = fluid.layers.fc(input=concated, size=400, act='relu', param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( scale=1 / math.sqrt(concated.shape[1])))) fc2 = fluid.layers.fc(input=fc1, size=400, act='relu', param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( scale=1 / math.sqrt(fc1.shape[1])))) fc3 = fluid.layers.fc(input=fc2, size=400, act='relu', param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( scale=1 / math.sqrt(fc2.shape[1])))) predict = fluid.layers.fc( input=[sparse_fm_first, sparse_fm_second, dense_fm_first, dense_fm_second, fc3], size=2, act="softmax", param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1 / math.sqrt(fc3.shape[1])))) cost = fluid.layers.cross_entropy(input=predict, label=words[-1]) avg_cost = fluid.layers.reduce_sum(cost) accuracy = fluid.layers.accuracy(input=predict, label=words[-1]) auc_var, batch_auc_var, auc_states = \ fluid.layers.auc(input=predict, label=words[-1], num_thresholds=2 ** 12, slide_steps=20) return avg_cost, auc_var, batch_auc_var, py_reader def ctr_dnn_model(embedding_size, sparse_feature_dim, use_py_reader=True): def embedding_layer(input): return fluid.layers.embedding( input=input, is_sparse=True, # you need to patch https://github.com/PaddlePaddle/Paddle/pull/14190 # if you want to set is_distributed to True is_distributed=False, size=[sparse_feature_dim, embedding_size], param_attr=fluid.ParamAttr(name="SparseFeatFactors", initializer=fluid.initializer.Uniform())) dense_input = fluid.layers.data( name="dense_input", shape=[dense_feature_dim], dtype='float32') sparse_input_ids = [ fluid.layers.data(name="C" + str(i), shape=[1], lod_level=1, dtype='int64') for i in range(1, 27)] label = fluid.layers.data(name='label', shape=[1], dtype='int64') words = [dense_input] + sparse_input_ids + [label] py_reader = None if use_py_reader: py_reader = fluid.layers.create_py_reader_by_data(capacity=64, feed_list=words, name='py_reader', use_double_buffer=True) words = fluid.layers.read_file(py_reader) sparse_embed_seq = list(map(embedding_layer, words[1:-1])) concated = fluid.layers.concat(sparse_embed_seq + words[0:1], axis=1) fc1 = fluid.layers.fc(input=concated, size=400, act='relu', param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( scale=1 / math.sqrt(concated.shape[1])))) fc2 = fluid.layers.fc(input=fc1, size=400, act='relu', param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( scale=1 / math.sqrt(fc1.shape[1])))) fc3 = fluid.layers.fc(input=fc2, size=400, act='relu', param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( scale=1 / math.sqrt(fc2.shape[1])))) predict = fluid.layers.fc(input=fc3, size=2, act='softmax', param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal( scale=1 / math.sqrt(fc3.shape[1])))) cost = fluid.layers.cross_entropy(input=predict, label=words[-1]) avg_cost = fluid.layers.reduce_sum(cost) accuracy = fluid.layers.accuracy(input=predict, label=words[-1]) auc_var, batch_auc_var, auc_states = \ fluid.layers.auc(input=predict, label=words[-1], num_thresholds=2 ** 12, slide_steps=20) return avg_cost, auc_var, batch_auc_var, py_reader, words