import paddle.v2 as paddle import paddle.v2.framework.layers as layers import paddle.v2.framework.nets as nets import paddle.v2.framework.core as core import paddle.v2.framework.optimizer as optimizer from paddle.v2.framework.framework import Program from paddle.v2.framework.executor import Executor import numpy as np startup_program = Program() main_program = Program() is_sparse = True use_gpu = False BATCH_SIZE = 256 def get_usr_combined_features(): # FIXME(dzh) : old API integer_value(10) may has range check. # currently we don't have user configurated check. USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1 uid = layers.data( name='user_id', shape=[1], data_type='int64', main_program=main_program, startup_program=startup_program) usr_emb = layers.embedding( input=uid, data_type='float32', size=[USR_DICT_SIZE, 32], param_attr={'name': 'user_table'}, is_sparse=is_sparse, main_program=main_program, startup_program=startup_program) usr_fc = layers.fc(input=usr_emb, size=32, main_program=main_program, startup_program=startup_program) USR_GENDER_DICT_SIZE = 2 usr_gender_id = layers.data( name='gender_id', shape=[1], data_type='int64', main_program=main_program, startup_program=startup_program) usr_gender_emb = layers.embedding( input=usr_gender_id, size=[USR_GENDER_DICT_SIZE, 16], param_attr={'name': 'gender_table'}, is_sparse=is_sparse, main_program=main_program, startup_program=startup_program) usr_gender_fc = layers.fc(input=usr_gender_emb, size=16, main_program=main_program, startup_program=startup_program) USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table) usr_age_id = layers.data( name='age_id', shape=[1], data_type="int64", main_program=main_program, startup_program=startup_program) usr_age_emb = layers.embedding( input=usr_age_id, size=[USR_AGE_DICT_SIZE, 16], is_sparse=is_sparse, param_attr={'name': 'age_table'}, main_program=main_program, startup_program=startup_program) usr_age_fc = layers.fc(input=usr_age_emb, size=16, main_program=main_program, startup_program=startup_program) USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1 usr_job_id = layers.data( name='job_id', shape=[1], data_type="int64", main_program=main_program, startup_program=startup_program) usr_job_emb = layers.embedding( input=usr_job_id, size=[USR_JOB_DICT_SIZE, 16], param_attr={'name': 'job_table'}, is_sparse=is_sparse, main_program=main_program, startup_program=startup_program) usr_job_fc = layers.fc(input=usr_job_emb, size=16, main_program=main_program, startup_program=startup_program) concat_embed = layers.concat( input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1, main_program=main_program, startup_program=startup_program) usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh", main_program=main_program, startup_program=startup_program) return usr_combined_features def get_mov_combined_features(): MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1 mov_id = layers.data( name='movie_id', shape=[1], data_type='int64', main_program=main_program, startup_program=startup_program) mov_emb = layers.embedding( input=mov_id, data_type='float32', size=[MOV_DICT_SIZE, 32], param_attr={'name': 'movie_table'}, is_sparse=is_sparse, main_program=main_program, startup_program=startup_program) mov_fc = layers.fc(input=mov_emb, size=32, main_program=main_program, startup_program=startup_program) CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories()) category_id = layers.data( name='category_id', shape=[1], data_type='int64', main_program=main_program, startup_program=startup_program) mov_categories_emb = layers.embedding( input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=is_sparse, main_program=main_program, startup_program=startup_program) mov_categories_hidden = layers.sequence_pool( input=mov_categories_emb, pool_type="sum", main_program=main_program, startup_program=startup_program) MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict()) mov_title_id = layers.data( name='movie_title', shape=[1], data_type='int64', main_program=main_program, startup_program=startup_program) mov_title_emb = layers.embedding( input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=is_sparse, main_program=main_program, startup_program=startup_program) mov_title_conv = nets.sequence_conv_pool( input=mov_title_emb, num_filters=32, filter_size=3, act="tanh", pool_type="sum", main_program=main_program, startup_program=startup_program) concat_embed = layers.concat( input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1, main_program=main_program, startup_program=startup_program) # FIXME(dzh) : need tanh operator mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh", main_program=main_program, startup_program=startup_program) return mov_combined_features def model(): usr_combined_features = get_usr_combined_features() mov_combined_features = get_mov_combined_features() # need cos sim inference = layers.cos_sim( X=usr_combined_features, Y=mov_combined_features, main_program=main_program, startup_program=startup_program) label = layers.data( name='score', shape=[1], data_type='float32', main_program=main_program, startup_program=startup_program) square_cost = layers.square_error_cost( input=inference, label=label, main_program=main_program, startup_program=startup_program) avg_cost = layers.mean( x=square_cost, main_program=main_program, startup_program=startup_program) return avg_cost def main(): cost = model() sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.2) opts = sgd_optimizer.minimize(cost, startup_program=startup_program) block = main_program.block(0) if use_gpu: place = core.GPUPlace(0) else: place = core.CPUPlace() exe = Executor(place) exe.run(startup_program, feed={}, fetch_list=[]) train_reader = paddle.batch( paddle.reader.shuffle( paddle.dataset.movielens.train(), buf_size=8192), batch_size=BATCH_SIZE) feeding = { 'user_id': 0, 'gender_id': 1, 'age_id': 2, 'job_id': 3, 'movie_id': 4, 'category_id': 5, 'movie_title': 6, 'score': 7 } def func_feed(feeding, data): feed_tensors = {} for (key, idx) in feeding.iteritems(): tensor = core.LoDTensor() if key != "category_id" and key != "movie_title": if key == "score": numpy_data = np.array(map(lambda x: x[idx], data)).astype( "float32") else: numpy_data = np.array(map(lambda x: x[idx], data)).astype( "int64") else: numpy_data = map(lambda x: np.array(x[idx]).astype("int64"), data) lod_info = [len(item) for item in numpy_data] offset = 0 lod = [offset] for item in lod_info: offset += item lod.append(offset) numpy_data = np.concatenate(numpy_data, axis=0) tensor.set_lod([lod]) numpy_data = numpy_data.reshape([numpy_data.shape[0], 1]) tensor.set(numpy_data, place) feed_tensors[key] = tensor return feed_tensors PASS_NUM = 100 for pass_id in range(PASS_NUM): for data in train_reader(): outs = exe.run(main_program, feed=func_feed(feeding, data), fetch_list=[cost]) out = np.array(outs[0]) if out[0] < 6.0: # if avg cost less than 6.0, we think our code is good. exit(0) main()