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train.py
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train.py
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import math
import sys
import argparse
import numpy as np
import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
import paddle.fluid.nets as nets
IS_SPARSE = True
BATCH_SIZE = 256
def parse_args():
parser = argparse.ArgumentParser("recommender_system")
parser.add_argument(
'--enable_ce',
action='store_true',
help="If set, run the task with continuous evaluation logs.")
parser.add_argument(
'--use_gpu', type=int, default=0, help="Whether to use GPU or not.")
parser.add_argument(
'--num_epochs', type=int, default=1, help="number of epochs.")
args = parser.parse_args()
return args
def get_usr_combined_features():
USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1
uid = layers.data(name='user_id', shape=[1], dtype='int64')
usr_emb = layers.embedding(
input=uid,
dtype='float32',
size=[USR_DICT_SIZE, 32],
param_attr='user_table',
is_sparse=IS_SPARSE)
usr_fc = layers.fc(input=usr_emb, size=32)
USR_GENDER_DICT_SIZE = 2
usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64')
usr_gender_emb = layers.embedding(
input=usr_gender_id,
size=[USR_GENDER_DICT_SIZE, 16],
param_attr='gender_table',
is_sparse=IS_SPARSE)
usr_gender_fc = layers.fc(input=usr_gender_emb, size=16)
USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table)
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64")
usr_age_emb = layers.embedding(
input=usr_age_id,
size=[USR_AGE_DICT_SIZE, 16],
is_sparse=IS_SPARSE,
param_attr='age_table')
usr_age_fc = layers.fc(input=usr_age_emb, size=16)
USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64")
usr_job_emb = layers.embedding(
input=usr_job_id,
size=[USR_JOB_DICT_SIZE, 16],
param_attr='job_table',
is_sparse=IS_SPARSE)
usr_job_fc = layers.fc(input=usr_job_emb, size=16)
concat_embed = layers.concat(
input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1)
usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
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], dtype='int64')
mov_emb = layers.embedding(
input=mov_id,
dtype='float32',
size=[MOV_DICT_SIZE, 32],
param_attr='movie_table',
is_sparse=IS_SPARSE)
mov_fc = layers.fc(input=mov_emb, size=32)
CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories())
category_id = layers.data(
name='category_id', shape=[1], dtype='int64', lod_level=1)
mov_categories_emb = layers.embedding(
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_categories_hidden = layers.sequence_pool(
input=mov_categories_emb, pool_type="sum")
MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict())
mov_title_id = layers.data(
name='movie_title', shape=[1], dtype='int64', lod_level=1)
mov_title_emb = layers.embedding(
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE)
mov_title_conv = nets.sequence_conv_pool(
input=mov_title_emb,
num_filters=32,
filter_size=3,
act="tanh",
pool_type="sum")
concat_embed = layers.concat(
input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1)
mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh")
return mov_combined_features
def inference_program():
usr_combined_features = get_usr_combined_features()
mov_combined_features = get_mov_combined_features()
inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features)
scale_infer = layers.scale(x=inference, scale=5.0)
label = layers.data(name='score', shape=[1], dtype='float32')
square_cost = layers.square_error_cost(input=scale_infer, label=label)
avg_cost = layers.mean(square_cost)
return scale_infer, avg_cost
def optimizer_func():
return fluid.optimizer.SGD(learning_rate=0.2)
def train(use_cuda, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
if args.enable_ce:
train_reader = paddle.batch(
paddle.dataset.movielens.train(), batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
else:
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.movielens.train(), buf_size=8192),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.movielens.test(), batch_size=BATCH_SIZE)
feed_order = [
'user_id', 'gender_id', 'age_id', 'job_id', 'movie_id', 'category_id',
'movie_title', 'score'
]
main_program = fluid.default_main_program()
star_program = fluid.default_startup_program()
if args.enable_ce:
main_program.random_seed = 90
star_program.random_seed = 90
scale_infer, avg_cost = inference_program()
test_program = main_program.clone(for_test=True)
sgd_optimizer = optimizer_func()
sgd_optimizer.minimize(avg_cost)
exe = fluid.Executor(place)
def train_test(program, reader):
count = 0
feed_var_list = [
program.global_block().var(var_name) for var_name in feed_order
]
feeder_test = fluid.DataFeeder(feed_list=feed_var_list, place=place)
test_exe = fluid.Executor(place)
accumulated = 0
for test_data in reader():
avg_cost_np = test_exe.run(
program=program,
feed=feeder_test.feed(test_data),
fetch_list=[avg_cost])
accumulated += avg_cost_np[0]
count += 1
return accumulated / count
def train_loop():
feed_list = [
main_program.global_block().var(var_name) for var_name in feed_order
]
feeder = fluid.DataFeeder(feed_list, place)
exe.run(star_program)
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
# train a mini-batch
outs = exe.run(
program=main_program,
feed=feeder.feed(data),
fetch_list=[avg_cost])
out = np.array(outs[0])
# get test avg_cost
test_avg_cost = train_test(test_program, test_reader)
# if test_avg_cost < 4.0: # Change this number to adjust accuracy
if batch_id == 20:
if args.enable_ce:
print("kpis\ttest_cost\t%f" % float(test_avg_cost))
if params_dirname is not None:
fluid.io.save_inference_model(params_dirname, [
"user_id", "gender_id", "age_id", "job_id",
"movie_id", "category_id", "movie_title"
], [scale_infer], exe)
return
print('EpochID {0}, BatchID {1}, Test Loss {2:0.2}'.format(
pass_id + 1, batch_id + 1, float(test_avg_cost)))
if math.isnan(float(out[0])):
sys.exit("got NaN loss, training failed.")
train_loop()
def infer(use_cuda, params_dirname):
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
# Use the first data from paddle.dataset.movielens.test() as input.
# Use create_lod_tensor(data, lod, place) API to generate LoD Tensor,
# where `data` is a list of sequences of index numbers, `lod` is
# the level of detail (lod) info associated with `data`.
# For example, data = [[10, 2, 3], [2, 3]] means that it contains
# two sequences of indexes, of length 3 and 2, respectively.
# Correspondingly, lod = [[3, 2]] contains one level of detail info,
# indicating that `data` consists of two sequences of length 3 and 2.
infer_movie_id = 783
infer_movie_name = paddle.dataset.movielens.movie_info()[
infer_movie_id].title
exe = fluid.Executor(place)
inference_scope = fluid.core.Scope()
with fluid.scope_guard(inference_scope):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[inferencer, feed_target_names,
fetch_targets] = fluid.io.load_inference_model(params_dirname, exe)
# Use the first data from paddle.dataset.movielens.test() as input
assert feed_target_names[0] == "user_id"
# Use create_lod_tensor(data, recursive_sequence_lengths, place) API
# to generate LoD Tensor where `data` is a list of sequences of index
# numbers, `recursive_sequence_lengths` is the length-based level of detail
# (lod) info associated with `data`.
# For example, data = [[10, 2, 3], [2, 3]] means that it contains
# two sequences of indexes, of length 3 and 2, respectively.
# Correspondingly, recursive_sequence_lengths = [[3, 2]] contains one
# level of detail info, indicating that `data` consists of two sequences
# of length 3 and 2, respectively.
user_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
assert feed_target_names[1] == "gender_id"
gender_id = fluid.create_lod_tensor([[np.int64(1)]], [[1]], place)
assert feed_target_names[2] == "age_id"
age_id = fluid.create_lod_tensor([[np.int64(0)]], [[1]], place)
assert feed_target_names[3] == "job_id"
job_id = fluid.create_lod_tensor([[np.int64(10)]], [[1]], place)
assert feed_target_names[4] == "movie_id"
movie_id = fluid.create_lod_tensor([[np.int64(783)]], [[1]], place)
assert feed_target_names[5] == "category_id"
category_id = fluid.create_lod_tensor(
[np.array([10, 8, 9], dtype='int64')], [[3]], place)
assert feed_target_names[6] == "movie_title"
movie_title = fluid.create_lod_tensor(
[np.array([1069, 4140, 2923, 710, 988], dtype='int64')], [[5]],
place)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results = exe.run(
inferencer,
feed={
feed_target_names[0]: user_id,
feed_target_names[1]: gender_id,
feed_target_names[2]: age_id,
feed_target_names[3]: job_id,
feed_target_names[4]: movie_id,
feed_target_names[5]: category_id,
feed_target_names[6]: movie_title
},
fetch_list=fetch_targets,
return_numpy=False)
predict_rating = np.array(results[0])
print("Predict Rating of user id 1 on movie \"" + infer_movie_name +
"\" is " + str(predict_rating[0][0]))
print("Actual Rating of user id 1 on movie \"" + infer_movie_name +
"\" is 4.")
def main(use_cuda):
if use_cuda and not fluid.core.is_compiled_with_cuda():
return
params_dirname = "recommender_system.inference.model"
train(use_cuda=use_cuda, params_dirname=params_dirname)
infer(use_cuda=use_cuda, params_dirname=params_dirname)
if __name__ == '__main__':
args = parse_args()
PASS_NUM = args.num_epochs
use_cuda = args.use_gpu
main(use_cuda)