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[High-Level-API] Rewrite Chapter 5 Personalized Recommendation in Book to use new Flui… #526
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import paddle.v2 as paddle | ||
import cPickle | ||
import copy | ||
import os | ||
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with_gpu = os.getenv('WITH_GPU', '0') != '0' | ||
# 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. | ||
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import math | ||
import sys | ||
import numpy as np | ||
import paddle | ||
import paddle.fluid as fluid | ||
import paddle.fluid.layers as layers | ||
import paddle.fluid.nets as nets | ||
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IS_SPARSE = True | ||
USE_GPU = False | ||
BATCH_SIZE = 256 | ||
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def get_usr_combined_features(): | ||
uid = paddle.layer.data( | ||
name='user_id', | ||
type=paddle.data_type.integer_value( | ||
paddle.dataset.movielens.max_user_id() + 1)) | ||
usr_emb = paddle.layer.embedding(input=uid, size=32) | ||
usr_fc = paddle.layer.fc(input=usr_emb, size=32) | ||
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usr_gender_id = paddle.layer.data( | ||
name='gender_id', type=paddle.data_type.integer_value(2)) | ||
usr_gender_emb = paddle.layer.embedding(input=usr_gender_id, size=16) | ||
usr_gender_fc = paddle.layer.fc(input=usr_gender_emb, size=16) | ||
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usr_age_id = paddle.layer.data( | ||
name='age_id', | ||
type=paddle.data_type.integer_value( | ||
len(paddle.dataset.movielens.age_table))) | ||
usr_age_emb = paddle.layer.embedding(input=usr_age_id, size=16) | ||
usr_age_fc = paddle.layer.fc(input=usr_age_emb, size=16) | ||
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usr_job_id = paddle.layer.data( | ||
name='job_id', | ||
type=paddle.data_type.integer_value( | ||
paddle.dataset.movielens.max_job_id() + 1)) | ||
usr_job_emb = paddle.layer.embedding(input=usr_job_id, size=16) | ||
usr_job_fc = paddle.layer.fc(input=usr_job_emb, size=16) | ||
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usr_combined_features = paddle.layer.fc( | ||
input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], | ||
size=200, | ||
act=paddle.activation.Tanh()) | ||
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USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1 | ||
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uid = layers.data(name='user_id', shape=[1], dtype='int64') | ||
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usr_emb = layers.embedding( | ||
input=uid, | ||
dtype='float32', | ||
size=[USR_DICT_SIZE, 32], | ||
param_attr='user_table', | ||
is_sparse=IS_SPARSE) | ||
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usr_fc = layers.fc(input=usr_emb, size=32) | ||
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USR_GENDER_DICT_SIZE = 2 | ||
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usr_gender_id = layers.data(name='gender_id', shape=[1], dtype='int64') | ||
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usr_gender_emb = layers.embedding( | ||
input=usr_gender_id, | ||
size=[USR_GENDER_DICT_SIZE, 16], | ||
param_attr='gender_table', | ||
is_sparse=IS_SPARSE) | ||
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usr_gender_fc = layers.fc(input=usr_gender_emb, size=16) | ||
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USR_AGE_DICT_SIZE = len(paddle.dataset.movielens.age_table) | ||
usr_age_id = layers.data(name='age_id', shape=[1], dtype="int64") | ||
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usr_age_emb = layers.embedding( | ||
input=usr_age_id, | ||
size=[USR_AGE_DICT_SIZE, 16], | ||
is_sparse=IS_SPARSE, | ||
param_attr='age_table') | ||
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usr_age_fc = layers.fc(input=usr_age_emb, size=16) | ||
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USR_JOB_DICT_SIZE = paddle.dataset.movielens.max_job_id() + 1 | ||
usr_job_id = layers.data(name='job_id', shape=[1], dtype="int64") | ||
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usr_job_emb = layers.embedding( | ||
input=usr_job_id, | ||
size=[USR_JOB_DICT_SIZE, 16], | ||
param_attr='job_table', | ||
is_sparse=IS_SPARSE) | ||
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usr_job_fc = layers.fc(input=usr_job_emb, size=16) | ||
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concat_embed = layers.concat( | ||
input=[usr_fc, usr_gender_fc, usr_age_fc, usr_job_fc], axis=1) | ||
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usr_combined_features = layers.fc(input=concat_embed, size=200, act="tanh") | ||
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return usr_combined_features | ||
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def get_mov_combined_features(): | ||
movie_title_dict = paddle.dataset.movielens.get_movie_title_dict() | ||
mov_id = paddle.layer.data( | ||
name='movie_id', | ||
type=paddle.data_type.integer_value( | ||
paddle.dataset.movielens.max_movie_id() + 1)) | ||
mov_emb = paddle.layer.embedding(input=mov_id, size=32) | ||
mov_fc = paddle.layer.fc(input=mov_emb, size=32) | ||
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mov_categories = paddle.layer.data( | ||
name='category_id', | ||
type=paddle.data_type.sparse_binary_vector( | ||
len(paddle.dataset.movielens.movie_categories()))) | ||
mov_categories_hidden = paddle.layer.fc(input=mov_categories, size=32) | ||
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mov_title_id = paddle.layer.data( | ||
name='movie_title', | ||
type=paddle.data_type.integer_value_sequence(len(movie_title_dict))) | ||
mov_title_emb = paddle.layer.embedding(input=mov_title_id, size=32) | ||
mov_title_conv = paddle.networks.sequence_conv_pool( | ||
input=mov_title_emb, hidden_size=32, context_len=3) | ||
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mov_combined_features = paddle.layer.fc( | ||
input=[mov_fc, mov_categories_hidden, mov_title_conv], | ||
size=200, | ||
act=paddle.activation.Tanh()) | ||
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MOV_DICT_SIZE = paddle.dataset.movielens.max_movie_id() + 1 | ||
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mov_id = layers.data(name='movie_id', shape=[1], dtype='int64') | ||
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mov_emb = layers.embedding( | ||
input=mov_id, | ||
dtype='float32', | ||
size=[MOV_DICT_SIZE, 32], | ||
param_attr='movie_table', | ||
is_sparse=IS_SPARSE) | ||
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mov_fc = layers.fc(input=mov_emb, size=32) | ||
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CATEGORY_DICT_SIZE = len(paddle.dataset.movielens.movie_categories()) | ||
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category_id = layers.data( | ||
name='category_id', shape=[1], dtype='int64', lod_level=1) | ||
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mov_categories_emb = layers.embedding( | ||
input=category_id, size=[CATEGORY_DICT_SIZE, 32], is_sparse=IS_SPARSE) | ||
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mov_categories_hidden = layers.sequence_pool( | ||
input=mov_categories_emb, pool_type="sum") | ||
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MOV_TITLE_DICT_SIZE = len(paddle.dataset.movielens.get_movie_title_dict()) | ||
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mov_title_id = layers.data( | ||
name='movie_title', shape=[1], dtype='int64', lod_level=1) | ||
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mov_title_emb = layers.embedding( | ||
input=mov_title_id, size=[MOV_TITLE_DICT_SIZE, 32], is_sparse=IS_SPARSE) | ||
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mov_title_conv = nets.sequence_conv_pool( | ||
input=mov_title_emb, | ||
num_filters=32, | ||
filter_size=3, | ||
act="tanh", | ||
pool_type="sum") | ||
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concat_embed = layers.concat( | ||
input=[mov_fc, mov_categories_hidden, mov_title_conv], axis=1) | ||
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mov_combined_features = layers.fc(input=concat_embed, size=200, act="tanh") | ||
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return mov_combined_features | ||
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def main(): | ||
paddle.init(use_gpu=with_gpu) | ||
def inference_program(): | ||
usr_combined_features = get_usr_combined_features() | ||
mov_combined_features = get_mov_combined_features() | ||
inference = paddle.layer.cos_sim( | ||
a=usr_combined_features, b=mov_combined_features, size=1, scale=5) | ||
cost = paddle.layer.square_error_cost( | ||
input=inference, | ||
label=paddle.layer.data( | ||
name='score', type=paddle.data_type.dense_vector(1))) | ||
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parameters = paddle.parameters.create(cost) | ||
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trainer = paddle.trainer.SGD( | ||
cost=cost, | ||
parameters=parameters, | ||
update_equation=paddle.optimizer.Adam(learning_rate=1e-4)) | ||
feeding = { | ||
'user_id': 0, | ||
'gender_id': 1, | ||
'age_id': 2, | ||
'job_id': 3, | ||
'movie_id': 4, | ||
'category_id': 5, | ||
'movie_title': 6, | ||
'score': 7 | ||
} | ||
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def event_handler(event): | ||
if isinstance(event, paddle.event.EndIteration): | ||
if event.batch_id % 100 == 0: | ||
print "Pass %d Batch %d Cost %.2f" % ( | ||
event.pass_id, event.batch_id, event.cost) | ||
inference = layers.cos_sim(X=usr_combined_features, Y=mov_combined_features) | ||
scale_infer = layers.scale(x=inference, scale=5.0) | ||
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trainer.train( | ||
reader=paddle.batch( | ||
paddle.reader.shuffle( | ||
paddle.dataset.movielens.train(), buf_size=8192), | ||
batch_size=256), | ||
event_handler=event_handler, | ||
feeding=feeding, | ||
num_passes=1) | ||
return scale_infer | ||
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user_id = 234 | ||
movie_id = 345 | ||
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user = paddle.dataset.movielens.user_info()[user_id] | ||
movie = paddle.dataset.movielens.movie_info()[movie_id] | ||
def train_program(): | ||
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feature = user.value() + movie.value() | ||
scale_infer = inference_program() | ||
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infer_dict = copy.copy(feeding) | ||
del infer_dict['score'] | ||
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) | ||
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prediction = paddle.infer( | ||
output_layer=inference, | ||
parameters=parameters, | ||
input=[feature], | ||
feeding=infer_dict) | ||
print(prediction + 5) / 2 | ||
return [avg_cost, scale_infer] | ||
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def optimizer_func(): | ||
return fluid.optimizer.SGD(learning_rate=0.2) | ||
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def train(use_cuda, train_program, params_dirname): | ||
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() | ||
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trainer = fluid.Trainer( | ||
train_func=train_program, place=place, optimizer_func=optimizer_func) | ||
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feed_order = [ | ||
'user_id', 'gender_id', 'age_id', 'job_id', 'movie_id', 'category_id', | ||
'movie_title', 'score' | ||
] | ||
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def event_handler(event): | ||
if isinstance(event, fluid.EndStepEvent): | ||
test_reader = paddle.batch( | ||
paddle.dataset.movielens.test(), batch_size=BATCH_SIZE) | ||
avg_cost_set = trainer.test( | ||
reader=test_reader, feed_order=feed_order) | ||
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# get avg cost | ||
avg_cost = np.array(avg_cost_set).mean() | ||
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print("avg_cost: %s" % avg_cost) | ||
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if float(avg_cost) < 4: # Change this number to adjust accuracy | ||
trainer.save_params(params_dirname) | ||
trainer.stop() | ||
else: | ||
print('BatchID {0}, Test Loss {1:0.2}'.format(event.epoch + 1, | ||
float(avg_cost))) | ||
if math.isnan(float(avg_cost)): | ||
sys.exit("got NaN loss, training failed.") | ||
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train_reader = paddle.batch( | ||
paddle.reader.shuffle( | ||
paddle.dataset.movielens.train(), buf_size=8192), | ||
batch_size=BATCH_SIZE) | ||
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trainer.train( | ||
num_epochs=1, | ||
event_handler=event_handler, | ||
reader=train_reader, | ||
feed_order=feed_order) | ||
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def infer(use_cuda, inference_program, params_dirname): | ||
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() | ||
inferencer = fluid.Inferencer( | ||
inference_program, param_path=params_dirname, place=place) | ||
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# 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. | ||
user_id = fluid.create_lod_tensor([[1]], [[1]], place) | ||
gender_id = fluid.create_lod_tensor([[1]], [[1]], place) | ||
age_id = fluid.create_lod_tensor([[0]], [[1]], place) | ||
job_id = fluid.create_lod_tensor([[10]], [[1]], place) | ||
movie_id = fluid.create_lod_tensor([[783]], [[1]], place) | ||
category_id = fluid.create_lod_tensor([[10, 8, 9]], [[3]], place) | ||
movie_title = fluid.create_lod_tensor([[1069, 4140, 2923, 710, 988]], [[5]], | ||
place) | ||
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results = inferencer.infer( | ||
{ | ||
'user_id': user_id, | ||
'gender_id': gender_id, | ||
'age_id': age_id, | ||
'job_id': job_id, | ||
'movie_id': movie_id, | ||
'category_id': category_id, | ||
'movie_title': movie_title | ||
}, | ||
return_numpy=False) | ||
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print("infer results: ", np.array(results[0])) | ||
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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, | ||
train_program=train_program, | ||
params_dirname=params_dirname) | ||
infer( | ||
use_cuda=use_cuda, | ||
inference_program=inference_program, | ||
params_dirname=params_dirname) | ||
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if __name__ == '__main__': | ||
main() | ||
main(USE_GPU) |
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Can we show a comparison between prediction and the real data? For example, user
23::M::35::0::90049
rated movie2278::Ronin (1998)::Action|Crime|Thriller
a 4.0 score. Our prediction is 3.458There was a problem hiding this comment.
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sure
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Good suggestion, i think it would be helpful