提交 55a7b981 编写于 作者: X Xin Pan

Add DeepCF model

test=develop
上级 15444430
# 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.
import unittest
import numpy as np
import random
import sys
import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
from test_imperative_base import new_program_scope
from paddle.fluid.imperative.base import to_variable
NUM_USERS = 100
NUM_ITEMS = 1000
BATCH_SIZE = 32
NUM_BATCHES = 2
class MLP(fluid.imperative.Layer):
def __init__(self, name_scope):
super(MLP, self).__init__(name_scope)
self._user_latent = fluid.imperative.FC(self.full_name(), 256)
self._item_latent = fluid.imperative.FC(self.full_name(), 256)
self._user_layers = []
self._item_layers = []
self._hid_sizes = [128, 64]
for i in range(len(self._hid_sizes)):
self._user_layers.append(
self.add_sublayer(
'user_layer_%d' % i,
fluid.imperative.FC(
self.full_name(), self._hid_sizes[i], act='relu')))
self._item_layers.append(
self.add_sublayer(
'item_layer_%d' % i,
fluid.imperative.FC(
self.full_name(), self._hid_sizes[i], act='relu')))
def forward(self, users, items):
users = self._user_latent(users)
items = self._item_latent(items)
for ul, il in zip(self._user_layers, self._item_layers):
users = ul(users)
items = il(items)
return fluid.layers.elementwise_mul(users, items)
class DMF(fluid.imperative.Layer):
def __init__(self, name_scope):
super(DMF, self).__init__(name_scope)
self._user_latent = fluid.imperative.FC(self.full_name(), 256)
self._item_latent = fluid.imperative.FC(self.full_name(), 256)
self._match_layers = []
self._hid_sizes = [128, 64]
for i in range(len(self._hid_sizes)):
self._match_layers.append(
self.add_sublayer(
'match_layer_%d' % i,
fluid.imperative.FC(
self.full_name(), self._hid_sizes[i], act='relu')))
self._mat
def forward(self, users, items):
users = self._user_latent(users)
items = self._item_latent(items)
match_vec = fluid.layers.concat(
[users, items], axis=len(users.shape) - 1)
for l in self._match_layers:
match_vec = l(match_vec)
return match_vec
class DeepCF(fluid.imperative.Layer):
def __init__(self, name_scope):
super(DeepCF, self).__init__(name_scope)
self._user_emb = fluid.imperative.Embedding(self.full_name(),
[NUM_USERS, 256])
self._item_emb = fluid.imperative.Embedding(self.full_name(),
[NUM_ITEMS, 256])
self._mlp = MLP(self.full_name())
self._dmf = DMF(self.full_name())
self._match_fc = fluid.imperative.FC(self.full_name(), 1, act='sigmoid')
def forward(self, users, items):
users_emb = self._user_emb(users)
items_emb = self._item_emb(items)
mlp_predictive = self._mlp(users_emb, items_emb)
dmf_predictive = self._dmf(users_emb, items_emb)
predictive = fluid.layers.concat(
[mlp_predictive, dmf_predictive],
axis=len(mlp_predictive.shape) - 1)
prediction = self._match_fc(predictive)
return prediction
def get_data():
user_ids = []
item_ids = []
labels = []
for uid in range(NUM_USERS):
for iid in range(NUM_ITEMS):
# 10% positive
label = float(random.randint(1, 10) == 1)
user_ids.append(uid)
item_ids.append(iid)
labels.append(label)
indices = np.arange(NUM_USERS * NUM_ITEMS)
np.random.shuffle(indices)
users_np = np.array(user_ids, dtype=np.int64)[indices]
items_np = np.array(item_ids, dtype=np.int64)[indices]
labels_np = np.array(labels, dtype=np.float32)[indices]
return np.expand_dims(users_np, -1), \
np.expand_dims(items_np, -1), \
np.expand_dims(labels_np, -1)
class TestImperativeDeepCF(unittest.TestCase):
def test_gan_float32(self):
seed = 90
users_np, items_np, labels_np = get_data()
startup = fluid.Program()
startup.random_seed = seed
main = fluid.Program()
main.random_seed = seed
scope = fluid.core.Scope()
with new_program_scope(main=main, startup=startup, scope=scope):
users = fluid.layers.data('users', [1], dtype='int64')
items = fluid.layers.data('items', [1], dtype='int64')
labels = fluid.layers.data('labels', [1], dtype='float32')
deepcf = DeepCF('deepcf')
prediction = deepcf(users, items)
loss = fluid.layers.reduce_sum(
fluid.layers.log_loss(prediction, labels))
adam = fluid.optimizer.AdamOptimizer(0.01)
adam.minimize(loss)
exe = fluid.Executor(fluid.CPUPlace(
) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
exe.run(startup)
for slice in range(0, BATCH_SIZE * NUM_BATCHES, BATCH_SIZE):
static_loss = exe.run(
main,
feed={
users.name: users_np[slice:slice + BATCH_SIZE],
items.name: items_np[slice:slice + BATCH_SIZE],
labels.name: labels_np[slice:slice + BATCH_SIZE]
},
fetch_list=[loss])[0]
sys.stderr.write('static loss %s\n' % static_loss)
with fluid.imperative.guard():
fluid.default_startup_program().random_seed = seed
fluid.default_main_program().random_seed = seed
deepcf = DeepCF('deepcf')
for slice in range(0, BATCH_SIZE * NUM_BATCHES, BATCH_SIZE):
prediction = deepcf(
to_variable(users_np[slice:slice + BATCH_SIZE]),
to_variable(items_np[slice:slice + BATCH_SIZE]))
loss = fluid.layers.reduce_sum(
fluid.layers.log_loss(prediction,
to_variable(labels_np[slice:slice +
BATCH_SIZE])))
loss._backward()
adam = fluid.optimizer.AdamOptimizer(0.01)
adam.minimize(loss)
deepcf.clear_gradients()
dy_loss = loss._numpy()
self.assertEqual(static_loss, dy_loss)
if __name__ == '__main__':
unittest.main()
......@@ -51,7 +51,7 @@ class Generator(fluid.imperative.Layer):
return self._fc3(x)
class TestImperativeMnist(unittest.TestCase):
class TestImperativeGAN(unittest.TestCase):
def test_gan_float32(self):
seed = 90
......
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