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vbpr_multiprocess.py
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vbpr_multiprocess.py
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# coding: utf-8
import tensorflow as tf
import os
import lmdb
import cPickle as pickle
import numpy
import random
from multiprocessing import Process, Queue
with open("man20k.pkl", "r") as f:
user_id_mapping, item_id_mapping, train_ratings, test_ratings = pickle.load(f)
image_features = {}
db = lmdb.open('./features20k')
with db.begin(write=False) as ctx:
for iid in item_id_mapping.values():
image_features[iid] = numpy.fromstring(ctx.get(str(iid)), dtype=numpy.float32)
assert(len(item_id_mapping) == len(image_features))
print len(user_id_mapping)
print len(item_id_mapping)
train_queue = Queue(4)
def uniform_sample_batch(train_ratings, item_count, image_features, sample_count=20000, batch_size=512):
for i in range(sample_count):
t = []
iv = []
jv = []
for b in xrange(batch_size):
u = random.sample(train_ratings.keys(), 1)[0]
i = random.sample(train_ratings[u], 1)[0]
j = random.randint(0, item_count-1)
while j in train_ratings[u]:
j = random.randint(0, item_count-1)
t.append([u, i, j])
iv.append(image_features[i])
jv.append(image_features[j])
# block if queue is full
train_queue.put( (numpy.asarray(t), numpy.vstack(tuple(iv)), numpy.vstack(tuple(jv))), True )
train_queue.put(None)
def train_data_process(sample_count=20000, batch_size=512):
p = Process(target=uniform_sample_batch, args=(train_ratings, len(item_id_mapping), image_features, sample_count, batch_size))
return p
def test_batch_generator_by_user(train_ratings, test_ratings, item_count, image_features):
# using leave one cv
for u in test_ratings.keys():
i = test_ratings[u]
t = []
ilist = []
jlist = []
for j in range(item_count):
if j != test_ratings[u] and not (j in train_ratings[u]):
# find item not in test[u] and train[u]
t.append([u, i, j])
ilist.append(image_features[i])
jlist.append(image_features[j])
yield numpy.asarray(t), numpy.vstack(tuple(ilist)), numpy.vstack(tuple(jlist))
def vbpr(user_count, item_count, hidden_dim=20, hidden_img_dim=128,
learning_rate = 0.001,
l2_regulization = 0.01,
bias_regulization=1.0):
"""
user_count: total number of users
item_count: total number of items
hidden_dim: hidden feature size of MF
hidden_img_dim: [4096, hidden_img_dim]
"""
u = tf.placeholder(tf.int32, [None])
i = tf.placeholder(tf.int32, [None])
j = tf.placeholder(tf.int32, [None])
iv = tf.placeholder(tf.float32, [None, 4096])
jv = tf.placeholder(tf.float32, [None, 4096])
with tf.device("/gpu:1"):
user_emb_w = tf.get_variable("user_emb_w", [user_count+1, hidden_dim],
initializer=tf.random_normal_initializer(0, 0.1))
user_img_w = tf.get_variable("user_img_w", [user_count+1, hidden_img_dim],
initializer=tf.random_normal_initializer(0, 0.1))
item_emb_w = tf.get_variable("item_emb_w", [item_count+1, hidden_dim],
initializer=tf.random_normal_initializer(0, 0.1))
item_b = tf.get_variable("item_b", [item_count+1, 1],
initializer=tf.constant_initializer(0.0))
u_emb = tf.nn.embedding_lookup(user_emb_w, u)
u_img = tf.nn.embedding_lookup(user_img_w, u)
i_emb = tf.nn.embedding_lookup(item_emb_w, i)
i_b = tf.nn.embedding_lookup(item_b, i)
j_emb = tf.nn.embedding_lookup(item_emb_w, j)
j_b = tf.nn.embedding_lookup(item_b, j)
with tf.device("/gpu:1"):
img_emb_w = tf.get_variable("image_embedding_weights", [4096, hidden_img_dim],
initializer=tf.random_normal_initializer(0, 0.1))
img_i_j = tf.matmul(iv - jv, img_emb_w)
# MF predict: u_i > u_j
x = i_b - j_b + tf.reduce_sum(tf.mul(u_emb, (i_emb - j_emb)), 1, keep_dims=True) + tf.reduce_sum(tf.mul(u_img, img_i_j),1, keep_dims=True)
# auc score is used in test/cv
# reduce_mean is reasonable BECAUSE
# all test (i, j) pairs of one user is in ONE batch
auc = tf.reduce_mean(tf.to_float(x > 0))
l2_norm = tf.add_n([
tf.reduce_sum(tf.mul(u_emb, u_emb)),
tf.reduce_sum(tf.mul(u_img, u_img)),
tf.reduce_sum(tf.mul(i_emb, i_emb)),
tf.reduce_sum(tf.mul(j_emb, j_emb)),
tf.reduce_sum(tf.mul(img_emb_w, img_emb_w)),
bias_regulization * tf.reduce_sum(tf.mul(i_b, i_b)),
bias_regulization * tf.reduce_sum(tf.mul(j_b, j_b))
])
loss = l2_norm - tf.reduce_mean(tf.log(tf.sigmoid(x)))
train_op = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
return u, i, j, iv, jv, loss, auc, train_op
# In[17]:
user_count = len(user_id_mapping)
item_count = len(item_id_mapping)
with tf.Graph().as_default(), tf.Session() as session:
with tf.variable_scope('vbpr'):
u, i, j, iv, jv, loss, auc, train_op = vbpr(user_count, item_count)
session.run(tf.initialize_all_variables())
for epoch in range(1, 20):
print "epoch ", epoch
_loss_train = 0.0
sample_count = 20000
batch_size = 512
p = train_data_process(sample_count, batch_size)
p.start()
data = train_queue.get(True) #block if queue is empty
while data:
d, _iv, _jv = data
_loss, _ = session.run([loss, train_op], feed_dict={
u:d[:,0], i:d[:,1], j:d[:,2], iv:_iv, jv:_jv
})
_loss_train += _loss
data = train_queue.get(True)
p.join()
print "train_loss:", _loss_train/sample_count
_auc_all = 0
_loss_test = 0.0
_test_user_count = len(test_ratings)
for d, _iv, _jv in test_batch_generator_by_user(train_ratings,
test_ratings, item_count, image_features):
_loss, _auc = session.run([loss, auc], feed_dict={
u:d[:,0], i:d[:,1], j:d[:,2], iv:_iv, jv:_jv
})
_loss_test += _loss
_auc_all += _auc
print "test_loss: ", _loss_test/_test_user_count, " auc: ", _auc_all/_test_user_count
print ""