# Peeking at the top 5 user values
print(df_train["user"].head())
print(df_test["user"].head())
# Peeking at the top 5 item values
print(df_train["item"].head())
print(df_test["item"].head())
# Peeking at the top 5 rate values
print(df_train["rate"].head())
print(df_test["rate"].head())
# Using a shuffle iterator to generate random batches, for training
iter_train = readers.ShuffleIterator([df_train["user"],
df_train["item"],
df_train["rate"]],
batch_size=batch_size)
# Sequentially generate one-epoch batches, for testing
iter_test = readers.OneEpochIterator([df_test["user"],
df_test["item"],
df_test["rate"]],
batch_size=-1)
user_batch = tf.placeholder(tf.int32, shape=[None], name="id_user")
item_batch = tf.placeholder(tf.int32, shape=[None], name="id_item")
rate_batch = tf.placeholder(tf.float32, shape=[None])
infer, regularizer = model(user_batch, item_batch, user_num=u_num, item_num=i_num, dim=dims, device=place_device)
_, train_op = loss(infer, regularizer, rate_batch, learning_rate=0.0010, reg=0.05, device=place_device)
def generate_model(df_train, df_test):
sample_per_batch = len(df_train) // batch_size
iter_train = readers.ShuffleIterator(
[df_train["userId"], df_train["newsId"], df_train["rate"]],
batch_size=batch_size)
iter_test = readers.OneEpochIterator(
[df_test["userId"], df_test["newsId"], df_test["rate"]], batch_size=-1)
user_batch = tf.placeholder(tf.int32, shape=[None], name="userId")
item_batch = tf.placeholder(tf.int32, shape=[None], name="newsId")
rate_batch = tf.placeholder(tf.float32, shape=[None])
infer, regularizer = news_model.model(user_batch,
item_batch,
user_num=u_num,
item_num=i_num,
dim=dims,
device=place_device)
global_step = tf.contrib.framework.get_or_create_global_step()
_, train_op = news_model.loss(infer,
regularizer,
rate_batch,
learning_rate=0.01,
reg=0.05,
device=place_device)
init_op = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_op)
saver = tf.train.Saver()
# summary_writer = tf.summary.FileWriter(logdir="/save", graph=sess.graph)
print("{} {} {} {}".format("epoch", "train_error", "val_error",
"elapsed_time"))
errors = deque(maxlen=sample_per_batch)
start = time.time()
for i in range(max_epochs * sample_per_batch):
users, items, rates = next(iter_train)
_, pred_batch = sess.run([train_op, infer],
feed_dict={
user_batch: users,
item_batch: items,
rate_batch: rates
})
pred_batch = clip(pred_batch)
errors.append(np.power(pred_batch - rates, 2))
if i % sample_per_batch == 0:
train_err = np.sqrt(np.mean(errors))
test_err2 = np.array([])
for users, items, rates in iter_test:
pred_batch = sess.run(infer,
feed_dict={
user_batch: users,
item_batch: items
})
pred_batch = clip(pred_batch)
test_err2 = np.append(test_err2,
np.power(pred_batch - rates, 2))
end = time.time()
test_err = np.sqrt(np.mean(test_err2))
print("{:3d} {:f} {:f} {:f}(s)".format(i // sample_per_batch,
train_err, test_err,
end - start))
# train_err_summary = make_scalar_summary("training_error", train_err)
# test_err_summary = make_scalar_summary("test_error", test_err)
# summary_writer.add_summary(train_err_summary, i)
# summary_writer.add_summary(test_err_summary, i)
start = end
saver.save(sess, './model/model')