def setUpClass(cls):
os.environ['CPU_NUM'] = str(4)
cls.word_dict_len = 5147
batch_size = 64
reader = fake_imdb_reader(cls.word_dict_len, batch_size * 100)
reader = paddle.batch(reader, batch_size=batch_size)()
cls.train_data = next(reader)
def setUp(self):
self.word_dict_len = 5147
self.BATCH_SIZE = 2
reader = fake_imdb_reader(self.word_dict_len, self.BATCH_SIZE * 100)
self.train_data = paddle.batch(reader, batch_size=self.BATCH_SIZE)
self.clip_gradient = lambda x: None
self.init()
def train(network, use_cuda, use_parallel_executor, batch_size=32, pass_num=2):
if use_cuda and not core.is_compiled_with_cuda():
print('Skip use_cuda=True because Paddle is not compiled with cuda')
return
if use_parallel_executor and os.name == 'nt':
print(
'Skip use_parallel_executor=True because Paddle comes without parallel support on windows'
)
return
word_dict_size = 5147
reader = fake_imdb_reader(word_dict_size, batch_size * 40)
train_reader = paddle.batch(reader, batch_size=batch_size)
data = fluid.layers.data(name="words",
shape=[1],
dtype="int64",
lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost = network(data, label, word_dict_size)
cost.persistable = True
optimizer = fluid.optimizer.Adagrad(learning_rate=0.2)
optimizer.minimize(cost)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
reader = feeder.decorate_reader(train_reader,
multi_devices=use_parallel_executor)
exe = fluid.Executor(place)
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
exe.run(fluid.default_startup_program())
train_cp = fluid.default_main_program()
if use_parallel_executor:
train_cp = compiler.CompiledProgram(
fluid.default_main_program()).with_data_parallel(
loss_name=cost.name)
fetch_list = [cost.name]
else:
fetch_list = [cost]
for pass_id in six.moves.xrange(pass_num):
batch_id = 0
for data in reader():
exe.run(train_cp,
feed=data,
fetch_list=fetch_list if batch_id % 4 == 0 else [])
batch_id += 1
if batch_id > 16:
break
def test_lstm(self):
def optimizer():
optimizer = fluid.optimizer.Adagrad(
learning_rate=0.001,
regularization=fluid.regularizer.L2Decay(1e-4))
return optimizer
with self.program_scope_guard():
word_dict_size = 5147
reader = fake_imdb_reader(word_dict_size, 1)
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
feeder = fluid.DataFeeder(
feed_list=[data, label], place=core.CPUPlace())
feed_data = feeder.feed(reader())
self.check_prune_correctness(
method=lstm_net, feed_dict=feed_data, optimizer=optimizer)
def setUp(self):
self.word_dict_len = 5147
self.BATCH_SIZE = 2
reader = fake_imdb_reader(self.word_dict_len, self.BATCH_SIZE * 100)
self.train_data = paddle.batch(reader, batch_size=self.BATCH_SIZE)
