def init(**kwargs):
import py_paddle.swig_paddle as api
args = []
args_dict = {}
# NOTE: append arguments if they are in ENV
for ek, ev in os.environ.iteritems():
if ek.startswith("PADDLE_INIT_"):
args_dict[ek.replace("PADDLE_INIT_", "").lower()] = str(ev)
args_dict.update(kwargs)
# NOTE: overwrite arguments from ENV if it is in kwargs
for key in args_dict.keys():
args.append('--%s=%s' % (key, str(args_dict[key])))
set_env_vars(kwargs.get('trainer_count', 1))
if 'use_gpu' in kwargs:
cp.g_command_config_args['use_gpu'] = kwargs['use_gpu']
if 'use_mkldnn' in kwargs:
cp.g_command_config_args['use_mkldnn'] = kwargs['use_mkldnn']
if 'use_mkl_packed' in kwargs:
cp.g_command_config_args['use_mkl_packed'] = kwargs['use_mkl_packed']
assert 'parallel_nn' not in kwargs, ("currently 'parallel_nn' is not "
"supported in v2 APIs.")
api.initPaddle(*args)
def init(**kwargs):
import py_paddle.swig_paddle as api
args = []
args_dict = {}
# NOTE: append arguments if they are in ENV
for ek, ev in os.environ.iteritems():
if ek.startswith("PADDLE_INIT_"):
args_dict[ek.replace("PADDLE_INIT_", "").lower()] = str(ev)
args_dict.update(kwargs)
# NOTE: overwrite arguments from ENV if it is in kwargs
for key in args_dict.keys():
args.append('--%s=%s' % (key, str(args_dict[key])))
set_env_vars(kwargs.get('trainer_count', 1))
if 'use_gpu' in kwargs:
cp.g_command_config_args['use_gpu'] = kwargs['use_gpu']
if 'use_mkldnn' in kwargs:
cp.g_command_config_args['use_mkldnn'] = kwargs['use_mkldnn']
if 'use_mkl_packed' in kwargs:
cp.g_command_config_args['use_mkl_packed'] = kwargs['use_mkl_packed']
assert 'parallel_nn' not in kwargs, ("currently 'parallel_nn' is not "
"supported in v2 APIs.")
api.initPaddle(*args)
def paddle_predict_main(q, result_q):
api.initPaddle("--use_gpu=false")
gm = api.GradientMachine.loadFromConfigFile("./output/model/pass-00000/trainer_config.py")
assert isinstance(gm, api.GradientMachine)
converter = DataProviderConverter(input_types=[dense_vector(28 * 28)])
while True:
features = q.get()
val = gm.forwardTest(converter([[features]]))[0]['value'][0]
result_q.put(val)
def predict(arr):
swig_paddle.initPaddle("--use_gpu=0")
data = [arr.tolist()]
#直接填充4个0
for i in range(4):
data[0][0].append(0)
sex = prediction_sex.predict(data)
age = prediction_age.predict(data)
return sex, age
def __init__(self, train_conf, model_dir=None,
resize_dim=256, crop_dim=224,
mean_file=None,
output_layer=None,
oversample=False, is_color=True):
"""
train_conf: network configure.
model_dir: string, directory of model.
resize_dim: int, resized image size.
crop_dim: int, crop size.
mean_file: string, image mean file.
oversample: bool, oversample means multiple crops, namely five
patches (the four corner patches and the center
patch) as well as their horizontal reflections,
ten crops in all.
"""
self.train_conf = train_conf
self.model_dir = model_dir
if model_dir is None:
self.model_dir = os.path.dirname(train_conf)
self.resize_dim = resize_dim
self.crop_dims = [crop_dim, crop_dim]
self.oversample = oversample
self.is_color = is_color
self.output_layer = output_layer
if self.output_layer:
assert isinstance(self.output_layer, basestring)
self.output_layer = self.output_layer.split(",")
self.transformer = image_util.ImageTransformer(is_color = is_color)
self.transformer.set_transpose((2,0,1))
self.transformer.set_channel_swap((2,1,0))
self.mean_file = mean_file
if self.mean_file is not None:
mean = np.load(self.mean_file)['data_mean']
mean = mean.reshape(3, self.crop_dims[0], self.crop_dims[1])
self.transformer.set_mean(mean) # mean pixel
else:
# if you use three mean value, set like:
# this three mean value is calculated from ImageNet.
self.transformer.set_mean(np.array([103.939,116.779,123.68]))
conf_args = "is_test=1,use_gpu=1,is_predict=1"
conf = parse_config(train_conf, conf_args)
swig_paddle.initPaddle("--use_gpu=1")
self.network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
assert isinstance(self.network, swig_paddle.GradientMachine)
self.network.loadParameters(self.model_dir)
data_size = 3 * self.crop_dims[0] * self.crop_dims[1]
slots = [DenseSlot(data_size)]
is_sequence = False
self.converter = util.DataProviderWrapperConverter(is_sequence, slots)
def main():
options, args = option_parser()
train_conf = options.train_conf
data = options.data
dict_file = options.dict_file
model_path = options.model_path
label = options.label
swig_paddle.initPaddle("--use_gpu=0")
predict = SentimentPrediction(train_conf, dict_file, model_path, label)
predict.predict(data)
def main():
options, args = option_parser()
train_conf = options.train_conf
data = options.data
dict_file = options.dict_file
model_path = options.model_path
label = options.label
swig_paddle.initPaddle("--use_gpu=0")
predict = SentimentPrediction(train_conf, dict_file, model_path, label)
predict.predict(data)
def __init__(self,
train_conf,
use_gpu=True,
model_dir=None,
resize_dim=None,
crop_dim=None,
mean_file=None,
oversample=False,
is_color=False):
"""
train_conf: 网络配置文件
model_dir: 模型路径
resize_dim: 设为原图大小
crop_dim: 图像裁剪大小，一般设为原图大小
oversample: bool, oversample表示多次裁剪,这里禁用
"""
self.train_conf = train_conf
self.model_dir = model_dir
if model_dir is None:
self.model_dir = os.path.dirname(train_conf)
self.resize_dim = resize_dim
self.crop_dims = [crop_dim, crop_dim]
self.oversample = oversample
self.is_color = is_color
self.transformer = image_util.ImageTransformer(is_color = is_color)
self.transformer.set_transpose((2,0,1))
self.mean_file = mean_file
mean = np.load(self.mean_file)['data_mean']
mean = mean.reshape(1, self.crop_dims[0], self.crop_dims[1])
self.transformer.set_mean(mean) # mean pixel
gpu = 1 if use_gpu else 0
conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (gpu)
#使用 parse_config() 解析训练时的配置文件
conf = parse_config(train_conf, conf_args)
#PaddlePaddle目前使用Swig对其常用的预测接口进行了封装，使在Python环境下的预测接口更加简单
#使用 swig_paddle.initPaddle() 传入命令行参数初始化 PaddlePaddle
swig_paddle.initPaddle("--use_gpu=%d" % (int(use_gpu)))
#使用 swig_paddle.GradientMachine.createFromConfigproto() 根据上一步解析好的配置创建神经网络
self.network = swig_paddle.GradientMachine.createFromConfigProto(conf.model_config)
assert isinstance(self.network, swig_paddle.GradientMachine)
#从模型文件加载参数
self.network.loadParameters(self.model_dir)
data_size = 1 * self.crop_dims[0] * self.crop_dims[1]
slots = [dense_vector(data_size)]
'''
创建一个 DataProviderConverter 对象converter。
swig_paddle接受的原始数据是C++的Matrix，也就是直接写内存的float数组。 这个接口并不用户友好。所以，我们提供了一个工具类DataProviderConverter。 这个工具类接收和PyDataProvider2一样的输入数据
'''
self.converter = DataProviderConverter(slots)
def main():
options = parse_arguments()
api.initPaddle("--use_gpu=%s" % options.use_gpu,
"--trainer_count=%s" % options.trainer_count)
word_dict = load_dict(options.dict_file)
train_dataset = list(load_data(options.train_data, word_dict))
if options.test_data:
test_dataset = list(load_data(options.test_data, word_dict))
else:
test_dataset = None
trainer_config = parse_config(options.config,
"dict_file=%s" % options.dict_file)
# No need to have data provider for trainer
trainer_config.ClearField('data_config')
trainer_config.ClearField('test_data_config')
# create a GradientMachine from the model configuratin
model = api.GradientMachine.createFromConfigProto(
trainer_config.model_config)
# create a trainer for the gradient machine
trainer = api.Trainer.create(trainer_config, model)
# create a data converter which converts data to PaddlePaddle
# internal format
input_types = [
integer_value_sequence(len(word_dict))
if options.seq else sparse_binary_vector(len(word_dict)),
integer_value(2)
]
converter = DataProviderConverter(input_types)
batch_size = trainer_config.opt_config.batch_size
trainer.startTrain()
for train_pass in xrange(options.num_passes):
trainer.startTrainPass()
random.shuffle(train_dataset)
for pos in xrange(0, len(train_dataset), batch_size):
batch = itertools.islice(train_dataset, pos, pos + batch_size)
size = min(batch_size, len(train_dataset) - pos)
trainer.trainOneDataBatch(size, converter(batch))
trainer.finishTrainPass()
if test_dataset:
trainer.startTestPeriod()
for pos in xrange(0, len(test_dataset), batch_size):
batch = itertools.islice(test_dataset, pos, pos + batch_size)
size = min(batch_size, len(test_dataset) - pos)
trainer.testOneDataBatch(size, converter(batch))
trainer.finishTestPeriod()
trainer.finishTrain()
def main():
options, args = option_parser()
train_conf = options.train_conf
data_file = options.data_file
dict_file = options.dict_file
model_path = options.model_path
label_file = options.label_file
predict_dict_file = options.predict_dict_file
output_file = options.output_file
swig_paddle.initPaddle("--use_gpu=0")
predict = Prediction(train_conf, dict_file, model_path, label_file,
predict_dict_file)
predict.predict(data_file, output_file)
def main():
options = parse_arguments()
api.initPaddle("--use_gpu=%s" % options.use_gpu,
"--trainer_count=%s" % options.trainer_count)
word_dict = load_dict(options.dict_file)
train_dataset = list(load_data(options.train_data, word_dict))
if options.test_data:
test_dataset = list(load_data(options.test_data, word_dict))
else:
test_dataset = None
trainer_config = parse_config(options.config,
"dict_file=%s" % options.dict_file)
# No need to have data provider for trainer
trainer_config.ClearField('data_config')
trainer_config.ClearField('test_data_config')
# create a GradientMachine from the model configuratin
model = api.GradientMachine.createFromConfigProto(
trainer_config.model_config)
# create a trainer for the gradient machine
trainer = api.Trainer.create(trainer_config, model)
# create a data converter which converts data to PaddlePaddle
# internal format
input_types = [
integer_value_sequence(len(word_dict)) if options.seq else
sparse_binary_vector(len(word_dict)), integer_value(2)
]
converter = DataProviderConverter(input_types)
batch_size = trainer_config.opt_config.batch_size
trainer.startTrain()
for train_pass in xrange(options.num_passes):
trainer.startTrainPass()
random.shuffle(train_dataset)
for pos in xrange(0, len(train_dataset), batch_size):
batch = itertools.islice(train_dataset, pos, pos + batch_size)
size = min(batch_size, len(train_dataset) - pos)
trainer.trainOneDataBatch(size, converter(batch))
trainer.finishTrainPass()
if test_dataset:
trainer.startTestPeriod()
for pos in xrange(0, len(test_dataset), batch_size):
batch = itertools.islice(test_dataset, pos, pos + batch_size)
size = min(batch_size, len(test_dataset) - pos)
trainer.testOneDataBatch(size, converter(batch))
trainer.finishTestPeriod()
trainer.finishTrain()
def main():
options, args = option_parser()
train_conf = options.train_conf
data_file = options.data_file
dict_file = options.dict_file
model_path = options.model_path
label_file = options.label_file
predict_dict_file = options.predict_dict_file
output_file = options.output_file
swig_paddle.initPaddle("--use_gpu=0")
predict = Prediction(train_conf, dict_file, model_path, label_file,
predict_dict_file)
predict.predict(data_file, output_file)
def init(**kwargs):
args = []
args_dict = {}
# NOTE: append arguments if they are in ENV
for ek, ev in os.environ.iteritems():
if ek.startswith("PADDLE_INIT_"):
args_dict[ek.replace("PADDLE_INIT_", "").lower()] = str(ev)
args_dict.update(kwargs)
# NOTE: overwrite arguments from ENV if it is in kwargs
for key in args_dict.keys():
args.append('--%s=%s' % (key, str(args_dict[key])))
api.initPaddle(*args)
def __init__(self,
train_conf,
use_gpu=True,
model_dir=None,
resize_dim=None,
crop_dim=None,
mean_file=None,
oversample=False,
is_color=True):
"""
train_conf: network configure.
model_dir: string, directory of model.
resize_dim: int, resized image size.
crop_dim: int, crop size.
mean_file: string, image mean file.
oversample: bool, oversample means multiple crops, namely five
patches (the four corner patches and the center
patch) as well as their horizontal reflections,
ten crops in all.
"""
self.train_conf = train_conf
self.model_dir = model_dir
if model_dir is None:
self.model_dir = os.path.dirname(train_conf)
self.resize_dim = resize_dim
self.crop_dims = [crop_dim, crop_dim]
self.oversample = oversample
self.is_color = is_color
self.transformer = image_util.ImageTransformer(is_color=is_color)
self.transformer.set_transpose((2, 0, 1))
self.mean_file = mean_file
mean = np.load(self.mean_file)['data_mean']
mean = mean.reshape(3, self.crop_dims[0], self.crop_dims[1])
self.transformer.set_mean(mean) # mean pixel
gpu = 1 if use_gpu else 0
conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (gpu)
conf = parse_config(train_conf, conf_args)
swig_paddle.initPaddle("--use_gpu=%d" % (gpu))
self.network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
assert isinstance(self.network, swig_paddle.GradientMachine)
self.network.loadParameters(self.model_dir)
data_size = 3 * self.crop_dims[0] * self.crop_dims[1]
slots = [dense_vector(data_size)]
self.converter = DataProviderConverter(slots)
def __init__(self,
train_conf,
use_gpu=True,
model_dir=None,
resize_dim=None,
crop_dim=None,
mean_file=None,
oversample=False,
is_color=True):
"""
train_conf: network configure.
model_dir: string, directory of model.
resize_dim: int, resized image size.
crop_dim: int, crop size.
mean_file: string, image mean file.
oversample: bool, oversample means multiple crops, namely five
patches (the four corner patches and the center
patch) as well as their horizontal reflections,
ten crops in all.
"""
self.train_conf = train_conf
self.model_dir = model_dir
if model_dir is None:
self.model_dir = os.path.dirname(train_conf)
self.resize_dim = resize_dim
self.crop_dims = [crop_dim, crop_dim]
self.oversample = oversample
self.is_color = is_color
self.transformer = image_util.ImageTransformer(is_color=is_color)
self.transformer.set_transpose((2, 0, 1))
self.mean_file = mean_file
mean = np.load(self.mean_file)['data_mean']
mean = mean.reshape(3, self.crop_dims[0], self.crop_dims[1])
self.transformer.set_mean(mean) # mean pixel
gpu = 1 if use_gpu else 0
conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (gpu)
conf = parse_config(train_conf, conf_args)
swig_paddle.initPaddle("--use_gpu=%d" % (gpu))
self.network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
assert isinstance(self.network, swig_paddle.GradientMachine)
self.network.loadParameters(self.model_dir)
data_size = 3 * self.crop_dims[0] * self.crop_dims[1]
slots = [DenseSlot(data_size)]
self.converter = util.DataProviderWrapperConverter(False, slots)
def main():
options, args = option_parser()
train_conf = options.train_conf
batch_size = options.batch_size
dict_file = options.dict_file
model_path = options.model_path
label = options.label
swig_paddle.initPaddle("--use_gpu=0")
predict = SentimentPrediction(train_conf, dict_file, model_path, label)
batch = []
for line in sys.stdin:
batch.append([predict.get_index(line)])
if len(batch) == batch_size:
predict.batch_predict(batch)
batch = []
if len(batch) > 0:
predict.batch_predict(batch)
def main():
options, args = option_parser()
train_conf = options.train_conf
batch_size = options.batch_size
dict_file = options.dict_file
model_path = options.model_path
label = options.label
swig_paddle.initPaddle("--use_gpu=0")
predict = QuickStartPrediction(train_conf, dict_file, model_path, label)
batch = []
labels = []
for line in sys.stdin:
[label, text] = line.split("\t")
labels.append(int(label))
batch.append([predict.get_index(text)])
print("labels is:")
print labels
predict.batch_predict(batch)
def main():
options, args = option_parser()
train_conf = options.train_conf
batch_size = options.batch_size
dict_file = options.dict_file
model_path = options.model_path
label = options.label
swig_paddle.initPaddle("--use_gpu=0")
predict = QuickStartPrediction(train_conf, dict_file, model_path, label)
batch = []
labels = []
for line in sys.stdin:
[label, text] = line.split("\t")
labels.append(int(label))
batch.append([predict.get_index(text)])
print("labels is:")
print labels
predict.batch_predict(batch)
def main():
options, args = option_parser()
train_conf = options.train_conf
batch_size = options.batch_size
dict_file = options.dict_file
model_path = options.model_path
label = options.label
swig_paddle.initPaddle("--use_gpu=0")
predict = SentimentPrediction(train_conf, dict_file, model_path, label)
batch = []
for line in sys.stdin:
words = predict.get_index(line)
if words:
batch.append([words])
else:
print('All the words in [%s] are not in the dictionary.' % line)
if len(batch) == batch_size:
predict.batch_predict(batch)
batch = []
if len(batch) > 0:
predict.batch_predict(batch)
def main():
options, args = option_parser()
train_conf = options.train_conf
batch_size = options.batch_size
dict_file = options.dict_file
model_path = options.model_path
label = options.label
swig_paddle.initPaddle("--use_gpu=0")
predict = SentimentPrediction(train_conf, dict_file, model_path, label)
batch = []
for line in sys.stdin:
words = predict.get_index(line)
if words:
batch.append([words])
else:
print('All the words in [%s] are not in the dictionary.' % line)
if len(batch) == batch_size:
predict.batch_predict(batch)
batch = []
if len(batch) > 0:
predict.batch_predict(batch)
data = []
for i in xrange(batch_size):
a = np.random.randint(10)
b = self.sparse_binary_reader(20000, 40, non_empty=True)
c = self.dense_reader(100)
each_sample = (a, b, c)
data.append(each_sample)
# test multiple features
data_types = [('fea0', data_type.dense_vector(100)),
('fea1', data_type.sparse_binary_vector(20000)),
('fea2', data_type.integer_value(10))]
feeder = DataFeeder(data_types, {'fea0': 2, 'fea1': 1, 'fea2': 0})
arg = feeder(data)
out_dense = arg.getSlotValue(0).copyToNumpyMat()
out_sparse = arg.getSlotValue(1)
out_index = arg.getSlotIds(2).copyToNumpyArray()
for i in xrange(batch_size):
self.assertEqual(out_dense[i].all(), data[i][2].all())
self.assertEqual(out_sparse.getSparseRowCols(i), data[i][1])
self.assertEqual(out_index[i], data[i][0])
if __name__ == '__main__':
api.initPaddle("--use_gpu=0")
suite = unittest.TestLoader().loadTestsFromTestCase(DataFeederTest)
unittest.TextTestRunner().run(suite)
if api.isGpuVersion():
api.setUseGpu(True)
unittest.main()
for h in xrange(m.getHeight()):
for w in xrange(m.getWidth()):
self.assertEqual(m.get(h, w), numpy_mat[h, w])
mat2 = m.toNumpyMatInplace()
mat2[1, 1] = 32.2
self.assertTrue(np.array_equal(mat2, numpy_mat))
def test_numpyGpu(self):
if swig_paddle.isGpuVersion():
numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype='float32')
gpu_m = swig_paddle.Matrix.createGpuDenseFromNumpy(numpy_mat)
assert isinstance(gpu_m, swig_paddle.Matrix)
self.assertEqual((int(gpu_m.getHeight()), int(gpu_m.getWidth())),
numpy_mat.shape)
self.assertTrue(gpu_m.isGpu())
numpy_mat = gpu_m.copyToNumpyMat()
numpy_mat[0, 1] = 3.23
for a, e in zip(gpu_m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
gpu_m.copyFromNumpyMat(numpy_mat)
for a, e in zip(gpu_m.getData(), [1.0, 3.23, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
if __name__ == "__main__":
swig_paddle.initPaddle("--use_gpu=0")
unittest.main()
self.assertTrue(util.doubleEqual(n, v))
numpy_2 = vec.toNumpyArrayInplace()
vec[0] = 1.3
for x, y in zip(numpy_arr, numpy_2):
self.assertTrue(util.doubleEqual(x, y))
for x, y in zip(numpy_arr, vec):
self.assertTrue(util.doubleEqual(x, y))
numpy_3 = vec.copyToNumpyArray()
numpy_3[0] = 0.4
self.assertTrue(util.doubleEqual(vec[0], 1.3))
self.assertTrue(util.doubleEqual(numpy_3[0], 0.4))
for i in xrange(1, len(numpy_3)):
util.doubleEqual(numpy_3[i], vec[i])
def testCopyFromNumpy(self):
vec = swig_paddle.Vector.createZero(1)
arr = np.array([1.3, 3.2, 2.4], dtype="float32")
vec.copyFromNumpyArray(arr)
for i in xrange(len(vec)):
self.assertTrue(util.doubleEqual(vec[i], arr[i]))
if __name__ == '__main__':
swig_paddle.initPaddle("--use_gpu=1"
if swig_paddle.isGpuVersion() else "--use_gpu=0")
unittest.main()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--data_source", help="mnist or cifar or uniform")
parser.add_argument(
"--use_gpu", default="1", help="1 means use gpu for training")
parser.add_argument("--gpu_id", default="0", help="the gpu_id parameter")
args = parser.parse_args()
data_source = args.data_source
use_gpu = args.use_gpu
assert data_source in ["mnist", "cifar", "uniform"]
assert use_gpu in ["0", "1"]
if not os.path.exists("./%s_samples/" % data_source):
os.makedirs("./%s_samples/" % data_source)
if not os.path.exists("./%s_params/" % data_source):
os.makedirs("./%s_params/" % data_source)
api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
'--log_period=100', '--gpu_id=' + args.gpu_id,
'--save_dir=' + "./%s_params/" % data_source)
if data_source == "uniform":
conf = "gan_conf.py"
num_iter = 10000
else:
conf = "gan_conf_image.py"
num_iter = 1000
gen_conf = parse_config(conf, "mode=generator_training,data=" + data_source)
dis_conf = parse_config(conf,
"mode=discriminator_training,data=" + data_source)
generator_conf = parse_config(conf, "mode=generator,data=" + data_source)
batch_size = dis_conf.opt_config.batch_size
noise_dim = get_layer_size(gen_conf.model_config, "noise")
if data_source == "mnist":
data_np = load_mnist_data("./data/mnist_data/train-images-idx3-ubyte")
elif data_source == "cifar":
data_np = load_cifar_data("./data/cifar-10-batches-py/")
else:
data_np = load_uniform_data()
# this creates a gradient machine for discriminator
dis_training_machine = api.GradientMachine.createFromConfigProto(
dis_conf.model_config)
# this create a gradient machine for generator
gen_training_machine = api.GradientMachine.createFromConfigProto(
gen_conf.model_config)
# generator_machine is used to generate data only, which is used for
# training discriminator
logger.info(str(generator_conf.model_config))
generator_machine = api.GradientMachine.createFromConfigProto(
generator_conf.model_config)
dis_trainer = api.Trainer.create(dis_conf, dis_training_machine)
gen_trainer = api.Trainer.create(gen_conf, gen_training_machine)
dis_trainer.startTrain()
gen_trainer.startTrain()
# Sync parameters between networks (GradientMachine) at the beginning
copy_shared_parameters(gen_training_machine, dis_training_machine)
copy_shared_parameters(gen_training_machine, generator_machine)
# constrain that either discriminator or generator can not be trained
# consecutively more than MAX_strike times
curr_train = "dis"
curr_strike = 0
MAX_strike = 5
for train_pass in xrange(100):
dis_trainer.startTrainPass()
gen_trainer.startTrainPass()
for i in xrange(num_iter):
# Do forward pass in discriminator to get the dis_loss
noise = get_noise(batch_size, noise_dim)
data_batch_dis_pos = prepare_discriminator_data_batch_pos(
batch_size, data_np)
dis_loss_pos = get_training_loss(dis_training_machine,
data_batch_dis_pos)
data_batch_dis_neg = prepare_discriminator_data_batch_neg(
generator_machine, batch_size, noise)
dis_loss_neg = get_training_loss(dis_training_machine,
data_batch_dis_neg)
dis_loss = (dis_loss_pos + dis_loss_neg) / 2.0
# Do forward pass in generator to get the gen_loss
data_batch_gen = prepare_generator_data_batch(batch_size, noise)
gen_loss = get_training_loss(gen_training_machine, data_batch_gen)
if i % 100 == 0:
print "d_pos_loss is %s d_neg_loss is %s" % (dis_loss_pos,
dis_loss_neg)
print "d_loss is %s g_loss is %s" % (dis_loss, gen_loss)
# Decide which network to train based on the training history
# And the relative size of the loss
if (not (curr_train == "dis" and curr_strike == MAX_strike)) and \
((curr_train == "gen" and curr_strike == MAX_strike) or dis_loss > gen_loss):
if curr_train == "dis":
curr_strike += 1
else:
curr_train = "dis"
curr_strike = 1
dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_neg)
dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos)
copy_shared_parameters(dis_training_machine,
gen_training_machine)
else:
if curr_train == "gen":
curr_strike += 1
else:
curr_train = "gen"
curr_strike = 1
gen_trainer.trainOneDataBatch(batch_size, data_batch_gen)
# TODO: add API for paddle to allow true parameter sharing between different GradientMachines
# so that we do not need to copy shared parameters.
copy_shared_parameters(gen_training_machine,
dis_training_machine)
copy_shared_parameters(gen_training_machine, generator_machine)
dis_trainer.finishTrainPass()
gen_trainer.finishTrainPass()
# At the end of each pass, save the generated samples/images
fake_samples = get_fake_samples(generator_machine, batch_size, noise)
if data_source == "uniform":
plot2DScatter(fake_samples, "./%s_samples/train_pass%s.png" %
(data_source, train_pass))
else:
save_images(fake_samples, "./%s_samples/train_pass%s.png" %
(data_source, train_pass))
dis_trainer.finishTrain()
gen_trainer.finishTrain()
:param rho: the :math:`\\rho` in the equation. The forgetting factor.
:type rho: float
:param epsilon: the :math:`\\epsilon` in the equation.
:type epsilon: float
"""
def __init__(self, rho=0.95, epsilon=1e-6, **kwargs):
learning_method = v1_optimizers.RMSPropOptimizer(rho=rho,
epsilon=epsilon)
super(RMSProp, self).__init__(learning_method=learning_method,
**kwargs)
ModelAverage = v1_optimizers.ModelAverage
L2Regularization = v1_optimizers.L2Regularization
if __name__ == '__main__':
swig_api.initPaddle('--use_gpu=false')
for opt in [
Momentum(),
Adam(),
Adamax(),
AdaGrad(),
DecayedAdaGrad(),
AdaDelta(),
RMSProp(),
Adam(model_average=ModelAverage(average_window=0.5),
regularization=L2Regularization(rate=0.5),
gradient_clipping_threshold=25)
]:
print opt, opt.enable_types()
if atEnd:
break
trainer.trainOneDataBatch(batch_size, data)
outs = trainer.getForwardOutput()
cost += sum(outs[0]['value'])
num += batch_size
trainer.finishTrainPass()
logger.info('train cost=%f' % (cost / num))
trainer.startTestPeriod()
num = 0
cost = 0
while True: # Test one batch
batch_size = 1000
data, atEnd = util.loadMNISTTrainData(batch_size)
if atEnd:
break
trainer.testOneDataBatch(batch_size, data)
outs = trainer.getForwardOutput()
cost += sum(outs[0]['value'])
num += batch_size
trainer.finishTestPeriod()
logger.info('test cost=%f' % (cost / num))
trainer.finishTrain()
if __name__ == '__main__':
swig_paddle.initPaddle("--use_gpu=0", "--trainer_count=1")
main()
def __init__(self,
train_conf,
model_dir=None,
resize_dim=256,
crop_dim=224,
use_gpu=True,
mean_file=None,
output_layer=None,
oversample=False,
is_color=True):
"""
train_conf: network configure.
model_dir: string, directory of model.
resize_dim: int, resized image size.
crop_dim: int, crop size.
mean_file: string, image mean file.
oversample: bool, oversample means multiple crops, namely five
patches (the four corner patches and the center
patch) as well as their horizontal reflections,
ten crops in all.
"""
self.train_conf = train_conf
self.model_dir = model_dir
if model_dir is None:
self.model_dir = os.path.dirname(train_conf)
self.resize_dim = resize_dim
self.crop_dims = [crop_dim, crop_dim]
self.oversample = oversample
self.is_color = is_color
self.output_layer = output_layer
if self.output_layer:
assert isinstance(self.output_layer, basestring)
self.output_layer = self.output_layer.split(",")
self.transformer = image_util.ImageTransformer(is_color=is_color)
self.transformer.set_transpose((2, 0, 1))
self.transformer.set_channel_swap((2, 1, 0))
self.mean_file = mean_file
if self.mean_file is not None:
mean = np.load(self.mean_file)['data_mean']
mean = mean.reshape(3, self.crop_dims[0], self.crop_dims[1])
self.transformer.set_mean(mean) # mean pixel
else:
# if you use three mean value, set like:
# this three mean value is calculated from ImageNet.
self.transformer.set_mean(np.array([103.939, 116.779, 123.68]))
conf_args = "is_test=1,use_gpu=%d,is_predict=1" % (int(use_gpu))
conf = parse_config(train_conf, conf_args)
swig_paddle.initPaddle("--use_gpu=%d" % (int(use_gpu)))
self.network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
assert isinstance(self.network, swig_paddle.GradientMachine)
self.network.loadParameters(self.model_dir)
data_size = 3 * self.crop_dims[0] * self.crop_dims[1]
slots = [dense_vector(data_size)]
self.converter = DataProviderConverter(slots)
# limitations under the License.
from py_paddle import swig_paddle, DataProviderConverter
from common_utils import *
from paddle.trainer.config_parser import parse_config
try:
import cPickle as pickle
except ImportError:
import pickle
import sys
if __name__ == '__main__':
model_path = sys.argv[1]
swig_paddle.initPaddle('--use_gpu=0')
conf = parse_config("trainer_config.py", "is_predict=1")
network = swig_paddle.GradientMachine.createFromConfigProto(
conf.model_config)
assert isinstance(network, swig_paddle.GradientMachine)
network.loadParameters(model_path)
with open('./data/meta.bin', 'rb') as f:
meta = pickle.load(f)
headers = [h[1] for h in meta_to_header(meta, 'movie')]
headers.extend([h[1] for h in meta_to_header(meta, 'user')])
cvt = DataProviderConverter(headers)
while True:
movie_id = int(raw_input("Input movie_id: "))
user_id = int(raw_input("Input user_id: "))
movie_meta = meta['movie'][movie_id] # Query Data From Meta.
user_meta = meta['user'][user_id]
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--use_gpu", default="1", help="1 means use gpu for training")
parser.add_argument("--gpu_id", default="0", help="the gpu_id parameter")
args = parser.parse_args()
use_gpu = args.use_gpu
assert use_gpu in ["0", "1"]
if not os.path.exists("./samples/"):
os.makedirs("./samples/")
if not os.path.exists("./params/"):
os.makedirs("./params/")
api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
'--log_period=1000', '--gpu_id=' + args.gpu_id,
'--save_dir=' + "./params/")
conf = "vae_conf.py"
trainer_conf = parse_config(conf, "is_generating=False")
gener_conf = parse_config(conf, "is_generating=True")
batch_size = trainer_conf.opt_config.batch_size
noise_dim = get_layer_size(gener_conf.model_config, "noise")
mnist = dataloader.MNISTloader(batch_size=batch_size)
mnist.load_data()
training_machine = api.GradientMachine.createFromConfigProto(
trainer_conf.model_config)
generator_machine = api.GradientMachine.createFromConfigProto(
gener_conf.model_config)
trainer = api.Trainer.create(trainer_conf, training_machine)
trainer.startTrain()
for train_pass in xrange(100):
trainer.startTrainPass()
mnist.reset_pointer()
i = 0
it = 0
while mnist.pointer != 0 or i == 0:
X = mnist.next_batch().astype('float32')
inputs = api.Arguments.createArguments(1)
inputs.setSlotValue(0, api.Matrix.createDenseFromNumpy(X))
trainer.trainOneDataBatch(batch_size, inputs)
if it % 1000 == 0:
outputs = api.Arguments.createArguments(0)
training_machine.forward(inputs, outputs, api.PASS_TEST)
loss = np.mean(outputs.getSlotValue(0).copyToNumpyMat())
print "\niter: {}".format(str(it).zfill(3))
print "VAE loss: {}".format(str(loss).zfill(3))
#Sync parameters between networks (GradientMachine) at the beginning
copy_shared_parameters(training_machine, generator_machine)
z_samples = np.random.randn(batch_size,
noise_dim).astype('float32')
samples = get_fake_samples(generator_machine, batch_size,
z_samples)
#Generating the first 16 images for a picture.
figure = plot_samples(samples[:16])
plt.savefig(
"./samples/{}_{}.png".format(
str(train_pass).zfill(3), str(i).zfill(3)),
bbox_inches='tight')
plt.close(figure)
i += 1
it += 1
trainer.finishTrainPass()
trainer.finishTrain()
def main():
api.initPaddle("-use_gpu=false", "-trainer_count=4") # use 4 cpu cores
optimizer = paddle_v2.optimizer.Adam(
learning_rate=1e-4,
batch_size=1000,
model_average=ModelAverage(average_window=0.5),
regularization=L2Regularization(rate=0.5))
# Create Local Updater. Local means not run in cluster.
# For a cluster training, here we can change to createRemoteUpdater
# in future.
updater = optimizer.create_local_updater()
assert isinstance(updater, api.ParameterUpdater)
# define network
images = paddle_v2.layer.data(name='pixel',
type=paddle_v2.data_type.dense_vector(784))
label = paddle_v2.layer.data(name='label',
type=paddle_v2.data_type.integer_value(10))
hidden1 = paddle_v2.layer.fc(input=images, size=200)
hidden2 = paddle_v2.layer.fc(input=hidden1, size=200)
inference = paddle_v2.layer.fc(input=hidden2,
size=10,
act=paddle_v2.activation.Softmax())
cost = paddle_v2.layer.classification_cost(input=inference, label=label)
# Create Simple Gradient Machine.
model_config = paddle_v2.layer.parse_network(cost)
m = api.GradientMachine.createFromConfigProto(model_config,
api.CREATE_MODE_NORMAL,
optimizer.enable_types())
# This type check is not useful. Only enable type hint in IDE.
# Such as PyCharm
assert isinstance(m, api.GradientMachine)
# Initialize Parameter by numpy.
init_parameter(network=m)
# Initialize ParameterUpdater.
updater.init(m)
# DataProvider Converter is a utility convert Python Object to Paddle C++
# Input. The input format is as same as Paddle's DataProvider.
converter = DataProviderConverter(input_types=[images.type, label.type])
train_file = './data/raw_data/train'
test_file = './data/raw_data/t10k'
# start gradient machine.
# the gradient machine must be started before invoke forward/backward.
# not just for training, but also for inference.
m.start()
# evaluator can print error rate, etc. It is a C++ class.
batch_evaluator = m.makeEvaluator()
test_evaluator = m.makeEvaluator()
# Get Train Data.
# TrainData will stored in a data pool. Currently implementation is not care
# about memory, speed. Just a very naive implementation.
train_data_generator = input_order_converter(read_from_mnist(train_file))
train_data = BatchPool(train_data_generator, 512)
# outArgs is Neural Network forward result. Here is not useful, just passed
# to gradient_machine.forward
outArgs = api.Arguments.createArguments(0)
for pass_id in xrange(2): # we train 2 passes.
updater.startPass()
for batch_id, data_batch in enumerate(train_data()):
# data_batch is input images.
# here, for online learning, we could get data_batch from network.
# Start update one batch.
pass_type = updater.startBatch(len(data_batch))
# Start BatchEvaluator.
# batch_evaluator can be used between start/finish.
batch_evaluator.start()
# forwardBackward is a shortcut for forward and backward.
# It is sometimes faster than invoke forward/backward separately,
# because in GradientMachine, it may be async.
m.forwardBackward(converter(data_batch), outArgs, pass_type)
for each_param in m.getParameters():
updater.update(each_param)
# Get cost. We use numpy to calculate total cost for this batch.
cost_vec = outArgs.getSlotValue(0)
cost_vec = cost_vec.copyToNumpyMat()
cost = cost_vec.sum() / len(data_batch)
# Make evaluator works.
m.eval(batch_evaluator)
# Print logs.
print 'Pass id', pass_id, 'Batch id', batch_id, 'with cost=', \
cost, batch_evaluator
batch_evaluator.finish()
# Finish batch.
# * will clear gradient.
# * ensure all values should be updated.
updater.finishBatch(cost)
# testing stage. use test data set to test current network.
updater.apply()
test_evaluator.start()
test_data_generator = input_order_converter(read_from_mnist(test_file))
for data_batch in generator_to_batch(test_data_generator, 512):
# in testing stage, only forward is needed.
m.forward(converter(data_batch), outArgs, api.PASS_TEST)
m.eval(test_evaluator)
# print error rate for test data set
print 'Pass', pass_id, ' test evaluator: ', test_evaluator
test_evaluator.finish()
updater.restore()
updater.catchUpWith()
params = m.getParameters()
for each_param in params:
assert isinstance(each_param, api.Parameter)
value = each_param.getBuf(api.PARAMETER_VALUE)
value = value.copyToNumpyArray()
# Here, we could save parameter to every where you want
print each_param.getName(), value
updater.finishPass()
m.finish()
self.assertTrue(util.doubleEqual(n, v))
numpy_2 = vec.toNumpyArrayInplace()
vec[0] = 1.3
for x, y in zip(numpy_arr, numpy_2):
self.assertTrue(util.doubleEqual(x, y))
for x, y in zip(numpy_arr, vec):
self.assertTrue(util.doubleEqual(x, y))
numpy_3 = vec.copyToNumpyArray()
numpy_3[0] = 0.4
self.assertTrue(util.doubleEqual(vec[0], 1.3))
self.assertTrue(util.doubleEqual(numpy_3[0], 0.4))
for i in xrange(1, len(numpy_3)):
util.doubleEqual(numpy_3[i], vec[i])
def testCopyFromNumpy(self):
vec = swig_paddle.Vector.createZero(1)
arr = np.array([1.3, 3.2, 2.4], dtype="float32")
vec.copyFromNumpyArray(arr)
for i in xrange(len(vec)):
self.assertTrue(util.doubleEqual(vec[i], arr[i]))
if __name__ == '__main__':
swig_paddle.initPaddle(
"--use_gpu=1" if swig_paddle.isGpuVersion() else "--use_gpu=0")
unittest.main()
def init(**kwargs):
args = []
for key in kwargs.keys():
args.append('--%s=%s' % (key, str(kwargs[key])))
api.initPaddle(*args)
def gen_data(batch_size, shape):
data = []
for i in xrange(batch_size):
each_sample = []
each_sample.append(np.random.random(shape))
data.append(each_sample)
return data
feeder = DataFeeder([('image', data_type.dense_array(2352))],
{'image': 0})
arg = feeder(gen_data(32, (3, 28, 28)))
h = arg.getSlotFrameHeight(0)
w = arg.getSlotFrameWidth(0)
self.assertEqual(h, 28)
self.assertEqual(w, 28)
arg = feeder(gen_data(32, (3, 30, 32)))
h = arg.getSlotFrameHeight(0)
w = arg.getSlotFrameWidth(0)
self.assertEqual(h, 30)
self.assertEqual(w, 32)
if __name__ == '__main__':
api.initPaddle("--use_gpu=0")
suite = unittest.TestLoader().loadTestsFromTestCase(DataFeederTest)
unittest.TextTestRunner().run(suite)
if api.isGpuVersion():
api.setUseGpu(True)
unittest.main()
machine.backward(backward_callback)
for k in optimizers:
opt = optimizers[k]
opt.finishBatch()
for k in optimizers:
opt = optimizers[k]
opt.finishPass()
self.assertTrue(self.isCalled)
def test_train_one_pass(self):
conf_file_path = './testTrainConfig.py'
trainer_config = swig_paddle.TrainerConfig.createFromTrainerConfigFile(
conf_file_path)
model_config = trainer_config.getModelConfig()
machine = swig_paddle.GradientMachine.createByModelConfig(model_config)
at_end = False
output = swig_paddle.Arguments.createArguments(0)
if not at_end:
input_, at_end = util.loadMNISTTrainData(1000)
machine.forwardBackward(input_, output, swig_paddle.PASS_TRAIN)
if __name__ == '__main__':
swig_paddle.initPaddle('--use_gpu=0')
unittest.main()
self.assertEqual((int(gpu_m.getHeight()), int(gpu_m.getWidth())),
numpy_mat.shape)
self.assertTrue(gpu_m.isGpu())
numpy_mat = gpu_m.copyToNumpyMat()
numpy_mat[0, 1] = 3.23
for a, e in zip(gpu_m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
gpu_m.copyFromNumpyMat(numpy_mat)
for a, e in zip(gpu_m.getData(), [1.0, 3.23, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
def test_numpy(self):
numpy_mat = np.matrix([[1, 2], [3, 4], [5, 6]], dtype="float32")
m = swig_paddle.Matrix.createDenseFromNumpy(numpy_mat)
self.assertEqual((int(m.getHeight()), int(m.getWidth())),
numpy_mat.shape)
self.assertEqual(m.isGpu(), swig_paddle.isUsingGpu())
for a, e in zip(m.getData(), [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]):
self.assertAlmostEqual(a, e)
if __name__ == "__main__":
swig_paddle.initPaddle("--use_gpu=0")
suite = unittest.TestLoader().loadTestsFromTestCase(TestMatrix)
unittest.TextTestRunner().run(suite)
if swig_paddle.isGpuVersion():
swig_paddle.setUseGpu(True)
unittest.main()
def main():
api.initPaddle("-use_gpu=false", "-trainer_count=4") # use 4 cpu cores
optimizer = paddle_v2.optimizer.Adam(
learning_rate=1e-4,
batch_size=1000,
model_average=ModelAverage(average_window=0.5),
regularization=L2Regularization(rate=0.5))
# Create Local Updater. Local means not run in cluster.
# For a cluster training, here we can change to createRemoteUpdater
# in future.
updater = optimizer.create_local_updater()
assert isinstance(updater, api.ParameterUpdater)
# define network
images = paddle_v2.layer.data(
name='pixel', type=paddle_v2.data_type.dense_vector(784))
label = paddle_v2.layer.data(
name='label', type=paddle_v2.data_type.integer_value(10))
hidden1 = paddle_v2.layer.fc(input=images, size=200)
hidden2 = paddle_v2.layer.fc(input=hidden1, size=200)
inference = paddle_v2.layer.fc(input=hidden2,
size=10,
act=paddle_v2.activation.Softmax())
cost = paddle_v2.layer.classification_cost(input=inference, label=label)
# Create Simple Gradient Machine.
model_config = paddle_v2.layer.parse_network(cost)
m = api.GradientMachine.createFromConfigProto(model_config,
api.CREATE_MODE_NORMAL,
optimizer.enable_types())
# This type check is not useful. Only enable type hint in IDE.
# Such as PyCharm
assert isinstance(m, api.GradientMachine)
# Initialize Parameter by numpy.
init_parameter(network=m)
# Initialize ParameterUpdater.
updater.init(m)
# DataProvider Converter is a utility convert Python Object to Paddle C++
# Input. The input format is as same as Paddle's DataProvider.
converter = DataProviderConverter(input_types=[images.type, label.type])
train_file = './data/raw_data/train'
test_file = './data/raw_data/t10k'
# start gradient machine.
# the gradient machine must be started before invoke forward/backward.
# not just for training, but also for inference.
m.start()
# evaluator can print error rate, etc. It is a C++ class.
batch_evaluator = m.makeEvaluator()
test_evaluator = m.makeEvaluator()
# Get Train Data.
# TrainData will stored in a data pool. Currently implementation is not care
# about memory, speed. Just a very naive implementation.
train_data_generator = input_order_converter(read_from_mnist(train_file))
train_data = BatchPool(train_data_generator, 512)
# outArgs is Neural Network forward result. Here is not useful, just passed
# to gradient_machine.forward
outArgs = api.Arguments.createArguments(0)
for pass_id in xrange(2): # we train 2 passes.
updater.startPass()
for batch_id, data_batch in enumerate(train_data()):
# data_batch is input images.
# here, for online learning, we could get data_batch from network.
# Start update one batch.
pass_type = updater.startBatch(len(data_batch))
# Start BatchEvaluator.
# batch_evaluator can be used between start/finish.
batch_evaluator.start()
# forwardBackward is a shortcut for forward and backward.
# It is sometimes faster than invoke forward/backward separately,
# because in GradientMachine, it may be async.
m.forwardBackward(converter(data_batch), outArgs, pass_type)
for each_param in m.getParameters():
updater.update(each_param)
# Get cost. We use numpy to calculate total cost for this batch.
cost_vec = outArgs.getSlotValue(0)
cost_vec = cost_vec.copyToNumpyMat()
cost = cost_vec.sum() / len(data_batch)
# Make evaluator works.
m.eval(batch_evaluator)
# Print logs.
print 'Pass id', pass_id, 'Batch id', batch_id, 'with cost=', \
cost, batch_evaluator
batch_evaluator.finish()
# Finish batch.
# * will clear gradient.
# * ensure all values should be updated.
updater.finishBatch(cost)
# testing stage. use test data set to test current network.
updater.apply()
test_evaluator.start()
test_data_generator = input_order_converter(read_from_mnist(test_file))
for data_batch in generator_to_batch(test_data_generator, 512):
# in testing stage, only forward is needed.
m.forward(converter(data_batch), outArgs, api.PASS_TEST)
m.eval(test_evaluator)
# print error rate for test data set
print 'Pass', pass_id, ' test evaluator: ', test_evaluator
test_evaluator.finish()
updater.restore()
updater.catchUpWith()
params = m.getParameters()
for each_param in params:
assert isinstance(each_param, api.Parameter)
value = each_param.getBuf(api.PARAMETER_VALUE)
value = value.copyToNumpyArray()
# Here, we could save parameter to every where you want
print each_param.getName(), value
updater.finishPass()
m.finish()
def main():
parser = argparse.ArgumentParser()
parser.add_argument("-d",
"--data_source",
help="mnist or cifar or uniform")
parser.add_argument("--use_gpu",
default="1",
help="1 means use gpu for training")
parser.add_argument("--gpu_id", default="0", help="the gpu_id parameter")
args = parser.parse_args()
data_source = args.data_source
use_gpu = args.use_gpu
assert data_source in ["mnist", "cifar", "uniform"]
assert use_gpu in ["0", "1"]
if not os.path.exists("./%s_samples/" % data_source):
os.makedirs("./%s_samples/" % data_source)
if not os.path.exists("./%s_params/" % data_source):
os.makedirs("./%s_params/" % data_source)
api.initPaddle('--use_gpu=' + use_gpu, '--dot_period=10',
'--log_period=100', '--gpu_id=' + args.gpu_id,
'--save_dir=' + "./%s_params/" % data_source)
if data_source == "uniform":
conf = "gan_conf.py"
num_iter = 10000
else:
conf = "gan_conf_image.py"
num_iter = 1000
gen_conf = parse_config(conf,
"mode=generator_training,data=" + data_source)
dis_conf = parse_config(conf,
"mode=discriminator_training,data=" + data_source)
generator_conf = parse_config(conf, "mode=generator,data=" + data_source)
batch_size = dis_conf.opt_config.batch_size
noise_dim = get_layer_size(gen_conf.model_config, "noise")
if data_source == "mnist":
data_np = load_mnist_data("./data/mnist_data/train-images-idx3-ubyte")
elif data_source == "cifar":
data_np = load_cifar_data("./data/cifar-10-batches-py/")
else:
data_np = load_uniform_data()
# this creates a gradient machine for discriminator
dis_training_machine = api.GradientMachine.createFromConfigProto(
dis_conf.model_config)
# this create a gradient machine for generator
gen_training_machine = api.GradientMachine.createFromConfigProto(
gen_conf.model_config)
# generator_machine is used to generate data only, which is used for
# training discriminator
logger.info(str(generator_conf.model_config))
generator_machine = api.GradientMachine.createFromConfigProto(
generator_conf.model_config)
dis_trainer = api.Trainer.create(dis_conf, dis_training_machine)
gen_trainer = api.Trainer.create(gen_conf, gen_training_machine)
dis_trainer.startTrain()
gen_trainer.startTrain()
# Sync parameters between networks (GradientMachine) at the beginning
copy_shared_parameters(gen_training_machine, dis_training_machine)
copy_shared_parameters(gen_training_machine, generator_machine)
# constrain that either discriminator or generator can not be trained
# consecutively more than MAX_strike times
curr_train = "dis"
curr_strike = 0
MAX_strike = 5
for train_pass in xrange(100):
dis_trainer.startTrainPass()
gen_trainer.startTrainPass()
for i in xrange(num_iter):
# Do forward pass in discriminator to get the dis_loss
noise = get_noise(batch_size, noise_dim)
data_batch_dis_pos = prepare_discriminator_data_batch_pos(
batch_size, data_np)
dis_loss_pos = get_training_loss(dis_training_machine,
data_batch_dis_pos)
data_batch_dis_neg = prepare_discriminator_data_batch_neg(
generator_machine, batch_size, noise)
dis_loss_neg = get_training_loss(dis_training_machine,
data_batch_dis_neg)
dis_loss = (dis_loss_pos + dis_loss_neg) / 2.0
# Do forward pass in generator to get the gen_loss
data_batch_gen = prepare_generator_data_batch(batch_size, noise)
gen_loss = get_training_loss(gen_training_machine, data_batch_gen)
if i % 100 == 0:
print "d_pos_loss is %s d_neg_loss is %s" % (dis_loss_pos,
dis_loss_neg)
print "d_loss is %s g_loss is %s" % (dis_loss, gen_loss)
# Decide which network to train based on the training history
# And the relative size of the loss
if (not (curr_train == "dis" and curr_strike == MAX_strike)) and \
((curr_train == "gen" and curr_strike == MAX_strike) or dis_loss > gen_loss):
if curr_train == "dis":
curr_strike += 1
else:
curr_train = "dis"
curr_strike = 1
dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_neg)
dis_trainer.trainOneDataBatch(batch_size, data_batch_dis_pos)
copy_shared_parameters(dis_training_machine,
gen_training_machine)
else:
if curr_train == "gen":
curr_strike += 1
else:
curr_train = "gen"
curr_strike = 1
gen_trainer.trainOneDataBatch(batch_size, data_batch_gen)
# TODO: add API for paddle to allow true parameter sharing between different GradientMachines
# so that we do not need to copy shared parameters.
copy_shared_parameters(gen_training_machine,
dis_training_machine)
copy_shared_parameters(gen_training_machine, generator_machine)
dis_trainer.finishTrainPass()
gen_trainer.finishTrainPass()
# At the end of each pass, save the generated samples/images
fake_samples = get_fake_samples(generator_machine, batch_size, noise)
if data_source == "uniform":
plot2DScatter(
fake_samples,
"./%s_samples/train_pass%s.png" % (data_source, train_pass))
else:
save_images(
fake_samples,
"./%s_samples/train_pass%s.png" % (data_source, train_pass))
dis_trainer.finishTrain()
gen_trainer.finishTrain()
v(w, t) & = \\rho v(w, t-1) + (1 - \\rho)(\\nabla Q_{i}(w))^2 \\\\
w & = w - \\frac{\\eta} {\\sqrt{v(w,t) + \\epsilon}} \\nabla Q_{i}(w)
:param rho: the :math:`\\rho` in the equation. The forgetting factor.
:type rho: float
:param epsilon: the :math:`\\epsilon` in the equation.
:type epsilon: float
"""
def __init__(self, rho=0.95, epsilon=1e-6, **kwargs):
learning_method = v1_optimizers.RMSPropOptimizer(
rho=rho, epsilon=epsilon)
super(RMSProp, self).__init__(learning_method=learning_method, **kwargs)
ModelAverage = v1_optimizers.ModelAverage
L2Regularization = v1_optimizers.L2Regularization
if __name__ == '__main__':
import py_paddle.swig_paddle as swig_api
swig_api.initPaddle('--use_gpu=false')
for opt in [
Momentum(), Adam(), Adamax(), AdaGrad(), DecayedAdaGrad(),
AdaDelta(), RMSProp(), Adam(
model_average=ModelAverage(average_window=0.5),
regularization=L2Regularization(rate=0.5),
gradient_clipping_threshold=25)
]:
print opt, opt.enable_types()