def main():
# init
paddle.init(use_gpu=False, trainer_count=1)
# network config
x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
y_predict = paddle.layer.fc(input=x,
param_attr=paddle.attr.Param(name='w'),
size=1,
act=paddle.activation.Linear(),
bias_attr=paddle.attr.Param(name='b'))
y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
cost = paddle.layer.mse_cost(input=y_predict, label=y)
# create parameters
parameters = paddle.parameters.create(cost)
# create optimizer of new remote updater to pserver
optimizer = paddle.optimizer.Momentum(momentum=0, learning_rate=1e-3)
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=optimizer,
is_local=False,
pserver_spec=etcd_endpoints,
use_etcd=True)
# event_handler to print training and testing info
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
# FIXME: for cloud data reader, pass number is managed by master
# should print the server side pass number
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f" % (
event.pass_id, event.batch_id, event.cost)
if isinstance(event, paddle.event.EndPass):
if (event.pass_id + 1) % 10 == 0:
result = trainer.test(reader=paddle.batch(uci_housing.test(),
batch_size=2),
feeding={
'x': 0,
'y': 1
})
print "Test %d, %.2f" % (event.pass_id, result.cost)
# training
# NOTE: use uci_housing.train() as reader for non-paddlecloud training
trainer.train(reader=paddle.batch(paddle.reader.shuffle(
cloud_reader(["/pfs/dlnel/public/dataset/uci_housing/uci_housing*"],
etcd_endpoints),
buf_size=500),
batch_size=2),
feeding={
'x': 0,
'y': 1
},
event_handler=event_handler,
num_passes=30)
def main():
# init
paddle.init(use_gpu=False, trainer_count=1)
# network config
x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
y_predict = paddle.layer.fc(input=x,
param_attr=paddle.attr.Param(name='w'),
size=1,
act=paddle.activation.Linear(),
bias_attr=paddle.attr.Param(name='b'))
y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
cost = paddle.layer.mse_cost(input=y_predict, label=y)
# create parameters
parameters = paddle.parameters.create(cost)
# create optimizer of new remote updater to pserver
optimizer = paddle.optimizer.Momentum(momentum=0, learning_rate=1e-3)
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=optimizer,
is_local=False,
pserver_spec=etcd_endpoints,
use_etcd=True)
# event_handler to print training and testing info
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
# FIXME: for cloud data reader, pass number is managed by master
# should print the server side pass number
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f" % (
event.pass_id, event.batch_id, event.cost)
if isinstance(event, paddle.event.EndPass):
if (event.pass_id + 1) % 10 == 0:
result = trainer.test(
reader=paddle.batch(
uci_housing.test(), batch_size=2),
feeding={'x': 0,
'y': 1})
print "Test %d, %.2f" % (event.pass_id, result.cost)
# training
# NOTE: use uci_housing.train() as reader for non-paddlecloud training
trainer.train(
reader=paddle.batch(
paddle.reader.shuffle(
cloud_reader(
["/pfs/dlnel/public/dataset/uci_housing/uci_housing*"],
etcd_endpoints),
buf_size=500),
batch_size=2),
feeding={'x': 0,
'y': 1},
event_handler=event_handler,
num_passes=30)
def main():
# init
paddle.init()
# network config
x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))
y_predict = paddle.layer.fc(input=x,
param_attr=paddle.attr.Param(name='w', learning_rate=1e-3),
size=1,
act=paddle.activation.Linear(),
bias_attr=paddle.attr.Param(name='b', learning_rate=1e-3))
y = paddle.layer.data(name='y', type=paddle.data_type.dense_vector(1))
cost = paddle.layer.square_error_cost(input=y_predict, label=y)
# create parameters
parameters = paddle.parameters.create(cost)
# create optimizer
optimizer = paddle.optimizer.Momentum(momentum=0, learning_rate=2e-4)
trainer = paddle.trainer.SGD(
cost=cost,
parameters=parameters,
update_equation=optimizer,
is_local=False,
pserver_spec=etcd_endpoint,
use_etcd=True)
feeding = {'x': 0, 'y': 1}
# event_handler to print training and testing info
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f" % (
event.pass_id, event.batch_id, event.cost)
if isinstance(event, paddle.event.EndPass):
result = trainer.test(
reader=paddle.batch(uci_housing.test(), batch_size=2),
feeding=feeding)
print "Test %d, Cost %f" % (event.pass_id, result.cost)
if trainer_id == "0":
with gzip.open("fit-a-line_pass_%05d.tar.gz" % event.pass_id,
"w") as f:
parameters.to_tar(f)
# training
trainer.train(
reader=paddle.batch(
paddle.reader.shuffle(cloud_reader(
["/workspace/data/uci_housing/uci_housing_train-*"],
etcd_endpoint), buf_size=500),
batch_size=2),
feeding=feeding,
event_handler=event_handler,
num_passes=30)
def main():
etcd_ip = os.getenv("ETCD_IP")
etcd_endpoint = "http://" + etcd_ip + ":" + "2379"
paddle.init(trainer_count=1)
# define network topology
images = paddle.layer.data(name='pixel',
type=paddle.data_type.dense_vector(784))
label = paddle.layer.data(name='label',
type=paddle.data_type.integer_value(10))
# Here we can build the prediction network in different ways. Please
# choose one by uncomment corresponding line.
# predict = softmax_regression(images)
# predict = multilayer_perceptron(images)
predict = convolutional_neural_network(images)
cost = paddle.layer.classification_cost(input=predict, label=label)
parameters = paddle.parameters.create(cost)
optimizer = paddle.optimizer.Momentum(
learning_rate=0.1 / 128.0,
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0005 * 128))
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=optimizer,
is_local=False,
pserver_spec=etcd_endpoint,
use_etcd=True)
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
if isinstance(event, paddle.event.EndPass):
result = trainer.test(
reader=paddle.batch(paddle.dataset.mnist.test(), batch_size=2))
print "Test with Pass %d, Cost %f, %s\n" % (
event.pass_id, result.cost, result.metrics)
trainer.train(reader=paddle.batch(cloud_reader([DATASET_PATH],
etcd_endpoint),
batch_size=10),
event_handler=event_handler,
num_passes=120)
def main():
paddle.init()
# define network topology
feature_out = db_lstm()
target = paddle.layer.data(name='target', type=d_type(label_dict_len))
crf_cost = paddle.layer.crf(size=label_dict_len,
input=feature_out,
label=target,
param_attr=paddle.attr.Param(
name='crfw',
initial_std=default_std,
learning_rate=mix_hidden_lr))
crf_dec = paddle.layer.crf_decoding(
size=label_dict_len,
input=feature_out,
label=target,
param_attr=paddle.attr.Param(name='crfw'))
evaluator.sum(input=crf_dec)
# create parameters
parameters = paddle.parameters.create(crf_cost)
parameters.set('emb', load_parameter(conll05.get_embedding(), 44068, 32))
# create optimizer
optimizer = paddle.optimizer.Momentum(
momentum=0,
learning_rate=2e-2,
regularization=paddle.optimizer.L2Regularization(rate=8e-4),
model_average=paddle.optimizer.ModelAverage(average_window=0.5,
max_average_window=10000),
)
trainer = paddle.trainer.SGD(cost=crf_cost,
parameters=parameters,
update_equation=optimizer,
extra_layers=crf_dec)
reader = paddle.batch(paddle.reader.shuffle(cloud_reader(
["/pfs/dlnel/public/dataset/conll05/conl105_train-*"], etcd_endpoint),
buf_size=8192),
batch_size=10)
feeding = {
'word_data': 0,
'ctx_n2_data': 1,
'ctx_n1_data': 2,
'ctx_0_data': 3,
'ctx_p1_data': 4,
'ctx_p2_data': 5,
'verb_data': 6,
'mark_data': 7,
'target': 8
}
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
if event.batch_id % 1000 == 0:
result = trainer.test(reader=reader, feeding=feeding)
print "\nTest with Pass %d, Batch %d, %s" % (
event.pass_id, event.batch_id, result.metrics)
if isinstance(event, paddle.event.EndPass):
# save parameters
with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=paddle.batch(conll05.test(), 10),
feeding=feeding)
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
trainer.train(reader=reader,
event_handler=event_handler,
num_passes=1,
feeding=feeding)
test_creator = paddle.dataset.conll05.test()
test_data = []
for item in test_creator():
test_data.append(item[0:8])
if len(test_data) == 1:
break
predict = paddle.layer.crf_decoding(
size=label_dict_len,
input=feature_out,
param_attr=paddle.attr.Param(name='crfw'))
probs = paddle.infer(output_layer=predict,
parameters=parameters,
input=test_data,
feeding=feeding,
field='id')
assert len(probs) == len(test_data[0][0])
labels_reverse = {}
for (k, v) in label_dict.items():
labels_reverse[v] = k
pre_lab = [labels_reverse[i] for i in probs]
print pre_lab
def main():
paddle.init()
usr_combined_features = get_usr_combined_features()
mov_combined_features = get_mov_combined_features()
inference = paddle.layer.cos_sim(a=usr_combined_features,
b=mov_combined_features,
size=1,
scale=5)
cost = paddle.layer.square_error_cost(
input=inference,
label=paddle.layer.data(name='score',
type=paddle.data_type.dense_vector(1)))
parameters = paddle.parameters.create(cost)
trainer = paddle.trainer.SGD(
cost=cost,
parameters=parameters,
update_equation=paddle.optimizer.Adam(learning_rate=1e-4))
feeding = {
'user_id': 0,
'gender_id': 1,
'age_id': 2,
'job_id': 3,
'movie_id': 4,
'category_id': 5,
'movie_title': 6,
'score': 7
}
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "Pass %d Batch %d Cost %.2f" % (
event.pass_id, event.batch_id, event.cost)
trainer.train(reader=paddle.batch(paddle.reader.shuffle(
cloud_reader(["/pfs/dlnel/public/dataset/movielens/movielens_train-*"],
etcd_endpoint),
buf_size=8192),
batch_size=256),
event_handler=event_handler,
feeding=feeding,
num_passes=1)
user_id = 234
movie_id = 345
user = paddle.dataset.movielens.user_info()[user_id]
movie = paddle.dataset.movielens.movie_info()[movie_id]
feature = user.value() + movie.value()
infer_dict = copy.copy(feeding)
del infer_dict['score']
prediction = paddle.infer(output_layer=inference,
parameters=parameters,
input=[feature],
feeding=infer_dict)
print(prediction + 5) / 2
def main():
datadim = 3 * 32 * 32
classdim = 10
# PaddlePaddle init
paddle.init(use_gpu=with_gpu, trainer_count=1)
image = paddle.layer.data(name="image",
type=paddle.data_type.dense_vector(datadim))
# Add neural network config
# option 1. resnet
# net = resnet_cifar10(image, depth=32)
# option 2. vgg
net = vgg_bn_drop(image)
out = paddle.layer.fc(input=net,
size=classdim,
act=paddle.activation.Softmax())
lbl = paddle.layer.data(name="label",
type=paddle.data_type.integer_value(classdim))
cost = paddle.layer.classification_cost(input=out, label=lbl)
# Create parameters
parameters = paddle.parameters.create(cost)
# Create optimizer
momentum_optimizer = paddle.optimizer.Momentum(
momentum=0.9,
regularization=paddle.optimizer.L2Regularization(rate=0.0002 * 128),
learning_rate=0.1 / 128.0,
learning_rate_decay_a=0.1,
learning_rate_decay_b=50000 * 100,
learning_rate_schedule='discexp')
feeding = {'image': 0, 'label': 1}
train_reader = paddle.batch(paddle.reader.shuffle(cloud_reader(
[cifar_train10_path], etcd_endpoint),
buf_size=50000),
batch_size=128)
# End batch and end pass event handler
def event_handler(event):
if isinstance(event, paddle.event.EndIteration):
if event.batch_id % 100 == 0:
print "\nPass %d, Batch %d, Cost %f, %s" % (
event.pass_id, event.batch_id, event.cost, event.metrics)
else:
sys.stdout.write('.')
sys.stdout.flush()
if isinstance(event, paddle.event.EndPass):
# save parameters
with open('params_pass_%d.tar' % event.pass_id, 'w') as f:
parameters.to_tar(f)
result = trainer.test(reader=paddle.batch(
paddle.dataset.cifar.test10(), batch_size=128),
feeding=feeding)
print "\nTest with Pass %d, %s" % (event.pass_id, result.metrics)
# Create trainer
trainer = paddle.trainer.SGD(cost=cost,
parameters=parameters,
update_equation=momentum_optimizer,
is_local=False,
pserver_spec=etcd_endpoint,
use_etcd=True)
# Save the inference topology to protobuf.
inference_topology = paddle.topology.Topology(layers=out)
with open("inference_topology.pkl", 'wb') as f:
inference_topology.serialize_for_inference(f)
trainer.train(reader=train_reader,
num_passes=1,
event_handler=event_handler,
feeding=feeding)
# inference
from PIL import Image
import numpy as np
import os
def load_image(file):
im = Image.open(file)
im = im.resize((32, 32), Image.ANTIALIAS)
im = np.array(im).astype(np.float32)
# The storage order of the loaded image is W(widht),
# H(height), C(channel). PaddlePaddle requires
# the CHW order, so transpose them.
im = im.transpose((2, 0, 1)) # CHW
# In the training phase, the channel order of CIFAR
# image is B(Blue), G(green), R(Red). But PIL open
# image in RGB mode. It must swap the channel order.
im = im[(2, 1, 0), :, :] # BGR
im = im.flatten()
im = im / 255.0
return im
test_data = []
test_data.append((load_image(testing_image_path), ))
# users can remove the comments and change the model name
# with open('params_pass_50.tar', 'r') as f:
# parameters = paddle.parameters.Parameters.from_tar(f)
probs = paddle.infer(output_layer=out,
parameters=parameters,
input=test_data,
feeding=feeding)
lab = np.argsort(-probs) # probs and lab are the results of one batch data
print "Label of image/dog.png is: %d" % lab[0][0]