提交 452fe1e1 编写于 作者: X Xi Chen

init checkin for distributed book chapter 6

上级 43dab723
from __future__ import print_function
import numpy as np
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
def convolution_net(data, label, input_dim, class_dim=2, emb_dim=32,
hid_dim=32):
emb = fluid.layers.embedding(input=data, size=[input_dim, emb_dim])
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=3,
act="tanh",
pool_type="sqrt")
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=4,
act="tanh",
pool_type="sqrt")
prediction = fluid.layers.fc(input=[conv_3, conv_4],
size=class_dim,
act="softmax")
cost = fluid.layers.cross_entropy(input=prediction, label=label)
avg_cost = fluid.layers.mean(x=cost)
adam_optimizer = fluid.optimizer.Adam(learning_rate=0.002)
optimize_ops, params_grads = adam_optimizer.minimize(avg_cost)
accuracy = fluid.evaluator.Accuracy(input=prediction, label=label)
return avg_cost, accuracy, accuracy.metrics[0], optimize_ops, params_grads
def to_lodtensor(data, place):
seq_lens = [len(seq) for seq in data]
cur_len = 0
lod = [cur_len]
for l in seq_lens:
cur_len += l
lod.append(cur_len)
flattened_data = np.concatenate(data, axis=0).astype("int64")
flattened_data = flattened_data.reshape([len(flattened_data), 1])
res = fluid.LoDTensor()
res.set(flattened_data, place)
res.set_lod([lod])
return res
def main():
BATCH_SIZE = 100
PASS_NUM = 5
word_dict = paddle.dataset.imdb.word_dict()
dict_dim = len(word_dict)
class_dim = 2
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
cost, accuracy, acc_out, optimize_ops, params_grads = convolution_net(
data, label, input_dim=dict_dim, class_dim=class_dim)
train_data = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.imdb.train(word_dict), buf_size=1000),
batch_size=BATCH_SIZE)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
t = fluid.DistributeTranspiler()
# all parameter server endpoints list for spliting parameters
pserver_endpoints = os.getenv("PSERVERS")
# server endpoint for current node
current_endpoint = os.getenv("SERVER_ENDPOINT")
# run as trainer or parameter server
training_role = os.getenv(
"TRAINING_ROLE", "TRAINER") # get the training role: trainer/pserver
t.transpile(
optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
exe.run(fluid.default_startup_program())
if training_role == "PSERVER":
if not current_endpoint:
print("need env SERVER_ENDPOINT")
exit(1)
pserver_prog = t.get_pserver_program(current_endpoint, optimize_ops)
exe.run(pserver_prog)
elif training_role == "TRAINER":
trainer_prog = t.get_trainer_program()
feeder = fluid.DataFeeder(feed_list=[data, label], place=place)
for pass_id in xrange(PASS_NUM):
accuracy.reset(exe)
for data in train_data():
cost_val, acc_val = exe.run(trainer_prog,
feed=feeder.feed(data),
fetch_list=[cost, acc_out])
pass_acc = accuracy.eval(exe)
print("cost=" + str(cost_val) + " acc=" + str(acc_val) +
" pass_acc=" + str(pass_acc))
if cost_val < 1.0 and pass_acc > 0.8:
exit(0)
else:
print("environment var TRAINER_ROLE should be TRAINER os PSERVER")
if __name__ == '__main__':
main()
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