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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import argparse
import paddle.v2.fluid as fluid
import paddle.v2 as paddle
import sys
import numpy
def parse_arg():
parser = argparse.ArgumentParser()
parser.add_argument(
"nn_type",
help="The neural network type, in ['mlp', 'conv']",
type=str,
choices=['mlp', 'conv'])
parser.add_argument(
"--parallel",
help='Run in parallel or not',
default=False,
action="store_true")
parser.add_argument(
"--use_cuda",
help="Run the program by using CUDA",
default=False,
action="store_true")
return parser.parse_args()
BATCH_SIZE = 64
def loss_net(hidden, label):
prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=prediction, label=label)
return fluid.layers.mean(x=loss), fluid.layers.accuracy(
input=prediction, label=label)
def mlp(img, label):
hidden = fluid.layers.fc(input=img, size=200, act='tanh')
hidden = fluid.layers.fc(input=hidden, size=200, act='tanh')
return loss_net(hidden, label)
def conv_net(img, label):
conv_pool_1 = fluid.nets.simple_img_conv_pool(
input=img,
filter_size=5,
num_filters=20,
pool_size=2,
pool_stride=2,
act="relu")
conv_pool_2 = fluid.nets.simple_img_conv_pool(
input=conv_pool_1,
filter_size=5,
num_filters=50,
pool_size=2,
pool_stride=2,
act="relu")
return loss_net(conv_pool_2, label)
def main():
args = parse_arg()
print("recognize digits with args: {0}".format(" ".join(sys.argv[1:])))
img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
if args.nn_type == 'mlp':
net_conf = mlp
else:
net_conf = conv_net
if args.parallel:
places = fluid.layers.get_places()
pd = fluid.layers.ParallelDo(places)
with pd.do():
img_ = pd.read_input(img)
label_ = pd.read_input(label)
for o in net_conf(img_, label_):
pd.write_output(o)
avg_loss, acc = pd()
# get mean loss and acc through every devices.
avg_loss = fluid.layers.mean(x=avg_loss)
acc = fluid.layers.mean(x=acc)
else:
avg_loss, acc = net_conf(img, label)
test_program = fluid.default_main_program().clone()
optimizer = fluid.optimizer.Adam(learning_rate=0.001)
optimizer.minimize(avg_loss)
place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
train_reader = paddle.batch(
paddle.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=500),
batch_size=BATCH_SIZE)
test_reader = paddle.batch(
paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for batch_id, data in enumerate(train_reader()):
# train a mini-batch, fetch nothing
exe.run(feed=feeder.feed(data))
if (batch_id + 1) % 10 == 0:
acc_set = []
avg_loss_set = []
for test_data in test_reader():
acc_np, avg_loss_np = exe.run(program=test_program,
feed=feeder.feed(test_data),
fetch_list=[acc, avg_loss])
acc_set.append(float(acc_np))
avg_loss_set.append(float(avg_loss_np))
# get test acc and loss
acc_val = numpy.array(acc_set).mean()
avg_loss_val = numpy.array(avg_loss_set).mean()
if float(acc_val) > 0.85: # test acc > 85%
exit(0)
else:
print(
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'.
format(pass_id, batch_id + 1,
float(avg_loss_val), float(acc_val)))
if __name__ == '__main__':
main()