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