You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
126 lines
4.1 KiB
126 lines
4.1 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# 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 absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import numpy as np
|
|
import argparse
|
|
import time
|
|
import cProfile
|
|
import os
|
|
|
|
import paddle
|
|
import paddle.fluid as fluid
|
|
import paddle.fluid.profiler as profiler
|
|
|
|
SEED = 1
|
|
DTYPE = "float32"
|
|
|
|
# random seed must set before configuring the network.
|
|
# fluid.default_startup_program().random_seed = SEED
|
|
|
|
|
|
def cnn_model(data):
|
|
conv_pool_1 = fluid.nets.simple_img_conv_pool(
|
|
input=data,
|
|
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")
|
|
|
|
# TODO(dzhwinter) : refine the initializer and random seed settting
|
|
SIZE = 10
|
|
input_shape = conv_pool_2.shape
|
|
param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
|
|
scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
|
|
|
|
predict = fluid.layers.fc(
|
|
input=conv_pool_2,
|
|
size=SIZE,
|
|
act="softmax",
|
|
param_attr=fluid.param_attr.ParamAttr(
|
|
initializer=fluid.initializer.NormalInitializer(
|
|
loc=0.0, scale=scale)))
|
|
return predict
|
|
|
|
|
|
def get_model(args):
|
|
if args.use_reader_op:
|
|
filelist = [
|
|
os.path.join(args.data_path, f) for f in os.listdir(args.data_path)
|
|
]
|
|
data_file = fluid.layers.open_files(
|
|
filenames=filelist,
|
|
shapes=[[-1, 1, 28, 28], (-1, 1)],
|
|
lod_levels=[0, 0],
|
|
dtypes=["float32", "int64"],
|
|
thread_num=args.gpus,
|
|
pass_num=args.pass_num)
|
|
data_file = fluid.layers.double_buffer(
|
|
fluid.layers.batch(
|
|
data_file, batch_size=args.batch_size))
|
|
images, label = fluid.layers.read_file(data_file)
|
|
else:
|
|
images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
|
|
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
|
|
|
|
if args.device == 'CPU' and args.cpus > 1:
|
|
places = fluid.layers.get_places(args.cpus)
|
|
pd = fluid.layers.ParallelDo(places)
|
|
with pd.do():
|
|
predict = cnn_model(pd.read_input(images))
|
|
label = pd.read_input(label)
|
|
cost = fluid.layers.cross_entropy(input=predict, label=label)
|
|
avg_cost = fluid.layers.mean(x=cost)
|
|
batch_acc = fluid.layers.accuracy(input=predict, label=label)
|
|
|
|
pd.write_output(avg_cost)
|
|
pd.write_output(batch_acc)
|
|
|
|
avg_cost, batch_acc = pd()
|
|
avg_cost = fluid.layers.mean(avg_cost)
|
|
batch_acc = fluid.layers.mean(batch_acc)
|
|
else:
|
|
# Train program
|
|
predict = cnn_model(images)
|
|
cost = fluid.layers.cross_entropy(input=predict, label=label)
|
|
avg_cost = fluid.layers.mean(x=cost)
|
|
|
|
# Evaluator
|
|
batch_acc = fluid.layers.accuracy(input=predict, label=label)
|
|
|
|
# inference program
|
|
inference_program = fluid.default_main_program().clone()
|
|
|
|
# Optimization
|
|
opt = fluid.optimizer.AdamOptimizer(
|
|
learning_rate=0.001, beta1=0.9, beta2=0.999)
|
|
|
|
# Reader
|
|
train_reader = paddle.batch(
|
|
paddle.dataset.mnist.train(), batch_size=args.batch_size * args.gpus)
|
|
test_reader = paddle.batch(
|
|
paddle.dataset.mnist.test(), batch_size=args.batch_size)
|
|
return avg_cost, inference_program, opt, train_reader, test_reader, batch_acc
|