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118 lines
3.9 KiB
118 lines
3.9 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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 absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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import argparse
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import time
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import cProfile
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import os
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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.profiler as profiler
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SEED = 1
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DTYPE = "float32"
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# random seed must set before configuring the network.
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# fluid.default_startup_program().random_seed = SEED
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def cnn_model(data):
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conv_pool_1 = fluid.nets.simple_img_conv_pool(
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input=data,
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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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# TODO(dzhwinter) : refine the initializer and random seed settting
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SIZE = 10
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input_shape = conv_pool_2.shape
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param_shape = [reduce(lambda a, b: a * b, input_shape[1:], 1)] + [SIZE]
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scale = (2.0 / (param_shape[0]**2 * SIZE))**0.5
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predict = fluid.layers.fc(
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input=conv_pool_2,
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size=SIZE,
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act="softmax",
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.NormalInitializer(
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loc=0.0, scale=scale)))
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return predict
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def get_model(args, is_train, main_prog, startup_prog):
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# NOTE: mnist is small, we don't implement data sharding yet.
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opt = None
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data_file_handle = None
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with fluid.program_guard(main_prog, startup_prog):
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if args.use_reader_op:
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filelist = [
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os.path.join(args.data_path, f)
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for f in os.listdir(args.data_path)
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]
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data_file_handle = fluid.layers.open_files(
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filenames=filelist,
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shapes=[[-1, 1, 28, 28], (-1, 1)],
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lod_levels=[0, 0],
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dtypes=["float32", "int64"],
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thread_num=1,
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pass_num=1)
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data_file = fluid.layers.double_buffer(
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fluid.layers.batch(
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data_file_handle, batch_size=args.batch_size))
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with fluid.unique_name.guard():
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if args.use_reader_op:
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input, label = fluid.layers.read_file(data_file)
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else:
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images = fluid.layers.data(
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name='pixel', shape=[1, 28, 28], dtype='float32')
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label = fluid.layers.data(
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name='label', shape=[1], dtype='int64')
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predict = cnn_model(images)
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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# Evaluator
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batch_acc = fluid.layers.accuracy(input=predict, label=label)
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# Optimization
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if is_train:
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opt = fluid.optimizer.AdamOptimizer(
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learning_rate=0.001, beta1=0.9, beta2=0.999)
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opt.minimize(avg_cost)
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if args.memory_optimize:
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fluid.memory_optimize(main_prog)
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# Reader
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if is_train:
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reader = paddle.dataset.mnist.train()
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else:
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reader = paddle.dataset.mnist.test()
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batched_reader = paddle.batch(
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reader, batch_size=args.batch_size * args.gpus)
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return avg_cost, opt, [batch_acc], batched_reader, data_file_handle
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