# 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) 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