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Turn on the tests for MNIST
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‎python/paddle/fluid/tests/book/high-level-api/test_recognize_digits/test_recognize_digits_conv.py‎

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# 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 print_function
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import argparse
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import paddle.fluid as fluid
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import paddle
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import sys
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import numpy
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import unittest
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import math
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import sys
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import os
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BATCH_SIZE = 64
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def inference_program():
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img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
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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_1 = fluid.layers.batch_norm(conv_pool_1)
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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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prediction = fluid.layers.fc(input=conv_pool_2, size=10, act='softmax')
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return prediction
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def train_program():
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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predict = inference_program()
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(cost)
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# acc = fluid.layers.accuracy(input=predict, label=label)
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# return avg_cost, acc
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return avg_cost
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def train(use_cuda, save_dirname):
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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optimizer = fluid.optimizer.Adam(learning_rate=0.001)
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trainer = fluid.Trainer(train_program, place=place, optimizer=optimizer)
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def event_handler(event):
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if isinstance(event, fluid.EndEpochEvent):
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if (event.epoch + 1) % 10 == 0:
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test_reader = paddle.batch(
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paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
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test_metrics = trainer.test(reader=test_reader)
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avg_cost_set = test_metrics[0]
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acc_set = test_metrics[1]
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# get test acc and loss
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acc = numpy.array(acc_set).mean()
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avg_cost = numpy.array(avg_cost_set).mean()
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print("avg_cost: %s" % avg_cost)
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print("acc : %s" % acc)
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if float(acc) > 0.2: # Smaller value to increase CI speed
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trainer.save_params(save_dirname)
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else:
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print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
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event.epoch + 1, float(avg_cost), float(acc)))
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if math.isnan(float(avg_cost)):
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sys.exit("got NaN loss, training failed.")
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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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trainer.train(
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num_epochs=100,
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event_handler=event_handler,
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reader=train_reader,
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feed_order=['img', 'label'])
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def infer(use_cuda, save_dirname=None):
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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inferencer = fluid.Inferencer(
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inference_program, param_path=save_dirname, place=place)
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batch_size = 1
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tensor_img = numpy.random.uniform(-1.0, 1.0,
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[batch_size, 1, 28, 28]).astype("float32")
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results = inferencer.infer({'img': tensor_img})
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print("infer results: ", results[0])
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def main(use_cuda):
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save_dirname = "recognize_digits_conv.inference.model"
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# call train() with is_local argument to run distributed train
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train(use_cuda=use_cuda, save_dirname=save_dirname)
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infer(use_cuda=use_cuda, save_dirname=save_dirname)
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if __name__ == '__main__':
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for use_cuda in (False, True):
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main(use_cuda=use_cuda)

‎python/paddle/fluid/tests/book/high-level-api/test_recognize_digits/test_recognize_digits_mlp.py‎

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# 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 print_function
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import argparse
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import paddle.fluid as fluid
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import paddle
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import sys
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import numpy
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import unittest
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import math
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import sys
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import os
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BATCH_SIZE = 64
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def inference_program():
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img = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
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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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prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
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return prediction
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def train_program():
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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predict = inference_program()
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(cost)
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# acc = fluid.layers.accuracy(input=predict, label=label)
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# return avg_cost, acc
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return avg_cost
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def train(use_cuda, save_dirname):
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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optimizer = fluid.optimizer.Adam(learning_rate=0.001)
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trainer = fluid.Trainer(train_program, place=place, optimizer=optimizer)
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def event_handler(event):
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if isinstance(event, fluid.EndEpochEvent):
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if (event.epoch + 1) % 10 == 0:
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test_reader = paddle.batch(
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paddle.dataset.mnist.test(), batch_size=BATCH_SIZE)
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test_metrics = trainer.test(reader=test_reader)
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avg_cost_set = test_metrics[0]
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acc_set = test_metrics[1]
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# get test acc and loss
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acc = numpy.array(acc_set).mean()
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avg_cost = numpy.array(avg_cost_set).mean()
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print("avg_cost: %s" % avg_cost)
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print("acc : %s" % acc)
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if float(acc) > 0.2: # Smaller value to increase CI speed
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trainer.save_params(save_dirname)
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else:
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print('BatchID {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format(
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event.epoch + 1, float(avg_cost), float(acc)))
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if math.isnan(float(avg_cost)):
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sys.exit("got NaN loss, training failed.")
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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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trainer.train(
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num_epochs=100,
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event_handler=event_handler,
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reader=train_reader,
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feed_order=['img', 'label'])
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def infer(use_cuda, save_dirname=None):
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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inferencer = fluid.Inferencer(
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inference_program, param_path=save_dirname, place=place)
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batch_size = 1
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tensor_img = numpy.random.uniform(-1.0, 1.0,
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[batch_size, 1, 28, 28]).astype("float32")
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results = inferencer.infer({'img': tensor_img})
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print("infer results: ", results[0])
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def main(use_cuda):
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save_dirname = "recognize_digits_mlp.inference.model"
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# call train() with is_local argument to run distributed train
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train(use_cuda=use_cuda, save_dirname=save_dirname)
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infer(use_cuda=use_cuda, save_dirname=save_dirname)
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if __name__ == '__main__':
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for use_cuda in (False, True):
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main(use_cuda=use_cuda)

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