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Add accuracy func and test mnist accuracy #40

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Jun 28, 2018
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Add accuracy func and test mnist accuracy #40

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b1011b6
add accuracy func and test mnist accuracy
kexinzhao Jun 28, 2018
4c729c9
address comments
kexinzhao Jun 28, 2018
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18 changes: 16 additions & 2 deletions src/example/mnist/create_mnist_recordio.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@


def create_mnist_recordio_files():
# Convert mnist to recordio file
# Convert mnist training set to recordio files
with fluid.program_guard(fluid.Program(), fluid.Program()):
reader = paddle.batch(mnist.train(), batch_size=32)
feeder = fluid.DataFeeder(
Expand All @@ -30,7 +30,21 @@ def create_mnist_recordio_files():
],
place=fluid.CPUPlace())
fluid.recordio_writer.convert_reader_to_recordio_file(
'/tmp/mnist.recordio', reader, feeder)
'/tmp/mnist_train.recordio', reader, feeder)

# Convert mnist testing set to recordio files
with fluid.program_guard(fluid.Program(), fluid.Program()):
reader = paddle.batch(mnist.test(), batch_size=32)
feeder = fluid.DataFeeder(
feed_list=[ # order is image and label
fluid.layers.data(
name='image', shape=[1, 28, 28], dtype='float32'),
fluid.layers.data(
name='label', shape=[1], dtype='int64'),
],
place=fluid.CPUPlace())
fluid.recordio_writer.convert_reader_to_recordio_file(
'/tmp/mnist_test.recordio', reader, feeder)


if __name__ == "__main__":
Expand Down
96 changes: 70 additions & 26 deletions src/example/mnist/test_mnist.cc
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Expand Up @@ -12,14 +12,17 @@
// See the License for the specific language governing permissions and
// limitations under the License.

#include <fstream>
#include <numeric>
#include <vector>

#include "gtest/gtest.h"
#include "src/function.h"

using paddle::tape::VariableHandle;
using paddle::tape::Linear;
using paddle::tape::SGD;
using paddle::tape::Adam;
using paddle::tape::accuracy;
using paddle::tape::mean;
using paddle::tape::softmax;
using paddle::tape::cross_entropy;
Expand All @@ -29,41 +32,36 @@ using paddle::tape::get_global_tape;
using paddle::tape::CreateRecordioFileReader;
using paddle::tape::ReadNext;

bool is_file_exist(const std::string& fileName) {
std::ifstream infile(fileName);
return infile.good();
}

TEST(Mnist, TestCPU) {
std::string filename = "/tmp/mnist.recordio";
PADDLE_ENFORCE(is_file_exist(filename),
"file doesn't exist; have you run create_mnist_recordio.py");
auto reader = CreateRecordioFileReader(
filename, {32, 1, 28, 28, 32, 1}, {4, 2}, {0, 0});

Linear linear1(784, 200, "relu");
Linear linear2(200, 200, "relu");
Linear linear3(200, 10, "relu");
std::string filename1 = "/tmp/mnist_train.recordio";
std::string filename2 = "/tmp/mnist_test.recordio";
auto train_reader = CreateRecordioFileReader(
filename1, {32, 1, 28, 28, 32, 1}, {4, 2}, {0, 0});
auto test_reader = CreateRecordioFileReader(
filename2, {32, 1, 28, 28, 32, 1}, {4, 2}, {0, 0});

Linear linear1(784, 200, "tanh");
Linear linear2(200, 200, "tanh");
Linear linear3(200, 10, "softmax");
Adam adam(0.001);

auto forward = [&](VariableHandle input) -> VariableHandle {
return linear3(linear2(linear1(input)));
};

int total_steps = 10000;
int print_step = 100;
float avg_loss = 0.0;
float threshold = 0.90f;

for (int i = 0; i < 1000; ++i) {
for (int i = 0; i < total_steps; ++i) {
reset_global_tape();
auto data_label = ReadNext(reader);
auto data_label = ReadNext(train_reader, true);
auto data = data_label[0];
auto label = data_label[1];

auto predict = softmax(linear3(linear2(linear1(data))));
auto predict = forward(data);
auto loss = mean(cross_entropy(predict, label));

avg_loss +=
loss->Value().Get<paddle::framework::LoDTensor>().data<float>()[0];
if ((i + 1) % print_step == 0) {
LOG(INFO) << avg_loss / print_step;
avg_loss = 0;
}
auto precision = accuracy(predict, label);

get_global_tape().Backward(loss);

Expand All @@ -76,6 +74,52 @@ TEST(Mnist, TestCPU) {
for (auto w : linear3.Params()) {
adam.Update(w);
}

// Every time certain amount of batches have been processed,
// we test the average loss and accuracy on the test data set,
// we stop training when the accuracy hit some threshold
if ((i + 1) % print_step == 0) {
std::vector<float> losses;
std::vector<float> accuracies;

while (true) {
reset_global_tape();

auto data_label = ReadNext(test_reader, false);
if (data_label.empty()) {
break;
}

auto data = data_label[0];
auto label = data_label[1];

auto predict = forward(data);
auto loss = mean(cross_entropy(predict, label));
auto precision = accuracy(predict, label);

get_global_tape().Forward();

losses.push_back(
loss->Get<paddle::framework::LoDTensor>().data<float>()[0]);
accuracies.push_back(
precision->Get<paddle::framework::LoDTensor>().data<float>()[0]);
}

float avg_loss =
std::accumulate(losses.begin(), losses.end(), 0.0f) / losses.size();
float avg_accu =
std::accumulate(accuracies.begin(), accuracies.end(), 0.0f) /
accuracies.size();

LOG(INFO) << "Pass #" << (i + 1) / print_step
<< ", test set evaluation result: Avg loss is " << avg_loss
<< ", Avg accuracy is " << avg_accu;

if (avg_accu >= threshold) {
LOG(INFO) << "Meets target accuracy, stop training";
break;
}
}
}
}

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53 changes: 47 additions & 6 deletions src/function.h
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Expand Up @@ -15,6 +15,7 @@
#pragma once

#include <cmath>
#include <fstream>
#include <string>
#include <tuple>
#include <vector>
Expand Down Expand Up @@ -76,7 +77,7 @@ void init_params(VariableHandle v,

class Linear {
public:
Linear(int in_dim, int out_dim, const std::string &act)
Linear(int in_dim, int out_dim, const std::string &act = "")
: w_(new Variable("LinearWeight")),
b_(new Variable("LinearBias")),
act_(act) {
Expand Down Expand Up @@ -110,6 +111,9 @@ class Linear {
{{"X", {pre_bias}}, {"Y", {b_}}},
{{"Out", {pre_act}}},
add_op_attrs);
if (act_.empty()) {
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Maybe also make Conv support act_.empty()

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Done for both conv and batchnorm

return pre_act;
}
VariableHandle post_act(new Variable("linear"));
get_global_tape().AddOp(
act_, {{"X", {pre_act}}}, {{"Out", {post_act}}}, {});
Expand All @@ -126,7 +130,7 @@ class Linear {

class Convolution2D {
public:
Convolution2D(int c_in, int c_out, int f, const std::string &act)
Convolution2D(int c_in, int c_out, int f, const std::string &act = "")
: w_(new Variable("ConvolutionWeight")),
b_(new Variable("ConvolutionBias")),
act_(act) {
Expand Down Expand Up @@ -162,6 +166,9 @@ class Convolution2D {
{{"X", {pre_bias}}, {"Y", {b_}}},
{{"Out", {pre_act}}},
add_op_attrs);
if (act_.empty()) {
return pre_act;
}
VariableHandle post_act(new Variable("conv"));
get_global_tape().AddOp(
act_, {{"X", {pre_act}}}, {{"Out", {post_act}}}, {});
Expand Down Expand Up @@ -282,7 +289,7 @@ class Adam {

class BatchNorm {
public:
BatchNorm(int channel_in, const std::string &act)
explicit BatchNorm(int channel_in, const std::string &act = "")
: scale_(new Variable("BatchNormScale")),
bias_(new Variable("BatchNormBias")),
mean_(new Variable("BatchNormMean")),
Expand Down Expand Up @@ -319,7 +326,9 @@ class BatchNorm {
{"SavedMean", {tmp_mean}},
{"SavedVariance", {tmp_var}}},
attrs);

if (act_.empty()) {
return pre_act;
}
VariableHandle post_act(new Variable("batch_norm"));
get_global_tape().AddOp(
act_, {{"X", {pre_act}}}, {{"Out", {post_act}}}, {});
Expand All @@ -337,6 +346,29 @@ class BatchNorm {
std::string act_;
};

// Calculate the top k accuracy of the prediction against the label
VariableHandle accuracy(VariableHandle prediction,
VariableHandle label,
int k = 1) {
// Use top_k op to get top k prediction class labels
VariableHandle topk_values(new Variable("accuracy"));
VariableHandle topk_indices(new Variable("accuracy"));
get_global_tape().AddOp("top_k",
{{"X", {prediction}}},
{{"Out", {topk_values}}, {"Indices", {topk_indices}}},
{{"k", k}});

VariableHandle acc_out(new Variable("accuracy"));
VariableHandle correct(new Variable("accuracy"));
VariableHandle total(new Variable("accuracy"));
get_global_tape().AddOp(
"accuracy",
{{"Out", {topk_values}}, {"Indices", {topk_indices}}, {"Label", {label}}},
{{"Accuracy", {acc_out}}, {"Correct", {correct}}, {"Total", {total}}},
{});
return acc_out;
}

VariableHandle pool2d(VariableHandle x,
const framework::AttributeMap &attrs = {}) {
VariableHandle out(new Variable("pool2d"));
Expand Down Expand Up @@ -382,6 +414,11 @@ VariableHandle CreateRecordioFileReader(std::string filename,
std::vector<int> shape_concat,
std::vector<int> ranks,
std::vector<int> lod_levels) {
std::ifstream infile(filename);
PADDLE_ENFORCE(infile.good(),
"%s doesn't exist; have you run create_mnist_recordio.py?",
filename);

VariableHandle reader(new paddle::tape::Variable("reader"));

framework::OpDesc op_desc = CreateOpDesc("create_recordio_file_reader",
Expand All @@ -397,7 +434,7 @@ VariableHandle CreateRecordioFileReader(std::string filename,
return reader;
}

std::vector<VariableHandle> ReadNext(VariableHandle reader) {
std::vector<VariableHandle> ReadNext(VariableHandle reader, bool repeat) {
PADDLE_ENFORCE(reader->Var().IsType<framework::ReaderHolder>());

paddle::framework::LoDTensorArray data_holder;
Expand All @@ -406,8 +443,12 @@ std::vector<VariableHandle> ReadNext(VariableHandle reader) {
reader->GetMutable<paddle::framework::ReaderHolder>()->ReInit();
reader->GetMutable<paddle::framework::ReaderHolder>()->ReadNext(
&data_holder);
PADDLE_ENFORCE(!data_holder.empty(), "Error reading file.");
if (!repeat) {
reader->GetMutable<paddle::framework::ReaderHolder>()->ReInit();
return {};
}
}
PADDLE_ENFORCE(!data_holder.empty(), "Error reading file.");

std::vector<VariableHandle> rval;
for (size_t i = 0; i < data_holder.size(); ++i) {
Expand Down
1 change: 1 addition & 0 deletions src/variable.h
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Expand Up @@ -15,6 +15,7 @@

#include <memory>
#include <string>
#include <unordered_map>
#include <vector>

#include "paddle/fluid/framework/operator.h" // framework::kGradVarSuffix
Expand Down