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Merge pull request #215 from shrit/mnist_float
Adding float mnist_simple example
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# This is a simple Makefile used to build the example source code. | ||
# This example might requires some modifications in order to work correctly on | ||
# your system. | ||
# If you're not using the Armadillo wrapper, replace `armadillo` with linker commands | ||
# for the BLAS and LAPACK libraries that you are using. | ||
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TARGET := mnist_cnn_f32 | ||
SRC := mnist_cnn_f32.cpp | ||
LIBS_NAME := armadillo | ||
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CXX := g++ | ||
CXXFLAGS += -std=c++14 -Wall -Wextra -O3 -DNDEBUG -fopenmp | ||
# Use these CXXFLAGS instead if you want to compile with debugging symbols and | ||
# without optimizations. | ||
# CXXFLAGS += -std=c++14 -Wall -Wextra -g -O0 | ||
LDFLAGS += -fopenmp | ||
LDFLAGS += -L . # /path/to/mlpack/library/ # if installed locally. | ||
# Add header directories for any includes that aren't on the | ||
# default compiler search path. | ||
INCLFLAGS := -I . | ||
# If you have mlpack or ensmallen installed somewhere nonstandard, uncomment and | ||
# update the lines below. | ||
# INCLFLAGS += -I/path/to/mlpack/include/ | ||
# INCLFLAGS += -I/path/to/ensmallen/include/ | ||
CXXFLAGS += $(INCLFLAGS) | ||
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OBJS := $(SRC:.cpp=.o) | ||
LIBS := $(addprefix -l,$(LIBS_NAME)) | ||
CLEAN_LIST := $(TARGET) $(OBJS) | ||
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# default rule | ||
default: all | ||
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$(TARGET): $(OBJS) | ||
$(CXX) $(CXXFLAGS) $(OBJS) -o $(TARGET) $(LDFLAGS) $(LIBS) | ||
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.PHONY: all | ||
all: $(TARGET) | ||
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.PHONY: clean | ||
clean: | ||
@echo CLEAN $(CLEAN_LIST) | ||
@rm -f $(CLEAN_LIST) |
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/** | ||
* An example of using Convolutional Neural Network (CNN) for | ||
* solving Digit Recognizer problem from Kaggle website. | ||
* | ||
* The full description of a problem as well as datasets for training | ||
* and testing are available here: https://www.kaggle.com/c/digit-recognizer. | ||
* | ||
* This example is similar to the mnist_cnn. The main difference is that, | ||
* this one loads the dataset as a float32 and creates a float32 model. | ||
* | ||
* mlpack is free software; you may redistribute it and/or modify it under the | ||
* terms of the 3-clause BSD license. You should have received a copy of the | ||
* 3-clause BSD license along with mlpack. If not, see | ||
* http://www.opensource.org/licenses/BSD-3-Clause for more information. | ||
* | ||
* @author Daivik Nema | ||
*/ | ||
#define MLPACK_ENABLE_ANN_SERIALIZATION | ||
#include <mlpack.hpp> | ||
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#if ((ENS_VERSION_MAJOR < 2) || ((ENS_VERSION_MAJOR == 2) && (ENS_VERSION_MINOR < 13))) | ||
#error "need ensmallen version 2.13.0 or later" | ||
#endif | ||
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using namespace arma; | ||
using namespace mlpack; | ||
using namespace std; | ||
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CEREAL_REGISTER_MLPACK_LAYERS(arma::fmat); | ||
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Row<size_t> getLabels(const arma::fmat& predOut) | ||
{ | ||
Row<size_t> predLabels(predOut.n_cols); | ||
for (uword i = 0; i < predOut.n_cols; ++i) | ||
{ | ||
predLabels(i) = predOut.col(i).index_max(); | ||
} | ||
return predLabels; | ||
} | ||
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int main() | ||
{ | ||
// Dataset is randomly split into validation | ||
// and training parts with following ratio. | ||
constexpr double RATIO = 0.1; | ||
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// Allow 60 passes over the training data, unless we are stopped early by | ||
// EarlyStopAtMinLoss. | ||
const int EPOCHS = 60; | ||
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// Number of data points in each iteration of SGD. | ||
const int BATCH_SIZE = 50; | ||
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// Step size of the optimizer. | ||
const double STEP_SIZE = 1.2e-3; | ||
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cout << "Reading data ..." << endl; | ||
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// Labeled dataset that contains data for training is loaded from CSV file. | ||
// Rows represent features, columns represent data points. | ||
arma::fmat dataset; | ||
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// The original file can be downloaded from | ||
// https://www.kaggle.com/c/digit-recognizer/data | ||
data::Load("../data/mnist_train.csv", dataset, true); | ||
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// Split the dataset into training and validation sets. | ||
arma::fmat train, valid; | ||
data::Split(dataset, train, valid, RATIO); | ||
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// The train and valid datasets contain both - the features as well as the | ||
// class labels. Split these into separate mats. | ||
const arma::fmat trainX = train.submat(1, 0, train.n_rows - 1, train.n_cols - 1) / | ||
256.0; | ||
const arma::fmat validX = valid.submat(1, 0, valid.n_rows - 1, valid.n_cols - 1) / | ||
256.0; | ||
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// Labels should specify the class of a data point and be in the interval [0, | ||
// numClasses). | ||
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// Create labels for training and validatiion datasets. | ||
const arma::fmat trainY = train.row(0); | ||
const arma::fmat validY = valid.row(0); | ||
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// Specify the NN model. NegativeLogLikelihood is the output layer that | ||
// is used for classification problem. RandomInitialization means that | ||
// initial weights are generated randomly in the interval from -1 to 1. | ||
FFN<NegativeLogLikelihoodType<arma::fmat>, RandomInitialization, arma::fmat> model; | ||
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// Specify the model architecture. | ||
// In this example, the CNN architecture is chosen similar to LeNet-5. | ||
// The architecture follows a Conv-ReLU-Pool-Conv-ReLU-Pool-Dense schema. We | ||
// have used leaky ReLU activation instead of vanilla ReLU. Standard | ||
// max-pooling has been used for pooling. The first convolution uses 6 filters | ||
// of size 5x5 (and a stride of 1). The second convolution uses 16 filters of | ||
// size 5x5 (stride = 1). The final dense layer is connected to a softmax to | ||
// ensure that we get a valid probability distribution over the output classes | ||
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// Layers schema. | ||
// 28x28x1 --- conv (6 filters of size 5x5. stride = 1) ---> 24x24x6 | ||
// 24x24x6 --------------- Leaky ReLU ---------------------> 24x24x6 | ||
// 24x24x6 --- max pooling (over 2x2 fields. stride = 2) --> 12x12x6 | ||
// 12x12x6 --- conv (16 filters of size 5x5. stride = 1) --> 8x8x16 | ||
// 8x8x16 --------------- Leaky ReLU ---------------------> 8x8x16 | ||
// 8x8x16 --- max pooling (over 2x2 fields. stride = 2) --> 4x4x16 | ||
// 4x4x16 ------------------- Dense ----------------------> 10 | ||
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// Add the first convolution layer. | ||
model.Add<ConvolutionType< | ||
NaiveConvolution<ValidConvolution>, | ||
NaiveConvolution<FullConvolution>, | ||
NaiveConvolution<ValidConvolution>, | ||
arma::fmat>>(6, // Number of output activation maps. | ||
5, // Filter width. | ||
5, // Filter height. | ||
1, // Stride along width. | ||
1, // Stride along height. | ||
0, // Padding width. | ||
0 // Padding height. | ||
); | ||
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// Add first ReLU. | ||
model.Add<LeakyReLUType<arma::fmat>>(); | ||
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// Add first pooling layer. Pools over 2x2 fields in the input. | ||
model.Add<MaxPoolingType<arma::fmat>>(2, // Width of field. | ||
2, // Height of field. | ||
2, // Stride along width. | ||
2, // Stride along height. | ||
true); | ||
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// Add the second convolution layer. | ||
model.Add<ConvolutionType< | ||
NaiveConvolution<ValidConvolution>, | ||
NaiveConvolution<FullConvolution>, | ||
NaiveConvolution<ValidConvolution>, | ||
arma::fmat>>(16, // Number of output activation maps. | ||
5, // Filter width. | ||
5, // Filter height. | ||
1, // Stride along width. | ||
1, // Stride along height. | ||
0, // Padding width. | ||
0 // Padding height. | ||
); | ||
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// Add the second ReLU. | ||
model.Add<LeakyReLUType<arma::fmat>>(); | ||
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// Add the second pooling layer. | ||
model.Add<MaxPoolingType<arma::fmat>>(2, 2, 2, 2, true); | ||
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// Add the final dense layer. | ||
model.Add<LinearType<arma::fmat>>(10); | ||
model.Add<LogSoftMaxType<arma::fmat>>(); | ||
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model.InputDimensions() = vector<size_t>({ 28, 28 }); | ||
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cout << "Start training ..." << endl; | ||
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// Set parameters for the Adam optimizer. | ||
ens::Adam optimizer( | ||
STEP_SIZE, // Step size of the optimizer. | ||
BATCH_SIZE, // Batch size. Number of data points that are used in each | ||
// iteration. | ||
0.9, // Exponential decay rate for the first moment estimates. | ||
0.999, // Exponential decay rate for the weighted infinity norm estimates. | ||
1e-8, // Value used to initialise the mean squared gradient parameter. | ||
EPOCHS * trainX.n_cols, // Max number of iterations. | ||
1e-8, // Tolerance. | ||
true); | ||
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// Train the CNN model. If this is the first iteration, weights are | ||
// randomly initialized between -1 and 1. Otherwise, the values of weights | ||
// from the previous iteration are used. | ||
model.Train(trainX, | ||
trainY, | ||
optimizer, | ||
ens::PrintLoss(), | ||
ens::ProgressBar(), | ||
// Stop the training using Early Stop at min loss. | ||
ens::EarlyStopAtMinLossType<arma::fmat>( | ||
[&](const arma::fmat& /* param */) | ||
{ | ||
double validationLoss = model.Evaluate(validX, validY); | ||
cout << "Validation loss: " << validationLoss << "." | ||
<< endl; | ||
return validationLoss; | ||
})); | ||
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// Matrix to store the predictions on train and validation datasets. | ||
arma::fmat predOut; | ||
// Get predictions on training data points. | ||
model.Predict(trainX, predOut); | ||
// Calculate accuracy on training data points. | ||
Row<size_t> predLabels = getLabels(predOut); | ||
double trainAccuracy = | ||
accu(predLabels == trainY) / (double) trainY.n_elem * 100; | ||
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// Get predictions on validation data points. | ||
model.Predict(validX, predOut); | ||
predLabels = getLabels(predOut); | ||
// Calculate accuracy on validation data points. | ||
double validAccuracy = | ||
accu(predLabels == validY) / (double) validY.n_elem * 100; | ||
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cout << "Accuracy: train = " << trainAccuracy << "%," | ||
<< "\t valid = " << validAccuracy << "%" << endl; | ||
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data::Save("model.bin", "model", model, false); | ||
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cout << "Predicting on test set..." << endl; | ||
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// Get predictions on test data points. | ||
// The original file could be download from | ||
// https://www.kaggle.com/c/digit-recognizer/data | ||
data::Load("../data/mnist_test.csv", dataset, true); | ||
const arma::fmat testX = dataset.submat(1, 0, dataset.n_rows - 1, dataset.n_cols - 1) | ||
/ 256.0; | ||
const arma::fmat testY = dataset.row(0); | ||
model.Predict(testX, predOut); | ||
// Calculate accuracy on test data points. | ||
predLabels = getLabels(predOut); | ||
double testAccuracy = | ||
accu(predLabels == testY) / (double) testY.n_elem * 100; | ||
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cout << "Accuracy: test = " << testAccuracy << "%" << endl; | ||
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cout << "Saving predicted labels to \"results.csv.\"..." << endl; | ||
// Saving results into Kaggle compatible CSV file. | ||
predLabels.save("results.csv", arma::csv_ascii); | ||
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cout << "Neural network model is saved to \"model.bin\"" << endl; | ||
cout << "Finished" << endl; | ||
} |
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@@ -0,0 +1,43 @@ | ||
# This is a simple Makefile used to build the example source code. | ||
# This example might requires some modifications in order to work correctly on | ||
# your system. | ||
# If you're not using the Armadillo wrapper, replace `armadillo` with linker commands | ||
# for the BLAS and LAPACK libraries that you are using. | ||
|
||
TARGET := mnist_simple_f32 | ||
SRC := mnist_simple_f32.cpp | ||
LIBS_NAME := armadillo | ||
|
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CXX := g++ | ||
CXXFLAGS += -std=c++14 -Wall -Wextra -O3 -DNDEBUG -fopenmp | ||
# Use these CXXFLAGS instead if you want to compile with debugging symbols and | ||
# without optimizations. | ||
# CXXFLAGS += -std=c++14 -Wall -Wextra -g -O0 | ||
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LDFLAGS += -fopenmp | ||
# Add header directories for any includes that aren't on the | ||
# default compiler search path. | ||
INCLFLAGS := -I . | ||
# If you have mlpack or ensmallen installed somewhere nonstandard, uncomment and | ||
# update the lines below. | ||
# INCLFLAGS += -I/path/to/mlpack/include/ | ||
# INCLFLAGS += -I/path/to/ensmallen/include/ | ||
CXXFLAGS += $(INCLFLAGS) | ||
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OBJS := $(SRC:.cpp=.o) | ||
LIBS := $(addprefix -l,$(LIBS_NAME)) | ||
CLEAN_LIST := $(TARGET) $(OBJS) | ||
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# default rule | ||
default: all | ||
|
||
$(TARGET): $(OBJS) | ||
$(CXX) $(OBJS) -o $(TARGET) $(LDFLAGS) $(LIBS) | ||
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.PHONY: all | ||
all: $(TARGET) | ||
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.PHONY: clean | ||
clean: | ||
@echo CLEAN $(CLEAN_LIST) | ||
@rm -f $(CLEAN_LIST) |
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