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added method to read and write matrices and tensors, Assert Functions…
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…, and tools to format the code
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@giovastabile
giovastabile committed Apr 10, 2019
1 parent 73654a9 commit 2c1a6a2
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Showing 30 changed files with 6,614 additions and 4,886 deletions.
79 changes: 79 additions & 0 deletions code_formatter.sh
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@@ -0,0 +1,79 @@
#!/bin/bash

#######################################

required_command="astyle"
code_directory="include/"
tutorial_directory="tutorials/"

#######################################

usage() {
echo
echo -e "\tUsage: $(basename $0) [files]"
echo
echo -e "\tIf files are not specified, $(basename $0) formats all ".C" and ".H" files"
echo -e "\tin source directory; otherwise, it formats all given files."
echo
echo -e "\tRequired command: $required_command"
echo
exit 0
}


[[ $1 == "-h" ]] && usage

# Test for required program
for comm in $required_command; do
command -v $comm >/dev/null 2>&1 || {
echo "I require $comm but it's not installed. Aborting." >&2;
exit 1
}
done

# Set the files to format
[[ $# != 0 ]] && src_files=$@ || src_files="--recursive $code_directory**.h,**.H"
[[ $# != 0 ]] && tutorial_files=$@ || tutorial_files="--recursive $tutorial_directory**.cpp,**.H"

echo $tutorial_files
echo $src_files

# Here the important part: astyle formats the src files.
astyle --style=bsd\
--indent=spaces=4\
--indent-classes\
--indent-switches\
--indent-col1-comments\
--break-blocks\
--pad-oper\
--pad-comma\
--pad-header\
--delete-empty-lines\
--align-pointer=type\
--align-reference=type\
--add-braces\
--convert-tabs\
--close-templates\
--max-code-length=80\
--mode=c\
$src_files

# Here the important part: astyle formats the tutorial files.
astyle --style=bsd\
--indent=spaces=4\
--indent-classes\
--indent-switches\
--indent-col1-comments\
--break-blocks\
--pad-oper\
--pad-comma\
--pad-header\
--delete-empty-lines\
--align-pointer=type\
--align-reference=type\
--add-braces\
--convert-tabs\
--close-templates\
--max-code-length=80\
--mode=c\
$tutorial_files
44 changes: 23 additions & 21 deletions include/Activation/Identity.h
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Expand Up @@ -4,7 +4,8 @@
#include <Eigen/Core>
#include "../Config.h"

namespace MiniDNN {
namespace MiniDNN
{


///
Expand All @@ -18,26 +19,27 @@ namespace MiniDNN {
///
class Identity
{
private:
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> Matrix;

public:
// a = activation(z) = z
// Z = [z1, ..., zn], A = [a1, ..., an], n observations
static inline void activate(const Matrix& Z, Matrix& A)
{
A.noalias() = Z;
}

// Apply the Jacobian matrix J to a vector f
// J = d_a / d_z = I
// g = J * f = f
// Z = [z1, ..., zn], G = [g1, ..., gn], F = [f1, ..., fn]
// Note: When entering this function, Z and G may point to the same matrix
static inline void apply_jacobian(const Matrix& Z, const Matrix& A, const Matrix& F, Matrix& G)
{
G.noalias() = F;
}
private:
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> Matrix;

public:
// a = activation(z) = z
// Z = [z1, ..., zn], A = [a1, ..., an], n observations
static inline void activate(const Matrix& Z, Matrix& A)
{
A.noalias() = Z;
}

// Apply the Jacobian matrix J to a vector f
// J = d_a / d_z = I
// g = J * f = f
// Z = [z1, ..., zn], G = [g1, ..., gn], F = [f1, ..., fn]
// Note: When entering this function, Z and G may point to the same matrix
static inline void apply_jacobian(const Matrix& Z, const Matrix& A,
const Matrix& F, Matrix& G)
{
G.noalias() = F;
}
};


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44 changes: 23 additions & 21 deletions include/Activation/ReLU.h
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Expand Up @@ -4,7 +4,8 @@
#include <Eigen/Core>
#include "../Config.h"

namespace MiniDNN {
namespace MiniDNN
{


///
Expand All @@ -14,26 +15,27 @@ namespace MiniDNN {
///
class ReLU
{
private:
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> Matrix;

public:
// a = activation(z) = max(z, 0)
// Z = [z1, ..., zn], A = [a1, ..., an], n observations
static inline void activate(const Matrix& Z, Matrix& A)
{
A.array() = Z.array().cwiseMax(Scalar(0));
}

// Apply the Jacobian matrix J to a vector f
// J = d_a / d_z = diag(sign(a)) = diag(a > 0)
// g = J * f = (a > 0) .* f
// Z = [z1, ..., zn], G = [g1, ..., gn], F = [f1, ..., fn]
// Note: When entering this function, Z and G may point to the same matrix
static inline void apply_jacobian(const Matrix& Z, const Matrix& A, const Matrix& F, Matrix& G)
{
G.array() = (A.array() > Scalar(0)).select(F, Scalar(0));
}
private:
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> Matrix;

public:
// a = activation(z) = max(z, 0)
// Z = [z1, ..., zn], A = [a1, ..., an], n observations
static inline void activate(const Matrix& Z, Matrix& A)
{
A.array() = Z.array().cwiseMax(Scalar(0));
}

// Apply the Jacobian matrix J to a vector f
// J = d_a / d_z = diag(sign(a)) = diag(a > 0)
// g = J * f = (a > 0) .* f
// Z = [z1, ..., zn], G = [g1, ..., gn], F = [f1, ..., fn]
// Note: When entering this function, Z and G may point to the same matrix
static inline void apply_jacobian(const Matrix& Z, const Matrix& A,
const Matrix& F, Matrix& G)
{
G.array() = (A.array() > Scalar(0)).select(F, Scalar(0));
}
};


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44 changes: 23 additions & 21 deletions include/Activation/Sigmoid.h
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Expand Up @@ -4,7 +4,8 @@
#include <Eigen/Core>
#include "../Config.h"

namespace MiniDNN {
namespace MiniDNN
{


///
Expand All @@ -14,26 +15,27 @@ namespace MiniDNN {
///
class Sigmoid
{
private:
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> Matrix;

public:
// a = activation(z) = 1 / (1 + exp(-z))
// Z = [z1, ..., zn], A = [a1, ..., an], n observations
static inline void activate(const Matrix& Z, Matrix& A)
{
A.array() = Scalar(1) / (Scalar(1) + (-Z.array()).exp());
}

// Apply the Jacobian matrix J to a vector f
// J = d_a / d_z = diag(a .* (1 - a))
// g = J * f = a .* (1 - a) .* f
// Z = [z1, ..., zn], G = [g1, ..., gn], F = [f1, ..., fn]
// Note: When entering this function, Z and G may point to the same matrix
static inline void apply_jacobian(const Matrix& Z, const Matrix& A, const Matrix& F, Matrix& G)
{
G.array() = A.array() * (Scalar(1) - A.array()) * F.array();
}
private:
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> Matrix;

public:
// a = activation(z) = 1 / (1 + exp(-z))
// Z = [z1, ..., zn], A = [a1, ..., an], n observations
static inline void activate(const Matrix& Z, Matrix& A)
{
A.array() = Scalar(1) / (Scalar(1) + (-Z.array()).exp());
}

// Apply the Jacobian matrix J to a vector f
// J = d_a / d_z = diag(a .* (1 - a))
// g = J * f = a .* (1 - a) .* f
// Z = [z1, ..., zn], G = [g1, ..., gn], F = [f1, ..., fn]
// Note: When entering this function, Z and G may point to the same matrix
static inline void apply_jacobian(const Matrix& Z, const Matrix& A,
const Matrix& F, Matrix& G)
{
G.array() = A.array() * (Scalar(1) - A.array()) * F.array();
}
};


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52 changes: 27 additions & 25 deletions include/Activation/Softmax.h
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@@ -1,7 +1,8 @@
#ifndef ACTIVATION_SOFTMAX_H_
#define ACTIVATION_SOFTMAX_H_

namespace MiniDNN {
namespace MiniDNN
{


///
Expand All @@ -11,30 +12,31 @@ namespace MiniDNN {
///
class Softmax
{
private:
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> Matrix;
typedef Eigen::Array<Scalar, 1, Eigen::Dynamic> RowArray;

public:
// a = activation(z) = softmax(z)
// Z = [z1, ..., zn], A = [a1, ..., an], n observations
static inline void activate(const Matrix& Z, Matrix& A)
{
A.array() = (Z.rowwise() - Z.colwise().maxCoeff()).array().exp();
RowArray colsums = A.colwise().sum();
A.array().rowwise() /= colsums;
}

// Apply the Jacobian matrix J to a vector f
// J = d_a / d_z = diag(a) - a * a'
// g = J * f = a .* f - a * (a' * f) = a .* (f - a'f)
// Z = [z1, ..., zn], G = [g1, ..., gn], F = [f1, ..., fn]
// Note: When entering this function, Z and G may point to the same matrix
static inline void apply_jacobian(const Matrix& Z, const Matrix& A, const Matrix& F, Matrix& G)
{
RowArray a_dot_f = A.cwiseProduct(F).colwise().sum();
G.array() = A.array() * (F.array().rowwise() - a_dot_f);
}
private:
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> Matrix;
typedef Eigen::Array<Scalar, 1, Eigen::Dynamic> RowArray;

public:
// a = activation(z) = softmax(z)
// Z = [z1, ..., zn], A = [a1, ..., an], n observations
static inline void activate(const Matrix& Z, Matrix& A)
{
A.array() = (Z.rowwise() - Z.colwise().maxCoeff()).array().exp();
RowArray colsums = A.colwise().sum();
A.array().rowwise() /= colsums;
}

// Apply the Jacobian matrix J to a vector f
// J = d_a / d_z = diag(a) - a * a'
// g = J * f = a .* f - a * (a' * f) = a .* (f - a'f)
// Z = [z1, ..., zn], G = [g1, ..., gn], F = [f1, ..., fn]
// Note: When entering this function, Z and G may point to the same matrix
static inline void apply_jacobian(const Matrix& Z, const Matrix& A,
const Matrix& F, Matrix& G)
{
RowArray a_dot_f = A.cwiseProduct(F).colwise().sum();
G.array() = A.array() * (F.array().rowwise() - a_dot_f);
}
};


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55 changes: 30 additions & 25 deletions include/Callback.h
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Expand Up @@ -4,7 +4,8 @@
#include <Eigen/Core>
#include "Config.h"

namespace MiniDNN {
namespace MiniDNN
{


class Network;
Expand All @@ -27,30 +28,34 @@ class Network;
///
class Callback
{
protected:
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> Matrix;
typedef Eigen::RowVectorXi IntegerVector;

public:
// Public members that will be set by the network during the training process
int m_nbatch; // Number of total batches
int m_batch_id; // The index for the current mini-batch (0, 1, ..., m_nbatch-1)
int m_nepoch; // Total number of epochs (one run on the whole data set) in the training process
int m_epoch_id; // The index for the current epoch (0, 1, ..., m_nepoch-1)

Callback() :
m_nbatch(0), m_batch_id(0), m_nepoch(0), m_epoch_id(0)
{}

virtual ~Callback() {}

// Before training a mini-batch
virtual void pre_training_batch(const Network* net, const Matrix& x, const Matrix& y) {}
virtual void pre_training_batch(const Network* net, const Matrix& x, const IntegerVector& y) {}

// After a mini-batch is trained
virtual void post_training_batch(const Network* net, const Matrix& x, const Matrix& y) {}
virtual void post_training_batch(const Network* net, const Matrix& x, const IntegerVector& y) {}
protected:
typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> Matrix;
typedef Eigen::RowVectorXi IntegerVector;

public:
// Public members that will be set by the network during the training process
int m_nbatch; // Number of total batches
int m_batch_id; // The index for the current mini-batch (0, 1, ..., m_nbatch-1)
int m_nepoch; // Total number of epochs (one run on the whole data set) in the training process
int m_epoch_id; // The index for the current epoch (0, 1, ..., m_nepoch-1)

Callback() :
m_nbatch(0), m_batch_id(0), m_nepoch(0), m_epoch_id(0)
{}

virtual ~Callback() {}

// Before training a mini-batch
virtual void pre_training_batch(const Network* net, const Matrix& x,
const Matrix& y) {}
virtual void pre_training_batch(const Network* net, const Matrix& x,
const IntegerVector& y) {}

// After a mini-batch is trained
virtual void post_training_batch(const Network* net, const Matrix& x,
const Matrix& y) {}
virtual void post_training_batch(const Network* net, const Matrix& x,
const IntegerVector& y) {}
};


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