Merge pull request #3 from giovastabile/development

ENH:: Added methods to read and write matrices and tensors, Assert Functions and tools to format the code.
yixuan · Apr 10, 2019 · 2c41dab · 2c41dab
2 parents 73654a9 + 2c1a6a2
commit 2c41dab
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diff --git a/code_formatter.sh b/code_formatter.sh
@@ -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
diff --git a/include/Activation/Identity.h b/include/Activation/Identity.h
@@ -4,7 +4,8 @@
 #include <Eigen/Core>
 #include "../Config.h"
 
-namespace MiniDNN {
+namespace MiniDNN
+{
 
 
 ///
@@ -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;
+        }
 };
 
 

diff --git a/include/Activation/ReLU.h b/include/Activation/ReLU.h
@@ -4,7 +4,8 @@
 #include <Eigen/Core>
 #include "../Config.h"
 
-namespace MiniDNN {
+namespace MiniDNN
+{
 
 
 ///
@@ -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));
+        }
 };
 
 

diff --git a/include/Activation/Sigmoid.h b/include/Activation/Sigmoid.h
@@ -4,7 +4,8 @@
 #include <Eigen/Core>
 #include "../Config.h"
 
-namespace MiniDNN {
+namespace MiniDNN
+{
 
 
 ///
@@ -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();
+        }
 };
 
 

diff --git a/include/Activation/Softmax.h b/include/Activation/Softmax.h
@@ -1,7 +1,8 @@
 #ifndef ACTIVATION_SOFTMAX_H_
 #define ACTIVATION_SOFTMAX_H_
 
-namespace MiniDNN {
+namespace MiniDNN
+{
 
 
 ///
@@ -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);
+        }
 };
 
 

diff --git a/include/Callback.h b/include/Callback.h
@@ -4,7 +4,8 @@
 #include <Eigen/Core>
 #include "Config.h"
 
-namespace MiniDNN {
+namespace MiniDNN
+{
 
 
 class Network;
@@ -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) {}
 };