nnet1: integrating comments from Dan.

egs/hkust/s5/local/nnet/run_cnn.sh

@@ -82,7 +82,7 @@ if [ $stage -le 4 ]; then
   cnn_dbn=$dir/cnn_dbn.nnet
   { # Concatenate CNN layers and DBN,
     num_components=$(nnet-info $feature_transform | grep -m1 num-components | awk '{print $2;}')
-    nnet-concat "nnet-copy --remove-first-layers=$num_components $feature_transform_dbn - |" $dbn $cnn_dbn \
+    nnet-concat "nnet-copy --remove-first-components=$num_components $feature_transform_dbn - |" $dbn $cnn_dbn \
       2>$dir/log/concat_cnn_dbn.log || exit 1 
   }
   # Train

egs/rm/s5/local/nnet/run_cnn.sh

@@ -66,7 +66,7 @@ if [ $stage -le 2 ]; then
   # Concat 'feature_transform' with convolutional layers,
   dir=exp/cnn4c
   nnet-concat $dir/final.feature_transform \
-    "nnet-copy --remove-last-layers=$(((hid_layers+1)*2)) $dir/final.nnet - |" \
+    "nnet-copy --remove-last-components=$(((hid_layers+1)*2)) $dir/final.nnet - |" \
     $dir/final.feature_transform_cnn
 fi
 
@@ -99,7 +99,7 @@ if [ $stage -le 5 ]; then
   cnn_dbn=$dir/cnn_dbn.nnet
   { # Concatenate CNN layers and DBN,
     num_components=$(nnet-info $feature_transform | grep -m1 num-components | awk '{print $2;}')
-    cnn="nnet-copy --remove-first-layers=$num_components $feature_transform_dbn - |"
+    cnn="nnet-copy --remove-first-components=$num_components $feature_transform_dbn - |"
     nnet-concat "$cnn" $dbn $cnn_dbn 2>$dir/log/concat_cnn_dbn.log || exit 1 
   }
   # Train

egs/rm/s5/local/nnet/run_cnn2d.sh

@@ -93,7 +93,7 @@ if [ $stage -le 4 ]; then
   cnn_dbn=$dir/cnn_dbn.nnet
   { # Concatenate CNN layers and DBN,
     num_components=$(nnet-info $feature_transform | grep -m1 num-components | awk '{print $2;}')
-    cnn="nnet-copy --remove-first-layers=$num_components $feature_transform_dbn - |"
+    cnn="nnet-copy --remove-first-components=$num_components $feature_transform_dbn - |"
     nnet-concat "$cnn" $dbn $cnn_dbn 2>$dir/log/concat_cnn_dbn.log || exit 1 
   }
   # Train

egs/rm/s5/local/nnet/run_multilingual.sh

@@ -148,7 +148,7 @@ if [ $stage -le 2 ]; then
     # Compose feature_transform for 2nd part,
     nnet-initialize <(echo "<Splice> <InputDim> $bn1_dim <OutputDim> $((13*bn1_dim)) <BuildVector> -10 -5:5 10 </BuildVector>") \
       $dir_part1/splice_for_bottleneck.nnet 
-    nnet-concat $dir_part1/final.feature_transform "nnet-copy --remove-last-layers=4 $dir_part1/final.nnet - |" \
+    nnet-concat $dir_part1/final.feature_transform "nnet-copy --remove-last-components=4 $dir_part1/final.nnet - |" \
       $dir_part1/splice_for_bottleneck.nnet $dir_part1/final.feature_transform.part1
     # Train 2nd part,
     $cuda_cmd $dir/log/train_nnet.log \

egs/swbd/s5b/local/nnet/run_dnn_tandem_uc.sh

@@ -89,7 +89,7 @@ if [ $stage -le 4 ]; then
   dir=exp/nnet5b_uc-part1
   feature_transform=$dir/final.feature_transform.part1
   nnet-concat $dir/final.feature_transform \
-    "nnet-copy --remove-last-layers=4 --binary=false $dir/final.nnet - |" \
+    "nnet-copy --remove-last-components=4 --binary=false $dir/final.nnet - |" \
     "utils/nnet/gen_splice.py --fea-dim=80 --splice=2 --splice-step=5 |" \
     $feature_transform || exit 1
   

egs/swbd/s5c/local/nnet/run_dnn_tandem_uc.sh

@@ -78,7 +78,7 @@ if [ $stage -le 4 ]; then
   nnet-initialize <(echo "<Splice> <InputDim> 80 <OutputDim> 1040 <BuildVector> -10 -5:5 10 </BuildVector>") \
     $dir/splice_for_bottleneck.nnet 
   # Concatanate the input-transform, 1stage network, splicing,
-  nnet-concat $dir/final.feature_transform "nnet-copy --remove-last-layers=4 $dir/final.nnet - |" \
+  nnet-concat $dir/final.feature_transform "nnet-copy --remove-last-components=4 $dir/final.nnet - |" \
     $dir/splice_for_bottleneck.nnet $feature_transform
   
   # Train 2nd network, overall context +/-15 frames,

egs/wsj/s5/steps/nnet/train.sh

@@ -455,7 +455,7 @@ steps/nnet/train_scheduler.sh \
   ${config:+ --config $config} \
   $nnet_init "$feats_tr" "$feats_cv" "$labels_tr" "$labels_cv" $dir
 
-echo "$0: Successfuly finished! '$dir'"
+echo "$0: Successfuly finished. '$dir'"
 
 sleep 3
 exit 0

src/cudamatrix/cu-array-inl.h

@@ -233,6 +233,22 @@ inline T CuArray<T>::Max() const {
 }
 
 
+template<typename T>
+void CuArray<T>::Read(std::istream& in, bool binary) {
+  std::vector<T> tmp;
+  ReadIntegerVector(in, binary, &tmp);
+  (*this) = tmp;
+}
+
+
+template<typename T>
+void CuArray<T>::Write(std::ostream& out, bool binary) const {
+  std::vector<T> tmp(this->Dim());
+  this->CopyToVec(&tmp);
+  WriteIntegerVector(out, binary, tmp);
+}
+
+
 /**
  * Print the vector to stream
  */
@@ -248,23 +264,6 @@ std::ostream &operator << (std::ostream &out, const CuArray<T> &vec) {
   return out;
 }
 
-
-template<typename T>
-void ReadIntegerVector(std::istream& in, bool binary, CuArray<T>* vec) {
-  std::vector<T> tmp;
-  ReadIntegerVector(in, binary, &tmp);
-  (*vec) = tmp;
-}
-
-
-template<typename T>
-void WriteIntegerVector(std::ostream& out, bool binary, const CuArray<T>& vec) {
-  std::vector<T> tmp(vec.Dim());
-  vec.CopyToVec(&tmp);
-  WriteIntegerVector(out, binary, tmp);
-}
-
-
 } // namespace kaldi
 
 #endif

src/cudamatrix/cu-array.cc

@@ -44,7 +44,7 @@ void CuArray<int32>::Set(const int32 &value) {
     dim3 dimGrid(n_blocks(Dim(), CU2DBLOCK));
     ::MatrixDim d = { 1, Dim(), Dim() };
 
-    cudaI32_set_const(dimGrid, dimBlock, data_, value, d);
+    cuda_int32_set_const(dimGrid, dimBlock, data_, value, d);
     CU_SAFE_CALL(cudaGetLastError());
 
     CuDevice::Instantiate().AccuProfile(__func__, tim.Elapsed());
@@ -69,7 +69,7 @@ void CuArray<int32>::Add(const int32 &value) {
     dim3 dimGrid(n_blocks(Dim(), CU2DBLOCK));
     ::MatrixDim d = { 1, Dim(), Dim() };
 
-    cudaI32_add(dimGrid, dimBlock, data_, value, d);
+    cuda_int32_add(dimGrid, dimBlock, data_, value, d);
     CU_SAFE_CALL(cudaGetLastError());
 
     CuDevice::Instantiate().AccuProfile(__func__, tim.Elapsed());

src/cudamatrix/cu-array.h

@@ -121,6 +121,10 @@ class CuArray {
   CuArray<T> &operator= (const std::vector<T> &in) {
     this->CopyFromVec(in); return *this;
   }
+
+  /// I/O
+  void Read(std::istream &is, bool binary);
+  void Write(std::ostream &is, bool binary) const;
   
  private:
   MatrixIndexT dim_;     ///< dimension of the vector
@@ -133,17 +137,8 @@ class CuArray {
 template<typename T>
 std::ostream &operator << (std::ostream &out, const CuArray<T> &vec);
 
-/// Wrapper for reading,
-template<typename T>
-void ReadIntegerVector(std::istream& in, bool binary, CuArray<T>* vec);
-
-/// Wrapper for writing,
-template<typename T>
-void WriteIntegerVector(std::ostream& out, bool binary, const CuArray<T>& vec);
-
 } // namespace
 
-
 #include "cudamatrix/cu-array-inl.h"
 
 #endif

src/cudamatrix/cu-kernels-ansi.h

@@ -35,8 +35,8 @@ extern "C" {
 /*********************************************************
  * int32 CUDA kernel calls (no template wrapper)
  */
-void cudaI32_set_const(dim3 Gr, dim3 Bl, int32_cuda *mat, int32_cuda value, MatrixDim d);
-void cudaI32_add(dim3 Gr, dim3 Bl, int32_cuda *mat, int32_cuda value, MatrixDim d);
+void cuda_int32_set_const(dim3 Gr, dim3 Bl, int32_cuda *mat, int32_cuda value, MatrixDim d);
+void cuda_int32_add(dim3 Gr, dim3 Bl, int32_cuda *mat, int32_cuda value, MatrixDim d);
 
 
 

src/cudamatrix/cu-kernels.cu

@@ -2103,10 +2103,10 @@ static void _diff_xent(const int32_cuda* vec_tgt, Real* mat_net_out, Real* vec_l
 /*
  * "int32"
  */
-void cudaI32_set_const(dim3 Gr, dim3 Bl, int32_cuda* mat, int32_cuda value, MatrixDim d) {
+void cuda_int32_set_const(dim3 Gr, dim3 Bl, int32_cuda* mat, int32_cuda value, MatrixDim d) {
   _set_const<<<Gr,Bl>>>(mat,value,d);
 }
-void cudaI32_add(dim3 Gr, dim3 Bl, int32_cuda* mat, int32_cuda value, MatrixDim d) {
+void cuda_int32_add(dim3 Gr, dim3 Bl, int32_cuda* mat, int32_cuda value, MatrixDim d) {
   _add<<<Gr,Bl>>>(mat,value,d);
 }
 

src/nnet/nnet-activation.h

@@ -35,20 +35,15 @@ namespace nnet1 {
 
 class Softmax : public Component {
  public:
-  Softmax(int32 dim_in, int32 dim_out) :
+  Softmax(int32 dim_in, int32 dim_out):
     Component(dim_in, dim_out)
   { }
 
   ~Softmax()
   { }
 
-  Component* Copy() const {
-    return new Softmax(*this);
-  }
-
-  ComponentType GetType() const {
-    return kSoftmax;
-  }
+  Component* Copy() const { return new Softmax(*this); }
+  ComponentType GetType() const { return kSoftmax; }
 
   void PropagateFnc(const CuMatrixBase<BaseFloat> &in,
                     CuMatrixBase<BaseFloat> *out) {
@@ -72,20 +67,15 @@ class Softmax : public Component {
 
 class BlockSoftmax : public Component {
  public:
-  BlockSoftmax(int32 dim_in, int32 dim_out) :
+  BlockSoftmax(int32 dim_in, int32 dim_out):
     Component(dim_in, dim_out)
   { }
 
   ~BlockSoftmax()
   { }
 
-  Component* Copy() const {
-    return new BlockSoftmax(*this);
-  }
-
-  ComponentType GetType() const {
-    return kBlockSoftmax;
-  }
+  Component* Copy() const { return new BlockSoftmax(*this); }
+  ComponentType GetType() const { return kBlockSoftmax; }
 
   void InitData(std::istream &is) {
     // parse config
@@ -175,20 +165,15 @@ class BlockSoftmax : public Component {
 
 class Sigmoid : public Component {
  public:
-  Sigmoid(int32 dim_in, int32 dim_out) :
+  Sigmoid(int32 dim_in, int32 dim_out):
     Component(dim_in, dim_out)
   { }
 
   ~Sigmoid()
   { }
 
-  Component* Copy() const {
-    return new Sigmoid(*this);
-  }
-
-  ComponentType GetType() const {
-    return kSigmoid;
-  }
+  Component* Copy() const { return new Sigmoid(*this); }
+  ComponentType GetType() const { return kSigmoid; }
 
   void PropagateFnc(const CuMatrixBase<BaseFloat> &in,
                     CuMatrixBase<BaseFloat> *out) {
@@ -209,20 +194,15 @@ class Sigmoid : public Component {
 
 class Tanh : public Component {
  public:
-  Tanh(int32 dim_in, int32 dim_out) :
+  Tanh(int32 dim_in, int32 dim_out):
     Component(dim_in, dim_out)
   { }
 
   ~Tanh()
   { }
 
-  Component* Copy() const {
-    return new Tanh(*this);
-  }
-
-  ComponentType GetType() const {
-    return kTanh;
-  }
+  Component* Copy() const { return new Tanh(*this); }
+  ComponentType GetType() const { return kTanh; }
 
   void PropagateFnc(const CuMatrixBase<BaseFloat> &in,
                     CuMatrixBase<BaseFloat> *out) {
@@ -243,7 +223,7 @@ class Tanh : public Component {
 
 class Dropout : public Component {
  public:
-  Dropout(int32 dim_in, int32 dim_out) :
+  Dropout(int32 dim_in, int32 dim_out):
       Component(dim_in, dim_out),
       dropout_retention_(0.5)
   { }
@@ -251,13 +231,8 @@ class Dropout : public Component {
   ~Dropout()
   { }
 
-  Component* Copy() const {
-    return new Dropout(*this);
-  }
-
-  ComponentType GetType() const {
-    return kDropout;
-  }
+  Component* Copy() const { return new Dropout(*this); }
+  ComponentType GetType() const { return kDropout; }
 
   void InitData(std::istream &is) {
     is >> std::ws;  // eat-up whitespace
@@ -308,9 +283,7 @@ class Dropout : public Component {
     in_diff->Scale(1.0/dropout_retention_);
   }
 
-  BaseFloat GetDropoutRetention() {
-    return dropout_retention_;
-  }
+  BaseFloat GetDropoutRetention() { return dropout_retention_; }
 
   void SetDropoutRetention(BaseFloat dr) {
     dropout_retention_ = dr;

src/nnet/nnet-affine-transform.h

@@ -32,11 +32,11 @@ namespace nnet1 {
 
 class AffineTransform : public UpdatableComponent {
  public:
-  AffineTransform(int32 dim_in, int32 dim_out)
-    : UpdatableComponent(dim_in, dim_out),
-      linearity_(dim_out, dim_in), bias_(dim_out),
-      linearity_corr_(dim_out, dim_in), bias_corr_(dim_out),
-      max_norm_(0.0)
+  AffineTransform(int32 dim_in, int32 dim_out):
+    UpdatableComponent(dim_in, dim_out),
+    linearity_(dim_out, dim_in), bias_(dim_out),
+    linearity_corr_(dim_out, dim_in), bias_corr_(dim_out),
+    max_norm_(0.0)
   { }
   ~AffineTransform()
   { }
@@ -216,18 +216,14 @@ class AffineTransform : public UpdatableComponent {
   }
 
   /// Accessors to the component parameters,
-  const CuVectorBase<BaseFloat>& GetBias() const {
-    return bias_;
-  }
+  const CuVectorBase<BaseFloat>& GetBias() const { return bias_; }
 
   void SetBias(const CuVectorBase<BaseFloat>& bias) {
     KALDI_ASSERT(bias.Dim() == bias_.Dim());
     bias_.CopyFromVec(bias);
   }
 
-  const CuMatrixBase<BaseFloat>& GetLinearity() const {
-    return linearity_;
-  }
+  const CuMatrixBase<BaseFloat>& GetLinearity() const { return linearity_; }
 
   void SetLinearity(const CuMatrixBase<BaseFloat>& linearity) {
     KALDI_ASSERT(linearity.NumRows() == linearity_.NumRows());

src/nnet/nnet-average-pooling-2d-component.h

@@ -40,11 +40,11 @@ namespace nnet1 {
  */
 class AveragePooling2DComponent : public Component {
  public:
-  AveragePooling2DComponent(int32 dim_in, int32 dim_out)
-    : Component(dim_in, dim_out),
-      fmap_x_len_(0), fmap_y_len_(0),
-      pool_x_len_(0), pool_y_len_(0),
-      pool_x_step_(0), pool_y_step_(0)
+  AveragePooling2DComponent(int32 dim_in, int32 dim_out):
+    Component(dim_in, dim_out),
+    fmap_x_len_(0), fmap_y_len_(0),
+    pool_x_len_(0), pool_y_len_(0),
+    pool_x_step_(0), pool_y_step_(0)
   { }
   ~AveragePooling2DComponent()
   { }
@@ -199,8 +199,8 @@ class AveragePooling2DComponent : public Component {
 
  private:
   int32 fmap_x_len_, fmap_y_len_,
-    pool_x_len_, pool_y_len_,
-    pool_x_step_, pool_y_step_;
+        pool_x_len_, pool_y_len_,
+        pool_x_step_, pool_y_step_;
 };
 
 }  // namespace nnet1

src/nnet/nnet-average-pooling-component.h

@@ -39,9 +39,13 @@ namespace nnet1 {
  */
 class AveragePoolingComponent : public Component {
  public:
-  AveragePoolingComponent(int32 dim_in, int32 dim_out)
-    : Component(dim_in, dim_out), pool_size_(0), pool_step_(0), pool_stride_(0)
+  AveragePoolingComponent(int32 dim_in, int32 dim_out):
+    Component(dim_in, dim_out),
+    pool_size_(0),
+    pool_step_(0),
+    pool_stride_(0)
   { }
+
   ~AveragePoolingComponent()
   { }
 

src/nnet/nnet-blstm-projected-streams.h

@@ -49,7 +49,7 @@ namespace nnet1 {
 
 class BLstmProjectedStreams : public UpdatableComponent {
  public:
-  BLstmProjectedStreams(int32 input_dim, int32 output_dim) :
+  BLstmProjectedStreams(int32 input_dim, int32 output_dim):
     UpdatableComponent(input_dim, output_dim),
     ncell_(0),
     nrecur_(static_cast<int32>(output_dim/2)),
@@ -61,17 +61,12 @@ class BLstmProjectedStreams : public UpdatableComponent {
   ~BLstmProjectedStreams()
   { }
 
-  Component* Copy() const {
-    return new BLstmProjectedStreams(*this);
-  }
-
-  ComponentType GetType() const {
-    return kBLstmProjectedStreams;
-  }
+  Component* Copy() const { return new BLstmProjectedStreams(*this); }
+  ComponentType GetType() const { return kBLstmProjectedStreams; }
 
   /// set the utterance length used for parallel training
   void SetSeqLengths(const std::vector<int32> &sequence_lengths) {
-        sequence_lengths_ = sequence_lengths;
+    sequence_lengths_ = sequence_lengths;
   }
 
   void InitData(std::istream &is) {

src/nnet/nnet-component.h

@@ -101,7 +101,7 @@ class Component {
 
  /// Generic interface of a component,
  public:
-  Component(int32 input_dim, int32 output_dim) :
+  Component(int32 input_dim, int32 output_dim):
     input_dim_(input_dim),
     output_dim_(output_dim)
   { }
@@ -201,7 +201,7 @@ class Component {
  */
 class UpdatableComponent : public Component {
  public:
-  UpdatableComponent(int32 input_dim, int32 output_dim) :
+  UpdatableComponent(int32 input_dim, int32 output_dim):
     Component(input_dim, output_dim),
     learn_rate_coef_(1.0),
     bias_learn_rate_coef_(1.0)

src/nnet/nnet-convolutional-2d-component.h

@@ -71,7 +71,7 @@ namespace nnet1 {
  */
 class Convolutional2DComponent : public UpdatableComponent {
  public:
-  Convolutional2DComponent(int32 dim_in, int32 dim_out) :
+  Convolutional2DComponent(int32 dim_in, int32 dim_out):
     UpdatableComponent(dim_in, dim_out),
     fmap_x_len_(0), fmap_y_len_(0),
     filt_x_len_(0), filt_y_len_(0),
@@ -82,13 +82,8 @@ class Convolutional2DComponent : public UpdatableComponent {
   ~Convolutional2DComponent()
   { }
 
-  Component* Copy() const {
-    return new Convolutional2DComponent(*this);
-  }
-
-  ComponentType GetType() const {
-    return kConvolutional2DComponent;
-  }
+  Component* Copy() const { return new Convolutional2DComponent(*this); }
+  ComponentType GetType() const { return kConvolutional2DComponent; }
 
   void InitData(std::istream &is) {
     // define options

src/nnet/nnet-convolutional-component.h

@@ -65,7 +65,7 @@ namespace nnet1 {
  */
 class ConvolutionalComponent : public UpdatableComponent {
  public:
-  ConvolutionalComponent(int32 dim_in, int32 dim_out) :
+  ConvolutionalComponent(int32 dim_in, int32 dim_out):
     UpdatableComponent(dim_in, dim_out),
     patch_dim_(0),
     patch_step_(0),
@@ -76,13 +76,8 @@ class ConvolutionalComponent : public UpdatableComponent {
   ~ConvolutionalComponent()
   { }
 
-  Component* Copy() const {
-    return new ConvolutionalComponent(*this);
-  }
-
-  ComponentType GetType() const {
-    return kConvolutionalComponent;
-  }
+  Component* Copy() const { return new ConvolutionalComponent(*this); }
+  ComponentType GetType() const { return kConvolutionalComponent; }
 
   void InitData(std::istream &is) {
     // define options

src/nnet/nnet-frame-pooling-component.h

@@ -42,7 +42,7 @@ namespace nnet1 {
  */
 class FramePoolingComponent : public UpdatableComponent {
  public:
-  FramePoolingComponent(int32 dim_in, int32 dim_out) :
+  FramePoolingComponent(int32 dim_in, int32 dim_out):
     UpdatableComponent(dim_in, dim_out),
     feature_dim_(0),
     normalize_(false)
@@ -51,13 +51,8 @@ class FramePoolingComponent : public UpdatableComponent {
   ~FramePoolingComponent()
   { }
 
-  Component* Copy() const {
-    return new FramePoolingComponent(*this);
-  }
-
-  ComponentType GetType() const {
-    return kFramePoolingComponent;
-  }
+  Component* Copy() const { return new FramePoolingComponent(*this); }
+  ComponentType GetType() const { return kFramePoolingComponent; }
 
   /**
    * Here the offsets are w.r.t. central frames, which has offset 0.

src/nnet/nnet-kl-hmm.h

@@ -36,21 +36,16 @@ namespace nnet1 {
 
 class KlHmm : public Component {
  public:
-  KlHmm(int32 dim_in, int32 dim_out)
-    : Component(dim_in, dim_out),
-      kl_stats_(dim_out, dim_in, kSetZero)
+  KlHmm(int32 dim_in, int32 dim_out):
+    Component(dim_in, dim_out),
+    kl_stats_(dim_out, dim_in, kSetZero)
   { }
 
   ~KlHmm()
   { }
 
-  Component* Copy() const {
-    return new KlHmm(*this);
-  }
-
-  ComponentType GetType() const {
-    return kKlHmm;
-  }
+  Component* Copy() const { return new KlHmm(*this); }
+  ComponentType GetType() const { return kKlHmm; }
 
   void PropagateFnc(const CuMatrixBase<BaseFloat> &in,
                     CuMatrixBase<BaseFloat> *out) {
@@ -153,9 +148,6 @@ class KlHmm : public Component {
   CuMatrix<BaseFloat> kl_inv_q_;
 };
 
-
-
-
 }  // namespace nnet1
 }  // namespace kaldi
 

src/nnet/nnet-linear-transform.h

@@ -32,7 +32,7 @@ namespace nnet1 {
 
 class LinearTransform : public UpdatableComponent {
  public:
-  LinearTransform(int32 dim_in, int32 dim_out) :
+  LinearTransform(int32 dim_in, int32 dim_out):
     UpdatableComponent(dim_in, dim_out),
     linearity_(dim_out, dim_in),
     linearity_corr_(dim_out, dim_in)
@@ -41,13 +41,8 @@ class LinearTransform : public UpdatableComponent {
   ~LinearTransform()
   { }
 
-  Component* Copy() const {
-    return new LinearTransform(*this);
-  }
-
-  ComponentType GetType() const {
-    return kLinearTransform;
-  }
+  Component* Copy() const { return new LinearTransform(*this); }
+  ComponentType GetType() const { return kLinearTransform; }
 
   void InitData(std::istream &is) {
     // define options
@@ -196,9 +191,7 @@ class LinearTransform : public UpdatableComponent {
   }
 
   /// Accessors to the component parameters
-  const CuMatrixBase<BaseFloat>& GetLinearity() {
-    return linearity_;
-  }
+  const CuMatrixBase<BaseFloat>& GetLinearity() { return linearity_; }
 
   void SetLinearity(const CuMatrixBase<BaseFloat>& linearity) {
     KALDI_ASSERT(linearity.NumRows() == linearity_.NumRows());
@@ -206,9 +199,7 @@ class LinearTransform : public UpdatableComponent {
     linearity_.CopyFromMat(linearity);
   }
 
-  const CuMatrixBase<BaseFloat>& GetLinearityCorr() {
-    return linearity_corr_;
-  }
+  const CuMatrixBase<BaseFloat>& GetLinearityCorr() { return linearity_corr_; }
 
  private:
   CuMatrix<BaseFloat> linearity_;

src/nnet/nnet-loss.h

@@ -61,7 +61,7 @@ class LossItf {
 
 class Xent : public LossItf {
  public:
-  Xent() :
+  Xent():
     frames_progress_(0.0),
     xentropy_progress_(0.0),
     entropy_progress_(0.0)
@@ -124,9 +124,15 @@ class Xent : public LossItf {
 
 class Mse : public LossItf {
  public:
-  Mse() : frames_(0.0), loss_(0.0),
-          frames_progress_(0.0), loss_progress_(0.0) { }
-  ~Mse() { }
+  Mse():
+    frames_(0.0),
+    loss_(0.0),
+    frames_progress_(0.0),
+    loss_progress_(0.0)
+  { }
+
+  ~Mse()
+  { }
 
   /// Evaluate mean square error using target-matrix,
   void Eval(const VectorBase<BaseFloat> &frame_weights,
@@ -164,7 +170,9 @@ class Mse : public LossItf {
 
 class MultiTaskLoss : public LossItf {
  public:
-  MultiTaskLoss() { }
+  MultiTaskLoss()
+  { }
+
   ~MultiTaskLoss() {
     while (loss_vec_.size() > 0) {
       delete loss_vec_.back();

src/nnet/nnet-lstm-projected-streams.h

@@ -48,7 +48,7 @@ namespace nnet1 {
 
 class LstmProjectedStreams : public UpdatableComponent {
  public:
-  LstmProjectedStreams(int32 input_dim, int32 output_dim) :
+  LstmProjectedStreams(int32 input_dim, int32 output_dim):
     UpdatableComponent(input_dim, output_dim),
     ncell_(0),
     nrecur_(output_dim),
@@ -60,13 +60,8 @@ class LstmProjectedStreams : public UpdatableComponent {
   ~LstmProjectedStreams()
   { }
 
-  Component* Copy() const {
-    return new LstmProjectedStreams(*this);
-  }
-
-  ComponentType GetType() const {
-    return kLstmProjectedStreams;
-  }
+  Component* Copy() const { return new LstmProjectedStreams(*this); }
+  ComponentType GetType() const { return kLstmProjectedStreams; }
 
   void InitData(std::istream &is) {
     // define options,

src/nnet/nnet-max-pooling-2d-component.h

@@ -40,22 +40,18 @@ namespace nnet1 {
  */
 class MaxPooling2DComponent : public Component {
  public:
-  MaxPooling2DComponent(int32 dim_in, int32 dim_out)
-      : Component(dim_in, dim_out),
-        fmap_x_len_(0), fmap_y_len_(0),
-        pool_x_len_(0), pool_y_len_(0), pool_x_step_(0), pool_y_step_(0)
+  MaxPooling2DComponent(int32 dim_in, int32 dim_out):
+    Component(dim_in, dim_out),
+    fmap_x_len_(0), fmap_y_len_(0),
+    pool_x_len_(0), pool_y_len_(0),
+    pool_x_step_(0), pool_y_step_(0)
   { }
 
   ~MaxPooling2DComponent()
   { }
 
-  Component* Copy() const {
-    return new MaxPooling2DComponent(*this);
-  }
-
-  ComponentType GetType() const {
-    return kMaxPooling2DComponent;
-  }
+  Component* Copy() const { return new MaxPooling2DComponent(*this); }
+  ComponentType GetType() const { return kMaxPooling2DComponent; }
 
   void InitData(std::istream &is) {
     // parse config
@@ -219,8 +215,8 @@ class MaxPooling2DComponent : public Component {
 
  private:
   int32 fmap_x_len_, fmap_y_len_,
-    pool_x_len_, pool_y_len_,
-    pool_x_step_, pool_y_step_;
+        pool_x_len_, pool_y_len_,
+        pool_x_step_, pool_y_step_;
 };
 
 }  // namespace nnet1

src/nnet/nnet-max-pooling-component.h

@@ -39,20 +39,18 @@ namespace nnet1 {
  */
 class MaxPoolingComponent : public Component {
  public:
-  MaxPoolingComponent(int32 dim_in, int32 dim_out)
-    : Component(dim_in, dim_out), pool_size_(0), pool_step_(0), pool_stride_(0)
+  MaxPoolingComponent(int32 dim_in, int32 dim_out):
+    Component(dim_in, dim_out),
+    pool_size_(0),
+    pool_step_(0),
+    pool_stride_(0)
   { }
 
   ~MaxPoolingComponent()
   { }
 
-  Component* Copy() const {
-    return new MaxPoolingComponent(*this);
-  }
-
-  ComponentType GetType() const {
-    return kMaxPoolingComponent;
-  }
+  Component* Copy() const { return new MaxPoolingComponent(*this); }
+  ComponentType GetType() const { return kMaxPoolingComponent; }
 
   void InitData(std::istream &is) {
     // parse config

src/nnet/nnet-parallel-component.h

@@ -35,28 +35,18 @@ namespace nnet1 {
 
 class ParallelComponent : public UpdatableComponent {
  public:
-  ParallelComponent(int32 dim_in, int32 dim_out)
-    : UpdatableComponent(dim_in, dim_out)
+  ParallelComponent(int32 dim_in, int32 dim_out):
+    UpdatableComponent(dim_in, dim_out)
   { }
 
   ~ParallelComponent()
   { }
 
-  Component* Copy() const {
-    return new ParallelComponent(*this);
-  }
+  Component* Copy() const { return new ParallelComponent(*this); }
+  ComponentType GetType() const { return kParallelComponent; }
 
-  ComponentType GetType() const {
-    return kParallelComponent;
-  }
-
-  const Nnet& GetNestedNnet(int32 id) const {
-    return nnet_.at(id);
-  }
-
-  Nnet& GetNestedNnet(int32 id) {
-    return nnet_.at(id);
-  }
+  const Nnet& GetNestedNnet(int32 id) const { return nnet_.at(id); }
+  Nnet& GetNestedNnet(int32 id) { return nnet_.at(id); }
 
   void InitData(std::istream &is) {
     // define options

src/nnet/nnet-pdf-prior.h

@@ -37,9 +37,11 @@ struct PdfPriorOptions {
   BaseFloat prior_scale;
   BaseFloat prior_floor;
 
-  PdfPriorOptions() : class_frame_counts(""),
-                      prior_scale(1.0),
-                      prior_floor(1e-10) {}
+  PdfPriorOptions():
+    class_frame_counts(""),
+    prior_scale(1.0),
+    prior_floor(1e-10)
+  { }
 
   void Register(OptionsItf *opts) {
     opts->Register("class-frame-counts", &class_frame_counts,

src/nnet/nnet-randomizer.h

@@ -40,7 +40,7 @@ struct NnetDataRandomizerOptions {
   int32 randomizer_seed;
   int32 minibatch_size;
 
-  NnetDataRandomizerOptions() :
+  NnetDataRandomizerOptions():
     randomizer_size(32768),
     randomizer_seed(777),
     minibatch_size(256)
@@ -86,12 +86,12 @@ class RandomizerMask {
  */
 class MatrixRandomizer {
  public:
-  MatrixRandomizer() :
+  MatrixRandomizer():
     data_begin_(0),
     data_end_(0)
   { }
 
-  explicit MatrixRandomizer(const NnetDataRandomizerOptions &conf) :
+  explicit MatrixRandomizer(const NnetDataRandomizerOptions &conf):
     data_begin_(0),
     data_end_(0)
   {
@@ -147,12 +147,12 @@ class MatrixRandomizer {
 /// Randomizes elements of a vector according to a mask
 class VectorRandomizer {
  public:
-  VectorRandomizer() :
+  VectorRandomizer():
     data_begin_(0),
     data_end_(0)
   { }
 
-  explicit VectorRandomizer(const NnetDataRandomizerOptions &conf) :
+  explicit VectorRandomizer(const NnetDataRandomizerOptions &conf):
     data_begin_(0),
     data_end_(0)
   {
@@ -208,12 +208,12 @@ class VectorRandomizer {
 template<typename T>
 class StdVectorRandomizer {
  public:
-  StdVectorRandomizer() :
+  StdVectorRandomizer():
     data_begin_(0),
     data_end_(0)
   { }
 
-  explicit StdVectorRandomizer(const NnetDataRandomizerOptions &conf) :
+  explicit StdVectorRandomizer(const NnetDataRandomizerOptions &conf):
     data_begin_(0),
     data_end_(0)
   {

src/nnet/nnet-rbm.h

@@ -39,7 +39,7 @@ class RbmBase : public Component {
     Gaussian
   } RbmNodeType;
 
-  RbmBase(int32 dim_in, int32 dim_out) :
+  RbmBase(int32 dim_in, int32 dim_out):
     Component(dim_in, dim_out)
   { }
 
@@ -95,7 +95,7 @@ class RbmBase : public Component {
 
 class Rbm : public RbmBase {
  public:
-  Rbm(int32 dim_in, int32 dim_out) :
+  Rbm(int32 dim_in, int32 dim_out):
     RbmBase(dim_in, dim_out)
   { }
 

src/nnet/nnet-sentence-averaging-component.h

@@ -41,7 +41,7 @@ namespace nnet1 {
  */
 class SimpleSentenceAveragingComponent : public Component {
  public:
-  SimpleSentenceAveragingComponent(int32 dim_in, int32 dim_out) :
+  SimpleSentenceAveragingComponent(int32 dim_in, int32 dim_out):
     Component(dim_in, dim_out),
     gradient_boost_(100.0),
     shrinkage_(0.0),
@@ -179,8 +179,8 @@ class SimpleSentenceAveragingComponent : public Component {
 /** Deprecated!!!, keeping it as Katka Zmolikova used it in JSALT 2015 */
 class SentenceAveragingComponent : public UpdatableComponent {
  public:
-  SentenceAveragingComponent(int32 dim_in, int32 dim_out)
-    : UpdatableComponent(dim_in, dim_out), learn_rate_factor_(100.0)
+  SentenceAveragingComponent(int32 dim_in, int32 dim_out):
+    UpdatableComponent(dim_in, dim_out), learn_rate_factor_(100.0)
   { }
   ~SentenceAveragingComponent()
   { }

src/nnet/nnet-trnopts.h

@@ -33,12 +33,15 @@ struct NnetTrainOptions {
   BaseFloat momentum;
   BaseFloat l2_penalty;
   BaseFloat l1_penalty;
+
   // default values
-  NnetTrainOptions() : learn_rate(0.008),
-                       momentum(0.0),
-                       l2_penalty(0.0),
-                       l1_penalty(0.0)
-                       { }
+  NnetTrainOptions():
+    learn_rate(0.008),
+    momentum(0.0),
+    l2_penalty(0.0),
+    l1_penalty(0.0)
+  { }
+
   // register options
   void Register(OptionsItf *opts) {
     opts->Register("learn-rate", &learn_rate, "Learning rate");
@@ -46,6 +49,7 @@ struct NnetTrainOptions {
     opts->Register("l2-penalty", &l2_penalty, "L2 penalty (weight decay)");
     opts->Register("l1-penalty", &l1_penalty, "L1 penalty (promote sparsity)");
   }
+
   // print for debug purposes
   friend std::ostream& operator<<(std::ostream& os, const NnetTrainOptions& opts) {
     os << "RbmTrainOptions : "
@@ -66,15 +70,18 @@ struct RbmTrainOptions {
   int32 momentum_steps;
   int32 momentum_step_period;
   BaseFloat l2_penalty;
+
   // default values
-  RbmTrainOptions() : learn_rate(0.4),
-                      momentum(0.5),
-                      momentum_max(0.9),
-                      momentum_steps(40),
-                      momentum_step_period(500000),
-                        // 500000 * 40 = 55h of linear increase of momentum
-                      l2_penalty(0.0002)
-                      { }
+  RbmTrainOptions():
+    learn_rate(0.4),
+    momentum(0.5),
+    momentum_max(0.9),
+    momentum_steps(40),
+    momentum_step_period(500000),
+    // 500000 * 40 = 55h of linear increase of momentum
+    l2_penalty(0.0002)
+  { }
+
   // register options
   void Register(OptionsItf *opts) {
     opts->Register("learn-rate", &learn_rate, "Learning rate");
@@ -91,6 +98,7 @@ struct RbmTrainOptions {
     opts->Register("l2-penalty", &l2_penalty,
                    "L2 penalty (weight decay, increases mixing-rate)");
   }
+
   // print for debug purposes
   friend std::ostream& operator<<(std::ostream& os, const RbmTrainOptions& opts) {
     os << "RbmTrainOptions : "

src/nnet/nnet-various.h

@@ -41,20 +41,15 @@ namespace nnet1 {
  */
 class Splice: public Component {
  public:
-  Splice(int32 dim_in, int32 dim_out) :
+  Splice(int32 dim_in, int32 dim_out):
     Component(dim_in, dim_out)
   { }
 
   ~Splice()
   { }
 
-  Component* Copy() const {
-    return new Splice(*this);
-  }
-
-  ComponentType GetType() const {
-    return kSplice;
-  }
+  Component* Copy() const { return new Splice(*this); }
+  ComponentType GetType() const { return kSplice; }
 
   void InitData(std::istream &is) {
     // define options,
@@ -64,7 +59,7 @@ class Splice: public Component {
     while (is >> std::ws, !is.eof()) {
       ReadToken(is, false, &token);
       /**/ if (token == "<ReadVector>") {
-        ReadIntegerVector(is, false, &frame_offsets_);
+        frame_offsets_.Read(is, false);
       } else if (token == "<BuildVector>") {
         // Parse the list of 'matlab-like' indices:
         // <BuildVector> 1:1:1000 1 2 3 1:10 </BuildVector>
@@ -92,12 +87,12 @@ class Splice: public Component {
   }
 
   void ReadData(std::istream &is, bool binary) {
-    ReadIntegerVector(is, binary, &frame_offsets_);
+    frame_offsets_.Read(is, binary);
     KALDI_ASSERT(frame_offsets_.Dim() * InputDim() == OutputDim());
   }
 
   void WriteData(std::ostream &os, bool binary) const {
-    WriteIntegerVector(os, binary, frame_offsets_);
+    frame_offsets_.Write(os, binary);
   }
 
   std::string Info() const {
@@ -131,20 +126,15 @@ class Splice: public Component {
  */
 class CopyComponent: public Component {
  public:
-  CopyComponent(int32 dim_in, int32 dim_out) :
+  CopyComponent(int32 dim_in, int32 dim_out):
     Component(dim_in, dim_out)
   { }
 
   ~CopyComponent()
   { }
 
-  Component* Copy() const {
-    return new CopyComponent(*this);
-  }
-
-  ComponentType GetType() const {
-    return kCopy;
-  }
+  Component* Copy() const { return new CopyComponent(*this); }
+  ComponentType GetType() const { return kCopy; }
 
   void InitData(std::istream &is) {
     // define options,
@@ -154,7 +144,7 @@ class CopyComponent: public Component {
     while (is >> std::ws, !is.eof()) {
       ReadToken(is, false, &token);
       /**/ if (token == "<ReadVector>") {
-        ReadIntegerVector(is, false, &copy_from_indices_);
+        copy_from_indices_.Read(is, false);
       } else if (token == "<BuildVector>") {
         // <BuildVector> 1:1:1000 1:1:1000 1 2 3 1:10 </BuildVector>
         // 'matlab-line' indexing, read the colon-separated-lists:
@@ -188,7 +178,7 @@ class CopyComponent: public Component {
   }
 
   void ReadData(std::istream &is, bool binary) {
-    ReadIntegerVector(is, binary, &copy_from_indices_);
+    copy_from_indices_.Read(is, binary);
     KALDI_ASSERT(copy_from_indices_.Dim() == OutputDim());
     copy_from_indices_.Add(-1);  // -1 from each element,
   }
@@ -196,7 +186,7 @@ class CopyComponent: public Component {
   void WriteData(std::ostream &os, bool binary) const {
     CuArray<int32> tmp(copy_from_indices_);
     tmp.Add(1);  // +1 to each element,
-    WriteIntegerVector(os, binary, tmp);
+    tmp.Write(os, binary);
   }
 
   std::string Info() const {
@@ -234,20 +224,15 @@ class CopyComponent: public Component {
  */
 class LengthNormComponent: public Component {
  public:
-  LengthNormComponent(int32 dim_in, int32 dim_out) :
+  LengthNormComponent(int32 dim_in, int32 dim_out):
     Component(dim_in, dim_out)
   { }
 
   ~LengthNormComponent()
   { }
 
-  Component* Copy() const {
-    return new LengthNormComponent(*this);
-  }
-
-  ComponentType GetType() const {
-    return kLengthNormComponent;
-  }
+  Component* Copy() const { return new LengthNormComponent(*this); }
+  ComponentType GetType() const { return kLengthNormComponent; }
 
   void PropagateFnc(const CuMatrixBase<BaseFloat> &in,
                     CuMatrixBase<BaseFloat> *out) {
@@ -286,20 +271,16 @@ class LengthNormComponent: public Component {
  */
 class AddShift : public UpdatableComponent {
  public:
-  AddShift(int32 dim_in, int32 dim_out) :
-    UpdatableComponent(dim_in, dim_out), shift_data_(dim_in)
+  AddShift(int32 dim_in, int32 dim_out):
+    UpdatableComponent(dim_in, dim_out),
+    shift_data_(dim_in)
   { }
 
   ~AddShift()
   { }
 
-  Component* Copy() const {
-    return new AddShift(*this);
-  }
-
-  ComponentType GetType() const {
-    return kAddShift;
-  }
+  Component* Copy() const { return new AddShift(*this); }
+  ComponentType GetType() const { return kAddShift; }
 
   void InitData(std::istream &is) {
     // define options
@@ -389,9 +370,7 @@ class AddShift : public UpdatableComponent {
     shift_data_.AddVec(-lr * learn_rate_coef_, shift_data_grad_);
   }
 
-  void SetLearnRateCoef(float c) {
-    learn_rate_coef_ = c;
-  }
+  void SetLearnRateCoef(float c) { learn_rate_coef_ = c; }
 
  protected:
   CuVector<BaseFloat> shift_data_;
@@ -405,7 +384,7 @@ class AddShift : public UpdatableComponent {
  */
 class Rescale : public UpdatableComponent {
  public:
-  Rescale(int32 dim_in, int32 dim_out) :
+  Rescale(int32 dim_in, int32 dim_out):
     UpdatableComponent(dim_in, dim_out),
     scale_data_(dim_in)
   { }
@@ -413,13 +392,8 @@ class Rescale : public UpdatableComponent {
   ~Rescale()
   { }
 
-  Component* Copy() const {
-    return new Rescale(*this);
-  }
-
-  ComponentType GetType() const {
-    return kRescale;
-  }
+  Component* Copy() const { return new Rescale(*this); }
+  ComponentType GetType() const { return kRescale; }
 
   void InitData(std::istream &is) {
     // define options
@@ -512,17 +486,13 @@ class Rescale : public UpdatableComponent {
     scale_data_.AddVec(-lr * learn_rate_coef_, scale_data_grad_);
   }
 
-  void SetLearnRateCoef(float c) {
-    learn_rate_coef_ = c;
-  }
+  void SetLearnRateCoef(float c) { learn_rate_coef_ = c; }
 
  protected:
   CuVector<BaseFloat> scale_data_;
   CuVector<BaseFloat> scale_data_grad_;
 };
 
-
-
 }  // namespace nnet1
 }  // namespace kaldi