Table of Contents - ruby-dnn-1.2.2 Documentation
Pages
Classes and Modules
- DNN
- DNN::CIFAR10
- DNN::CIFAR100
- DNN::CIFAR100::DNN_CIFAR100_LoadError
- DNN::CIFAR10::DNN_CIFAR10_LoadError
- DNN::Callbacks
- DNN::Callbacks::Callback
- DNN::Callbacks::CheckPoint
- DNN::Callbacks::EarlyStopping
- DNN::Callbacks::LambdaCallback
- DNN::Callbacks::Logger
- DNN::Callbacks::NaNStopping
- DNN::DNNError
- DNN::DNNShapeError
- DNN::DNN_DownloadError
- DNN::DNN_Iris_LoadError
- DNN::Downloader
- DNN::FashionMNIST
- DNN::FashionMNIST::DNN_MNIST_LoadError
- DNN::Image
- DNN::Image::ImageError
- DNN::Image::ImageReadError
- DNN::Image::ImageShapeError
- DNN::Image::ImageWriteError
- DNN::Initializers
- DNN::Initializers::Const
- DNN::Initializers::He
- DNN::Initializers::Initializer
- DNN::Initializers::RandomNormal
- DNN::Initializers::RandomUniform
- DNN::Initializers::Xavier
- DNN::Initializers::Zeros
- DNN::Iris
- DNN::Iterator
- DNN::Layers
- DNN::Layers::Add
- DNN::Layers::AvgPool2D
- DNN::Layers::BatchNormalization
- DNN::Layers::Concatenate
- DNN::Layers::Connection
- DNN::Layers::Conv2D
- DNN::Layers::Conv2DTranspose
- DNN::Layers::Conv2DUtils
- DNN::Layers::Dense
- DNN::Layers::Div
- DNN::Layers::Dot
- DNN::Layers::Dropout
- DNN::Layers::ELU
- DNN::Layers::Embedding
- DNN::Layers::Exp
- DNN::Layers::Flatten
- DNN::Layers::GRU
- DNN::Layers::GRUDense
- DNN::Layers::GlobalAvgPool2D
- DNN::Layers::InputLayer
- DNN::Layers::LSTM
- DNN::Layers::LSTMDense
- DNN::Layers::Lasso
- DNN::Layers::Layer
- DNN::Layers::LayerNode
- DNN::Layers::LeakyReLU
- DNN::Layers::Log
- DNN::Layers::MathUtils
- DNN::Layers::MaxPool2D
- DNN::Layers::Mean
- DNN::Layers::MergeLayer
- DNN::Layers::Mish
- DNN::Layers::Mul
- DNN::Layers::Neg
- DNN::Layers::Pool2D
- DNN::Layers::Pow
- DNN::Layers::RNN
- DNN::Layers::ReLU
- DNN::Layers::Reshape
- DNN::Layers::Ridge
- DNN::Layers::Sigmoid
- DNN::Layers::SimpleRNN
- DNN::Layers::SimpleRNNDense
- DNN::Layers::Softmax
- DNN::Layers::Softplus
- DNN::Layers::Softsign
- DNN::Layers::Split
- DNN::Layers::Sqrt
- DNN::Layers::Sub
- DNN::Layers::Sum
- DNN::Layers::Swish
- DNN::Layers::Tanh
- DNN::Layers::TrainableLayer
- DNN::Layers::UnPool2D
- DNN::Link
- DNN::Loaders
- DNN::Loaders::JSONLoader
- DNN::Loaders::Loader
- DNN::Loaders::MarshalLoader
- DNN::Losses
- DNN::Losses::Hinge
- DNN::Losses::HuberLoss
- DNN::Losses::Loss
- DNN::Losses::MeanAbsoluteError
- DNN::Losses::MeanSquaredError
- DNN::Losses::SigmoidCrossEntropy
- DNN::Losses::SoftmaxCrossEntropy
- DNN::MNIST
- DNN::MNIST::DNN_MNIST_LoadError
- DNN::Models
- DNN::Models::Chain
- DNN::Models::FixedModel
- DNN::Models::LayersList
- DNN::Models::Model
- DNN::Models::Sequential
- DNN::Optimizers
- DNN::Optimizers::AdaBound
- DNN::Optimizers::AdaDelta
- DNN::Optimizers::AdaGrad
- DNN::Optimizers::Adam
- DNN::Optimizers::Nesterov
- DNN::Optimizers::Optimizer
- DNN::Optimizers::RMSProp
- DNN::Optimizers::RMSPropGraves
- DNN::Optimizers::SGD
- DNN::Param
- DNN::Regularizers
- DNN::Regularizers::L1
- DNN::Regularizers::L1L2
- DNN::Regularizers::L2
- DNN::Regularizers::Regularizer
- DNN::STL10
- DNN::STL10::DNN_STL10_LoadError
- DNN::Savers
- DNN::Savers::JSONSaver
- DNN::Savers::MarshalSaver
- DNN::Savers::Saver
- DNN::Stb
- DNN::Tensor
- DNN::Utils
- DNNKerasLayerNotConvertSupportError
- DNNKerasModelConvertError
- Float
- Hash
- Integer
- KerasModelConvertor
- Numpy
- NumpyToNumoError
- Object
Methods
- ::activation — DNN::Losses::SoftmaxCrossEntropy
- ::activation — DNN::Losses::SigmoidCrossEntropy
- ::broadcast_to — DNN::Utils
- ::call — DNN::Layers::Layer
- ::call — DNN::Layers::MergeLayer
- ::call — DNN::Losses::Loss
- ::convert — DNN::Tensor
- ::cudnn_available? — DNN
- ::cumo2numo — DNN::Utils
- ::download — DNN::Downloader
- ::downloads — DNN::CIFAR10
- ::downloads — DNN::CIFAR100
- ::downloads — DNN::FashionMNIST
- ::downloads — DNN::Iris
- ::downloads — DNN::MNIST
- ::downloads — DNN::STL10
- ::from_binary — DNN::Image
- ::from_hash — DNN::Initializers::Initializer
- ::from_hash — DNN::Layers::Layer
- ::from_hash — DNN::Losses::Loss
- ::from_hash — DNN::Optimizers::Optimizer
- ::from_hash — DNN::Regularizers::Regularizer
- ::from_hash_list — DNN::Models::LayersList
- ::from_na — Numpy
- ::get_file_path — DNN::FashionMNIST
- ::get_file_path — DNN::MNIST
- ::hash_to_obj — DNN::Utils
- ::img_check — DNN::Image
- ::learning_phase — DNN
- ::learning_phase= — DNN
- ::load — DNN::Models::Model
- ::load — KerasModelConvertor
- ::load — DNN::Iris
- ::load_images — DNN::FashionMNIST
- ::load_images — DNN::MNIST
- ::load_labels — DNN::FashionMNIST
- ::load_labels — DNN::MNIST
- ::load_test — DNN::CIFAR10
- ::load_test — DNN::CIFAR100
- ::load_test — DNN::FashionMNIST
- ::load_test — DNN::MNIST
- ::load_test — DNN::STL10
- ::load_train — DNN::CIFAR10
- ::load_train — DNN::CIFAR100
- ::load_train — DNN::FashionMNIST
- ::load_train — DNN::MNIST
- ::load_train — DNN::STL10
- ::load_unlabeled — DNN::STL10
- ::mnist_dir — DNN::FashionMNIST
- ::mnist_dir — DNN::MNIST
- ::new — DNN::Callbacks::LambdaCallback
- ::new — DNN::Callbacks::CheckPoint
- ::new — DNN::Callbacks::EarlyStopping
- ::new — DNN::Callbacks::Logger
- ::new — DNN::Initializers::Initializer
- ::new — DNN::Initializers::Const
- ::new — DNN::Initializers::RandomNormal
- ::new — DNN::Initializers::RandomUniform
- ::new — DNN::Initializers::Xavier
- ::new — DNN::Initializers::He
- ::new — DNN::Iterator
- ::new — DNN::Layers::LeakyReLU
- ::new — DNN::Layers::ELU
- ::new — DNN::Layers::Layer
- ::new — DNN::Layers::TrainableLayer
- ::new — DNN::Layers::InputLayer
- ::new — DNN::Layers::Connection
- ::new — DNN::Layers::Dense
- ::new — DNN::Layers::Reshape
- ::new — DNN::Layers::Lasso
- ::new — DNN::Layers::Ridge
- ::new — DNN::Layers::Dropout
- ::new — DNN::Layers::Conv2D
- ::new — DNN::Layers::Conv2DTranspose
- ::new — DNN::Layers::Pool2D
- ::new — DNN::Layers::UnPool2D
- ::new — DNN::Layers::Embedding
- ::new — DNN::Layers::Pow
- ::new — DNN::Layers::Sum
- ::new — DNN::Layers::Mean
- ::new — DNN::Layers::Concatenate
- ::new — DNN::Layers::BatchNormalization
- ::new — DNN::Layers::RNN
- ::new — DNN::Layers::SimpleRNNDense
- ::new — DNN::Layers::SimpleRNN
- ::new — DNN::Layers::LSTMDense
- ::new — DNN::Layers::LSTM
- ::new — DNN::Layers::GRUDense
- ::new — DNN::Layers::GRU
- ::new — DNN::Layers::Split
- ::new — DNN::Link
- ::new — DNN::Losses::SoftmaxCrossEntropy
- ::new — DNN::Losses::SigmoidCrossEntropy
- ::new — DNN::Models::Chain
- ::new — DNN::Models::Model
- ::new — DNN::Models::Sequential
- ::new — DNN::Models::FixedModel
- ::new — DNN::Optimizers::Optimizer
- ::new — DNN::Optimizers::SGD
- ::new — DNN::Optimizers::Nesterov
- ::new — DNN::Optimizers::AdaGrad
- ::new — DNN::Optimizers::RMSProp
- ::new — DNN::Optimizers::AdaDelta
- ::new — DNN::Optimizers::RMSPropGraves
- ::new — DNN::Optimizers::Adam
- ::new — DNN::Optimizers::AdaBound
- ::new — DNN::Param
- ::new — DNN::Regularizers::L1
- ::new — DNN::Regularizers::L2
- ::new — DNN::Regularizers::L1L2
- ::new — DNN::Loaders::Loader
- ::new — DNN::Savers::Saver
- ::new — DNN::Savers::MarshalSaver
- ::new — DNN::Tensor
- ::new — DNN::Downloader
- ::new — KerasModelConvertor
- ::numerical_grad — DNN::Utils
- ::numo2cumo — DNN::Utils
- ::read — DNN::Image
- ::resize — DNN::Image
- ::sigmoid — DNN::Losses::SigmoidCrossEntropy
- ::sigmoid — DNN::Utils
- ::softmax — DNN::Losses::SoftmaxCrossEntropy
- ::softmax — DNN::Utils
- ::stbi_load — DNN::Stb
- ::stbi_write_bmp — DNN::Stb
- ::stbi_write_hdr — DNN::Stb
- ::stbi_write_jpg — DNN::Stb
- ::stbi_write_png — DNN::Stb
- ::stbi_write_tga — DNN::Stb
- ::stbir_resize_uint8 — DNN::Stb
- ::stbir_resize_uint8_srgb — DNN::Stb
- ::stbir_resize_uint8_srgb_edgemode — DNN::Stb
- ::to_categorical — DNN::Utils
- ::to_f — DNN::Utils
- ::to_gray_scale — DNN::Image
- ::to_na — Numpy
- ::to_rgb — DNN::Image
- ::to_rgba — DNN::Image
- ::trim — DNN::Image
- ::url_to_file_name — DNN::Iris
- ::use_cudnn? — DNN
- ::use_cumo? — DNN
- ::write — DNN::Image
- #* — Integer
- #* — Float
- #* — DNN::Param
- #* — DNN::Tensor
- #** — DNN::Param
- #** — DNN::Tensor
- #+ — Integer
- #+ — Float
- #+ — DNN::Param
- #+ — DNN::Tensor
- #+@ — DNN::Param
- #+@ — DNN::Tensor
- #- — Integer
- #- — Float
- #- — DNN::Param
- #- — DNN::Tensor
- #-@ — DNN::Param
- #-@ — DNN::Tensor
- #/ — Integer
- #/ — Float
- #/ — DNN::Param
- #/ — DNN::Tensor
- #<< — DNN::Layers::Layer
- #<< — DNN::Models::Sequential
- #>> — DNN::Tensor
- #_backward_cpu — DNN::Layers::Reshape
- #_backward_gpu — DNN::Layers::Reshape
- #_forward_cpu — DNN::Layers::Reshape
- #_forward_gpu — DNN::Layers::Reshape
- #accuracy — DNN::Models::Model
- #activation_to_dnn_layer — KerasModelConvertor
- #add — DNN::Models::Sequential
- #add_callback — DNN::Models::Model
- #add_lambda_callback — DNN::Models::Model
- #after_epoch — DNN::Callbacks::CheckPoint
- #after_epoch — DNN::Callbacks::EarlyStopping
- #after_epoch — DNN::Callbacks::Logger
- #after_train_on_batch — DNN::Callbacks::EarlyStopping
- #after_train_on_batch — DNN::Callbacks::NaNStopping
- #after_train_on_batch — DNN::Callbacks::Logger
- #align_ndim — DNN::Layers::MathUtils
- #backward — DNN::Layers::SimpleRNNDense
- #backward — DNN::Layers::LSTMDense
- #backward — DNN::Layers::GRUDense
- #backward — DNN::Link
- #backward — DNN::Param
- #backward_node — DNN::Layers::Sigmoid
- #backward_node — DNN::Layers::Tanh
- #backward_node — DNN::Layers::Softsign
- #backward_node — DNN::Layers::Softplus
- #backward_node — DNN::Layers::Swish
- #backward_node — DNN::Layers::ReLU
- #backward_node — DNN::Layers::LeakyReLU
- #backward_node — DNN::Layers::ELU
- #backward_node — DNN::Layers::Mish
- #backward_node — DNN::Layers::Dense
- #backward_node — DNN::Layers::Reshape
- #backward_node — DNN::Layers::Lasso
- #backward_node — DNN::Layers::Ridge
- #backward_node — DNN::Layers::Dropout
- #backward_node — DNN::Layers::Conv2D
- #backward_node — DNN::Layers::Conv2DTranspose
- #backward_node — DNN::Layers::MaxPool2D
- #backward_node — DNN::Layers::AvgPool2D
- #backward_node — DNN::Layers::UnPool2D
- #backward_node — DNN::Layers::Embedding
- #backward_node — DNN::Layers::Neg
- #backward_node — DNN::Layers::Add
- #backward_node — DNN::Layers::Sub
- #backward_node — DNN::Layers::Mul
- #backward_node — DNN::Layers::Div
- #backward_node — DNN::Layers::Dot
- #backward_node — DNN::Layers::Exp
- #backward_node — DNN::Layers::Log
- #backward_node — DNN::Layers::Pow
- #backward_node — DNN::Layers::Sqrt
- #backward_node — DNN::Layers::Sum
- #backward_node — DNN::Layers::Mean
- #backward_node — DNN::Layers::Concatenate
- #backward_node — DNN::Layers::BatchNormalization
- #backward_node — DNN::Layers::RNN
- #backward_node — DNN::Layers::LSTM
- #backward_node — DNN::Layers::Split
- #backward_node — DNN::Losses::MeanSquaredError
- #backward_node — DNN::Losses::MeanAbsoluteError
- #backward_node — DNN::Losses::Hinge
- #backward_node — DNN::Losses::HuberLoss
- #backward_node — DNN::Losses::SoftmaxCrossEntropy
- #backward_node — DNN::Losses::SigmoidCrossEntropy
- #backward_node — DNN::Layers::LayerNode
- #broadcast_to — DNN::Layers::MathUtils
- #build — DNN::Layers::Layer
- #build — DNN::Layers::InputLayer
- #build — DNN::Layers::Dense
- #build — DNN::Layers::Conv2D
- #build — DNN::Layers::Conv2DTranspose
- #build — DNN::Layers::Pool2D
- #build — DNN::Layers::GlobalAvgPool2D
- #build — DNN::Layers::UnPool2D
- #build — DNN::Layers::Embedding
- #build — DNN::Layers::BatchNormalization
- #build — DNN::Layers::RNN
- #build — DNN::Layers::SimpleRNN
- #build — DNN::Layers::LSTM
- #build — DNN::Layers::GRU
- #build_dnn_layer — KerasModelConvertor
- #built? — DNN::Layers::Layer
- #built? — DNN::Models::Model
- #calc_conv2d_out_size — DNN::Layers::Conv2DUtils
- #calc_conv2d_padding_size — DNN::Layers::Conv2DUtils
- #calc_conv2d_transpose_out_size — DNN::Layers::Conv2DUtils
- #calc_conv2d_transpose_padding_size — DNN::Layers::Conv2DUtils
- #call — DNN::Layers::Layer
- #call — DNN::Layers::MergeLayer
- #call — DNN::Losses::Loss
- #call — DNN::Models::Chain
- #call — DNN::Models::Model
- #call_callbacks — DNN::Models::Model
- #check_xy_type — DNN::Models::Model
- #clean — DNN::Layers::Layer
- #clean — DNN::Layers::TrainableLayer
- #clean — DNN::Losses::Loss
- #clean_layers — DNN::Models::Model
- #clear_callbacks — DNN::Models::Model
- #clip_grads — DNN::Optimizers::Optimizer
- #clip_lr — DNN::Optimizers::AdaBound
- #col2im — DNN::Layers::Conv2DUtils
- #col2im_cpu — DNN::Layers::Conv2DUtils
- #col2im_gpu — DNN::Layers::Conv2DUtils
- #compute_output_shape — DNN::Layers::Layer
- #compute_output_shape — DNN::Layers::Dense
- #compute_output_shape — DNN::Layers::Flatten
- #compute_output_shape — DNN::Layers::Reshape
- #compute_output_shape — DNN::Layers::Conv2D
- #compute_output_shape — DNN::Layers::Conv2DTranspose
- #compute_output_shape — DNN::Layers::Pool2D
- #compute_output_shape — DNN::Layers::UnPool2D
- #compute_output_shape — DNN::Layers::RNN
- #convert — KerasModelConvertor
- #convert_Activation — KerasModelConvertor
- #convert_AveragePooling2D — KerasModelConvertor
- #convert_BatchNormalization — KerasModelConvertor
- #convert_Conv2D — KerasModelConvertor
- #convert_Conv2DTranspose — KerasModelConvertor
- #convert_Dense — KerasModelConvertor
- #convert_Dropout — KerasModelConvertor
- #convert_Flatten — KerasModelConvertor
- #convert_GlobalAveragePooling2D — KerasModelConvertor
- #convert_InputLayer — KerasModelConvertor
- #convert_MaxPooling2D — KerasModelConvertor
- #convert_Reshape — KerasModelConvertor
- #convert_UpSampling2D — KerasModelConvertor
- #convert_layers — KerasModelConvertor
- #copy — DNN::Models::Model
- #create_hidden_layer — DNN::Layers::RNN
- #create_hidden_layer — DNN::Layers::SimpleRNN
- #create_hidden_layer — DNN::Layers::LSTM
- #create_hidden_layer — DNN::Layers::GRU
- #dnn__add — Integer
- #dnn__add — Float
- #dnn__div — Integer
- #dnn__div — Float
- #dnn__mul — Integer
- #dnn__mul — Float
- #dnn__sub — Integer
- #dnn__sub — Float
- #dnn__to_h — Hash
- #download — DNN::Downloader
- #dump_bin — DNN::Savers::Saver
- #dump_bin — DNN::Savers::MarshalSaver
- #dump_bin — DNN::Savers::JSONSaver
- #evaluate — DNN::Models::Model
- #evaluate_by_iterator — DNN::Models::Model
- #filters — DNN::Layers::Conv2D
- #filters — DNN::Layers::Conv2DTranspose
- #filters= — DNN::Layers::Conv2D
- #filters= — DNN::Layers::Conv2DTranspose
- #fit — DNN::Models::Model
- #fit_by_iterator — DNN::Models::Model
- #foreach — DNN::Iterator
- #forward — DNN::Layers::Layer
- #forward — DNN::Layers::InputLayer
- #forward — DNN::Layers::Flatten
- #forward — DNN::Layers::GlobalAvgPool2D
- #forward — DNN::Layers::SimpleRNNDense
- #forward — DNN::Layers::LSTMDense
- #forward — DNN::Layers::GRUDense
- #forward — DNN::Link
- #forward — DNN::Losses::Loss
- #forward — DNN::Models::Chain
- #forward — DNN::Models::Sequential
- #forward — DNN::Models::FixedModel
- #forward — DNN::Regularizers::Regularizer
- #forward — DNN::Regularizers::L1
- #forward — DNN::Regularizers::L2
- #forward — DNN::Regularizers::L1L2
- #forward — DNN::Layers::Softmax
- #forward — DNN::Layers::LayerNode
- #forward_node — DNN::Layers::Sigmoid
- #forward_node — DNN::Layers::Tanh
- #forward_node — DNN::Layers::Softsign
- #forward_node — DNN::Layers::Softplus
- #forward_node — DNN::Layers::Swish
- #forward_node — DNN::Layers::ReLU
- #forward_node — DNN::Layers::LeakyReLU
- #forward_node — DNN::Layers::ELU
- #forward_node — DNN::Layers::Mish
- #forward_node — DNN::Layers::Dense
- #forward_node — DNN::Layers::Reshape
- #forward_node — DNN::Layers::Lasso
- #forward_node — DNN::Layers::Ridge
- #forward_node — DNN::Layers::Dropout
- #forward_node — DNN::Layers::Conv2D
- #forward_node — DNN::Layers::Conv2DTranspose
- #forward_node — DNN::Layers::MaxPool2D
- #forward_node — DNN::Layers::AvgPool2D
- #forward_node — DNN::Layers::UnPool2D
- #forward_node — DNN::Layers::Embedding
- #forward_node — DNN::Layers::Neg
- #forward_node — DNN::Layers::Add
- #forward_node — DNN::Layers::Sub
- #forward_node — DNN::Layers::Mul
- #forward_node — DNN::Layers::Div
- #forward_node — DNN::Layers::Dot
- #forward_node — DNN::Layers::Exp
- #forward_node — DNN::Layers::Log
- #forward_node — DNN::Layers::Pow
- #forward_node — DNN::Layers::Sqrt
- #forward_node — DNN::Layers::Sum
- #forward_node — DNN::Layers::Mean
- #forward_node — DNN::Layers::Concatenate
- #forward_node — DNN::Layers::BatchNormalization
- #forward_node — DNN::Layers::RNN
- #forward_node — DNN::Layers::LSTM
- #forward_node — DNN::Layers::Split
- #forward_node — DNN::Losses::MeanSquaredError
- #forward_node — DNN::Losses::MeanAbsoluteError
- #forward_node — DNN::Losses::Hinge
- #forward_node — DNN::Losses::HuberLoss
- #forward_node — DNN::Losses::SoftmaxCrossEntropy
- #forward_node — DNN::Losses::SigmoidCrossEntropy
- #forward_node — DNN::Layers::LayerNode
- #get_all_params_base64_data — DNN::Savers::JSONSaver
- #get_all_params_data — DNN::Models::Model
- #get_all_trainable_params — DNN::Models::Model
- #get_batch — DNN::Iterator
- #get_input_link — DNN::Models::FixedModel
- #get_k_layer_shape — KerasModelConvertor
- #get_layer — DNN::Models::Model
- #get_log — DNN::Callbacks::Logger
- #get_params — DNN::Layers::TrainableLayer
- #get_params — DNN::Layers::Connection
- #get_params — DNN::Layers::Embedding
- #get_params — DNN::Layers::BatchNormalization
- #get_params — DNN::Layers::RNN
- #get_params — DNN::Layers::LSTM
- #has_next? — DNN::Iterator
- #im2col — DNN::Layers::Conv2DUtils
- #im2col_cpu — DNN::Layers::Conv2DUtils
- #im2col_gpu — DNN::Layers::Conv2DUtils
- #init_param — DNN::Initializers::Initializer
- #init_param — DNN::Initializers::Zeros
- #init_param — DNN::Initializers::Const
- #init_param — DNN::Initializers::RandomNormal
- #init_param — DNN::Initializers::RandomUniform
- #init_param — DNN::Initializers::Xavier
- #init_param — DNN::Initializers::He
- #init_weight_and_bias — DNN::Layers::Connection
- #init_weight_and_bias — DNN::Layers::RNN
- #insert — DNN::Models::Sequential
- #judge_early_stopping_test — DNN::Callbacks::EarlyStopping
- #judge_early_stopping_train — DNN::Callbacks::EarlyStopping
- #l1_lambda — DNN::Regularizers::L1
- #l1_lambda — DNN::Regularizers::L1L2
- #l1_lambda= — DNN::Regularizers::L1
- #l1_lambda= — DNN::Regularizers::L1L2
- #l2_lambda — DNN::Regularizers::L2
- #l2_lambda — DNN::Regularizers::L1L2
- #l2_lambda= — DNN::Regularizers::L2
- #l2_lambda= — DNN::Regularizers::L1L2
- #layer_convert — KerasModelConvertor
- #layers — DNN::Models::LayersList
- #layers — DNN::Models::Chain
- #load — DNN::Loaders::Loader
- #load_bin — DNN::Loaders::Loader
- #load_bin — DNN::Loaders::MarshalLoader
- #load_bin — DNN::Loaders::JSONLoader
- #load_hash — DNN::Initializers::Initializer
- #load_hash — DNN::Initializers::Const
- #load_hash — DNN::Initializers::RandomNormal
- #load_hash — DNN::Initializers::RandomUniform
- #load_hash — DNN::Layers::LeakyReLU
- #load_hash — DNN::Layers::ELU
- #load_hash — DNN::Layers::Layer
- #load_hash — DNN::Layers::InputLayer
- #load_hash — DNN::Layers::Dense
- #load_hash — DNN::Layers::Reshape
- #load_hash — DNN::Layers::Lasso
- #load_hash — DNN::Layers::Ridge
- #load_hash — DNN::Layers::Dropout
- #load_hash — DNN::Layers::Conv2D
- #load_hash — DNN::Layers::Conv2DTranspose
- #load_hash — DNN::Layers::Pool2D
- #load_hash — DNN::Layers::UnPool2D
- #load_hash — DNN::Layers::Embedding
- #load_hash — DNN::Layers::Sum
- #load_hash — DNN::Layers::Mean
- #load_hash — DNN::Layers::Concatenate
- #load_hash — DNN::Layers::BatchNormalization
- #load_hash — DNN::Layers::RNN
- #load_hash — DNN::Layers::SimpleRNN
- #load_hash — DNN::Layers::Split
- #load_hash — DNN::Losses::Loss
- #load_hash — DNN::Losses::SoftmaxCrossEntropy
- #load_hash — DNN::Losses::SigmoidCrossEntropy
- #load_hash — DNN::Models::Chain
- #load_hash — DNN::Optimizers::Optimizer
- #load_hash — DNN::Optimizers::SGD
- #load_hash — DNN::Optimizers::AdaGrad
- #load_hash — DNN::Optimizers::RMSProp
- #load_hash — DNN::Optimizers::AdaDelta
- #load_hash — DNN::Optimizers::RMSPropGraves
- #load_hash — DNN::Optimizers::Adam
- #load_hash — DNN::Optimizers::AdaBound
- #load_hash — DNN::Regularizers::Regularizer
- #load_hash — DNN::Regularizers::L1
- #load_hash — DNN::Regularizers::L2
- #load_hash — DNN::Regularizers::L1L2
- #load_params — DNN::Models::Model
- #logging — DNN::Callbacks::Logger
- #loss — DNN::Losses::Loss
- #loss_func — DNN::Models::Model
- #loss_func= — DNN::Models::Model
- #metrics_to_str — DNN::Models::Model
- #next_batch — DNN::Iterator
- #predict — DNN::Models::Model
- #predict1 — DNN::Models::Model
- #regularizers — DNN::Layers::Connection
- #regularizers — DNN::Layers::Embedding
- #regularizers — DNN::Layers::RNN
- #regularizers_forward — DNN::Losses::Loss
- #remove — DNN::Models::Sequential
- #reset — DNN::Iterator
- #reset_state — DNN::Layers::RNN
- #reset_state — DNN::Layers::LSTM
- #save — DNN::Models::Model
- #save — DNN::Savers::Saver
- #save_params — DNN::Models::Model
- #set_all_params_base64_data — DNN::Loaders::JSONLoader
- #set_all_params_data — DNN::Models::Model
- #setup — DNN::Models::Model
- #shape — DNN::Param
- #shape — DNN::Tensor
- #sum_to — DNN::Layers::MathUtils
- #test_on_batch — DNN::Models::Model
- #to_cpu — DNN::Models::Model
- #to_gpu — DNN::Models::Model
- #to_h — Hash
- #to_hash — DNN::Initializers::Initializer
- #to_hash — DNN::Initializers::Const
- #to_hash — DNN::Initializers::RandomNormal
- #to_hash — DNN::Initializers::RandomUniform
- #to_hash — DNN::Layers::LeakyReLU
- #to_hash — DNN::Layers::ELU
- #to_hash — DNN::Layers::Layer
- #to_hash — DNN::Layers::InputLayer
- #to_hash — DNN::Layers::Connection
- #to_hash — DNN::Layers::Dense
- #to_hash — DNN::Layers::Reshape
- #to_hash — DNN::Layers::Lasso
- #to_hash — DNN::Layers::Ridge
- #to_hash — DNN::Layers::Dropout
- #to_hash — DNN::Layers::Conv2D
- #to_hash — DNN::Layers::Conv2DTranspose
- #to_hash — DNN::Layers::Pool2D
- #to_hash — DNN::Layers::UnPool2D
- #to_hash — DNN::Layers::Embedding
- #to_hash — DNN::Layers::Sum
- #to_hash — DNN::Layers::Mean
- #to_hash — DNN::Layers::Concatenate
- #to_hash — DNN::Layers::BatchNormalization
- #to_hash — DNN::Layers::RNN
- #to_hash — DNN::Layers::SimpleRNN
- #to_hash — DNN::Layers::Split
- #to_hash — DNN::Losses::Loss
- #to_hash — DNN::Losses::SoftmaxCrossEntropy
- #to_hash — DNN::Losses::SigmoidCrossEntropy
- #to_hash — DNN::Models::Chain
- #to_hash — DNN::Optimizers::Optimizer
- #to_hash — DNN::Optimizers::SGD
- #to_hash — DNN::Optimizers::AdaGrad
- #to_hash — DNN::Optimizers::RMSProp
- #to_hash — DNN::Optimizers::AdaDelta
- #to_hash — DNN::Optimizers::RMSPropGraves
- #to_hash — DNN::Optimizers::Adam
- #to_hash — DNN::Optimizers::AdaBound
- #to_hash — DNN::Regularizers::Regularizer
- #to_hash — DNN::Regularizers::L1
- #to_hash — DNN::Regularizers::L2
- #to_hash — DNN::Regularizers::L1L2
- #to_hash_list — DNN::Models::LayersList
- #to_proc — DNN::Layers::InputLayer
- #train — DNN::Models::Model
- #train_by_iterator — DNN::Models::Model
- #train_on_batch — DNN::Models::Model
- #train_step — DNN::Models::Model
- #trainable_layers — DNN::Models::Model
- #update — DNN::Optimizers::Optimizer
- #update_layers — DNN::Optimizers::Optimizer
- #update_params — DNN::Optimizers::Optimizer
- #update_params — DNN::Optimizers::SGD
- #update_params — DNN::Optimizers::Nesterov
- #update_params — DNN::Optimizers::AdaGrad
- #update_params — DNN::Optimizers::RMSProp
- #update_params — DNN::Optimizers::AdaDelta
- #update_params — DNN::Optimizers::RMSPropGraves
- #update_params — DNN::Optimizers::Adam
- #update_params — DNN::Optimizers::AdaBound
- #use_bias — DNN::Layers::Connection
- #zero_padding — DNN::Layers::Conv2DUtils
- #zero_padding_bwd — DNN::Layers::Conv2DUtils