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Class Hierarchy

Go to the graphical class hierarchy

This inheritance list is sorted roughly, but not completely, alphabetically:

tiny_dnn::absolute |
tiny_dnn::absolute_eps< fraction > |
tiny_dnn::aligned_allocator< T, alignment > |
tiny_dnn::core::backend |
tiny_dnn::core::avx_backend |
tiny_dnn::core::dnn_backend |
tiny_dnn::core::nnp_backend |
tiny_dnn::core::tiny_backend |
tiny_dnn::blocked_range |
tiny_dnn::detail::caffe_layer_vector |
tiny_dnn::core::connection_table |
tiny_dnn::core::Conv2dPadding |
tiny_dnn::core::conv_layer_worker_specific_storage |
tiny_dnn::cross_entropy |
tiny_dnn::cross_entropy_multiclass |
tiny_dnn::core::deconv_layer_worker_specific_storage |
tiny_dnn::core::deconv_params |
tiny_dnn::deserialization_helper< InputArchive > |
tiny_dnn::Device |
tiny_dnn::edge | Class containing input/output data
std::enable_shared_from_this |
tiny_dnn::node | Base class of all kind of tinny-cnn data
tiny_dnn::layer | Base class of all kind of NN layers
tiny_dnn::feedforward_layer< activation::identity > |
tiny_dnn::batch_normalization_layer | Batch Normalization
tiny_dnn::concat_layer | Concat N layers along depth
tiny_dnn::dropout_layer | Applies dropout to the input
tiny_dnn::elementwise_add_layer | Element-wise add N vectors `y_i = x0_i + x1_i + ... + xnum_i`
tiny_dnn::feedforward_layer< Activation > | Single-input, single-output network with activation function
tiny_dnn::partial_connected_layer< activation::identity > |
tiny_dnn::convolutional_layer< Activation > | 2D convolution layer
tiny_dnn::deconvolutional_layer< Activation > | 2D deconvolution layer
tiny_dnn::fully_connected_layer< Activation > | Compute fully-connected(matmul) operation
tiny_dnn::linear_layer< Activation > | Element-wise operation: `f(x) = h(scale*x+bias)`
tiny_dnn::lrn_layer< Activation > | Local response normalization
tiny_dnn::max_pooling_layer< Activation > | Applies max-pooing operaton to the spatial data
tiny_dnn::max_unpooling_layer< Activation > | Applies max-pooing operaton to the spatial data
tiny_dnn::partial_connected_layer< Activation > |
tiny_dnn::average_pooling_layer< Activation > | Average pooling with trainable weights
tiny_dnn::average_unpooling_layer< Activation > | Average pooling with trainable weights
tiny_dnn::quantized_convolutional_layer< Activation > | 2D convolution layer
tiny_dnn::quantized_deconvolutional_layer< Activation > | 2D deconvolution layer
tiny_dnn::quantized_fully_connected_layer< Activation > | Compute fully-connected(matmul) operation
tiny_dnn::input_layer |
tiny_dnn::power_layer | Element-wise pow: `y = scale*x^factor`
tiny_dnn::slice_layer | Slice an input data into multiple outputs along a given slice dimension
std::exception |
tiny_dnn::nn_error | Error exception class for tiny-dnn
tiny_dnn::nn_not_implemented_error |
std::false_type |
foobar< n > |
tiny_dnn::activation::function |
tiny_dnn::activation::elu |
tiny_dnn::activation::identity |
tiny_dnn::activation::leaky_relu |
tiny_dnn::activation::relu |
tiny_dnn::activation::sigmoid |
tiny_dnn::activation::softmax |
tiny_dnn::activation::tan_h |
tiny_dnn::activation::tan_hp1m2 |
tiny_dnn::weight_init::function |
tiny_dnn::weight_init::scalable |
tiny_dnn::weight_init::constant |
tiny_dnn::weight_init::gaussian |
tiny_dnn::weight_init::he |
tiny_dnn::weight_init::lecun | Use fan-in(number of input weight for each neuron) for scaling
tiny_dnn::weight_init::xavier | Use fan-in and fan-out for scaling
vectorize::detail::generic_vec_type< T > |
tiny_dnn::graph_visualizer | Utility for graph visualization
tiny_dnn::image< T > | Simple image utility class
tiny_dnn::index3d< T > |
tiny_dnn::index3d< serial_size_t > |
tiny_dnn::detail::layer_node |
tiny_dnn::core::max_pooling_layer_worker_specific_storage |
tiny_dnn::detail::mnist_header |
tiny_dnn::mse |
network |
models::alexnet |
tiny_dnn::network< NetType > | A model of neural networks in tiny-dnn
tiny_dnn::nn_info | Info class for tiny-dnn (for debug)
tiny_dnn::nn_warn | Warning class for tiny-dnn (for debug)
tiny_dnn::node_tuple< T > |
tiny_dnn::nodes | Basic class of various network types (sequential, multi-in/multi-out)
tiny_dnn::graph | Generic graph network
tiny_dnn::sequential | Single-input, single-output feedforward network
tiny_dnn::core::OpKernel |
tiny_dnn::Conv2dGradOp |
tiny_dnn::Conv2dLibDNNBackwardOp |
tiny_dnn::Conv2dLibDNNForwardOp |
tiny_dnn::Conv2dOp |
tiny_dnn::Conv2dOpenCLBackwardOp |
tiny_dnn::Conv2dOpenCLForwardOp |
tiny_dnn::FullyConnectedGradOp |
tiny_dnn::FullyConnectedOp |
tiny_dnn::MaxPoolGradOp |
tiny_dnn::MaxPoolOp |
tiny_dnn::core::OpKernelConstruction |
tiny_dnn::core::OpKernelContext |
tiny_dnn::core::OpKernelContext::OpParams |
tiny_dnn::optimizer | Base class of optimizer usesHessian : true if an optimizer uses hessian (2nd order derivative of loss function)
tiny_dnn::stateful_optimizer< 1 > |
tiny_dnn::RMSprop | RMSprop
tiny_dnn::adagrad | Adaptive gradient method
tiny_dnn::momentum | SGD with momentum
tiny_dnn::stateful_optimizer< 2 > |
tiny_dnn::adam | [a new optimizer (2015)]
tiny_dnn::gradient_descent | SGD without momentum
tiny_dnn::stateful_optimizer< N > |
tiny_dnn::core::Params |
tiny_dnn::core::conv_params |
tiny_dnn::core::fully_params |
tiny_dnn::core::maxpool_params |
tiny_dnn::Program |
tiny_dnn::ProgramHash |
tiny_dnn::ProgramManager |
tiny_dnn::progress_display |
tiny_dnn::random_generator |
tiny_dnn::aligned_allocator< T, alignment >::rebind< U > |
tiny_dnn::result |
tiny_dnn::serialization_helper< OutputArchive > |
tiny_dnn::core::session |
tiny_dnn::Tensor< U > |
tiny_dnn::timer |