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Class List
Here are the classes, structs, unions and interfaces with brief descriptions:

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