29 #include "tiny_dnn/config.h"
30 #include "tiny_dnn/network.h"
31 #include "tiny_dnn/nodes.h"
33 #include "tiny_dnn/core/framework/tensor.h"
35 #include "tiny_dnn/core/framework/device.h"
36 #include "tiny_dnn/core/framework/program_manager.h"
38 #include "tiny_dnn/layers/input_layer.h"
39 #include "tiny_dnn/layers/feedforward_layer.h"
40 #include "tiny_dnn/layers/convolutional_layer.h"
41 #include "tiny_dnn/layers/quantized_convolutional_layer.h"
42 #include "tiny_dnn/layers/deconvolutional_layer.h"
43 #include "tiny_dnn/layers/quantized_deconvolutional_layer.h"
44 #include "tiny_dnn/layers/fully_connected_layer.h"
45 #include "tiny_dnn/layers/quantized_fully_connected_layer.h"
46 #include "tiny_dnn/layers/average_pooling_layer.h"
47 #include "tiny_dnn/layers/max_pooling_layer.h"
48 #include "tiny_dnn/layers/linear_layer.h"
49 #include "tiny_dnn/layers/lrn_layer.h"
50 #include "tiny_dnn/layers/dropout_layer.h"
51 #include "tiny_dnn/layers/arithmetic_layer.h"
52 #include "tiny_dnn/layers/concat_layer.h"
53 #include "tiny_dnn/layers/max_unpooling_layer.h"
54 #include "tiny_dnn/layers/average_unpooling_layer.h"
55 #include "tiny_dnn/layers/batch_normalization_layer.h"
56 #include "tiny_dnn/layers/slice_layer.h"
57 #include "tiny_dnn/layers/power_layer.h"
59 #include "tiny_dnn/activations/activation_function.h"
60 #include "tiny_dnn/lossfunctions/loss_function.h"
61 #include "tiny_dnn/optimizers/optimizer.h"
63 #include "tiny_dnn/util/weight_init.h"
64 #include "tiny_dnn/util/image.h"
65 #include "tiny_dnn/util/deform.h"
66 #include "tiny_dnn/util/product.h"
67 #include "tiny_dnn/util/graph_visualizer.h"
69 #include "tiny_dnn/io/mnist_parser.h"
70 #include "tiny_dnn/io/cifar10_parser.h"
71 #include "tiny_dnn/io/display.h"
72 #include "tiny_dnn/io/layer_factory.h"
73 #include "tiny_dnn/util/serialization_helper.h"
74 #include "tiny_dnn/util/deserialization_helper.h"
76 #ifdef CNN_USE_CAFFE_CONVERTER
78 #include "tiny_dnn/io/caffe/layer_factory.h"
128 #include "tiny_dnn/models/alexnet.h"
average pooling with trainable weights
Definition: average_pooling_layer.h:136
average pooling with trainable weights
Definition: average_unpooling_layer.h:134
Batch Normalization.
Definition: batch_normalization_layer.h:42
concat N layers along depth
Definition: concat_layer.h:44
2D convolution layer
Definition: convolutional_layer.h:52
2D deconvolution layer
Definition: deconvolutional_layer.h:54
applies dropout to the input
Definition: dropout_layer.h:37
element-wise add N vectors y_i = x0_i + x1_i + ...
Definition: arithmetic_layer.h:36
compute fully-connected(matmul) operation
Definition: fully_connected_layer.h:39
local response normalization
Definition: lrn_layer.h:42
applies max-pooing operaton to the spatial data
Definition: max_pooling_layer.h:53
applies max-pooing operaton to the spatial data
Definition: max_unpooling_layer.h:38
element-wise pow: y = scale*x^factor
Definition: power_layer.h:38
2D convolution layer
Definition: quantized_convolutional_layer.h:54
slice an input data into multiple outputs along a given slice dimension.
Definition: slice_layer.h:42