A light deep learning tools box by c++
Network Architecture
- Convolutional Neural Network
- Normal Neural Network
- Reccurent Neural Network with three mainstream varieties(LSTM, LSTM-peelhole, GRU)(deep architecture supported)
- bi-directional LSTM(peephole) & GRU & RNN (deep architecture supported)
Nonlinearities
- ReLU
- Sigmoid
- tanh
TODO
- GPU supported (No Gpu for testing :( )
- network architecture configurable by proto Done (protoc is needed to be installed first)
Compile
sh build.sh(cmake is needed)
Usage
net architecture is built by proto file that you defined, just like what the examples do.
rnn.prototxt
name: "test"
layer {
name: "DataFeedLayer"
type: "DataFeedLayer"
top: "input_data"
}
layer {
name: "WordEmbeddingLayer"
type: "WordEmbeddingLayer"
top: "emb1"
bottoms: "input_data"
fc_param {
output_dim: 14
input_dim: 0
}
}
layer {
name: "LstmCell"
type: "LstmCell"
top: "lstm1"
bottoms: "emb1"
rnn_cell_param {
input_dim: 14
output_dim: 16
use_peephole: false
}
}
layer {
name: "LstmCell1"
type: "LstmCell"
top: "lstm2"
bottoms: "lstm1"
rnn_cell_param {
input_dim: 16
output_dim: 16
use_peephole: true
}
}
layer {
name: "SeqFullConnSoftmaxLayer"
type: "SeqFullConnSoftmaxLayer"
top: "seqsoftmax1"
bottoms: "lstm2"
fc_param {
input_dim: 16
output_dim: 4
}
}
layer {
name: "SeqCrossEntropyLossLayer"
type: "SeqCrossEntropyLossLayer"
top: "loss"
bottoms: "seqsoftmax1"
}
cnn.prototxt
name: "cnn"
layer {
name: "DataFeedLayer"
type: "DataFeedLayer"
top: "input_data"
}
layer {
name: "ConvLayer1"
type: "ConvLayer"
bottoms: "input_data"
top: "conv1"
conv_param {
input_dim: 1
output_dim: 2
kernel_x_dim: 11
kernel_y_dim: 11
feature_x_dim: 18
feature_y_dim: 18
}
}
layer {
name: "ReluLayer1"
type: "ReluLayer"
bottoms: "conv1"
top: "relu1"
}
layer {
name: "PoolingLayer1"
type: "PoolingLayer"
bottoms: "relu1"
top: "pool1"
pool_param {
input_dim: 2
output_dim: 2
pooling_x_dim: 2
pooling_y_dim: 2
feature_x_dim: 9
feature_y_dim: 9
}
}
layer {
name: "ConvLayer2"
type: "ConvLayer"
bottoms: "pool1"
top: "conv2"
conv_param {
input_dim: 2
output_dim: 2
kernel_x_dim: 4
kernel_y_dim: 4
feature_x_dim: 6
feature_y_dim: 6
}
}
layer {
name: "ReluLayer2"
type: "ReluLayer"
bottoms: "conv2"
top: "relu2"
}
layer {
name: "FlattenLayer1"
type: "FlattenLayer"
bottoms: "relu2"
top: "flat1"
}
layer {
name: "FullConnLayer"
type: "FullConnLayer"
bottoms: "flat1"
top: "full1"
fc_param {
input_dim: 72
output_dim: 32
}
}
layer {
name: "SigmoidLayer"
type: "SigmoidLayer"
bottoms: "full1"
top: "sigmoid1"
}
layer {
name: "FullConnSoftmaxLayer"
type: "FullConnSoftmaxLayer"
bottoms: "sigmoid1"
top: "full2"
fc_param {
input_dim: 32
output_dim: 10
}
}
layer {
name: "loss"
type: "CrossEntropyLossLayer"
bottoms: "full2"
top: "cross"
}