mini_cnn is a light-weight convolutional neural network implementation based on c++11, mutli threading and head only

Features

• mutli threading
• gradient checking for all layer weights/bias
• weight initializer
  • xavier initialize
  • he initializer
• layer-types
  • fully connected layer
  • convolutional layer
  • activation layer
  • flatten layer
  • softmax loglikelihood output layer
  • sigmod cross entropy output layer
  • average pooling layer
  • max pooling layer
  • dropout layer
  • batch normalization layer
• activation functions
  • sigmoid
  • softmax
  • rectified linear(relu)
• loss functions
  • mean squared error
  • cross-entropy
  • loglikelihood
• optimization algorithms
  • stochastic gradient descent
• fast convolution(im2col + gemm)

Todo list

- fast convolution(winograd)
- train on gpu
- more optimization algorithms such as adagrad，momentum etc	
- serilize/deserilize

Examples

train mnist dataset

2-layer conv on mnist dataset

	conv 3x3x32 relu
	   |
	maxpool 2x2
	   |
	conv 3x3x64 relu
	   |
	maxpool 2x2
	   |
	fc 1024
	   |
	log-likelihood softmax 10

network create_cnn()
{
	network nn;
	nn.add_layer(new input_layer(W_input, H_input, D_input));
	nn.add_layer(new convolutional_layer(3, 3, 1, 32, 1, 1, padding_type::eValid, activation_type::eRelu));
	nn.add_layer(new max_pooling_layer(2, 2, 2, 2));
	nn.add_layer(new convolutional_layer(3, 3, 32, 64, 1, 1, padding_type::eValid, activation_type::eRelu));
	nn.add_layer(new max_pooling_layer(2, 2, 2, 2));
	nn.add_layer(new fully_connected_layer(1024, activation_type::eRelu));
	nn.add_layer(new output_layer(C_classCount, lossfunc_type::eSoftMax_LogLikelihood, activation_type::eSoftMax));
	return nn;
}

more details in main.cpp

Result

References

[1] Neural Networks and Deep Learning by By Michael Nielsen
[2] Deep Learning, book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
[3] http://cs231n.github.io/convolutional-networks
[4] http://cs231n.stanford.edu/slides/2018/cs231n_2018_lecture05.pdf
[5] http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
[6] http://deeplearning.net/software/theano_versions/dev/tutorial/conv_arithmetic.html#transposed-convolution-arithmetic
[7] Gradient checking
[8] 2D Max Pooling Backward Layer
[9] https://blog.csdn.net/mrhiuser/article/details/52672824
[10] https://kevinzakka.github.io/2016/09/14/batch_normalization/