v0.42 BETA
A lightweight MATLAB deeplearning toolbox,based on gpuArray.
One of the fastest matlab's RNN libs.

model:A LSTM model has [1024,1024,1024] hidensizes and 10 timestep with a 256 dims input.
Device: i7-4710hq,GTX940m
matDL: 60sec/epoch Keras(1.2.2,Tensorflow backend,cudnn5.1): 29sec/epoch

High parallel Implementation.

• Concatance the weights of 4 gates to W and the values of x and h of every timesteps in a batch to a 3D tensor xh.Compute x*W for every timesteps of every samples in a batch at one time.
• Compute the activated values of input,forget ,ouput gates at one time.

OOP style

• Use struct type to define a layer class and a model class.Define ff, bp, optimize methods by using a FunctionHandle.

• A model is a set of layers,data and optimizer.
• build
  • model=model_init(input_shape,configs ,flag,optimizer)
  • arguments:
    • input_shape : a vector,[input_dim,batchsize] or [input_dim,timestep,batchsize]
    • configs : cell ,configures of each layers
    • flag : bool ,0 is predict model,1 is trrain model
    • optimizer : struct ,keywords: opt(type of optimizer) ,learningrate
• attributes :
  • model.input_shape
  • model.output_shape
  • model.batchsize
  • model.configs
  • model.flag
  • model.layers
  • model.optimizer (if flag)
  • model.loss
• methods:
  • private:
    • model.eval_loss=@(outputlayer,y_true,flag)eval_loss(outputlayer,y_true,flag)
    • model.optimize=@(layer,optimizer,batch,epoch)layer_optimize(layer,optimizer,batch,epoch)
  • public:
    • model.train=@(model,x,y,nb_epoch,verbose,filename)model_train(model,x,y,nb_epoch,verbose,filename)
      • model=model.train(model,x,y,nb_epoch,verbose,filename)
        • arguments:
          • model : self
          • x:input,shape:[dim,timestep,nb_samples],or [dim,nb_samples]
          • y:targets
          • nb_epoch: how many epochs you want to train
          • verbose :0,1,2,3,0 means no waitbar an figure,1 means showing waitbar only,2 means showing waitbar and plotting figures every epoch,3 means showing waitbar and plotting figures every epoch an batch.
    • model.predict=@(model,x)model_predict(model,x)
      • y=model.predict(model,x)
    • model.evaluate=@(model,x,y_true)model_evaluate(model,x,y_true)
      • mean_loss=model.evaluate(model,x,y_true)
    • model.save=@(filename)model_save(model,filename)
      • model.save(filename)
      • Save layers weigths and configs to a.mat file.
• reload:
  • model=model_load(minimodel,batch_size,flag,optimizer)
    • minimodel is the minimodel saved by model.save(),can be a struct variable or a string of filename.
• example: x=rand(100,10,3200,'single','gpuArray');
  y=(zeros(512,10,3200'single','gpuArray'));
  y(1,:,:)=1;
  %% Define a model which has 2 lstm layers with 512 hiddenunits,and a timedistrbuted dense layer with 512 hiddenunits
  input_shape=[100,10,64];%input dim is 100,timestep is 10,batchsize is 64
  hiddensizes=[512,512,512];
  for l=1:length(hiddensize)
  configs{l}.type='lstm';
  configs{l}.hiddensize=hiddensize(l);
  configs{l}.return_sequence=1;
  end
  configs{l+1}.type='activation';
  configs{l+1}.act_fun='softmax';
  configs{l+1}.loss='categorical_cross_entropy';
  optimizer.learningrate=0.1;
  optimizer.momentum=0.2;
  optimizer.opt='sgd'; model=model_init(input_shape,configs,1,optimizer);
  %% Train the model
  model=model.train(model,x,y,nb_epoch,3,'example/minimodel_f.mat');
  or
  test_lstm(50,[512,512,512],256,10,64,5);

• attributes:
  • type : string,type of the layer,available types:input,dense,lstm,activation
  • prelayer_type : string,type of the previous layer,available types:input,dense,lstm,activation
  • trainable : bool,is the layer trainable
  • flag : train model or predict model
  • configs :configures of the layer
  • input_shape : vector,[input_dim,batchsize] or [input_dim,timestep,batchsize]
  • output_shape : vector,[hiddensize,batchsize]or[hiddensize,timestep,batchsize]
  • batch : int,how many batches have been passed
  • epoch : same to batch
• methods:
  • layer=**layer_init(prelayer,loss,kwgrs)
    • Built and init a layer.If the layer is a input layer,prelayer argument should be input_shape
  • layer=layer.ff(layer,prelayer)
  • layer=layer.bp(layer,nextlayer)

  LSTM layer(layer)

    * `layer=lstm_init_gpu(prelayer,hiddensize,return_sequence,flag,loss)`
    * A LSTM(**Long-Short Term Memory unit - Hochreiter 1997**) layer,see [there]:http://deeplearning.net/tutorial/lstm.html for a step-by-step description of the algorithm.
        * aviliable configures:
            * `config.hiddensize` : `int`(`double`),number of hidden units(output dim)
            * `config.return_sequence` :`bool`(`double`),return sequences or not.if `return_sequences`,output will be a 3D tensor with shape (hiddensize,timestep,batchsize). Else ,a 2D tensor with shape (hiddensize,batchsize). 
            * `config.loss` : `string`,type of loss function.Optional,only be used if the layer is an ouput layer.
            * **example**