Activity recognition through Neural Network
==The data provides from 50 person from wrist.==
Data folder contains 6 files in whuch each file contain 1 collumn per axis (x,y,z) and all of the data from the activity Label folder contains 6 files in which we can found the label for each file in the data folder
The batchsize is 500
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I make one evaluation with the same data to train and test my model and an other evaluation with 85% of my all data to train and to test my model
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Each model in directory
modelwas trained with 100% of the data available.
NetworkD4J_CNN500 is the model which corresponds to CNN in the model folder. It uses data and label folder
- 500 epochs,
- 0.01 of learning rate,
- No normalization of the data input
- DataInput.java : height of 1, width of 500 and depth of 3
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.iterations(iteration)
.activation(Activation.RELU)
.learningRate(learningRate)
.weightInit(WeightInit.XAVIER)
.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
.updater(Updater.RMSPROP).momentum(0.9)
.list()
.layer(0, new ConvolutionLayer.Builder(1,10) //depends height
.nIn(3)//depth
.nOut(150)
.stride(1,1)
.build())
.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX) //max pooling
.kernelSize(1,2)
.stride(1,2)
.build())
.layer(2, new ConvolutionLayer.Builder(1,10)
.nIn(150)
.nOut(100)
.stride(1,1)
.build())
.layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(1,2)
.stride(1,2)
.build())
.layer(4, new ConvolutionLayer.Builder(1,10)
.nIn(100)
.nOut(80)
.stride(1,1)
.build())
.layer(5, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(1,2)
.stride(1,2)
.build())
.layer(6, new ConvolutionLayer.Builder(1,10)
.nIn(80)
.nOut(60)
.stride(1,1)
.build())
.layer(7, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(1,2)
.stride(1,2)
.build())
.layer(8, new ConvolutionLayer.Builder(1,10)
.stride(1,1)
.nIn(60)
.nOut(40)
.build())
.layer(9, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
.kernelSize(1,2)
.stride(1,2)
.build())
.layer(10, new DenseLayer.Builder() //fullyConnected
.nOut(900)
.activation(Activation.TANH)
.build())
.layer(11, new DenseLayer.Builder()
.nOut(300)
.activation(Activation.TANH)
.dropOut(0.5)
.build())
.layer(12, new OutputLayer.Builder()
.activation(Activation.SOFTMAX)
.lossFunction(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
.nOut(numOutputs)
.build())
.setInputType(InputType.convolutional(1, 500, 3))
.backprop(true)
.pretrain(false)
.build();Accuracy: 0.9456
Precision: 0.9326
Recall: 0.9216
F1 Score: 0.9271
About labelisation of the examples :
Examples labeled as 0 classified by model as 0: 618 times
Examples labeled as 0 classified by model as 1: 2 times
Examples labeled as 0 classified by model as 3: 1 times
Examples labeled as 0 classified by model as 4: 50 times
Examples labeled as 0 classified by model as 5: 21 times
Examples labeled as 1 classified by model as 0: 2 times
Examples labeled as 1 classified by model as 1: 1929 times
Examples labeled as 1 classified by model as 2: 2 times
Examples labeled as 1 classified by model as 3: 7 times
Examples labeled as 1 classified by model as 4: 5 times
Examples labeled as 1 classified by model as 5: 8 times
Examples labeled as 2 classified by model as 2: 1509 times
Examples labeled as 2 classified by model as 3: 16 times
Examples labeled as 2 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 0: 2 times
Examples labeled as 3 classified by model as 2: 28 times
Examples labeled as 3 classified by model as 3: 1504 times
Examples labeled as 3 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 5: 4 times
Examples labeled as 4 classified by model as 0: 17 times
Examples labeled as 4 classified by model as 2: 2 times
Examples labeled as 4 classified by model as 3: 4 times
Examples labeled as 4 classified by model as 4: 563 times
Examples labeled as 4 classified by model as 5: 175 times
Examples labeled as 5 classified by model as 0: 10 times
Examples labeled as 5 classified by model as 2: 1 times
Examples labeled as 5 classified by model as 3: 2 times
Examples labeled as 5 classified by model as 4: 74 times
Examples labeled as 5 classified by model as 5: 1442 times
Accuracy: 0.874
Precision: 0.8237
Recall: 0.8136
F1 Score: 0.8187
About labelisation of the examples :
Examples labeled as 0 classified by model as 0: 70 times
Examples labeled as 0 classified by model as 1: 1 times
Examples labeled as 0 classified by model as 3: 1 times
Examples labeled as 0 classified by model as 4: 15 times
Examples labeled as 0 classified by model as 5: 5 times
Examples labeled as 1 classified by model as 0: 3 times
Examples labeled as 1 classified by model as 1: 258 times
Examples labeled as 1 classified by model as 5: 1 times
Examples labeled as 2 classified by model as 0: 1 times
Examples labeled as 2 classified by model as 1: 2 times
Examples labeled as 2 classified by model as 2: 172 times
Examples labeled as 2 classified by model as 3: 1 times
Examples labeled as 2 classified by model as 5: 1 times
Examples labeled as 3 classified by model as 1: 1 times
Examples labeled as 3 classified by model as 2: 7 times
Examples labeled as 3 classified by model as 3: 180 times
Examples labeled as 3 classified by model as 5: 6 times
Examples labeled as 4 classified by model as 0: 4 times
Examples labeled as 4 classified by model as 3: 1 times
Examples labeled as 4 classified by model as 4: 33 times
Examples labeled as 4 classified by model as 5: 44 times
Examples labeled as 5 classified by model as 0: 8 times
Examples labeled as 5 classified by model as 2: 1 times
Examples labeled as 5 classified by model as 3: 1 times
Examples labeled as 5 classified by model as 4: 22 times
Examples labeled as 5 classified by model as 5: 161 times
