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fireml machine learning framework

Project description

README

Caffe-like machine learning framework in python

layers types

ImageData

layer to read images. Possible sources: txt file with path labels on each line, cifar archive.

image_data_param:

source: string - path to cifar archive or txt file

batch_size: int - how many images to process in each iteration

shuffle: bool - images will be shuffled when sampling for a batch

new_height: int - new image height(can be same as original)

new_width: int - new image width(can be same as original)

new_labels: int - expected number of labels(txt file may contain multiple labels for each path)

example:

 layer {
   top: "data"
   top: "label"
   name: "data"
   type: "ImageData"
   image_data_param {
     source: "../cifar/cifar-10-python.tar.gz"
     source: "data.txt.3"  # use cifar or txt file
     batch_size: 65
     shuffle: true
     new_height: 32
     new_width: 32
     n_labels: 10
   }
   transform_param {
     mean_value: 126 # r
     mean_value: 123 # g
     mean_value: 114 # b
     mirror: true
     scale: 0.02728125
     standard_params {
         var_average: 5000
         mean_average: 5000
         mean_per_channel: false
         var_per_channel: false
     }
   }
   include: { phase: TRAIN }
 }

transform_param

Parameters for data preprocessing

standard_params

Parameters for preprocessor for data standardization. To achieve zero mean and unit variance the preprocessor will subtract iterative mean from each sample and divide the result by standard deviation.

standard_params {
    var_average: 1
    mean_average: 1
    mean_per_channel: false
    var_per_channel: true
}

var_average: int [default = 0] - use last var_average samples to compute variance and std
disabled if var_average == 0
mean_average: int [default = 0] - use last var_average samples to compute mean
disabled if mean_average == 0
mean_per_channel: bool [default = false] - subtract from each channel mean for that channel
var_per_channel: [default = false] - divide each channel by separate std value

Convolution

Convolution of 2-3 d images(matrices)

convolution_param

num_output: int number of filters(output feature maps)

kernel_size: int size of receptive field of filters. Receptive field is kernel_size * kernel_size

stride: int - filter will be applied after stride pixels

weight_filler: see weight filler

example:

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 40
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
      variance_norm: AVERAGE
    }
  }
}

Pooling

Subsampling layer for max or average pooling

pooling_param

pool: MAX or AVE

kernel_size: int subsampling window size

stride: int perform pooling each stride pixels

example:

layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

Accuracy

Layer for computing accuracy Accuracy of a classifier is defined as (true positive + true negative)/total
In multilabel classification example counts as correctly classified iff all outputs are correct.
Example:

layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "pool10"
  bottom: "label"
  top: "accuracy"
}

weight_filler

Weigth filler parameters are common for all layers with weights

type: string
"xavier", "gaussian", "uniform"

mean: float
mean value for gaussian initialization

std: float
standard deviation for gaussian initialization

min: float
lower bound for uniform initialization

max: float
upper bound for uniform initialization

Activation functions

SeLU

Self-regularized linear unit:

example:
{ name: "relu_conv1" type: "SeLU" bottom: "conv1" top: "conv1" }

Loss layers

SigmoidCrossEntropyLoss

Layer that applies sigmoid elementwise, followed by cross-entropy log loss -mean(sum(y * log(p(y)) + (1 - y) * log(1 - p(y))))

where p(y) - sigmoid transformation of layer's input, that is vector of independent probabilities for each class.

example:  
    layer { 
      name: "loss"
      type: "SigmoidCrossEntropyLoss"
      bottom: "pool1"
      bottom: "label"
      top: "loss"
      include {
          phase: TRAIN
      }
    }

Maxout layer

Apply max operator for each size channels

size: int [default = 0] - take max over each size channels
lambda: int [default = 0.0] - apply probabilistic max if lambda != 0

layer {
  name: "maxout_1"
  type: "Maxout"
  maxout_param {
    lambda: 1
    size: 2
  }
  bottom: "conv1"
  top: "conv1"
}

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