Class: MachineLearningWorkbench::NeuralNetwork::Base

Inherits:
Object
  • Object
show all
Defined in:
lib/machine_learning_workbench/neural_network/base.rb

Overview

Neural Network base class

Direct Known Subclasses

FeedForward, Recurrent

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(struct, act_fn: nil) ⇒ Base

Returns a new instance of Base.

Parameters:

  • struct (Array<Integer>) —

    list of layer sizes

  • act_fn (Symbol) (defaults to: nil) —

    choice of activation function for the neurons



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 29

def initialize struct, act_fn: nil
  @struct = struct
  @act_fn_name = act_fn || :sigmoid
  @act_fn = send(act_fn_name)
  # @state holds both inputs, possibly recurrency, and bias
  # it is a complete input for the next layer, hence size from layer sizes
  @state = layer_row_sizes.collect do |size|
    NArray.zeros [1, size]
  end
  # to this, append a matrix to hold the final network output
  @state.push NArray.zeros [1, nneurs(-1)]
  reset_state
end

Instance Attribute Details

#act_fn ⇒ #call (readonly)

activation function, common to all neurons (for now)

Returns:

  • (#call) —

    activation function



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 22

attr_reader :layers, :state, :act_fn, :act_fn_name, :struct

#act_fn_name ⇒ Object (readonly)

Returns the value of attribute act_fn_name.



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 22

def act_fn_name
  @act_fn_name
end

#layers ⇒ Array<NArray> (readonly)

List of matrices, each being the weights connecting a layer’s inputs (rows) to a layer’s neurons (columns), hence its shape is ‘[ninputs, nneurs]` TODO: return a NArray after the usage of `#map` is figured out

Returns:

  • (Array<NArray>) —

    list of weight matrices, each uniquely describing a layer



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 22

def layers
  @layers
end

#state ⇒ Array<NArray> (readonly)

It’s a list of one-dimensional matrices, each an input to a layer, plus the output layer’s output. The first element is the input to the first layer of the network, which is composed of the network’s input, possibly the first layer’s activation on the last input (recursion), and a bias (fixed ‘1`). The second to but-last entries follow the same structure, but with the previous layer’s output in place of the network’s input. The last entry is the activation of the output layer, without additions since it’s not used as an input by anyone. TODO: return a NArray after the usage of ‘#map` is figured out

Returns:

  • (Array<NArray>) —

    current state of the network.



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 22

attr_reader :layers, :state, :act_fn, :act_fn_name, :struct

#struct ⇒ Object (readonly)

Returns the value of attribute struct.



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 22

attr_reader :layers, :state, :act_fn, :act_fn_name, :struct

Instance Method Details

#activate(input) ⇒ Array

Activate the network on a given input

Parameters:

  • input (Array<Float>) —

    the given input

Returns:

  • (Array) —

    the activation of the output layer

Raises:

  • (ArgumentError) 


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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 145

def activate input
  raise ArgumentError unless input.size == struct.first
  # load input in first state
  state[0][0...struct.first] = input
  # activate layers in sequence
  nlayers.times.each do |i|
    act = activate_layer i
    state[i+1][0...act.size] = act
  end
  return out
end

#deep_reset ⇒ Object

Resets memoization: needed to play with structure modification



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 61

def deep_reset
  # reset memoization
  [:@layer_row_sizes, :@layer_col_sizes, :@nlayers, :@layer_shapes,
   :@nweights_per_layer, :@nweights].each do |sym|
     instance_variable_set sym, nil
  end
  reset_state
end

#init_random ⇒ Object

Initialize the network with random weights



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 53

def init_random
  # Reusing `#load_weights` instead helps catching bugs
  load_weights NArray.new(nweights).rand(-1,1)
end

#interface_methods ⇒ Object

Declaring interface methods - implement in child class!



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 187

[:layer_row_sizes, :activate_layer].each do |sym|
  define_method sym do
    raise NotImplementedError, "Implement ##{sym} in child class!"
  end
end

#layer_col_sizes ⇒ Array

Number of neurons per layer. Although this implementation includes inputs in the layer counts, this methods correctly ignores the input as not having neurons.

Returns:

  • (Array) —

    list of neurons per each (proper) layer (i.e. no inputs)



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 99

def layer_col_sizes
  @layer_col_sizes ||= struct.drop(1)
end

#layer_shapes ⇒ Array<Array[Integer, Integer]>

Shapes for the weight matrices, each corresponding to a layer

Returns:

  • (Array<Array[Integer, Integer]>) —

    Weight matrix shapes



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 107

def layer_shapes
  @layer_shapes ||= layer_row_sizes.zip layer_col_sizes
end

#lecun_hyperbolic ⇒ Object

LeCun hyperbolic activation

See Also:



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 175

def lecun_hyperbolic
  -> (vec) { 1.7159 * NMath.tanh(2.0*vec/3.0) + 1e-3*vec }
end

#load_weights(weights) ⇒ true

Loads a plain list of weights into the weight matrices (one per layer). Preserves order. Reuses allocated memory if available.

Returns:

  • (true) —

    always true. If something’s wrong it simply fails, and if all goes well there’s nothing to return but a confirmation to the caller.

Raises:

  • (ArgumentError) 


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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 125

def load_weights weights
  raise ArgumentError unless weights.size == nweights
  weights = weights.to_na unless weights.kind_of? NArray
  from = 0
  @layers = layer_shapes.collect do |shape|
    to = from + shape.reduce(:*)
    lay_w = weights[from...to].reshape *shape
    from = to
    lay_w
  end
  reset_state
  return true
end

#nlayers ⇒ Integer

Count the layers. This is a computation helper, and for this implementation the inputs are considered as if a layer like the others.

Returns:

  • (Integer) —

    number of layers



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 85

def nlayers
  @nlayers ||= layer_shapes.size
end

#nneurs(nlay = nil) ⇒ Integer

Count the neurons in a particular layer or in the whole network.

Parameters:

  • nlay (Integer, nil) (defaults to: nil) —

    the layer of interest, 1-indexed. ‘0` will return the number of inputs. `nil` will compute the total neurons in the network.

Returns:

  • (Integer) —

    the number of neurons in a given layer, or in all network, or the number of inputs



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 116

def nneurs nlay=nil
  nlay.nil? ? struct.reduce(:+) : struct[nlay]
end

#nweights ⇒ Integer

Total weights in the network

Returns:

  • (Integer) —

    total number of weights



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 72

def nweights
  @nweights ||= nweights_per_layer.reduce(:+)
end

#nweights_per_layer ⇒ Array<Integer>

List of per-layer number of weights

Returns:

  • (Array<Integer>) —

    list of weights per each layer



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 78

def nweights_per_layer
  @nweights_per_layer ||= layer_shapes.collect { |shape| shape.reduce(:*) }
end

#out ⇒ NArray

Extract and convert the output layer’s activation

Returns:

  • (NArray) —

    the activation of the output layer



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 159

def out
  state.last.flatten
end

#relu ⇒ Object

Rectified Linear Unit (ReLU)



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 180

def relu
  -> (vec) { (vec>0).all? && vec || vec.class.zeros(vec.shape) }
end

#reset_state ⇒ Object

Reset the network to the initial state



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 44

def reset_state
  state.each do |s|
    s.fill 0           # reset state to zero
    s[-1] = 1        # add bias
  end
  state[-1][-1] = 0  # last layer has no bias
end

#sigmoid(k = 0.5) ⇒ Object Also known as: logistic

Traditional sigmoid (logistic) with variable steepness



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 166

def sigmoid k=0.5
  # k is steepness:  0<k<1 is flatter, 1<k is flatter
  # flatter makes activation less sensitive, better with large number of inputs
  -> (vec) { 1.0 / (NMath.exp(-k * vec) + 1.0) }
end

#weights ⇒ Array<NArray>

Returns the weight matrix

Returns:

  • (Array<NArray>) —

    list of NArray matrices of weights (one per layer).



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# File 'lib/machine_learning_workbench/neural_network/base.rb', line 91

def weights
  layers
end