Class: RANN::Backprop

Inherits:
Object
  • Object
show all
Includes:
Util::ArrayExt
Defined in:
lib/rann/backprop.rb

Constant Summary collapse

ACTIVATION_DERIVATIVES = 
{
  relu:   ->(x){ x > 0 ? 1.to_d : 0.to_d },
  sig:    ->(x){ x * (1 - x) },
  linear: ->(_){ 1.to_d },
  tanh:   ->(x){ 1 - x ** 2 },
  step:   ->(_){ 0.to_d },
}

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Methods included from Util::ArrayExt

#in_groups

Constructor Details

#initialize(network, opts = {}) ⇒ Backprop

Returns a new instance of Backprop.



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# File 'lib/rann/backprop.rb', line 23

def initialize network, opts = {}
  @network          = network
  @connections_hash = network.connections.each.with_object({}){ |c, h| h[c.id] = c }
  @optimiser        = RANN::Optimisers.const_get(opts[:optimiser] || 'RMSProp').new opts
  @batch_count      = 0.to_d
end

Instance Attribute Details

#network ⇒ Object

Returns the value of attribute network.



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# File 'lib/rann/backprop.rb', line 21

def network
  @network
end

Class Method Details

.bptt_connecting_to(neuron, network, timestep) ⇒ Object 



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# File 'lib/rann/backprop.rb', line 246

def self.bptt_connecting_to neuron, network, timestep
  # halt traversal if we're at a context and we're at the base timestep
  return [] if neuron.context? && timestep == 0

  timestep -= 1 if neuron.context?

  network.connections_to(neuron).each.with_object [] do |c, a|
    # don't enqueue connections from inputs
    next if c.input_neuron.input?

    a << [c.input_neuron, timestep]
  end
end

.mse(targets, outputs) ⇒ Object 



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# File 'lib/rann/backprop.rb', line 232

def self.mse targets, outputs
  total_squared_error = 0.to_d

  targets.size.times do |i|
    total_squared_error += (targets[i] - outputs[i]) ** 2 / 2
  end

  total_squared_error
end

.mse_delta(target, actual) ⇒ Object 



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# File 'lib/rann/backprop.rb', line 242

def self.mse_delta target, actual
  actual - target
end

.reset!(network) ⇒ Object 



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# File 'lib/rann/backprop.rb', line 227

def self.reset! network
  network.reset!
  network.neurons.select(&:context?).each{ |n| n.value = 0.to_d }
end

.run_single(network, inputs, targets) ⇒ Object 



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# File 'lib/rann/backprop.rb', line 98

def self.run_single network, inputs, targets
  states = []
  inputs = [inputs] if inputs.flatten == inputs

  # run the data into the network. (feed forward)
  # all but last
  (inputs.size - 1).times do |timestep|
    network.evaluate inputs[timestep]
    states[timestep] = network.reset!
  end
  # last
  outputs = network.evaluate inputs.last
  states[inputs.size - 1] = network.reset!

  # calculate error
  error = mse targets, outputs

  # backward pass with unravelling for recurrent networks
  node_deltas = Hash.new{ |h, k| h[k] = {} }
  initial_timestep = inputs.size - 1
  neuron_stack = network.output_neurons.map{ |n| [n, initial_timestep] }
  # initialize network end-point node_deltas in all timesteps with zero
  network.neurons_with_no_outgoing_connections.each do |n|
    (0...(inputs.size - 1)).each do |i|
      node_deltas[i][n.id] = 0.to_d
      neuron_stack << [n, i]
    end
  end
  gradients = Hash.new 0.to_d

  while current = neuron_stack.shift
    neuron, timestep = current
    next if node_deltas[timestep].key? neuron.id

    # neuron delta is summation of neuron deltas deltas for the connections
    # from this neuron
    if neuron.output?
      output_index = network.output_neurons.index neuron
      step_one = mse_delta targets[output_index], outputs[output_index]
    else
      sum =
        network.connections_from(neuron).reduce 0.to_d do |m, c|
          out_timestep = c.output_neuron.context? ? timestep + 1 : timestep
          output_node_delta = node_deltas[out_timestep][c.output_neuron.id]

          if out_timestep > initial_timestep
            m
          elsif !output_node_delta
            break
          else
            # connection delta is the output neuron delta multiplied by the
            # connection's weight
            connection_delta =
              if c.output_neuron.is_a? ProductNeuron
                intermediate =
                  network.connections_to(c.output_neuron).reject{ |c2| c2 == c }.reduce 1.to_d do |m, c2|
                    m * states[timestep][:values][c2.input_neuron.id] * c2.weight
                  end
                output_node_delta * intermediate * c.weight
              else
                output_node_delta * c.weight
              end

            m + connection_delta
          end
        end

      step_one = sum || next
    end

    from_here = bptt_connecting_to neuron, network, timestep
    neuron_stack |= from_here

    node_delta =
      ACTIVATION_DERIVATIVES[neuron.activation_function]
        .call(states[timestep][:values][neuron.id]) *
        step_one

    node_deltas[timestep][neuron.id] = node_delta

    in_timestep = neuron.context? ? timestep - 1 : timestep
    network.connections_to(neuron).each do |c|
      # connection gradient is the output neuron delta multipled by the
      # connection's input neuron value.
      gradient =
        if c.output_neuron.is_a? ProductNeuron
          intermediate = states[timestep][:intermediates][c.output_neuron.id]
          node_delta * intermediate / c.weight
        elsif c.input_neuron.context? && timestep == 0
          0.to_d
        else
          node_delta * states[in_timestep][:values][c.input_neuron.id]
        end

      gradients[c.id] += gradient
    end
  end

  reset! network
  [gradients, error]
end

Instance Method Details

#restore(filepath = nil) ⇒ Object 



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# File 'lib/rann/backprop.rb', line 211

def restore filepath = nil
  unless filepath
    filepath = Dir['*'].select{ |f| f =~ /rann_savepoint_.*/ }.sort.last

    unless filepath
      @network.init_normalised!
      puts "No savepoints found—initialised normalised weights"
      return
    end
  end

  weights, opt_vars = YAML.load_file(filepath)
  @network.impose(weights)
  @network.optimiser.load_state(opt_vars)
end

#run_batch(inputs, targets, opts = {}) ⇒ Object 



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# File 'lib/rann/backprop.rb', line 30

def run_batch inputs, targets, opts = {}
  @batch_count += 1

  batch_size      = inputs.size
  avg_gradients   = Hash.new{ |h, k| h[k] = 0 }
  avg_batch_error = 0

  # force longer bits of work per iteration, to maximise CPU usage less
  # marshalling data and process overhead etc. best for small networks. for
  # larger networks where one unit of work takes a long time, and the work
  # can vary in time taken, use num_groups == inputs.size
  num_groups     = opts[:num_groups] || ([1, opts[:processes]].max * 10)
  grouped_inputs = in_groups(inputs, num_groups, false).reject &:empty?
  reduce_proc =
    lambda do |_, _, result|
      group_avg_gradients, group_avg_error = result

      avg_gradients.merge!(group_avg_gradients){ |_, o, n| o + n }
      avg_batch_error += group_avg_error
    end

  Parallel.each_with_index(
    grouped_inputs,
    in_processes: opts[:processes],
    finish: reduce_proc
  ) do |inputs, i|
    group_avg_gradients = Hash.new{ |h, k| h[k] = 0.to_d }
    group_avg_error = 0.to_d

    inputs.each_with_index do |input, j|
      gradients, error = Backprop.run_single network, input, targets[i + j]

      gradients.each do |cid, g|
        group_avg_gradients[cid] += g / batch_size
      end
      group_avg_error += error / batch_size
    end

    group_avg_gradients.default_proc = nil
    [group_avg_gradients, group_avg_error]
  end

  if opts[:checking]
    # check assumes batchsize 1 for now
    sorted_gradients = avg_gradients.values_at *network.connections.map(&:id)
    invalid = GradientChecker.check network, inputs.first, targets.first, sorted_gradients
    if invalid.empty?
      puts "gradient valid"
    else
      puts "gradients INVALID for connections:"
      invalid.each do |i|
        puts "#{network.connections[i].input_neuron.name} -> #{network.connections[i].output_neuron.name}"
      end
    end
  end

  avg_gradients.each do |con_id, gradient|
    con = @connections_hash[con_id]
    next if con.locked?

    update = @optimiser.update gradient, con.id

    con.weight += update
  end

  avg_batch_error
end

#save(filepath = nil) ⇒ Object 



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# File 'lib/rann/backprop.rb', line 200

def save filepath = nil
  filepath ||= "rann_savepoint_#{DateTime.now.strftime('%Y-%m-%d-%H-%M-%S')}.yml"

  weights  = @network.params
  opt_vars = @optimiser.state

  File.open filepath, "w" do |f|
    f.write YAML.dump [weights, opt_vars]
  end
end