Class: DNN::Model

Inherits:
Object
  • Object
show all
Includes:
Numo
Defined in:
lib/dnn/core/model.rb

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize ⇒ Model

Returns a new instance of Model.



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# File 'lib/dnn/core/model.rb', line 13

def initialize
  @layers = []
  @optimizer = nil
  @batch_size = nil
  @compiled = false
end

Instance Attribute Details

#batch_size ⇒ Object (readonly)

Returns the value of attribute batch_size.



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# File 'lib/dnn/core/model.rb', line 10

def batch_size
  @batch_size
end

#layers ⇒ Object

Returns the value of attribute layers.



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# File 'lib/dnn/core/model.rb', line 8

def layers
  @layers
end

#optimizer ⇒ Object (readonly)

Returns the value of attribute optimizer.



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# File 'lib/dnn/core/model.rb', line 9

def optimizer
  @optimizer
end

#training ⇒ Object (readonly)

Returns the value of attribute training.



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# File 'lib/dnn/core/model.rb', line 11

def training
  @training
end

Class Method Details

.load(file_name) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 20

def self.load(file_name)
  Marshal.load(File.binread(file_name))
end

.load_json(json_str) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 24

def self.load_json(json_str)
  hash = JSON.parse(json_str, symbolize_names: true)
  model = self.new
  model.layers = hash[:layers].map { |hash_layer| Util.load_hash(hash_layer) }
  model.compile(Util.load_hash(hash[:optimizer]))
  model
end

Instance Method Details

#<<(layer) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 65

def <<(layer)
  unless layer.is_a?(Layers::Layer)
    raise DNN_TypeError.new("layer is not an instance of the DNN::Layers::Layer class.")
  end
  @layers << layer
  self
end

#accurate(x, y, batch_size = nil, &batch_proc) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 138

def accurate(x, y, batch_size = nil, &batch_proc)
  @batch_size = batch_size if batch_size
  correct = 0
  (x.shape[0].to_f / @batch_size).ceil.times do |i|
    x_batch = SFloat.zeros(@batch_size, *x.shape[1..-1])
    y_batch = SFloat.zeros(@batch_size, *y.shape[1..-1])
    @batch_size.times do |j|
      k = i * @batch_size + j
      break if k >= x.shape[0]
      x_batch[j, false] = x[k, false]
      y_batch[j, false] = y[k, false]
    end
    x_batch, y_batch = batch_proc.call(x_batch, y_batch) if batch_proc
    out = forward(x_batch, false)
    @batch_size.times do |j|
     correct += 1 if out[j, true].max_index == y_batch[j, true].max_index
    end
  end
  correct.to_f / x.shape[0]
end

#backward(y) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 171

def backward(y)
  dout = y
  @layers[0..-1].reverse.each do |layer|
    dout = layer.backward(dout)
  end
  dout
end

#compile(optimizer) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 73

def compile(optimizer)
  unless optimizer.is_a?(Optimizers::Optimizer)
    raise DNN_TypeError.new("optimizer is not an instance of the DNN::Optimizers::Optimizer class.")
  end
  @compiled = true
  layers_check
  @optimizer = optimizer
  @layers.each do |layer|
    layer.build(self)
  end
  layers_shape_check
end

#compiled? ⇒ Boolean

Returns:

  • (Boolean) 


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# File 'lib/dnn/core/model.rb', line 86

def compiled?
  @compiled
end

#forward(x, training) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 163

def forward(x, training)
  @training = training
  @layers.each do |layer|
    x = layer.forward(x)
  end
  x
end

#load_json_params(json_str) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 32

def load_json_params(json_str)
  has_param_layers_params = JSON.parse(json_str, symbolize_names: true)
  has_param_layers_index = 0
  @layers.each do |layer|
    next unless layer.is_a?(HasParamLayer)
    hash_params = has_param_layers_params[has_param_layers_index]
    hash_params.each do |key, param|
      layer.params[key] = SFloat.cast(param)
    end
    has_param_layers_index += 1
  end
end

#params_to_json ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 57

def params_to_json
  has_param_layers = @layers.select { |layer| layer.is_a?(HasParamLayer) }
  has_param_layers_params = has_param_layers.map do |layer|
    layer.params.map { |key, param| [key, param.to_a] }.to_h
  end
  JSON.dump(has_param_layers_params)
end

#predict(x) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 159

def predict(x)
  forward(x, false)
end

#save(file_name) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 45

def save(file_name)
  dir_name = file_name.match(%r`(.*)/.+$`)[1]
  Dir.mkdir(dir_name) unless Dir.exist?(dir_name)
  File.binwrite(file_name, Marshal.dump(self))
end

#to_json ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 51

def to_json
  hash_layers = @layers.map { |layer| layer.to_hash }
  hash = {version: VERSION, layers: hash_layers, optimizer: @optimizer.to_hash}
  JSON.dump(hash)
end

#train(x, y, epochs, batch_size: 1, test: nil, verbose: true, batch_proc: nil, &epoch_proc) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 90

def train(x, y, epochs,
          batch_size: 1,
          test: nil,
          verbose: true,
          batch_proc: nil,
          &epoch_proc)
  @batch_size = batch_size
  num_train_data = x.shape[0]
  (1..epochs).each do |epoch|
    puts "【 epoch #{epoch}/#{epochs} 】" if verbose
    (num_train_data.to_f / @batch_size).ceil.times do |index|
      x_batch, y_batch = Util.get_minibatch(x, y, @batch_size)
      loss = train_on_batch(x_batch, y_batch, @batch_size, &batch_proc)
      if loss.nan?
        puts "\nloss is nan" if verbose
        return
      end
      num_trained_data = (index + 1) * batch_size
      num_trained_data = num_trained_data > num_train_data ? num_train_data : num_trained_data
      log = "\r"
      20.times do |i|
        if i < num_trained_data * 20 / num_train_data
          log << "■"
        else
          log << "・"
        end
      end
      log << "  #{num_trained_data}/#{num_train_data} loss: #{loss}"
      print log if verbose
    end
    if verbose && test
      acc = accurate(test[0], test[1], batch_size,&batch_proc)
      print "  accurate: #{acc}"
    end
    puts "" if verbose
    epoch_proc.call(epoch) if epoch_proc
  end
end

#train_on_batch(x, y, batch_size, &batch_proc) ⇒ Object 



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# File 'lib/dnn/core/model.rb', line 129

def train_on_batch(x, y, batch_size, &batch_proc)
  @batch_size = batch_size
  x, y = batch_proc.call(x, y) if batch_proc
  forward(x, true)
  backward(y)
  @layers.each { |layer| layer.update if layer.respond_to?(:update) }
  @layers[-1].loss(y)
end