Class: DNN::Models::Model

Inherits:
Chain
  • Object
show all
Defined in:
lib/dnn/core/models.rb

Overview

This class deals with the model of the network.

Direct Known Subclasses

FixedModel, Sequential

Instance Attribute Summary collapse

Class Method Summary collapse

Instance Method Summary collapse

Methods inherited from Chain

#forward, #layers, #load_hash, #to_hash

Constructor Details

#initialize ⇒ Model 



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# File 'lib/dnn/core/models.rb', line 125

def initialize
  super
  @optimizer = nil
  @loss_func = nil
  @built = false
  @loss_weights = nil
  @callbacks = []
  @last_log = {}
end

Instance Attribute Details

#last_log ⇒ Object (readonly)

Returns the value of attribute last_log.



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# File 'lib/dnn/core/models.rb', line 113

def last_log
  @last_log
end

#loss_weights ⇒ Object

Returns the value of attribute loss_weights.



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# File 'lib/dnn/core/models.rb', line 112

def loss_weights
  @loss_weights
end

#optimizer ⇒ Object

Returns the value of attribute optimizer.



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# File 'lib/dnn/core/models.rb', line 111

def optimizer
  @optimizer
end

Class Method Details

.load(file_name) ⇒ DNN::Models::Model

Load marshal model.



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# File 'lib/dnn/core/models.rb', line 118

def self.load(file_name)
  model = self.allocate
  loader = Loaders::MarshalLoader.new(model)
  loader.load(file_name)
  model
end

Instance Method Details

#add_callback(callback) ⇒ Object

Add callback function.



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# File 'lib/dnn/core/models.rb', line 466

def add_callback(callback)
  callback.model = self
  @callbacks << callback
end

#built? ⇒ Boolean 



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# File 'lib/dnn/core/models.rb', line 518

def built?
  @built
end

#call(input_tensors) ⇒ Object 



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# File 'lib/dnn/core/models.rb', line 135

def call(input_tensors)
  output_tensors = forward(input_tensors)
  @built = true unless @built
  output_tensors
end

#clean_layers ⇒ Object

Clean all layers.



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# File 'lib/dnn/core/models.rb', line 523

def clean_layers
  layers.each(&:clean)
  @loss_func.clean
  @layers_cache = nil
end

#clear_callbacks ⇒ Object

Clear the callback function registered for each event.



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# File 'lib/dnn/core/models.rb', line 472

def clear_callbacks
  @callbacks = []
end

#copy ⇒ DNN::Models::Model 



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# File 'lib/dnn/core/models.rb', line 498

def copy
  Marshal.load(Marshal.dump(self))
end

#evaluate(x, y, batch_size: 100, accuracy: true) ⇒ Array

Evaluate model and get accuracy and loss of test data.



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# File 'lib/dnn/core/models.rb', line 329

def evaluate(x, y, batch_size: 100, accuracy: true)
  check_xy_type(x, y)
  evaluate_by_iterator(Iterator.new(x, y, random: false), batch_size: batch_size, accuracy: accuracy)
end

#evaluate_by_iterator(test_iterator, batch_size: 100, accuracy: true) ⇒ Array

Evaluate model by iterator.



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# File 'lib/dnn/core/models.rb', line 339

def evaluate_by_iterator(test_iterator, batch_size: 100, accuracy: true)
  num_test_datas = test_iterator.num_datas
  batch_size = batch_size >= num_test_datas ? num_test_datas : batch_size
  if @loss_func.is_a?(Array)
    total_correct = Array.new(@loss_func.length, 0)
    sum_loss = Array.new(@loss_func.length, 0)
  else
    total_correct = 0
    sum_loss = 0
  end
  max_steps = (num_test_datas.to_f / batch_size).ceil
  test_iterator.foreach(batch_size) do |x_batch, y_batch|
    correct, loss_value = test_on_batch(x_batch, y_batch, accuracy: accuracy)
    if @loss_func.is_a?(Array)
      @loss_func.each_index do |i|
        total_correct[i] += correct[i] if accuracy
        sum_loss[i] += loss_value[i]
      end
    else
      total_correct += correct if accuracy
      sum_loss += loss_value
    end
  end
  acc = nil
  if @loss_func.is_a?(Array)
    mean_loss = Array.new(@loss_func.length, 0)
    acc = Array.new(@loss_func.length, 0) if accuracy
    @loss_func.each_index do |i|
      mean_loss[i] += sum_loss[i] / max_steps
      acc[i] += total_correct[i].to_f / num_test_datas if accuracy
    end
  else
    mean_loss = sum_loss / max_steps
    acc = total_correct.to_f / num_test_datas if accuracy
  end
  @last_log[:test_loss] = mean_loss
  @last_log[:test_accuracy] = acc
  [acc, mean_loss]
end

#get_all_params_data ⇒ Array

Get parameter data of all layers.



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# File 'lib/dnn/core/models.rb', line 531

def get_all_params_data
  trainable_layers.map do |layer|
    layer.get_params.to_h do |key, param|
      [key, param.data]
    end
  end
end

#get_layer(name) ⇒ DNN::Layers::Layer

Get the layer that the model has.



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# File 'lib/dnn/core/models.rb', line 511

def get_layer(name)
  layer = instance_variable_get("@#{name}")
  return layer if layer.is_a?(Layers::Layer) || layer.is_a?(Chain) || layer.is_a?(LayersList)
  nil
end

#load_params(file_name) ⇒ Object

Load marshal params.



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# File 'lib/dnn/core/models.rb', line 478

def load_params(file_name)
  loader = Loaders::MarshalLoader.new(self)
  loader.load(file_name)
end

#loss_func ⇒ Object 



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# File 'lib/dnn/core/models.rb', line 157

def loss_func
  @loss_func
end

#loss_func=(lfs) ⇒ Object 



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# File 'lib/dnn/core/models.rb', line 161

def loss_func=(lfs)
  if lfs.is_a?(Array)
    @loss_func = []
    lfs.each.with_index do |lf, i|
      unless lf.is_a?(Losses::Loss)
        raise TypeError, "loss_func[#{i}]:#{lf.class} is not an instance of DNN::Losses::Loss class."
      end
      @loss_func << lf
    end
  else
    @loss_func = lfs
  end
end

#predict(x, use_loss_activation: true) ⇒ Object

Predict data.



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# File 'lib/dnn/core/models.rb', line 430

def predict(x, use_loss_activation: true)
  check_xy_type(x)
  DNN.learning_phase = false
  output_tensors = call(Tensor.convert(x))
  if output_tensors.is_a?(Array)
    lfs = @loss_func
    ary_output_tensors = output_tensors
  else
    lfs = [@loss_func]
    ary_output_tensors = [output_tensors]
  end
  ys = []
  ary_output_tensors.each.with_index do |out, i|
    y = out.data
    if use_loss_activation && lfs[i].class.respond_to?(:activation)
      y = lfs[i].class.activation(y)
    end
    ys << y
  end
  output_tensors.is_a?(Array) ? ys : ys.first
end

#predict1(x, use_loss_activation: true) ⇒ Object

Predict one data.



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# File 'lib/dnn/core/models.rb', line 454

def predict1(x, use_loss_activation: true)
  check_xy_type(x)
  input = if x.is_a?(Array)
            x.map { |v| v.reshape(1, *v.shape) }
          else
            x.reshape(1, *x.shape)
          end
  predict(input, use_loss_activation: use_loss_activation)[0, false]
end

#save(file_name) ⇒ Object

Save the model in marshal format.



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# File 'lib/dnn/core/models.rb', line 485

def save(file_name)
  saver = Savers::MarshalSaver.new(self, include_model: true)
  saver.save(file_name)
end

#save_params(file_name) ⇒ Object

Save the params in marshal format.



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# File 'lib/dnn/core/models.rb', line 492

def save_params(file_name)
  saver = Savers::MarshalSaver.new(self, include_model: false)
  saver.save(file_name)
end

#set_all_params_data(params_data) ⇒ Object

Set parameter data of all layers.



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# File 'lib/dnn/core/models.rb', line 541

def set_all_params_data(params_data)
  trainable_layers.each.with_index do |layer, i|
    params_data[i].each do |(key, data)|
      layer.get_params[key].data = data
    end
  end
end

#setup(optimizer, loss_func, loss_weights: nil) ⇒ Object

Set optimizer and loss_func to model.



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# File 'lib/dnn/core/models.rb', line 145

def setup(optimizer, loss_func, loss_weights: nil)
  unless optimizer.is_a?(Optimizers::Optimizer)
    raise TypeError, "optimizer:#{optimizer.class} is not an instance of DNN::Optimizers::Optimizer class."
  end
  unless loss_func.is_a?(Losses::Loss) || loss_func.is_a?(Array)
    raise TypeError, "loss_func:#{loss_func.class} is not an instance of DNN::Losses::Loss or Array class."
  end
  @optimizer = optimizer
  self.loss_func = loss_func
  @loss_weights = loss_weights
end

#test_on_batch(x, y, accuracy: true) ⇒ Array

Evaluate once.



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# File 'lib/dnn/core/models.rb', line 384

def test_on_batch(x, y, accuracy: true)
  call_callbacks(:before_test_on_batch)
  DNN.learning_phase = false
  output_tensors = call(Tensor.convert(x))
  correct = nil
  if output_tensors.is_a?(Array)
    correct = [] if accuracy
    loss_data = []
    output_tensors.each.with_index do |out, i|
      correct << accuracy(out.data, y[i]) if accuracy
      loss = @loss_func[i].(out, Tensor.convert(y[i]))
      loss_data << loss.data.to_f
    end
  else
    out = output_tensors
    correct = accuracy(out.data, y) if accuracy
    loss = @loss_func.(out, Tensor.convert(y))
    loss_data = loss.data.to_f
  end
  call_callbacks(:after_test_on_batch)
  [correct, loss_data]
end

#train(x, y, epochs, batch_size: 1, initial_epoch: 1, test: nil, verbose: true, accuracy: true) ⇒ Object Also known as: fit

Start training. Setup the model before use this method.



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# File 'lib/dnn/core/models.rb', line 186

def train(x, y, epochs,
          batch_size: 1,
          initial_epoch: 1,
          test: nil,
          verbose: true,
          accuracy: true)
  check_xy_type(x, y)
  train_iterator = Iterator.new(x, y)
  train_by_iterator(train_iterator, epochs,
                    batch_size: batch_size,
                    initial_epoch: initial_epoch,
                    test: test,
                    verbose: verbose,
                    accuracy: accuracy)
end

#train_by_iterator(train_iterator, epochs, batch_size: 1, initial_epoch: 1, test: nil, verbose: true, accuracy: true) ⇒ Object Also known as: fit_by_iterator

Start training by iterator. Setup the model before use this method.

Raises:



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# File 'lib/dnn/core/models.rb', line 214

def train_by_iterator(train_iterator, epochs,
                      batch_size: 1,
                      initial_epoch: 1,
                      test: nil,
                      verbose: true,
                      accuracy: true)
  raise DNNError, "The model is not optimizer setup complete." unless @optimizer
  raise DNNError, "The model is not loss_func setup complete." unless @loss_func

  num_train_datas = train_iterator.num_datas
  num_train_datas = num_train_datas / batch_size * batch_size if train_iterator.last_round_down

  stopped = catch(:stop) do
    (initial_epoch..epochs).each do |epoch|
      @last_log[:epoch] = epoch
      call_callbacks(:before_epoch)
      puts "【 epoch #{epoch}/#{epochs} 】" if verbose

      train_iterator.foreach(batch_size) do |x_batch, y_batch, index|
        train_step_met = train_step(x_batch, y_batch)
        num_trained_datas = (index + 1) * batch_size
        num_trained_datas = num_trained_datas > num_train_datas ? num_train_datas : num_trained_datas
        log = "\r"
        40.times do |i|
          if i < num_trained_datas * 40 / num_train_datas
            log << "="
          elsif i == num_trained_datas * 40 / num_train_datas
            log << ">"
          else
            log << "_"
          end
        end

        log << "  #{num_trained_datas}/#{num_train_datas} "
        log << metrics_to_str(train_step_met)
        print log if verbose
      end

      if test
        acc, loss = if test.is_a?(Array)
                      evaluate(test[0], test[1], batch_size: batch_size, accuracy: accuracy)
                    else
                      evaluate_by_iterator(test, batch_size: batch_size, accuracy: accuracy)
                    end
        if verbose
          metrics = if accuracy
                      { accuracy: acc, test_loss: loss }
                    else
                      { test_loss: loss }
                    end
          print "  " + metrics_to_str(metrics)
        end
      end
      puts "" if verbose
      call_callbacks(:after_epoch)
    end
    nil
  end

  if stopped
    puts "\n#{stopped}" if verbose
  end
end

#train_on_batch(x, y) ⇒ Float | Numo::SFloat

Training once. Setup the model before use this method.

Raises:



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# File 'lib/dnn/core/models.rb', line 294

def train_on_batch(x, y)
  raise DNNError, "The model is not optimizer setup complete." unless @optimizer
  raise DNNError, "The model is not loss_func setup complete." unless @loss_func
  check_xy_type(x, y)
  call_callbacks(:before_train_on_batch)
  DNN.learning_phase = true
  output_tensors = call(Tensor.convert(x))
  if output_tensors.is_a?(Array)
    loss_data = []
    output_tensors.each.with_index do |out, i|
      loss_opt = {}
      loss_opt[:layers] = layers if i == 0
      loss_opt[:loss_weight] = @loss_weights[i] if @loss_weights
      loss = @loss_func[i].loss(out, Tensor.convert(y[i]), **loss_opt)
      loss_data << loss.data.to_f
      loss.link.backward(Xumo::SFloat.ones(y[i][0...1, false].shape[0], 1))
    end
  else
    out = output_tensors
    loss = @loss_func.loss(out, Tensor.convert(y), layers: layers)
    loss_data = loss.data.to_f
    loss.link.backward(Xumo::SFloat.ones(y[0...1, false].shape[0], 1))
  end
  @optimizer.update(get_all_trainable_params)
  @last_log[:train_loss] = loss_data
  call_callbacks(:after_train_on_batch)
  loss_data
end

#trainable_layers ⇒ Array

Get the all trainable layers.



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# File 'lib/dnn/core/models.rb', line 504

def trainable_layers
  layers.select { |layer| layer.is_a?(Layers::TrainableLayer) }
end