Class: Chainer::Functions::Activation::LogSoftmaxGrad

Inherits:
Chainer::FunctionNode show all
Defined in:
lib/chainer/functions/activation/log_softmax.rb

Instance Attribute Summary

Attributes inherited from Chainer::FunctionNode

#inputs, #outputs, #rank

Instance Method Summary collapse

Methods inherited from Chainer::FunctionNode

#apply, #backward_accumulate, #forward_cpu, #get_retained_inputs, #get_retained_outputs, #label, #output_data, #retain_inputs, #retain_outputs, #unchain

Constructor Details

#initialize(x_shape, x_dtype) ⇒ LogSoftmaxGrad

Returns a new instance of LogSoftmaxGrad.



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# File 'lib/chainer/functions/activation/log_softmax.rb', line 78

def initialize(x_shape, x_dtype)
  @x_shape = x_shape
  @x_dtype = x_dtype
end

Instance Method Details

#backward(indexes, ggx) ⇒ Object 



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# File 'lib/chainer/functions/activation/log_softmax.rb', line 92

def backward(indexes, ggx)
  y, gy = get_retained_inputs
  ret = []
  exp_y = Chainer::Functions::Math::Exp.exp(y)

  if indexes.include?(0)
    gy_sum = Chainer::Functions::Math::Sum.sum(gy, axis: 1, keepdims: true)
    gy_sum = Chainer::Functions::Array::BroadcastTo.broadcast_to(gy_sum, gy.shape)

    g0 = -ggx.first * exp_y * gy_sum
    ret << g0
  end
  if indexes.include?(1)
    a = Chainer::Functions::Math::Sum.sum(ggx.first * exp_y, axis: 1, keepdims: true)
    a = Chainer::Functions::Array::BroadcastTo.broadcast_to(a, gy.shape)
    g1 = ggx.first - a
    ret << g1
  end

  ret
end

#forward(inputs) ⇒ Object 



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# File 'lib/chainer/functions/activation/log_softmax.rb', line 83

def forward(inputs)
  retain_inputs([0, 1])
  y, gy = inputs

  xm = Chainer.get_array_module(y)
  gx = gy - xm::NMath.exp(y) * gy.sum(axis: 1, keepdims: true)
  [gx]
end