gistfile1.txt

# for adagrad test
  defnetwork init_network4(_x) do
    _x |> f(10,10) |> flatten
    |> w(361,300) |> b(300) |> relu
    |> w(300,100) |> b(100) |> relu
    |> w(100,10) |> b(10) |> softmax
  end

def all(m,n) do
    IO.puts("preparing data")
    image = MNIST.train_image(60000) |> Ctensor.to_matrex
    label = MNIST.train_label_onehot(60000)
    network = init_network4(0)
    test_image = MNIST.test_image(10000) |> Ctensor.to_matrex
    test_label = MNIST.test_label(10000)
    IO.puts("ready")
    network1 = all1(image,network,label,m,n,test_image,test_label)
    correct = DP.accuracy(test_image,network1,test_label)
    IO.write("accuracy rate = ")
    IO.puts(correct / 10000)
  end


  def all1(_,network,_,_,0,_,_) do network end
  def all1(image,network,train,m,n,test_image,test_label) do
    network1 = all2(image,network,train,m)
    image1 = Enum.take(image,100)
    train1 = Enum.take(train,100) |> Cmatrix.to_matrex
    y = DP.forward(image1,network1)
    loss = DP.loss(y,train1,:cross)
    DP.print(loss)
    DP.newline()
    correct = DP.accuracy(test_image,network1,test_label)
    IO.write("accuracy rate = ")
    IO.puts(correct / 10000)
    all1(image,network1,train,m,n-1,test_image,test_label)
  end

  def all2(image,network,train,size) do
    if length(image) <= size do
      train1 = train |> Cmatrix.to_matrex
      network1 = DP.gradient(image,network,train1)
      network2 = DP.learning(network,network1,:adagrad)
      IO.puts(".")
      network2
    else
      train1 = Enum.take(train,size) |> Cmatrix.to_matrex
      network1 = DP.gradient(Enum.take(image,size),network,train1)
      network2 = DP.learning(network,network1,:adagrad)
      IO.write(".")
      all2(Enum.drop(image,size),network2,Enum.drop(train,size),size)
    end
  end