public static void Run()
{
//Write the configuration of the network you want to read into FunctionStack and adjust the parameters of each function
//Make sure to match name to the variable name of Chainer
FunctionStack nn = new FunctionStack(
new Convolution2D(1, 2, 3, name: "conv1", gpuEnable: true),//Do not forget the GPU flag if necessary
new ReLU(),
new MaxPooling(2, 2),
new Convolution2D(2, 2, 2, name: "conv2", gpuEnable: true),
new ReLU(),
new MaxPooling(2, 2),
new Linear(8, 2, name: "fl3"),
new ReLU(),
new Linear(2, 2, name: "fl4")
);
/* Declaration in Chainer
* class NN (chainer.Chain):
* def __init __ (self):
* super (NN, self).__ init __ (
* conv 1 = L. Convolution 2 D (1, 2, 3),
* conv 2 = L. Convolution 2 D (2, 2, 2),
* fl3 = L. Linear (8, 2),
* fl4 = L. Linear (2, 2)
* )
*
* def __call __ (self, x):
* h_conv 1 = F.relu (self.conv 1 (x))
* h_pool 1 = F.max_pooling - 2 d (h_conv 1, 2)
* h_conv 2 = F.relu (self.conv 2 (h_pool 1))
* h_pool 2 = F.max_pooling - 2 d (h_conv 2, 2)
* h_fc1 = F.relu (self.fl3 (h_pool2))
* y = self.fl 4 (h_fc 1)
* return y
* */
//Read parameters
ChainerModelDataLoader.ModelLoad(MODEL_FILE_PATH, nn);
//Use it as usual
nn.SetOptimizer(new SGD());
//Input data
NdArray x = new NdArray(new Real[, , ] {
{
{ 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.9, 0.2, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.2, 0.8, 0.9, 0.1, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.1, 0.8, 0.5, 0.8, 0.1, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.3, 0.3, 0.1, 0.7, 0.2, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.1, 0.0, 0.1, 0.7, 0.2, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.7, 0.1, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.0, 0.4, 0.8, 0.1, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.4, 0.1, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.2, 0.8, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.1, 0.8, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.1, 0.7, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }
}
});
//Teacher signal
Real[] t = { 0.0, 1.0 };
//Training conducted
Trainer.Train(nn, x, t, new MeanSquaredError(), false);
//Evacuate for results display
Convolution2D l2 = (Convolution2D)nn.Functions[0];
//When updating is executed grad will be consumed, so output the value first
Console.WriteLine("gw1");
Console.WriteLine(l2.Weight.ToString("Grad"));
Console.WriteLine("gb1");
Console.WriteLine(l2.Bias.ToString("Grad"));
//update
nn.Update();
Console.WriteLine("w1");
Console.WriteLine(l2.Weight);
Console.WriteLine("b1");
Console.WriteLine(l2.Bias);
}
public static void Run()
{
// Write the configuration of the network you want to read into FunctionStack and adjust the parameters of each function
// Make sure to match name to the variable name of Chainer here
FunctionStack nn = new FunctionStack("Test16",
new Convolution2D(true, 1, 2, 3, name: "conv1", gpuEnable: true),// Do not forget the GPU flag if necessary
new ReLU(),
new MaxPooling(2, 2),
new Convolution2D(true, 2, 2, 2, name: "conv2", gpuEnable: true),
new ReLU(),
new MaxPooling(2, 2),
new Linear(true, 8, 2, name: "fl3"),
new ReLU(),
new Linear(true, 2, 2, name: "fl4")
);
/* Chainerでの宣言
* class NN(chainer.Chain):
* def __init__(self):
* super(NN, self).__init__(
* conv1 = L.Convolution2D(1,2,3),
* conv2 = L.Convolution2D(2,2,2),
* fl3 = L.Linear(8,2),
* fl4 = L.Linear(2,2)
* )
*
* def __call__(self, x):
* h_conv1 = F.relu(self.conv1(x))
* h_pool1 = F.max_pooling_2d(h_conv1, 2)
* h_conv2 = F.relu(self.conv2(h_pool1))
* h_pool2 = F.max_pooling_2d(h_conv2, 2)
* h_fc1 = F.relu(self.fl3(h_pool2))
* y = self.fl4(h_fc1)
* return y
*/
// Read parameters
ChainerModelDataLoader.ModelLoad(MODEL_FILE_PATH, nn);
// We will use the rest as usual
nn.SetOptimizer(new SGD());
NdArray x = new NdArray(new Real[, , ] {
{
{ 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.9, 0.2, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.2, 0.8, 0.9, 0.1, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.1, 0.8, 0.5, 0.8, 0.1, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.3, 0.3, 0.1, 0.7, 0.2, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.1, 0.0, 0.1, 0.7, 0.2, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.0, 0.1, 0.7, 0.1, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.0, 0.4, 0.8, 0.1, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, 0.4, 0.1, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.2, 0.8, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.1, 0.8, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.1, 0.7, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0 },
{ 0.0, 0.0, 0.0, 0.0, 0.0, 0.3, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 }
}
});
Real[] t = { 0.0, 1.0 };
Trainer.Train(nn, x, t, new MeanSquaredError(), false);
Convolution2D l2 = (Convolution2D)nn.Functions[0];
RILogManager.Default?.SendDebug("gw1");
RILogManager.Default?.SendDebug(l2.Weight.ToString("Grad"));
RILogManager.Default?.SendDebug("gb1");
RILogManager.Default?.SendDebug(l2.Bias.ToString("Grad"));
// If Update is executed, grad is consumed, so output the value first
nn.Update();
RILogManager.Default?.SendDebug("w1");
RILogManager.Default?.SendDebug(l2.Weight.ToString());
RILogManager.Default?.SendDebug("b1");
RILogManager.Default?.SendDebug(l2.Bias.ToString());
}
public static void Run()
{
//読み込みたいネットワークの構成を FunctionStack に書き連ね、各 Function のパラメータを合わせる
//ここで必ず name を Chainer の変数名に合わせておくこと
FunctionStack <Real> nn = new FunctionStack <Real>(
new Convolution2D <Real>(1, 2, 3, name: "conv1", gpuEnable: true),//必要であればGPUフラグも忘れずに
new ReLU <Real>(),
new MaxPooling2D <Real>(2, 2),
new Convolution2D <Real>(2, 2, 2, name: "conv2", gpuEnable: true),
new ReLU <Real>(),
new MaxPooling2D <Real>(2, 2),
new Linear <Real>(8, 2, name: "fl3"),
new ReLU <Real>(),
new Linear <Real>(2, 2, name: "fl4")
);
/* Chainerでの宣言
* class NN(chainer.Chain):
* def __init__(self):
* super(NN, self).__init__(
* conv1 = L.Convolution2D(1,2,3),
* conv2 = L.Convolution2D(2,2,2),
* fl3 = L.Linear(8,2),
* fl4 = L.Linear(2,2)
* )
*
* def __call__(self, x):
* h_conv1 = F.relu(self.conv1(x))
* h_pool1 = F.max_pooling_2d(h_conv1, 2)
* h_conv2 = F.relu(self.conv2(h_pool1))
* h_pool2 = F.max_pooling_2d(h_conv2, 2)
* h_fc1 = F.relu(self.fl3(h_pool2))
* y = self.fl4(h_fc1)
* return y
*/
//パラメータを読み込み
ChainerModelDataLoader.ModelLoad(MODEL_FILE_PATH, nn);
//あとは通常通り使用する
SGD <Real> sgd = new SGD <Real>(0.1f);
sgd.SetUp(nn);
//入力データ
NdArray <Real> x = new NdArray <Real>(new Real[, , ] {
{
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.2f, 0.9f, 0.2f, 0.0f, 0.0f, 0.0f, 0.0f },
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.2f, 0.8f, 0.9f, 0.1f, 0.0f, 0.0f, 0.0f, 0.0f },
{ 0.0f, 0.0f, 0.0f, 0.1f, 0.8f, 0.5f, 0.8f, 0.1f, 0.0f, 0.0f, 0.0f, 0.0f },
{ 0.0f, 0.0f, 0.0f, 0.3f, 0.3f, 0.1f, 0.7f, 0.2f, 0.0f, 0.0f, 0.0f, 0.0f },
{ 0.0f, 0.0f, 0.0f, 0.1f, 0.0f, 0.1f, 0.7f, 0.2f, 0.0f, 0.0f, 0.0f, 0.0f },
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.1f, 0.7f, 0.1f, 0.0f, 0.0f, 0.0f, 0.0f },
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.4f, 0.8f, 0.1f, 0.0f, 0.0f, 0.0f, 0.0f },
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.8f, 0.4f, 0.1f, 0.0f, 0.0f, 0.0f, 0.0f },
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.2f, 0.8f, 0.3f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f },
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.1f, 0.8f, 0.2f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f },
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.1f, 0.7f, 0.2f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f },
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.3f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f }
}
});
//教師信号
NdArray <Real> t = new NdArray <Real>(new Real[] { 0.0f, 1.0f });
//訓練を実施
Trainer.Train(nn, x, t, new MeanSquaredError <Real>());
//結果表示用に退避
Convolution2D <Real> l2 = (Convolution2D <Real>)nn.Functions[0];
//Updateを実行するとgradが消費されてしまうため値を先に出力
Console.WriteLine("gw1");
Console.WriteLine(l2.Weight.ToString("Grad"));
Console.WriteLine("gb1");
Console.WriteLine(l2.Bias.ToString("Grad"));
//更新
sgd.Update();
Console.WriteLine("w1");
Console.WriteLine(l2.Weight);
Console.WriteLine("b1");
Console.WriteLine(l2.Bias);
}
