|
|
@@ -18,7 +18,7 @@ namespace TensorFlowNET.Examples |
|
|
|
public string Name => "NN XOR";
|
|
|
|
public bool ImportGraph { get; set; } = false;
|
|
|
|
|
|
|
|
public int num_steps = 5000;
|
|
|
|
public int num_steps = 10000;
|
|
|
|
|
|
|
|
private NDArray data;
|
|
|
|
|
|
|
@@ -55,7 +55,7 @@ namespace TensorFlowNET.Examples |
|
|
|
if (ImportGraph)
|
|
|
|
loss_value = RunWithImportedGraph();
|
|
|
|
else
|
|
|
|
loss_value=RunWithBuiltGraph();
|
|
|
|
loss_value = RunWithBuiltGraph();
|
|
|
|
|
|
|
|
return loss_value < 0.0628;
|
|
|
|
}
|
|
|
@@ -96,6 +96,7 @@ namespace TensorFlowNET.Examples |
|
|
|
}
|
|
|
|
Console.WriteLine($"Final loss: {loss_value}");
|
|
|
|
});
|
|
|
|
|
|
|
|
return loss_value;
|
|
|
|
}
|
|
|
|
|
|
|
@@ -120,11 +121,6 @@ namespace TensorFlowNET.Examples |
|
|
|
var y_ = np.array(new int[] { 1, 0, 0, 1 }, dtype: np.int32);
|
|
|
|
while (step < num_steps)
|
|
|
|
{
|
|
|
|
// original python:
|
|
|
|
//_, step, loss_value = sess.run(
|
|
|
|
// [train_op, gs, loss],
|
|
|
|
// feed_dict={features: xy, labels: y_}
|
|
|
|
// )
|
|
|
|
var result = sess.run(new ITensorOrOperation[] { train_op, gs, loss }, new FeedItem(features, data), new FeedItem(labels, y_));
|
|
|
|
loss_value = result[2];
|
|
|
|
//step = result[1];
|
|
|
@@ -134,6 +130,7 @@ namespace TensorFlowNET.Examples |
|
|
|
}
|
|
|
|
Console.WriteLine($"Final loss: {loss_value}");
|
|
|
|
});
|
|
|
|
|
|
|
|
return loss_value;
|
|
|
|
}
|
|
|
|
|
|
|
|