public void TestBasicBackendAndOptimizerAndExportGraph()
{
//create model first
var input = K.placeholder(new int?[] { -1, 3 });
var target = K.placeholder(new int?[] { -1, 1 });
var weight = K.variable((new Constant(1)).Call(new int[] { 3, 1 }, DataType.Float));
//var normalizedWeight = K.spectral_norm(weight);
var output = K.dot(input, weight);
output = K.reshape(output, new int[] { -1 });
target = K.reshape(target, new int[] { -1 });
var lossM = new MeanSquareError();
var loss = lossM.Call(target, output);
loss = K.constant(1.0f) * loss;
//training related
var weights = new List <Tensor>();
weights.Add(weight);
var optimizer = new SGD();
var updates = optimizer.get_updates(weights, new Dictionary <Tensor, IWeightConstraint>(), loss);
var inputs = new List <Tensor>();
inputs.Add(input);
inputs.Add(target);
var outputs = new List <Tensor>();
outputs.Add(loss);
var function = K.function(inputs, outputs, updates, "Train");
var inputData = new List <Array>();
inputData.Add(new float[] { 1.2f, 3.3f, 4.3f, 5, 5, 5 });
inputData.Add(new float[] { 2, 10 });
for (int i = 0; i < 10; ++i)
{
var functionResult = function.Call(inputData);
float resultLoss = (float)functionResult[0].eval();
print(resultLoss);
var opWeights = optimizer.get_weights();
foreach (var w in opWeights)
{
string toPrint = "";
for (int j = 0; j < w.Length; ++j)
{
if (w is float[])
{
toPrint += " " + w.GetValue(j);
}
else
{
toPrint += " " + w.GetValue(j, 0);
}
}
print("Weight:" + toPrint);
}
}
((UnityTFBackend)K).ExportGraphDef("SavedGraph/test.pb");
}
