public static void teste()
{
Console.WriteLine("\n\nA imagem deve estar na pasta debug, com o nome 'char.bmp' (já deve ter uma lá)");
var net2 = SerializationExtensions.FromJSON(File.ReadAllText("best"));
byte[] imgbytes = null;
var newImage = MnistReader.ScaleImage(new Bitmap(@"char.bmp"), 24, 24);
var x = new Volume(newImage.Width, newImage.Height, 1, 0.0);
for (var i = 0; i < newImage.Width; i++)
{
for (var j = 0; j < newImage.Width; j++)
{
x.Weights[j + i * newImage.Width] = (newImage.GetPixel(j, i).B + newImage.GetPixel(j, i).R + newImage.GetPixel(j, i).G) / 3;
}
}
if (File.Exists("oi.txt"))
{
File.Delete("oi.txt");
}
for (int i = 0; i < x.Weights.Length; i++)
{
using (StreamWriter file = new StreamWriter(@"oi.txt", true))
file.WriteLine(x.Weights[i].ToString());
}
net2.Forward(x);
var yhat = net2.GetPrediction();
Console.WriteLine("label prevista = {0}", yhat);
}
public static void segment_n_rec()
{
var image = new Bitmap(@"a.bmp");
image = image.Clone(new Rectangle(0, 0, image.Width, image.Height), PixelFormat.Format8bppIndexed);
byte[] imgbytes = new byte[image.Height * image.Width];
#region parallel grey image conv
var tasks = new Task[Environment.ProcessorCount];
int oi = -1;
for (var ai = 0; ai < image.Width; ai++)
{
for (int taskNumber = 0; taskNumber < Environment.ProcessorCount; taskNumber++)
{
tasks[taskNumber] = Task.Factory.StartNew(() =>
{
int j = Interlocked.Increment(ref oi);
while (j < image.Width)
{
imgbytes[j + ai * image.Width] = (byte)(((int)image.GetPixel(j, ai).B + (int)image.GetPixel(j, ai).R + (int)image.GetPixel(j, ai).G) / 3);
j = Interlocked.Increment(ref oi);
}
});
}
Task.WaitAll(tasks);
}
#endregion
new OtsuThreshold().ApplyInPlace(image);
// check threshold value
var net2 = SerializationExtensions.FromJSON(File.ReadAllText("best"));
var newImage = MnistReader.ScaleImage(new Bitmap(@"C:\Users\leoni.win7-PC\Desktop\Nova pasta\a.bmp"), 24, 24);
var x = new Volume(newImage.Width, newImage.Height, 1, 0.0);
for (var i = 0; i < newImage.Width; i++)
{
for (var j = 0; j < newImage.Width; j++)
{
x.Weights[j + i * newImage.Width] = (newImage.GetPixel(j, i).B + newImage.GetPixel(j, i).R + newImage.GetPixel(j, i).G) / 3;
}
}
if (File.Exists("oi.txt"))
{
File.Delete("oi.txt");
}
for (int i = 0; i < x.Weights.Length; i++)
{
using (StreamWriter file = new StreamWriter(@"oi.txt", true))
file.WriteLine(x.Weights[i].ToString());
}
net2.Forward(x);
var yhat = net2.GetPrediction();
}
public static Brain ReadBrainFromFile(string filename)
{
var brain = JsonConvert.DeserializeObject <Brain>(File.ReadAllText(filename));
brain.Net = SerializationExtensions.FromJSON(brain.NetJson);
brain.NetJson = string.Empty;
return(brain);
}
public void JsonNetSerializerTest()
{
var net = new Net();
net.AddLayer(new InputLayer(5, 5, 3));
var conv = new ConvLayer(2, 2, 16);
net.AddLayer(conv);
var fullycon = new FullyConnLayer(3);
net.AddLayer(fullycon);
net.AddLayer(new SoftmaxLayer(3));
// Serialize to json
var json = net.ToJSON();
// Deserialize from json
Net deserialized = SerializationExtensions.FromJSON(json);
// Make sure deserialized is identical to serialized
Assert.IsNotNull(deserialized.Layers);
Assert.AreEqual(net.Layers.Count, deserialized.Layers.Count);
Assert.IsTrue(net.Layers[0] is InputLayer);
var deserializedConv = net.Layers[1] as ConvLayer;
Assert.NotNull(deserializedConv);
Assert.NotNull(deserializedConv.Filters);
Assert.AreEqual(16, deserializedConv.Filters.Count);
for (int i = 0; i < deserializedConv.Filters.Count; i++)
{
for (int k = 0; k < deserializedConv.Filters[i].Length; k++)
{
Assert.AreEqual(conv.Filters[i].Get(k), deserializedConv.Filters[i].Get(k));
Assert.AreEqual(conv.Filters[i].GetGradient(k), deserializedConv.Filters[i].GetGradient(k));
}
}
var deserializedFullyCon = net.Layers[2] as FullyConnLayer;
Assert.NotNull(deserializedFullyCon);
Assert.NotNull(deserializedFullyCon.Filters);
Assert.AreEqual(3, deserializedFullyCon.Filters.Count);
for (int i = 0; i < deserializedFullyCon.Filters.Count; i++)
{
for (int k = 0; k < deserializedFullyCon.Filters[i].Length; k++)
{
Assert.AreEqual(fullycon.Filters[i].Get(k), deserializedFullyCon.Filters[i].Get(k));
Assert.AreEqual(fullycon.Filters[i].GetGradient(k), deserializedFullyCon.Filters[i].GetGradient(k));
}
}
Assert.IsTrue(net.Layers[3] is SoftmaxLayer);
Assert.AreEqual(3, ((SoftmaxLayer)net.Layers[3]).ClassCount);
}
private void openToolStripMenuItem_Click(object sender, EventArgs e)
{
net = null;
var json_temp = File.ReadAllLines("NetworkStructure.json");
string json = string.Join("", json_temp);
net = SerializationExtensions.FromJSON(json);
testingToolStripMenuItem.Enabled = true;
saveToolStripMenuItem.Enabled = true;
}
public void JsonAnotherNetSerilizerTest()
{
var numInputs = 64;
var numActions = 5;
var net = new Net();
net.AddLayer(new InputLayer(1, 1, numInputs));
net.AddLayer(new FullyConnLayer((int)(numInputs * 2.5)));
net.AddLayer(new ReluLayer());
net.AddLayer(new FullyConnLayer((int)(numInputs * 1.5)));
net.AddLayer(new ReluLayer());
net.AddLayer(new FullyConnLayer(numActions));
net.AddLayer(new RegressionLayer());
// Serialize to json
var json = net.ToJSON();
// Deserialize from json
var deserialized = SerializationExtensions.FromJSON(json);
var input = new Volume(1, 1, numInputs);
var output1 = net.Forward(input);
var output2 = deserialized.Forward(input);
}