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asm-jaimeasm-jaime
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add tath activation example
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5 files changed

+130
-7
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5 files changed

+130
-7
lines changed

‎DigitalRecognizer/NDLDigitalRecognizer.cs

Lines changed: 7 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,6 +1,7 @@
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using Keras.Datasets;
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using Numpy;
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using System.Linq;
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using System.Text.RegularExpressions;
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namespace DigitalRecognizer
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{
@@ -35,21 +36,26 @@ public void TrainOnMNISTData()
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np.random.seed(1);
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var (alpha, iterations, hidden_size, pixels_per_image, num_labels) = (0.005f, 30, 40, 784, 10);
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var (alpha, iterations, hidden_size, pixels_per_image, num_labels) = (0.005f, 30, 10, 784, 10);
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_weights01 = 0.2f * np.random.rand(pixels_per_image, hidden_size) - 0.1f;
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_weights12 = 0.2f * np.random.rand(hidden_size, num_labels) - 0.1f;
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//Regex.IsMatch("))))dfdf",@"(;|:)(~|-)?(\)|D)");
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for (var i = 0; i < iterations; i++)
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{
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for (var imageId = 0; imageId < images.len; ++imageId)
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{
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var layer_0 = images[$"{imageId}:{imageId + 1}"];
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var layer_1 = Relu(np.dot(layer_0, _weights01));
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var dropout_mask = np.random.randint(2, null, layer_1.shape.Dimensions);
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layer_1 *= dropout_mask * 2;
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var layer_2 = np.dot(layer_1, _weights12);
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var layer_2_delta = labels[$"{imageId}:{imageId + 1}"] - layer_2;
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var layer_1_delta = layer_2_delta.dot(_weights12.T) * ReluToDerivative(layer_1);
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layer_1_delta *= dropout_mask;
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_weights12 += alpha * layer_1.T.dot(layer_2_delta);
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_weights01 += alpha * layer_0.T.dot(layer_1_delta);

‎LearnML.sln

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Original file line numberDiff line numberDiff line change
@@ -5,8 +5,6 @@ VisualStudioVersion = 17.3.32922.545
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MinimumVisualStudioVersion = 10.0.40219.1
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "MLTests", "MLTests\MLTests.csproj", "{8B0D33BC-74F2-4E4A-8415-07026E3624E4}"
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EndProject
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Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "DigitalRecognizer", "DigitalRecognizer\DigitalRecognizer.csproj", "{72FC2029-F95B-4059-A156-55A1BB113AFB}"
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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Debug|Any CPU = Debug|Any CPU
@@ -17,10 +15,6 @@ Global
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{8B0D33BC-74F2-4E4A-8415-07026E3624E4}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{8B0D33BC-74F2-4E4A-8415-07026E3624E4}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{8B0D33BC-74F2-4E4A-8415-07026E3624E4}.Release|Any CPU.Build.0 = Release|Any CPU
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{72FC2029-F95B-4059-A156-55A1BB113AFB}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{72FC2029-F95B-4059-A156-55A1BB113AFB}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{72FC2029-F95B-4059-A156-55A1BB113AFB}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{72FC2029-F95B-4059-A156-55A1BB113AFB}.Release|Any CPU.Build.0 = Release|Any CPU
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE

‎MLTests/NDLActivationTathDigitalRec.cs

Lines changed: 93 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,93 @@
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using Keras.Datasets;
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using Numpy;
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namespace MLTests
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{
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public class NDLActivationTathDigitalRec
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{
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private NDarray _weights01;
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private NDarray _weights12;
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private string _dataPath;
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private static NDarray Relu(NDarray layer) => layer * (layer > 0);
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private static NDarray ReluToDerivative(NDarray layer) => np.where(layer > 0, np.array(1), np.array(0));
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private static NDarray Tanh(NDarray layer) => np.tanh(layer);
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private static NDarray TanhToDerivative(NDarray layer) => 1 - np.power(layer, np.array(2));
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private static NDarray Softmax(NDarray layer)
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{
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var temp = np.exp(layer);
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return temp / np.sum(temp, axis: 1, keepdims: true);
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}
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22+
public NDLActivationTathDigitalRec(string dataPath)
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{
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_weights01 = np.array(0f);
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_weights12 = np.array(0f);
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_dataPath = dataPath;
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}
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29+
public void TrainOnMNISTDataWhithTath()
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{
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var ((x_train, y_train), (_, _)) = MNIST.LoadData(_dataPath);
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var (images, labels) = (x_train["0:1000"].reshape(1000, 28 * 28) / 255, y_train["0:1000"]);
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var one_hot_labels = np.zeros(labels.len, 10);
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for (var id = 0; id < labels.len; id++)
36+
{
37+
var label = labels[id];
38+
one_hot_labels[id][label] = np.array(1);
39+
}
40+
labels = one_hot_labels;
41+
42+
np.random.seed(1);
43+
44+
var (alpha, iterations, hidden_size) = (2.0f, 30, 100);
45+
var (pixels_per_image, num_labels) = (784, 10);
46+
var batch_size = 100;
47+
48+
_weights01 = 0.02f * np.random.rand(pixels_per_image, hidden_size) - 0.01f;
49+
_weights12 = 0.2f * np.random.rand(hidden_size, num_labels) - 0.1f;
50+
51+
for (var i = 0; i < iterations; i++)
52+
{
53+
for (var imageId = 0; imageId < images.len/batch_size; ++imageId)
54+
{
55+
var (batch_start, batch_end) = (imageId * batch_size, (imageId + 1) * batch_size);
56+
var layer_0 = images[$"{batch_start}:{batch_end}"];
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var layer_1 = Tanh(np.dot(layer_0, _weights01));
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var dropout_mask = np.random.randint(2, null, layer_1.shape.Dimensions);
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layer_1 *= dropout_mask * 2;
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var layer_2 = Softmax(np.dot(layer_1, _weights12));
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62+
var layer_2_delta = (labels[$"{batch_start}:{batch_end}"] - layer_2) / (batch_size * layer_2.shape[0]);
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var layer_1_delta = layer_2_delta.dot(_weights12.T) * TanhToDerivative(layer_1);
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layer_1_delta *= dropout_mask;
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66+
67+
_weights12 += alpha * layer_1.T.dot(layer_2_delta);
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_weights01 += alpha * layer_0.T.dot(layer_1_delta);
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}
70+
}
71+
}
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public int PredictNumber(int testId)
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{
75+
var ((x_train, y_train), (x_test, y_test)) = MNIST.LoadData(_dataPath);
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var test_images = x_test.reshape(x_test.len, 28 * 28) / 255;
78+
var test_labels = np.zeros(y_test.len, 10);
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for (var id = 0; id < y_test.len; id++)
80+
{
81+
var test = y_test[id];
82+
test_labels[id][test] = np.array(1);
83+
}
84+
85+
var layer_0 = test_images[$"{testId}:{testId + 1}"];
86+
var layer_1 = Tanh(np.dot(layer_0, _weights01));
87+
var layer_2 = np.dot(layer_1, _weights12);
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var result = layer_2.GetData<double>();
89+
var max = result.Max();
90+
return result.Select((e, index) => new { e, index }).OrderBy(e => e.e).Last().index;
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}
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}
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}

‎MLTests/NDLActivationTathDigitalRecTests.cs

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@@ -0,0 +1,18 @@
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using FluentAssertions;
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using NUnit.Framework;
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4+
namespace MLTests
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{
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public class NDLActivationTathDigitalRecTests
7+
{
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[Test]
9+
public void ShouldPredictByUsingTathActivationFunc()
10+
{
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var recognizer = new NDLActivationTathDigitalRec(Path.GetFullPath(@"D:/projects.active/LearnML/data/mnist.npz"));
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recognizer.TrainOnMNISTDataWhithTath();
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var result = recognizer.PredictNumber(0);
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result.Should().Be(7);
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}
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}
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}

‎MLTests/NDLConvolutionDigitalRecTest.cs

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using System;
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using System.Collections.Generic;
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using System.Linq;
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using System.Text;
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using System.Threading.Tasks;
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7+
namespace MLTests
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{
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internal class NDLConvolutionDigitalRecTest
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{
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}
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}

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