■ 텐서플로 GPU 버전 실행시 세션을 설정하는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 |
import tensorflow as tf ... config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config = config) as session: ... |
■ 파이썬 텐서플로 GPU 버전을 설치하는 방법을 보여준다. 1. 엔비디아 사이트(https://developer.nvidia.com/cuda-toolkit-archive)에서 Cuda 9.0 버전을 다운로드 받아 설치한다. • cuda_9.0.176_win10.exe • cuda_9.0.176.1_windows.exe •
■ 파이썬 텐서플로 GPU 버전을 설치하는 방법을 보여준다. [파이썬 설치] 1. 웹 브라우저에서 아래 URL을 입력한다. ▶ URL
|
1 2 3 |
https://www.python.org/ |
2. 아래 화면에서
■ 텐서플로우를 사용해 물체를 인식하는 방법을 보여준다. ▶ MainForm.cs
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 |
using System; using System.Drawing; using System.Drawing.Imaging; using System.IO; using System.Windows.Forms; using AForge.Video; using AForge.Video.DirectShow; using TensorFlow; namespace TestProject { /// <summary> /// 메인 폼 /// </summary> public partial class MainForm : Form { //////////////////////////////////////////////////////////////////////////////////////////////////// Field ////////////////////////////////////////////////////////////////////////////////////////// Private #region Field /// <summary> /// 디스플레이 레이블 그래픽스 /// </summary> private Graphics displayLabelGraphics = null; /// <summary> /// 필터 정보 컬렉션 /// </summary> private FilterInfoCollection filterInfoCollection = null; /// <summary> /// 비디오 캡처 장치 /// </summary> private VideoCaptureDevice videoCaptureDevice = null; /// <summary> /// 모델 배열 /// </summary> private byte[] modelArray = null; /// <summary> /// 레이블 배열 /// </summary> private string[] labelArray = null; /// <summary> /// 텐서플로우 그래프 /// </summary> private TFGraph graph = null; /// <summary> /// 텐서플로우 세션 /// </summary> private TFSession session = null; #endregion //////////////////////////////////////////////////////////////////////////////////////////////////// Constructor ////////////////////////////////////////////////////////////////////////////////////////// Public #region MainForm /// <summary> /// 생성자 /// </summary> public MainForm() { InitializeComponent(); #region 이벤트를 설정한다. Load += Form_Load; FormClosing += Form_FormClosing; this.startButton.Click += startButton_Click; #endregion } #endregion //////////////////////////////////////////////////////////////////////////////////////////////////// Method ////////////////////////////////////////////////////////////////////////////////////////// Private //////////////////////////////////////////////////////////////////////////////// Event #region 폼 로드시 처리하기 - Form_Load(sender, e) /// <summary> /// 폼 로드시 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void Form_Load(object sender, EventArgs e) { this.displayLabelGraphics = this.displayLabel.CreateGraphics(); #region 카메라 장치 콤보 박스를 설정한다. this.filterInfoCollection = new FilterInfoCollection(FilterCategory.VideoInputDevice); for(int i = 0; i < this.filterInfoCollection.Count; i++) { this.cameraDeviceComboBox.Items.Add(this.filterInfoCollection[i].Name); } if(this.cameraDeviceComboBox.Items.Count > 0) { this.cameraDeviceComboBox.SelectedIndex = 0; } #endregion #region 시작 버튼을 설정한다. this.startButton.Enabled = (this.cameraDeviceComboBox.Items.Count > 0); #endregion #region 모델 배열을 설정한다. this.modelArray = File.ReadAllBytes("DATA\\frozen_process_no_filter_tiny.pb"); #endregion #region 레이블 배열을 설정한다. this.labelArray = File.ReadAllLines("DATA\\yolo_labels.txt"); #endregion #region 텐서플로우 그래프를 설정한다. this.graph = new TFGraph(); this.graph.Import(this.modelArray, ""); #endregion #region 텐서플로우 세션을 설정한다. this.session = new TFSession(graph); #endregion Bitmap bitmap = new Bitmap(100, 100, PixelFormat.Format24bppRgb); Predict(bitmap); } #endregion #region 폼 닫을 경우 처리하기 - Form_FormClosing(sender, e) /// <summary> /// 폼 닫을 경우 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void Form_FormClosing(object sender, FormClosingEventArgs e) { StopCameraCaoture(); } #endregion #region 시작 버튼 클릭시 처리하기 - startButton_Click(sender, e) /// <summary> /// 시작 버튼 클릭시 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void startButton_Click(object sender, EventArgs e) { if(this.startButton.Text == "시작") { this.startButton.Text = "중지"; StartCameraCapture(); } else { StopCameraCaoture(); this.startButton.Text = "시작"; } } #endregion #region 비디오 캡처 장치 신규 프레임 처리하기 - videoCaptureDevice_NewFrame(sender, e) /// <summary> /// 비디오 캡처 장치 신규 프레임 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void videoCaptureDevice_NewFrame(object sender, NewFrameEventArgs e) { Bitmap bitmap = e.Frame; TFTensor[] outputTensorArray = Predict(bitmap); float[,] rectangleArray = (float[,])outputTensorArray[0].GetValue(); float[] probabilityArray = (float[] )outputTensorArray[1].GetValue(); long[] labelIndexArray = (long[] )outputTensorArray[2].GetValue(); using(Graphics graphics = Graphics.FromImage(bitmap)) { for(int i = 0; i < probabilityArray.Length; i++) { if(probabilityArray[i] > 0.2) { int rectangleLeft = (int)(rectangleArray[i, 0] * (float)bitmap.Width ); int rectangleTop = (int)(rectangleArray[i, 1] * (float)bitmap.Height); int rectangleWidth = (int)(rectangleArray[i, 2] * (float)bitmap.Width ); int rectangleHeight = (int)(rectangleArray[i, 3] * (float)bitmap.Height); graphics.DrawRectangle ( new Pen(Color.Red, 2), rectangleLeft - rectangleWidth / 2, rectangleTop - rectangleHeight / 2, rectangleWidth, rectangleHeight ); string label = this.labelArray[labelIndexArray[i]] + " " + probabilityArray[i].ToString(); graphics.DrawString ( label, Font, Brushes.Red, rectangleLeft - rectangleWidth / 2, rectangleTop - rectangleHeight / 2 ); } } } this.displayLabelGraphics.DrawImage(bitmap, 0, 0, this.displayLabel.Width, this.displayLabel.Height); } #endregion //////////////////////////////////////////////////////////////////////////////// Function #region 카메라 캡처 시작하기 - StartCameraCapture() /// <summary> /// 카메라 캡처 시작하기 /// </summary> private void StartCameraCapture() { if(this.videoCaptureDevice == null || !this.videoCaptureDevice.IsRunning) { FilterInfo filterInfo = this.filterInfoCollection[this.cameraDeviceComboBox.SelectedIndex]; this.videoCaptureDevice = new VideoCaptureDevice(filterInfo.MonikerString); this.videoCaptureDevice.NewFrame += videoCaptureDevice_NewFrame; this.videoCaptureDevice.Start(); } } #endregion #region 카메라 캡처 중단하기 - StopCameraCaoture() /// <summary> /// 카메라 캡처 중단하기 /// </summary> private void StopCameraCaoture() { if(this.videoCaptureDevice != null && this.videoCaptureDevice.IsRunning) { this.videoCaptureDevice.NewFrame -= videoCaptureDevice_NewFrame; this.videoCaptureDevice.SignalToStop(); this.videoCaptureDevice.WaitForStop(); this.videoCaptureDevice = null; } } #endregion #region 텐서 구하기 - GetTensor(sourceArray) /// <summary> /// 텐서 구하기 /// </summary> /// <param name="sourceArray">소스 배열</param> /// <returns>텐서</returns> private TFTensor GetTensor(byte[] sourceArray) { TFGraph graph = new TFGraph(); TFOutput input = graph.Placeholder(TFDataType.String); TFOutput output = graph.Cast(graph.DecodeJpeg(contents : input, channels : 3), DstT : TFDataType.Float); TFTensor tensor = TFTensor.CreateString(sourceArray); using(TFSession session = new TFSession(graph)) { TFTensor[] normalizedTensorArray = session.Run ( inputs : new[] { input }, outputs : new[] { output }, inputValues : new[] { tensor } ); return normalizedTensorArray[0]; } } #endregion #region 예측하기 - Predict(bitmap) /// <summary> /// 예측하기 /// </summary> /// <param name="bitmap">비트맵</param> /// <returns>결과 텐서 배열</returns> private TFTensor[] Predict(Bitmap bitmap) { MemoryStream memoryStream = new MemoryStream(); bitmap.Save(memoryStream, ImageFormat.Jpeg); TFTensor tensor = GetTensor(memoryStream.GetBuffer()); TFSession.Runner runner = this.session.GetRunner(); runner.AddInput(this.graph["input"][0], tensor).Fetch("boxes", "classes_prob", "classes_arg"); TFTensor[] outputTensorArray = runner.Run(); return outputTensorArray; } #endregion } } |
TestProject.zip
■ 텐서플로우를 사용해 물체를 인식하는 방법을 보여준다. ▶ MainForm.cs
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 |
using System; using System.Drawing; using System.Drawing.Imaging; using System.IO; using System.Windows.Forms; using AForge.Video.DirectShow; using TensorFlow; namespace TestProject { /// <summary> /// 메인 폼 /// </summary> public partial class MainForm : Form { //////////////////////////////////////////////////////////////////////////////////////////////////// Field ////////////////////////////////////////////////////////////////////////////////////////// Private #region Field /// <summary> /// 타이머 /// </summary> private Timer timer = null; /// <summary> /// 필터 정보 컬렉션 /// </summary> private FilterInfoCollection filterInfoCollection = null; /// <summary> /// 모델 배열 /// </summary> private byte[] modelArray; /// <summary> /// 레이블 배열 /// </summary> private string[] labelArray = null; /// <summary> /// 텐서플로우 세션 /// </summary> private TFSession session = null; /// <summary> /// 텐서플로우 그래프 /// </summary> private TFGraph graph = null; #endregion //////////////////////////////////////////////////////////////////////////////////////////////////// Constructor ////////////////////////////////////////////////////////////////////////////////////////// Public #region 생성자 - MainForm() /// <summary> /// 생성자 /// </summary> public MainForm() { InitializeComponent(); #region 타이머를 설정한다. this.timer = new Timer(); this.timer.Interval = 1000; #endregion #region 이벤트를 설정한다. Load += Form_Load; FormClosing += Form_FormClosing; this.startButton.Click += startButton_Click; this.timer.Tick += timer_Tick; #endregion } #endregion //////////////////////////////////////////////////////////////////////////////////////////////////// Method ////////////////////////////////////////////////////////////////////////////////////////// Private //////////////////////////////////////////////////////////////////////////////// Event #region 폼 로드시 처리하기 - Form_Load(sender, e) /// <summary> /// 폼 로드시 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void Form_Load(object sender, EventArgs e) { #region 카메라 장치 콤보 박스를 설정한다. this.filterInfoCollection = new FilterInfoCollection(FilterCategory.VideoInputDevice); for(int i = 0; i < this.filterInfoCollection.Count; i++) { this.cameraDeviceComboBox.Items.Add(this.filterInfoCollection[i].Name); } if(this.cameraDeviceComboBox.Items.Count > 0) { this.cameraDeviceComboBox.SelectedIndex = 0; } #endregion #region 시작 버튼을 설정한다. this.startButton.Enabled = (this.cameraDeviceComboBox.Items.Count > 0); #endregion #region 모델 배열을 설정한다. this.modelArray = File.ReadAllBytes("DATA\\tensorflow_inception_graph.pb"); #endregion #region 레이블 배열을 설정한다. this.labelArray = File.ReadAllLines("DATA\\imagenet_comp_graph_label_strings.txt"); #endregion this.graph = new TFGraph(); this.session = new TFSession(graph); this.graph.Import(this.modelArray, ""); } #endregion #region 폼 닫을 경우 처리하기 - Form_FormClosing(sender, e) /// <summary> /// 폼 닫을 경우 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void Form_FormClosing(object sender, FormClosingEventArgs e) { this.timer.Stop(); StopCameraCaoture(); } #endregion #region 시작 버튼 클릭시 처리하기 - startButton_Click(sender, e) /// <summary> /// 시작 버튼 클릭시 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void startButton_Click(object sender, EventArgs e) { if(this.startButton.Text == "시작") { this.startButton.Text = "중지"; StartCameraCapture(); this.timer.Start(); } else { this.timer.Stop(); StopCameraCaoture(); this.startButton.Text = "시작"; } } #endregion #region 타이머 틱 처리하기 - timer_Tick(sender, e) /// <summary> /// 타이머 틱 처리하기 /// </summary> /// <param name="sender">이벤트 발생자</param> /// <param name="e">이벤트 인자</param> private void timer_Tick(object sender, EventArgs e) { Bitmap bitmap = this.videoSourcePlayer.GetCurrentVideoFrame(); if(bitmap == null) { return; } MemoryStream memoryStream = new MemoryStream(); bitmap.Save(memoryStream, ImageFormat.Jpeg); TFTensor tensor = GetTensor(memoryStream.GetBuffer()); TFSession.Runner runner = this.session.GetRunner(); runner.AddInput(graph["input"][0], tensor).Fetch(graph["output"][0]); TFTensor[] outputTensorArray = runner.Run(); TFTensor outputTensor = outputTensorArray[0]; long[] shapeArray = outputTensor.Shape; if(outputTensor.NumDims != 2 || shapeArray[0] != 1) { string shapeString = string.Empty; foreach(long shape in shapeArray) { shapeString += $"{shape} "; } shapeString = shapeString.Trim(); Console.WriteLine($"에러 : [1 N] 차원 텐서의 생성을 기대했는데(여기서 N은 라벨의 개수), [{shapeString}] 차원 텐서를 생성했습니다."); Environment.Exit(1); } bool jagged = true; int bestIndex = 0; float best = 0; if(jagged) { float[] probabilityArray = ((float[][])outputTensor.GetValue(jagged : true))[0]; for(int i = 0; i < probabilityArray.Length; i++) { if(probabilityArray[i] > best) { bestIndex = i; best = probabilityArray[i]; } } } else { float[,] valueArray = (float[,])outputTensor.GetValue(jagged : false); for(int i = 0; i < valueArray.GetLength(1); i++) { if(valueArray[0, i] > best) { bestIndex = i; best = valueArray[0, i]; } } } this.informationLabel.Text = $"판정 : [{bestIndex}] {best * 100.0}% {this.labelArray[bestIndex]}"; Console.WriteLine($"판정 : [{bestIndex}] {best * 100.0}% {this.labelArray[bestIndex]}"); } #endregion //////////////////////////////////////////////////////////////////////////////// Function #region 카메라 캡처 시작하기 - StartCameraCapture() /// <summary> /// 카메라 캡처 시작하기 /// </summary> private void StartCameraCapture() { FilterInfo filterInfo; filterInfo = this.filterInfoCollection[this.cameraDeviceComboBox.SelectedIndex]; VideoCaptureDevice videoCaptureDevice = new VideoCaptureDevice(filterInfo.MonikerString); this.videoSourcePlayer.VideoSource = videoCaptureDevice; this.videoSourcePlayer.AutoSizeControl = true; this.videoSourcePlayer.Start(); } #endregion #region 카메라 캡처 중단하기 - StopCameraCaoture() /// <summary> /// 카메라 캡처 중단하기 /// </summary> private void StopCameraCaoture() { this.videoSourcePlayer.SignalToStop(); this.videoSourcePlayer.WaitForStop(); } #endregion #region 값 설정하기 - SetValue(graph, input, output) /// <summary> /// 값 설정하기 /// </summary> /// <param name="graph">그래프</param> /// <param name="input">입력</param> /// <param name="output">출력</param> private void SetValue(out TFGraph graph, out TFOutput input, out TFOutput output) { const int IMAGE_WIDTH = 224; const int IMAGE_HEIGHT = 224; const float MEAN = 117; const float SCALE = 1; graph = new TFGraph(); input = graph.Placeholder(TFDataType.String); output = graph.Div ( x : graph.Sub ( x : graph.ResizeBilinear ( images : graph.ExpandDims ( input : graph.Cast ( graph.DecodeJpeg(contents : input, channels : 3), DstT : TFDataType.Float ), dim : graph.Const(0, "make_batch") ), size : graph.Const(new int[] { IMAGE_WIDTH, IMAGE_HEIGHT }, "size")), y : graph.Const(MEAN, "mean") ), y : graph.Const(SCALE, "scale") ); } #endregion #region 텐서 구하기 - GetTensor(sourceArray) /// <summary> /// 텐서 구하기 /// </summary> /// <param name="sourceArray">소스 배열</param> /// <returns>텐서</returns> private TFTensor GetTensor(byte[] sourceArray) { TFTensor tensor = TFTensor.CreateString(sourceArray); TFGraph graph; TFOutput input; TFOutput output; SetValue(out graph, out input, out output); using(TFSession session = new TFSession(graph)) { TFTensor[] normalizedTensorArray = session.Run ( inputs : new[] { input }, inputValues : new[] { tensor }, outputs : new[] { output } ); return normalizedTensorArray[0]; } } #endregion } } |
※ 닷넷 프레임워크 4.7.1 버전을 사용해야 한다. ※ 프로젝트 속성의 빌드 탭에서
■ 양방향 순환 신경망을 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 |
import numpy as np import tensorflow as tf import tensorflow.contrib.rnn as rnn import tensorflow.examples.tutorials.mnist as mnist learningRate = 0.001 trainingCount = 100000 batchSize = 128 displayStep = 10 inputLayerNodeCount = 28 inputLayerNodeStepCount = 28 hiddenLayerNodeCount = 128 outputLayerNodeCount = 10 mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) inputLayerTensor = tf.placeholder("float", [None, inputLayerNodeStepCount, inputLayerNodeCount]) correctOutputTensor = tf.placeholder("float", [None, outputLayerNodeCount]) outputLayerWeightVariable = tf.Variable(tf.random_normal([2 * hiddenLayerNodeCount, outputLayerNodeCount])) outputLayerBiasVariable = tf.Variable(tf.random_normal([outputLayerNodeCount])) inputLayerTensorTranspose = tf.transpose(inputLayerTensor, [1, 0, 2]) inputLayerTensorReshape = tf.reshape(inputLayerTensorTranspose, [-1, inputLayerNodeCount]) inputLayerTensorSplit = tf.split(axis = 0, num_or_size_splits = inputLayerNodeStepCount, value = inputLayerTensorReshape) forwardBasicLSTMCell = rnn.BasicLSTMCell(hiddenLayerNodeCount, forget_bias = 1.0) backwardBasicLSTMCell = rnn.BasicLSTMCell(hiddenLayerNodeCount, forget_bias = 1.0) outputList, _, _ = rnn.static_bidirectional_rnn(forwardBasicLSTMCell, backwardBasicLSTMCell, inputLayerTensorSplit, dtype = tf.float32) outputLayerOutperTensor = tf.matmul(outputList[-1], outputLayerWeightVariable) + outputLayerBiasVariable costTensor = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = outputLayerOutperTensor, labels = correctOutputTensor)) optimizerOperation = tf.train.AdamOptimizer(learningRate).minimize(costTensor) correctPredictionTensor = tf.equal(tf.argmax(outputLayerOutperTensor, 1), tf.argmax(correctOutputTensor, 1)) accuracyTensor = tf.reduce_mean(tf.cast(correctPredictionTensor, tf.float32)) with tf.Session() as session: session.run(tf.global_variables_initializer()) step = 1 while step * batchSize < trainingCount: batchInputNDArray, batchCorrectOutputNDArray = mnistDatasets.train.next_batch(batchSize) batchInputNDArray = batchInputNDArray.reshape((batchSize, inputLayerNodeStepCount, inputLayerNodeCount)) session.run(optimizerOperation, feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray}) if step % displayStep == 0: accuracy = session.run(accuracyTensor, feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray}) loss = session.run(costTensor, feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray}) print("훈련 카운트 : " + str(step * batchSize) + ", 미니 배치 손실 = " + "{:.6f}".format(loss) + ", 훈련 정확도 = " + "{:.5f}".format(accuracy)) step += 1 print("최적화가 완료되었습니다!") testSize = 128 testInputNDArray = mnistDatasets.test.images[:testSize].reshape((-1, inputLayerNodeStepCount, inputLayerNodeCount)) testCorrectOutputNDArray = mnistDatasets.test.labels[:testSize] print("테스트 정확도 : ", session.run(accuracyTensor, feed_dict = {inputLayerTensor : testInputNDArray, correctOutputTensor : testCorrectOutputNDArray})) |
■ 순환 신경망을 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
import tensorflow as tf import tensorflow.contrib.rnn as rnn import tensorflow.examples.tutorials.mnist as mnist learningRate = 0.001 trainingCount = 100000 batchSize = 128 displayStep = 10 inputLayerNodeCount = 28 inputLayerNodeStepCount = 28 hiddenLayerNodeCount = 128 outputLayerNodeCount = 10 mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) inputLayerTensor = tf.placeholder("float", [None, inputLayerNodeStepCount, inputLayerNodeCount]) correctOutputTensor = tf.placeholder("float", [None, outputLayerNodeCount]) outputLayerWeightVariable = tf.Variable(tf.random_normal([hiddenLayerNodeCount, outputLayerNodeCount])) outputLayerBiasVariable = tf.Variable(tf.random_normal([outputLayerNodeCount])) inputLayerTensorTranspose = tf.transpose(inputLayerTensor, [1, 0, 2]) inputLayerTensorReshape = tf.reshape(inputLayerTensorTranspose, [-1, inputLayerNodeCount]) inputLayerTensorSplit = tf.split(axis = 0, num_or_size_splits = inputLayerNodeStepCount, value = inputLayerTensorReshape) basicLSTMCell = rnn.BasicLSTMCell(hiddenLayerNodeCount, forget_bias = 1.0) outputList, lstmStateTuple = rnn.static_rnn(basicLSTMCell, inputLayerTensorSplit, dtype = tf.float32) outputLayerOutperTensor = tf.matmul(outputList[-1], outputLayerWeightVariable) + outputLayerBiasVariable costTensor = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = outputLayerOutperTensor, labels = correctOutputTensor)) optimizerOperation = tf.train.AdamOptimizer(learningRate).minimize(costTensor) correctPredictionTensor = tf. equal(tf.argmax(outputLayerOutperTensor,1), tf.argmax(correctOutputTensor, 1)) accuracyTensor = tf.reduce_mean(tf.cast(correctPredictionTensor, tf.float32)) with tf.Session() as session: session.run(tf.global_variables_initializer()) step = 1 while step * batchSize < trainingCount: batchInputNDArray, batchCorrectOutputNDArray = mnistDatasets.train.next_batch(batchSize) batchInputNDArray = batchInputNDArray.reshape((batchSize, inputLayerNodeStepCount, inputLayerNodeCount)) session.run(optimizerOperation, feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray}) if step % displayStep == 0: accuracy = session.run(accuracyTensor, feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray}) loss = session.run(costTensor, feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray}) print("훈련 카운트 : " + str(step * batchSize) + ", 미니 배치 손실 = " + "{:.6f}".format(loss) + ", 훈련 정확도 = " + "{:.5f}".format(accuracy)) step += 1 print("최적화가 완료되었습니다!") testSize = 128 testInputNDArray = mnistDatasets.test.images[:testSize].reshape((-1, inputLayerNodeStepCount, inputLayerNodeCount)) testCorrectOutputNDArray = mnistDatasets.test.labels[:testSize] print("테스트 정확도 : ", session.run(accuracyTensor, feed_dict = {inputLayerTensor : testInputNDArray, correctOutpu |
■ 컨볼루션 오토 인코더를 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
import matplotlib.pyplot as pp import numpy as np import tensorflow as tf import tensorflow.examples.tutorials.mnist as mnist from tensorflow.python.framework import ops import warnings import random import os warnings.filterwarnings("ignore") os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" ops.reset_default_graph() # MNIST 데이터를 로드한다. print("MNIST 데이터 로드를 시작합니다.") mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) print("MNIST 데이터 로드를 종료합니다.") # 네트워크 파라미터 inputLayerNodeCount = 784 learningRate = 0.001 batchSize = 128 epochCount = 50 n1 = 16 n2 = 32 n3 = 64 ksize = 5 inputLayerTensor = tf.placeholder(tf.float32, [None, inputLayerNodeCount]) inputLayerTensorReshape = tf.reshape(inputLayerTensor, shape = [-1, 28, 28, 1]) correctOutputTensor = tf.placeholder(tf.float32, [None, inputLayerNodeCount]) correctOutputTensorReshape = tf.reshape(correctOutputTensor, shape = [-1, 28, 28, 1]) dropoutRateTensor = tf.placeholder(tf.float32) encoderConvolutionLayer1WeightVariable = tf.Variable(tf.random_normal([ksize, ksize, 1 , n1], stddev = 0.1)) encoderConvolutionLayer2WeightVariable = tf.Variable(tf.random_normal([ksize, ksize, n1, n2], stddev = 0.1)) encoderConvolutionLayer3WeightVariable = tf.Variable(tf.random_normal([ksize, ksize, n2, n3], stddev = 0.1)) decoderConvolutionLayer3WeightVariable = tf.Variable(tf.random_normal([ksize, ksize, n2, n3], stddev = 0.1)) decoderConvolutionLayer2WeightVariable = tf.Variable(tf.random_normal([ksize, ksize, n1, n2], stddev = 0.1)) decoderConvolutionLayer1WeightVariable = tf.Variable(tf.random_normal([ksize, ksize, 1 , n1], stddev = 0.1)) encoderConvolutionLayer1BiasVariable = tf.Variable(tf.random_normal([n1], stddev = 0.1)) encoderConvolutionLayer2BiasVariable = tf.Variable(tf.random_normal([n2], stddev = 0.1)) encoderConvolutionLayer3BiasVariable = tf.Variable(tf.random_normal([n3], stddev = 0.1)) decoderConvolutionLayer3BiasVariable = tf.Variable(tf.random_normal([n2], stddev = 0.1)) decoderConvolutionLayer2BiasVariable = tf.Variable(tf.random_normal([n1], stddev = 0.1)) decoderConvolutionLayer1BiasVariable = tf.Variable(tf.random_normal([1 ], stddev = 0.1)) # 인코더 encoderConvolutionLayer1OutputTensor = tf.nn.sigmoid(tf.add(tf.nn.conv2d(inputLayerTensorReshape ,\ encoderConvolutionLayer1WeightVariable, strides = [1, 2, 2, 1], padding = "SAME"), encoderConvolutionLayer1BiasVariable)) encoderConvolutionLayer1OutputTensorDropout = tf.nn.dropout(encoderConvolutionLayer1OutputTensor, dropoutRateTensor) encoderConvolutionLayer2OutputTensor = tf.nn.sigmoid(tf.add(tf.nn.conv2d(encoderConvolutionLayer1OutputTensorDropout,\ encoderConvolutionLayer2WeightVariable, strides = [1, 2, 2, 1], padding = "SAME"), encoderConvolutionLayer2BiasVariable)) encoderConvolutionLayer2OutputTensorDropout = tf.nn.dropout(encoderConvolutionLayer2OutputTensor, dropoutRateTensor) encoderConvolutionLayer3OutputTensor = tf.nn.sigmoid(tf.add(tf.nn.conv2d(encoderConvolutionLayer2OutputTensorDropout,\ encoderConvolutionLayer3WeightVariable, strides = [1, 2, 2, 1], padding = "SAME"), encoderConvolutionLayer3BiasVariable)) encoderConvolutionLayer3OutputTensorDropout = tf.nn.dropout(encoderConvolutionLayer3OutputTensor, dropoutRateTensor) # 디코더 decoderConvolutionLayer3OutputTensor = tf.nn.sigmoid(tf.add(tf.nn.conv2d_transpose(encoderConvolutionLayer3OutputTensorDropout,\ decoderConvolutionLayer3WeightVariable, tf.stack([tf.shape(inputLayerTensor)[0], 7 , 7 , n2]), strides = [1, 2, 2, 1], padding = "SAME"),\ decoderConvolutionLayer3BiasVariable)) decoderConvolutionLayer3OutputTensorDropout = tf.nn.dropout(decoderConvolutionLayer3OutputTensor, dropoutRateTensor) decoderConvolutionLayer2OutputTensor = tf.nn.sigmoid(tf.add(tf.nn.conv2d_transpose(decoderConvolutionLayer3OutputTensorDropout,\ decoderConvolutionLayer2WeightVariable, tf.stack([tf.shape(inputLayerTensor)[0], 14, 14, n1]), strides = [1, 2, 2, 1], padding = "SAME"),\ decoderConvolutionLayer2BiasVariable)) decoderConvolutionLayer2OutputTensorDropout = tf.nn.dropout(decoderConvolutionLayer2OutputTensor, dropoutRateTensor) decoderConvolutionLayer1OutputTensor = tf.nn.sigmoid(tf.add(tf.nn.conv2d_transpose(decoderConvolutionLayer2OutputTensorDropout,\ decoderConvolutionLayer1WeightVariable, tf.stack([tf.shape(inputLayerTensor)[0], 28, 28, 1 ]), strides = [1, 2, 2, 1], padding = "SAME"),\ decoderConvolutionLayer1BiasVariable)) decoderConvolutionLayer1OutputTensorDropout = tf.nn.dropout(decoderConvolutionLayer1OutputTensor, dropoutRateTensor) outputLayerOuputTensor = decoderConvolutionLayer1OutputTensorDropout costTensor = tf.reduce_sum(tf.square(outputLayerOuputTensor - correctOutputTensorReshape)) optimizerOperation = tf.train.AdamOptimizer(learningRate).minimize(costTensor) # 그래프를 실행한다. print("학습을 시작합니다.") with tf.Session() as session: session.run(tf.global_variables_initializer()) meanImageNDArray = np.zeros((inputLayerNodeCount)) for epoch in range(epochCount): totalBatch = mnistDatasets.train.num_examples // batchSize for batch in range(totalBatch): batchInputNDArray, _ = mnistDatasets.train.next_batch(batchSize) trainInputNDArray = np.array([sourceImageNDArray - meanImageNDArray for sourceImageNDArray in batchInputNDArray]) noiseTrainInputNDArray = trainInputNDArray + 0.3 * np.random.randn(trainInputNDArray.shape[0], inputLayerNodeCount) session.run(optimizerOperation, feed_dict = {inputLayerTensor : noiseTrainInputNDArray, correctOutputTensor : trainInputNDArray, dropoutRateTensor : 0.7}) print("[%02d/%02d] 비용 : %.4f" % (epoch, epochCount, session.run(costTensor, feed_dict = {inputLayerTensor : noiseTrainInputNDArray,\ correctOutputTensor : trainInputNDArray, dropoutRateTensor: 1.}))) if (epoch % 1) == 0: exampleCount = 5 testInputNDArray, _ = mnistDatasets.test.next_batch(exampleCount) noisyTestInputNDArray = testInputNDArray + 0.3 * np.random.randn(testInputNDArray.shape[0], inputLayerNodeCount) outputNDArray = session.run(outputLayerOuputTensor, feed_dict = {inputLayerTensor : noisyTestInputNDArray, dropoutRateTensor : 1.}) figure, axesNDArray = pp.subplots(2, exampleCount, figsize = (15, 4)) for example in range(exampleCount): axesNDArray[0][example].matshow(np.reshape(noisyTestInputNDArray[example, :], (28, 28)), cmap = pp.get_cmap("gray")) axesNDArray[1][example].matshow(np.reshape(np.reshape(outputNDArray[example, ...], (inputLayerNodeCount,)) + meanImageNDArray, (28, 28)),\ cmap = pp.get_cmap("gray")) pp.show() |
■ 노이즈 제거 오토 인코더를 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
import matplotlib.pyplot as pp import numpy as np import tensorflow as tf import tensorflow.examples.tutorials.mnist as mnist def DrawResult(originalImageNDArray, noisyImageNDArray, outputImageNDArray): pp.matshow(np.reshape(originalImageNDArray, (28, 28)), cmap = pp.get_cmap("gray")) pp.title("Original Image") pp.colorbar() pp.matshow(np.reshape(noisyImageNDArray, (28, 28)), cmap = pp.get_cmap("gray")) pp.title("Input Image") pp.colorbar() pp.matshow(np.reshape(outputImageNDArray, (28, 28)), cmap = pp.get_cmap("gray")) pp.title("Reconstructed Image") pp.colorbar() pp.show() # 네트워크 파라미터 InputLayerNodeCount = 784 hiddenLayer1NodeCount = 256 hiddenLayer2NodeCount = 256 outputLayerNodeCount = 784 epochCount = 100 batchSize = 100 displayStep = 10 print("MNIST 데이터 로드를 시작합니다.") mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) print("MNIST 데이터 로드를 종료합니다.") # 네트워크 모델을 정의한다. inputLayerTensor = tf.placeholder("float", [None, InputLayerNodeCount ]) correctOutputTensor = tf.placeholder("float", [None, outputLayerNodeCount]) dropoutRateTensor = tf.placeholder("float") hiddenLayer1WeightVariable = tf.Variable(tf.random_normal([InputLayerNodeCount , hiddenLayer1NodeCount])) hiddenLayer2WeightVariable = tf.Variable(tf.random_normal([hiddenLayer1NodeCount, hiddenLayer2NodeCount])) outputLayerWeightVariable = tf.Variable(tf.random_normal([hiddenLayer2NodeCount, outputLayerNodeCount ])) hiddenLayer1BiasVariable = tf.Variable(tf.random_normal([hiddenLayer1NodeCount])) hiddenLayer2BiasVariable = tf.Variable(tf.random_normal([hiddenLayer2NodeCount])) outputLayerBiasVariable = tf.Variable(tf.random_normal([outputLayerNodeCount ])) hiddenLayer1OutputTensor = tf.nn.sigmoid(tf.add(tf.matmul(inputLayerTensor, hiddenLayer1WeightVariable), hiddenLayer1BiasVariable)) hiddenLayer1OutputTensorDropout = tf.nn.dropout(hiddenLayer1OutputTensor, dropoutRateTensor) hiddenLayer2OutputTensor = tf.nn.sigmoid(tf.add(tf.matmul(hiddenLayer1OutputTensorDropout, hiddenLayer2WeightVariable), hiddenLayer2BiasVariable)) hiddenLayer2OutputTensorDropout = tf.nn.dropout(hiddenLayer2OutputTensor, dropoutRateTensor) outputLayerOutputTensor = tf.nn.sigmoid(tf.matmul(hiddenLayer2OutputTensorDropout, outputLayerWeightVariable) + outputLayerBiasVariable) costTensor = tf.reduce_mean(tf.pow(outputLayerOutputTensor - correctOutputTensor, 2)) optmizerOperation = tf.train.RMSPropOptimizer(0.01).minimize(costTensor) # 그래를 실행한다. with tf.Session() as session: session.run(tf.global_variables_initializer()) print("훈련을 시작합니다.") for epoch in range(epochCount): batchCount = int(mnistDatasets.train.num_examples / batchSize) totalCost = 0. for batch in range(batchCount): batchInputNDArray, batchCorrectOutputNDArray = mnistDatasets.train.next_batch(batchSize) noisyBatchInputNDArray = batchInputNDArray + 0.3 * np.random.randn(batchSize, 784) feedDictionary = {inputLayerTensor : noisyBatchInputNDArray, correctOutputTensor : batchInputNDArray, dropoutRateTensor : 0.8} session.run(optmizerOperation, feed_dict = feedDictionary) totalCost += session.run(costTensor, feed_dict = feedDictionary) if epoch % displayStep == 0: print("Epoch : %02d/%02d, 평균 비용 : %.6f" % (epoch, epochCount, totalCost / batchCount)) print("테스트를 시작합니다.") testIndex = np.random.randint(mnistDatasets.test.images.shape[0], size = 1) originalImageNDArray = mnistDatasets.test.images[testIndex, :] testImageNDArray = mnistDatasets.test.images[testIndex, :] testImageLabel = np.argmax(mnistDatasets.test.labels[testIndex, :], 1) print("테스트 이미지 라벨 : %d" % (testImageLabel)) noisyTestImageNDArray = testImageNDArray + 0.3 * np.random.randn(1, 784) outputImageNDArray = session.run(outputLayerOutputTensor, feed_dict = {inputLayerTensor : noisyTestImageNDArray, dropoutRateTensor : 1}) DrawResult(originalImageNDArray, noisyTestImageNDArray, outputImageNDArray) print("훈련을 다시 시작합니다.") |
■ 컨볼루션 신경망을 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import numpy as np import tensorflow as tf import tensorflow.examples.tutorials.mnist as mnist batchSize = 128 testSize = 256 imageSize = 28 outputLayerNodeCount = 10 convolutionDropoutRate = 0.8 fullyConnectedDropoutRate = 0.5 mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) trainInputNDArray = mnistDatasets.train.images trainCorrectOutputNDArray = mnistDatasets.train.labels testInputNDArray = mnistDatasets.test.images testCorrectOutputNDArray = mnistDatasets.test.labels trainInputNDArray = trainInputNDArray.reshape(-1, imageSize, imageSize, 1) testInputNDArray = testInputNDArray.reshape (-1, imageSize, imageSize, 1) inputLayerTensor = tf.placeholder("float", [None, imageSize, imageSize, 1]) correctOutputTensor = tf.placeholder("float", [None, outputLayerNodeCount ]) convolutionLayer1WeightVariable = tf.Variable(tf.random_normal([3, 3, 1 , 32 ], stddev = 0.01)) convolutionLayer2WeightVariable = tf.Variable(tf.random_normal([3, 3, 32, 64 ], stddev = 0.01)) convolutionLayer3WeightVariable = tf.Variable(tf.random_normal([3, 3, 64, 128 ], stddev = 0.01)) fullyConnectedLayerWeightVariable = tf.Variable(tf.random_normal([128 * 4 * 4, 625 ], stddev = 0.01)) outputLayerWeightVariable = tf.Variable(tf.random_normal([625, outputLayerNodeCount], stddev = 0.01)) convolutionDropoutRateTensor = tf.placeholder("float") fullyConnectedDropoutRateTensor = tf.placeholder("float") convolutionLayer1OutputTensor = tf.nn.conv2d(inputLayerTensor, convolutionLayer1WeightVariable, strides = [1, 1, 1, 1], padding = "SAME") convolutionLayer1OutputTensorReLU = tf.nn.relu(convolutionLayer1OutputTensor) convolutionLayer1OutputTensorMaxPool = tf.nn.max_pool(convolutionLayer1OutputTensorReLU, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = "SAME") convolutionLayer1OutputTensorDropout = tf.nn.dropout(convolutionLayer1OutputTensorMaxPool, convolutionDropoutRateTensor) convolutionLayer2OutputTensor = tf.nn.conv2d(convolutionLayer1OutputTensorDropout, convolutionLayer2WeightVariable, strides = [1, 1, 1, 1], padding = "SAME") convolutionLayer2OutputTensorReLU = tf.nn.relu(convolutionLayer2OutputTensor) convolutionLayer2OutputTensorMaxPool = tf.nn.max_pool(convolutionLayer2OutputTensorReLU, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = "SAME") convolutionLayer2OutputTensorDropout = tf.nn.dropout(convolutionLayer2OutputTensorMaxPool, convolutionDropoutRateTensor) convolutionLayer3OutputTensor = tf.nn.conv2d(convolutionLayer2OutputTensorDropout, convolutionLayer3WeightVariable, strides = [1, 1, 1, 1], padding = "SAME") convolutionLayer3OutputTensorReLU = tf.nn.relu(convolutionLayer3OutputTensor) convolutionLayer3OutputTensorMaxPool = tf.nn.max_pool(convolutionLayer3OutputTensorReLU, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = "SAME") convolutionLayer3OutputTensorReshape = tf.reshape(convolutionLayer3OutputTensorMaxPool, [-1, fullyConnectedLayerWeightVariable.get_shape().as_list()[0]]) convolutionLayer3OutputTensorDropout = tf.nn.dropout(convolutionLayer3OutputTensorReshape, convolutionDropoutRateTensor) fullyConnectedLayerOutputTensor = tf.matmul(convolutionLayer3OutputTensorDropout, fullyConnectedLayerWeightVariable) fullyConnectedLayerOutputTensorReLU = tf.nn.relu(fullyConnectedLayerOutputTensor) fullyConnectedLayerOutputTensorDropout = tf.nn.dropout(fullyConnectedLayerOutputTensorReLU, fullyConnectedDropoutRateTensor) outputLayerOutputTensor = tf.matmul(fullyConnectedLayerOutputTensorDropout, outputLayerWeightVariable) costTensor = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = outputLayerOutputTensor, labels = correctOutputTensor)) optimizerOperation = tf.train.RMSPropOptimizer(0.001, 0.9).minimize(costTensor) predictTensor = tf.argmax(outputLayerOutputTensor, 1) with tf.Session() as session: session.run(tf.global_variables_initializer()) for i in range(10): trainingBatch = zip(range(0, len(trainInputNDArray), batchSize), range(batchSize, len(trainInputNDArray) + 1, batchSize)) for startIndex, endIndex in trainingBatch: session.run(optimizerOperation, feed_dict = {inputLayerTensor : trainInputNDArray[startIndex:endIndex],\ correctOutputTensor: trainCorrectOutputNDArray[startIndex:endIndex], convolutionDropoutRateTensor : convolutionDropoutRate,\ fullyConnectedDropoutRateTensor : fullyConnectedDropoutRate}) testIndexNDArray = np.arange(len(testInputNDArray)) np.random.shuffle(testIndexNDArray) testIndexNDArray = testIndexNDArray[0:testSize] print("Epoch : ", i + 1, "정확도 : ", np.mean(np.argmax(testCorrectOutputNDArray[testIndexNDArray], axis = 1) == session.run(predictTensor,\ feed_dict = {inputLayerTensor : testInputNDArray[testIndexNDArray], correctOutputTensor : testCorrectOutputNDArray[testIndexNDArray],\ convolutionDropoutRateTensor : 1.0, fullyConnectedDropoutRateTensor : 1.0}))) scoreTensor = tf.equal(tf.argmax(outputLayerOutputTensor, 1), tf.argmax(correctOutputTensor, 1)) accuracyTensor = tf.reduce_mean(tf.cast(scoreTensor, tf.float32)) print("정확도 : ", session.run(accuracyTensor, feed_dict = {inputLayerTensor : testInputNDArray, correctOutputTensor : testCorrectOutputNDArray,\ convolutionDropoutRateTensor : 1.0, fullyConnectedDropoutRateTensor : 1.0})) |
■ 오토 인코더를 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 |
import matplotlib.pyplot as pp import numpy as np import tensorflow as tf import tensorflow.examples.tutorials.mnist as mnist # MNIST 데이터를 로드한다. mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) # 파라미터를 설정한다. learningRate = 0.01 epochCount = 20 batchSize = 256 displayStep = 1 exampleCount = 10 # 네트워크 파라미터를 설정한다. inputLayerNodeCount = 784 # 28×28픽셀 hiddenLayer1NodeCount = 256 hiddenLayer2NodeCount = 128 # 모델을 정의한다. inputLayerTensor = tf.placeholder("float", [None, inputLayerNodeCount]) encoderHiddenLayer1WeightVariable = tf.Variable(tf.random_normal([inputLayerNodeCount , hiddenLayer1NodeCount])) encoderHiddenLayer2WeightVariable = tf.Variable(tf.random_normal([hiddenLayer1NodeCount, hiddenLayer2NodeCount])) decoderHiddenLayer1WeightVariable = tf.Variable(tf.random_normal([hiddenLayer2NodeCount, hiddenLayer1NodeCount])) decoderHiddenLayer2WeightVariable = tf.Variable(tf.random_normal([hiddenLayer1NodeCount, inputLayerNodeCount ])) encoderHiddenLayer1BiasVariable = tf.Variable(tf.random_normal([hiddenLayer1NodeCount])) encoderHiddenLayer2BiasVariable = tf.Variable(tf.random_normal([hiddenLayer2NodeCount])) decoderHiddenLayer1BiasVariable = tf.Variable(tf.random_normal([hiddenLayer1NodeCount])) decoderHiddenLayer2BiasVariable = tf.Variable(tf.random_normal([inputLayerNodeCount ])) encoderHiddenLayer1OutputTensor = tf.nn.sigmoid(tf.add(tf.matmul(inputLayerTensor , encoderHiddenLayer1WeightVariable), encoderHiddenLayer1BiasVariable)) encoderHiddenLayer2OutputTensor = tf.nn.sigmoid(tf.add(tf.matmul(encoderHiddenLayer1OutputTensor, encoderHiddenLayer2WeightVariable), encoderHiddenLayer2BiasVariable)) decoderHiddenLayer1OutputTensor = tf.nn.sigmoid(tf.add(tf.matmul(encoderHiddenLayer2OutputTensor, decoderHiddenLayer1WeightVariable), decoderHiddenLayer1BiasVariable)) decoderHiddenLayer2OutputTensor = tf.nn.sigmoid(tf.add(tf.matmul(decoderHiddenLayer1OutputTensor, decoderHiddenLayer2WeightVariable), decoderHiddenLayer2BiasVariable)) outputLayerOutputTensor = decoderHiddenLayer2OutputTensor correctOutputTensor = inputLayerTensor costTensor = tf.reduce_mean(tf.pow(correctOutputTensor - outputLayerOutputTensor, 2)) optimizerOperation = tf.train.RMSPropOptimizer(learningRate).minimize(costTensor) # 그래프를 실행한다. with tf.Session() as session: session.run(tf.global_variables_initializer()) batchCount = int(mnistDatasets.train.num_examples / batchSize) for epoch in range(epochCount): for batch in range(batchCount): batchInputNDArray, _ = mnistDatasets.train.next_batch(batchSize) _, cost = session.run([optimizerOperation, costTensor], feed_dict = {inputLayerTensor : batchInputNDArray}) if epoch % displayStep == 0: print("Epoch :", '%04d' % (epoch + 1), "비용 = ", "{:.9f}".format(cost)) print("최적화가 완료되었습니다!") # 테스트 집합에 대해 인코딩/디코딩을 처리한다. outputImageNDArray = session.run(outputLayerOutputTensor, feed_dict= {inputLayerTensor : mnistDatasets.test.images[:exampleCount]}) # 원본/복원 이미지를 비교한다. figure, axesNDArray = pp.subplots(2, 10, figsize = (10, 2)) for example in range(exampleCount): axesNDArray [0][example].imshow(np.reshape(mnistDatasets.test.images[example], (28, 28))) axesNDArray [1][example].imshow(np.reshape(outputImageNDArray[example], (28, 28))) figure.show() pp.draw() pp.show() |
■ 컨볼루션 신경망을 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
import tensorflow as tf import tensorflow.examples.tutorials.mnist as mnist imageSize = 28 batchSize = 100 outputLayerNodeCount = 10 learningRate = 0.001 epochCount = 10 dropoutRate = 0.8 mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) inputLayerTensor = tf.placeholder(tf.float32, [None, imageSize, imageSize, 1]) correctOutputTensor = tf.placeholder(tf.float32, [None, outputLayerNodeCount]) dropoutRateTensor = tf.placeholder(tf.float32) convolutionLayer1WeightVariable = tf.Variable(tf.random_normal([4, 4, 1, 16 ], stddev = 0.01)) convolutionLayer2WeightVariable = tf.Variable(tf.random_normal([4, 4, 16, 32 ], stddev = 0.01)) fullyConnectedLayerWeightVariable = tf.Variable(tf.random_normal([7 * 7 * 32, 256 ], stddev = 0.01)) outputLayerWeightVariable = tf.Variable(tf.random_normal([256, outputLayerNodeCount], stddev = 0.01)) convolutionLayer1OutputTensor = tf.nn.conv2d(inputLayerTensor, convolutionLayer1WeightVariable, strides = [1, 1, 1, 1], padding = "SAME") convolutionLayer1OutputTensor = tf.nn.relu(convolutionLayer1OutputTensor) convolutionLayer1OutputTensor = tf.nn.max_pool(convolutionLayer1OutputTensor, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = "SAME") convolutionLayer2OutputTensor = tf.nn.conv2d(convolutionLayer1OutputTensor, convolutionLayer2WeightVariable, strides = [1, 1, 1, 1], padding = "SAME") convolutionLayer2OutputTensor = tf.nn.relu(convolutionLayer2OutputTensor) convolutionLayer2OutputTensor = tf.nn.max_pool(convolutionLayer2OutputTensor, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = "SAME") fullyConnectedLayerOutputTensor = tf.reshape(convolutionLayer2OutputTensor, [-1, 7 * 7 * 32]) fullyConnectedLayerOutputTensor = tf.matmul(fullyConnectedLayerOutputTensor, fullyConnectedLayerWeightVariable) fullyConnectedLayerOutputTensor = tf.nn.relu(fullyConnectedLayerOutputTensor) fullyConnectedLayerOutputTensor = tf.nn.dropout(fullyConnectedLayerOutputTensor, dropoutRateTensor) outputLayerOutputTensor = tf.matmul(fullyConnectedLayerOutputTensor, outputLayerWeightVariable) costTensor = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = outputLayerOutputTensor, labels = correctOutputTensor)) optimizerOperation = tf.train.AdamOptimizer(learningRate).minimize(costTensor) with tf.Session() as session: session.run(tf.global_variables_initializer()) totalBatch = int(mnistDatasets.train.num_examples / batchSize) for epoch in range(epochCount): totalCost = 0 for batch in range(totalBatch): batchInputNDArray, batchCorrectOutputNDArray = mnistDatasets.train.next_batch(batchSize) batchInputNDArray = batchInputNDArray.reshape(-1, imageSize, imageSize, 1) _, cost = session.run([optimizerOperation, costTensor], feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray,\ dropoutRateTensor : dropoutRate}) totalCost += cost print("반복 : ", "%04d" % (epoch + 1), "평균 비용 : ", "{:.4f}".format(totalCost / totalBatch)) print("학습 완료!") scoreTensor = tf.equal(tf.argmax(outputLayerOutputTensor, 1), tf.argmax(correctOutputTensor, 1)) accuracyTensor = tf.reduce_mean(tf.cast(scoreTensor, tf.float32)) print("정확도 : ", session.run(accuracyTensor, feed_dict = {inputLayerTensor : mnistDatasets.test.images.reshape(-1, imageSize, imageSize, 1),\ correctOutputTensor : mnistDatasets.test.labels, dropoutRateTensor : 1})) |
■ 4계층 ReLU Dropout 1계층 Softmax로 구성된 다층 퍼셉트론 신경망을 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
import math import tensorflow as tf import tensorflow.examples.tutorials.mnist as mnist inputLayerNodeCount = 784 hiddenLayer1NodeCount = 200 hiddenLayer2NodeCount = 100 hiddenLayer3NodeCount = 60 hiddenLayer4NodeCount = 30 outputLayerNodeCount = 10 summaryLogDirectoryPath = "log_mnist_4_layer_relu_dropout_1_layer_softmax" batchSize = 100 epochCount = 10 minimumLearningRate = 0.0001 maximumLearningRate = 0.003 decaySpeed = 2000 dropoutRate = 0.75 mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) inputLayerTensor = tf.placeholder(tf.float32, [None, inputLayerNodeCount]) learningRateTensor = tf.placeholder(tf.float32) dropoutRateTensor = tf.placeholder(tf.float32) hiddenLayer1WeightVariable = tf.Variable(tf.truncated_normal([inputLayerNodeCount , hiddenLayer1NodeCount], stddev = 0.1)) hiddenLayer1BiasVariable = tf.Variable(tf.ones([hiddenLayer1NodeCount]) / 10) hiddenLayer2WeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer1NodeCount, hiddenLayer2NodeCount], stddev = 0.1)) hiddenLayer2BiasVariable = tf.Variable(tf.ones([hiddenLayer2NodeCount]) / 10) hiddenLayer3WeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer2NodeCount, hiddenLayer3NodeCount], stddev = 0.1)) hiddenLayer3BiasVariable = tf.Variable(tf.ones([hiddenLayer3NodeCount]) / 10) hiddenLayer4WeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer3NodeCount, hiddenLayer4NodeCount], stddev = 0.1)) hiddenLayer4BiasVariable = tf.Variable(tf.ones([hiddenLayer4NodeCount]) / 10) outputLayerWeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer4NodeCount, outputLayerNodeCount ], stddev = 0.1)) outputLayerBiasVariable = tf.Variable(tf.zeros([outputLayerNodeCount])) hiddenLayer1OutputTensor = tf.nn.relu(tf.matmul(inputLayerTensor , hiddenLayer1WeightVariable) + hiddenLayer1BiasVariable) hiddenLayer1OutputTensorDropout = tf.nn.dropout(hiddenLayer1OutputTensor, dropoutRateTensor) hiddenLayer2OutputTensor = tf.nn.relu(tf.matmul(hiddenLayer1OutputTensorDropout, hiddenLayer2WeightVariable) + hiddenLayer2BiasVariable) hiddenLayer2OutputTensorDropout = tf.nn.dropout(hiddenLayer2OutputTensor, dropoutRateTensor) hiddenLayer3OutputTensor = tf.nn.relu(tf.matmul(hiddenLayer2OutputTensorDropout, hiddenLayer3WeightVariable) + hiddenLayer3BiasVariable) hiddenLayer3OutputTensorDropout = tf.nn.dropout(hiddenLayer3OutputTensor, dropoutRateTensor) hiddenLayer4OutputTensor = tf.nn.relu(tf.matmul(hiddenLayer3OutputTensorDropout, hiddenLayer4WeightVariable) + hiddenLayer4BiasVariable) hiddenLayer4OutputTensorDropout = tf.nn.dropout(hiddenLayer4OutputTensor, dropoutRateTensor) outputLayerOutputTensor = tf.matmul(hiddenLayer4OutputTensorDropout, outputLayerWeightVariable ) + outputLayerBiasVariable outputLayerOutputTensorSoftmax = tf.nn.softmax(outputLayerOutputTensor) correctOutputTensor = tf.placeholder(tf.float32, [None, outputLayerNodeCount]) costTensor = tf.nn.softmax_cross_entropy_with_logits(logits = outputLayerOutputTensor, labels = correctOutputTensor) costTensor = tf.reduce_mean(costTensor) * 100 correctPredictionTensor = tf.equal(tf.argmax(outputLayerOutputTensorSoftmax, 1), tf.argmax(correctOutputTensor, 1)) accuracyTensor = tf.reduce_mean(tf.cast(correctPredictionTensor, tf.float32)) optimizerOperation = tf.train.AdamOptimizer(learningRateTensor).minimize(costTensor) tf.summary.scalar("cost" , costTensor ) tf.summary.scalar("accuracy", accuracyTensor) summaryTensor = tf.summary.merge_all() with tf.Session() as session: session.run(tf.global_variables_initializer()) fileWriter = tf.summary.FileWriter(summaryLogDirectoryPath, graph = tf.get_default_graph()) batchCount = int(mnistDatasets.train.num_examples / batchSize) for epoch in range(epochCount): for batch in range(batchCount): batchInputNDArray, batchCorrectOutputNDArray = mnistDatasets.train.next_batch(batchSize) learningRatio = minimumLearningRate + (maximumLearningRate - minimumLearningRate) * math.exp(-batch / decaySpeed) _, summary = session.run([optimizerOperation, summaryTensor], feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray,\ dropoutRateTensor : dropoutRate, learningRateTensor : learningRatio}) fileWriter.add_summary(summary, epoch * batchCount + batch) print("Epoch : ", epoch) print("정확도 : ", accuracyTensor.eval(feed_dict = {inputLayerTensor : mnistDatasets.test.images, correctOutputTensor : mnistDatasets.test.labels,\ dropoutRateTensor : dropoutRate})) print("학습을 완료했습니다.") |
■ 4계층 Sigmoid 1계층 Softmax로 구성된 다층 퍼셉트론 신경망을 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import tensorflow as tf import tensorflow.examples.tutorials.mnist as mnist inputLayerNodeCount = 784 hiddenLayer1NodeCount = 200 hiddenLayer2NodeCount = 100 hiddenLayer3NodeCount = 60 hiddenLayer4NodeCount = 30 outputLayerNodeCount = 10 summaryLogDirectoryPath = "log_mnist_4_layer_sigmoid_1_layer_softmax" batchSize = 100 learningRate = 0.005 epochCount = 10 mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) inputLayerTensor = tf.placeholder(tf.float32, [None, inputLayerNodeCount]) hiddenLayer1WeightVariable = tf.Variable(tf.truncated_normal([inputLayerNodeCount , hiddenLayer1NodeCount], stddev = 0.1)) hiddenLayer1BiasVariable = tf.Variable(tf.zeros([hiddenLayer1NodeCount])) hiddenLayer2WeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer1NodeCount, hiddenLayer2NodeCount], stddev = 0.1)) hiddenLayer2BiasVariable = tf.Variable(tf.zeros([hiddenLayer2NodeCount])) hiddenLayer3WeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer2NodeCount, hiddenLayer3NodeCount], stddev = 0.1)) hiddenLayer3BiasVariable = tf.Variable(tf.zeros([hiddenLayer3NodeCount])) hiddenLayer4WeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer3NodeCount, hiddenLayer4NodeCount], stddev = 0.1)) hiddenLayer4BiasVariable = tf.Variable(tf.zeros([hiddenLayer4NodeCount])) outputLayerWeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer4NodeCount, outputLayerNodeCount ], stddev = 0.1)) outputLayerBiasVariable = tf.Variable(tf.zeros([outputLayerNodeCount ])) hiddenLayer1OutputTensor = tf.nn.sigmoid(tf.matmul(inputLayerTensor , hiddenLayer1WeightVariable) + hiddenLayer1BiasVariable) hiddenLayer2OutputTensor = tf.nn.sigmoid(tf.matmul(hiddenLayer1OutputTensor, hiddenLayer2WeightVariable) + hiddenLayer2BiasVariable) hiddenLayer3OutputTensor = tf.nn.sigmoid(tf.matmul(hiddenLayer2OutputTensor, hiddenLayer3WeightVariable) + hiddenLayer3BiasVariable) hiddenLayer4OutputTensor = tf.nn.sigmoid(tf.matmul(hiddenLayer3OutputTensor, hiddenLayer4WeightVariable) + hiddenLayer4BiasVariable) outputLayerOutputTensor = tf.matmul(hiddenLayer4OutputTensor, outputLayerWeightVariable ) + outputLayerBiasVariable outputLayerOutputTensorSoftmax = tf.nn.softmax(outputLayerOutputTensor) correctOutputTensor = tf.placeholder(tf.float32, [None, outputLayerNodeCount]) costTensor = tf.nn.softmax_cross_entropy_with_logits(logits = outputLayerOutputTensor, labels = correctOutputTensor) costTensor = tf.reduce_mean(costTensor) * 100 correctPredictionTensor = tf.equal(tf.argmax(outputLayerOutputTensorSoftmax, 1), tf.argmax(correctOutputTensor, 1)) accuracyTensor = tf.reduce_mean(tf.cast(correctPredictionTensor, tf.float32)) optimizerOperation = tf.train.AdamOptimizer(learningRate).minimize(costTensor) tf.summary.scalar("cost" , costTensor ) tf.summary.scalar("accuracy", accuracyTensor) summaryTensor = tf.summary.merge_all() with tf.Session() as session: session.run(tf.global_variables_initializer()) fileWriter = tf.summary.FileWriter(summaryLogDirectoryPath, graph = tf.get_default_graph()) batchCount = int(mnistDatasets.train.num_examples / batchSize) for epoch in range(epochCount): for batch in range(batchCount): batchInputNDArray, batchCorrectOutputNDArray = mnistDatasets.train.next_batch(batchSize) _, summary = session.run([optimizerOperation, summaryTensor], feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray}) fileWriter.add_summary(summary, epoch * batchCount + batch) print("Epoch : ", epoch) print("정확도 : ", accuracyTensor.eval(feed_dict = {inputLayerTensor : mnistDatasets.test.images, correctOutputTensor : mnistDatasets.test.labels})) print("학습이 완료되었습니다.") |
■ 4계층 ReLU 1계층 Softmax로 구성된 다층 퍼셉트론 신경망을 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import tensorflow as tf import tensorflow.examples.tutorials.mnist as mnist inputLayerNodeCount = 784 hiddenLayer1NodeCount = 200 hiddenLayer2NodeCount = 100 hiddenLayer3NodeCount = 60 hiddenLayer4NodeCount = 30 outputLayerNodeCount = 10 summaryLogDirectoryPath = "log_mnist_4_layer_relu_1_layer_softmax" batchSize = 100 learningRate = 0.005 epochCount = 10 mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) inputLayerTensor = tf.placeholder(tf.float32, [None, inputLayerNodeCount]) hiddenLayer1WeightVariable = tf.Variable(tf.truncated_normal([inputLayerNodeCount , hiddenLayer1NodeCount], stddev = 0.1)) hiddenLayer1BiasVariable = tf.Variable(tf.zeros([hiddenLayer1NodeCount])) hiddenLayer2WeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer1NodeCount, hiddenLayer2NodeCount], stddev = 0.1)) hiddenLayer2BiasVariable = tf.Variable(tf.zeros([hiddenLayer2NodeCount])) hiddenLayer3WeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer2NodeCount, hiddenLayer3NodeCount], stddev = 0.1)) hiddenLayer3BiasVariable = tf.Variable(tf.zeros([hiddenLayer3NodeCount])) hiddenLayer4WeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer3NodeCount, hiddenLayer4NodeCount], stddev = 0.1)) hiddenLayer4BiasVariable = tf.Variable(tf.zeros([hiddenLayer4NodeCount])) outputLayerWeightVariable = tf.Variable(tf.truncated_normal([hiddenLayer4NodeCount, outputLayerNodeCount ], stddev = 0.1)) outputLayerBiasVariable = tf.Variable(tf.zeros([outputLayerNodeCount])) hiddenLayer1OutputTensor = tf.nn.relu(tf.matmul(inputLayerTensor , hiddenLayer1WeightVariable) + hiddenLayer1BiasVariable) hiddenLayer2OutputTensor = tf.nn.relu(tf.matmul(hiddenLayer1OutputTensor, hiddenLayer2WeightVariable) + hiddenLayer2BiasVariable) hiddenLayer3OutputTensor = tf.nn.relu(tf.matmul(hiddenLayer2OutputTensor, hiddenLayer3WeightVariable) + hiddenLayer3BiasVariable) hiddenLayer4OutputTensor = tf.nn.relu(tf.matmul(hiddenLayer3OutputTensor, hiddenLayer4WeightVariable) + hiddenLayer4BiasVariable) outputLayerOutputTensor = tf.matmul(hiddenLayer4OutputTensor, outputLayerWeightVariable ) + outputLayerBiasVariable outputLayerOutputTensorSoftmax = tf.nn.softmax(outputLayerOutputTensor) correctOutputTensor = tf.placeholder(tf.float32, [None, outputLayerNodeCount]) costTensor = tf.nn.softmax_cross_entropy_with_logits(logits = outputLayerOutputTensor, labels = correctOutputTensor) costTensor = tf.reduce_mean(costTensor) * 100 correctPredictionTensor = tf.equal(tf.argmax(outputLayerOutputTensorSoftmax, 1), tf.argmax(correctOutputTensor, 1)) accuracyTensor = tf.reduce_mean(tf.cast(correctPredictionTensor, tf.float32)) optimizerOperation = tf.train.AdamOptimizer(learningRate).minimize(costTensor) tf.summary.scalar("cost" , costTensor ) tf.summary.scalar("accuracy", accuracyTensor) summaryTensor = tf.summary.merge_all() with tf.Session() as session: session.run(tf.global_variables_initializer()) fileWriter = tf.summary.FileWriter(summaryLogDirectoryPath, graph = tf.get_default_graph()) batchCount = int(mnistDatasets.train.num_examples / batchSize) for epoch in range(epochCount): for batch in range(batchCount): batchInputNDArray, batchCorrectOutputNDArray = mnistDatasets.train.next_batch(batchSize) _, summary = session.run([optimizerOperation, summaryTensor], feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray}) fileWriter.add_summary(summary, epoch * batchCount + batch) print("Epoch : ", epoch) print("정확도 : ", accuracyTensor.eval(feed_dict = {inputLayerTensor : mnistDatasets.test.images, correctOutputTensor : mnistDatasets.test.labels})) print("학습이 완료되었습니다.") |
■ 신경망 모델을 로드하는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 |
import random import tensorflow as tf import tensorflow.examples.tutorials.mnist as mnist mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) session = tf.InteractiveSession() saver = tf.train.import_meta_graph("data/mnist_1_layer_softmax.ckpt.meta") saver.restore(session, "data/mnist_1_layer_softmax.ckpt") tf.get_default_graph().as_graph_def() inputTensor = session.graph.get_tensor_by_name("input:0" ) outputTensor = session.graph.get_tensor_by_name("output:0") testImageIndex = random.randint(0, mnistDatasets.test.images.shape[0]) testImageNDArray = mnistDatasets.test.images[testImageIndex] result = session.run(["input:0", outputTensor], feed_dict = {inputTensor : [testImageNDArray]}) print("정답 : ", session.run(tf.argmax(mnistDatasets.test.labels[testImageIndex]))) print("판단 : ", session.run(tf.argmax(result[1], 1)[0])) |
■ 순방향 신경망(Feed-Forward Neural Network, FFNN)을 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
import numpy as np import random import tensorflow as tf import tensorflow.examples.tutorials.mnist as mnist inputLayerNodeCount = 784 outputLayerNodeCount = 10 learningRate = 0.5 epochCount = 10 batchSize = 100 summaryLogDirectoryPath = "log_mnist_1_layer_softmax" mnistDatasets = mnist.input_data.read_data_sets("data", one_hot = True) inputLayerTensor = tf.placeholder(tf.float32, [None, inputLayerNodeCount], name = "input") hiddenLayerWeightVariable = tf.Variable(tf.zeros([inputLayerNodeCount, outputLayerNodeCount])) hiddenLayerBiasVariable = tf.Variable(tf.zeros([outputLayerNodeCount])) outputLayerOutputTensor = tf.nn.softmax(tf.matmul(inputLayerTensor, hiddenLayerWeightVariable) + hiddenLayerBiasVariable, name = "output") correctOutputTensor = tf.placeholder(tf.float32, [None, outputLayerNodeCount]) costTensor = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = correctOutputTensor, logits = outputLayerOutputTensor)) correctPredictionTensor = tf.equal(tf.argmax(outputLayerOutputTensor, 1), tf.argmax(correctOutputTensor, 1)) accuracyTensor = tf.reduce_mean(tf.cast(correctPredictionTensor, tf.float32)) optimizerOperation = tf.train.GradientDescentOptimizer(learningRate).minimize(costTensor) tf.summary.scalar("cost" , costTensor ) tf.summary.scalar("accuracy", accuracyTensor) summaryTensor = tf.summary.merge_all() with tf.Session() as session: session.run(tf.global_variables_initializer()) fileWriter = tf.summary.FileWriter(summaryLogDirectoryPath, graph = tf.get_default_graph()) batchCount = int(mnistDatasets.train.num_examples / batchSize) for epoch in range(epochCount): for batch in range(batchCount): batchInputNDArray, batchCorrectOutputNDArray = mnistDatasets.train.next_batch(batchSize) _, summaryBytes = session.run([optimizerOperation, summaryTensor], feed_dict = {inputLayerTensor : batchInputNDArray, correctOutputTensor : batchCorrectOutputNDArray}) fileWriter.add_summary(summaryBytes, epoch * batchCount + batch) print("Epoch : ", epoch) print("정확도 : ", accuracyTensor.eval(feed_dict = {inputLayerTensor : mnistDatasets.test.images, correctOutputTensor : mnistDatasets.test.labels})) print("학습이 완료되었습니다.") testImageIndex = random.randint(0, mnistDatasets.test.images.shape[0]) testImageNDArray = mnistDatasets.test.images[testImageIndex] classificationNDArray = session.run(tf.argmax(outputLayerOutputTensor, 1), feed_dict = {inputLayerTensor : [testImageNDArray]}) print("정답 : ", np.argmax(mnistDatasets.test.labels[testImageIndex])) print("판단 : ", classificationNDArray[0]) saver = tf.train.Saver() saveFilePath = saver.save(session, "data/mnist_1_layer_softmax.ckpt") print("모델이 저장되었습니다 : %s" % saveFilePath) |
■ Session 클래스의 run 메소드를 사용해 피드를 연산하는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 |
import tensorflow as tf import numpy as np aInteger = 3 bInteger = 2 xTensor = tf.placeholder(tf.float32, shape = (aInteger, bInteger)) yTensor = tf.add(xTensor, xTensor) sourceNDArray = np.random.rand(aInteger, bInteger) with tf.Session() as session: result = session.run(yTensor, feed_dict = {xTensor : sourceNDArray}) print(result) [결과] [[0.35339484 0.6795656 ] [1.6821228 0.5671149 ] [1.783075 0.05875171]] |
■ 단일 입력 뉴런을 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 |
import tensorflow as tf weightVariable = tf.Variable(1.0, name = "weight") inputValueTensor = tf.constant(0.5, name = "inputValue") expectedOuptutValueTensor = tf.constant(0.0, name = "expectedOutputValue") actualOutputValueTensor = tf.multiply(inputValueTensor, weightVariable, "actualOutputValue") lossFunctionTensor = tf.pow(expectedOuptutValueTensor - actualOutputValueTensor, 2, name = "lossFunction") optimizerOperation = tf.train.GradientDescentOptimizer(0.025).minimize(lossFunctionTensor) for value in [inputValueTensor, weightVariable, expectedOuptutValueTensor, actualOutputValueTensor, lossFunctionTensor]: tf.summary.scalar(value.op.name, value) symmaryTensor = tf.summary.merge_all() with tf.Session() as session: summaryFileWriter = tf.summary.FileWriter("c:/SummaryLog", session.graph) session.run(tf.global_variables_initializer()) for i in range(100): summaryFileWriter.add_summary(session.run(symmaryTensor), i) session.run(optimizerOperation) |
■ Session 클래스의 run 메소드를 사용해 1개 이상의 텐서 값을 구하는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 |
import tensorflow as tf aConstantTensor = tf.constant([100.0]) bConstantTensor = tf.constant([300.0]) cConstantTensor = tf.constant([3.0]) addTensor = tf.add(aConstantTensor, bConstantTensor) multiplyTensor = tf.multiply(aConstantTensor, cConstantTensor) with tf.Session() as session: result = session.run([addTensor, multiplyTensor]) print(result) [결과] [array([400.], dtype=float32), array([300.], dtype=float32)] |
■ Variable 클래스를 사용해 변수를 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 |
import tensorflow as tf valueVariable = tf.Variable(0, name = "value") oneConstantTensor = tf.constant(1) addValueTensor = tf.add(valueVariable, oneConstantTensor) assignValueTensor = tf.assign(valueVariable, addValueTensor) globalVariableInitializerOperation = tf.global_variables_initializer() with tf.Session() as session: session.run(globalVariableInitializerOperation) print(session.run(valueVariable)) for _ in range(10): session.run(assignValueTensor) print(session.run(valueVariable)) [결과] 0 1 2 3 4 5 6 7 8 9 10 |
■ InteractiveSession 클래스를 사용하는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 |
import numpy as np import tensorflow as tf interactiveSession = tf.InteractiveSession() ndArray = np.array([1, 2, 3, 4, 5]) constantTensor = tf.constant(ndArray) print(constantTensor.eval()) interactiveSession.close() [결과] [1 2 3 4 5] |
■ convert_to_tensor 함수를 사용해 텐서를 구하는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 |
import numpy as np import tensorflow as tf ndArray = np.array([[[ 0, 1, 2], [ 3, 4, 5], [ 6, 7, 8]], [[ 8, 10, 11], [12, 13, 14], [15, 16, 17]], [[18, 19, 20], [21, 22, 23], [24, 25, 26]]]) tensor = tf.convert_to_tensor(ndArray, dtype = tf.float64) with tf.Session() as session: print(tensor.get_shape()) print(session.run(tensor)) [결과] (3, 3, 3) [[[ 0. 1. 2.] [ 3. 4. 5.] [ 6. 7. 8.]] [[ 8. 10. 11.] [12. 13. 14.] [15. 16. 17.]] [[18. 19. 20.] [21. 22. 23.] [24. 25. 26.]]] |
■ Variable 클래스를 사용해 변수를 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 |
import numpy as np import tensorflow as tf ndArray = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)]) variable = tf.Variable(ndArray) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) print(variable.get_shape()) print(sess.run(variable)) [결과] (3, 3) [[1 2 3] [4 5 6] [7 8 9]] |
■ constant 함수를 사용해 상수를 만드는 방법을 보여준다. ▶ 예제 코드 (PY)
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 |
import numpy as np import tensorflow as tf ndArray = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) constantTensor = tf.constant(ndArray) with tf.Session() as session: print(constantTensor.get_shape()) print(session.run(constantTensor)) [결과] (10,) [ 1 2 3 4 5 6 7 8 9 10] |