├── Hebb.ipynb └── PCA.ipynb /Hebb.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 32, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stdout", 10 | "output_type": "stream", 11 | "text": [ 12 | "Learning stared. It takes sometime.\n", 13 | "(0, 2.073753)\n", 14 | "(200, 0.0027522093)\n", 15 | "(400, 0.001435751)\n", 16 | "(600, 0.0009716793)\n", 17 | "(800, 0.00073436607)\n", 18 | "(1000, 0.00059026404)\n", 19 | "(1200, 0.00049339264)\n", 20 | "(1400, 0.00042384118)\n", 21 | "(1600, 0.0003715467)\n", 22 | "(1800, 0.00033072275)\n", 23 | "(2000, 0.00029788984)\n", 24 | "(2200, 0.00027105995)\n", 25 | "(2400, 0.00024868237)\n", 26 | "(2600, 0.00022968375)\n", 27 | "(2800, 0.00021340797)\n", 28 | "(3000, 0.00019925881)\n", 29 | "(3200, 0.00018685858)\n", 30 | "(3400, 0.00017592905)\n", 31 | "(3600, 0.00016623165)\n", 32 | "(3800, 0.00015752793)\n", 33 | "(4000, 0.00014973836)\n", 34 | "(4200, 0.00014258473)\n", 35 | "(4400, 0.00013614651)\n", 36 | "(4600, 0.00013026471)\n", 37 | "(4800, 0.00012485986)\n", 38 | "(5000, 0.00011989218)\n", 39 | "(5200, 0.00011528219)\n", 40 | "(5400, 0.00011102991)\n", 41 | "(5600, 0.00010705582)\n", 42 | "(5800, 0.000103399674)\n", 43 | "(6000, 9.994224e-05)\n", 44 | "(6200, 9.676301e-05)\n", 45 | "(6400, 9.374274e-05)\n", 46 | "(6600, 9.0881455e-05)\n", 47 | "(6800, 8.825861e-05)\n", 48 | "(7000, 8.5715255e-05)\n", 49 | "(7200, 8.333085e-05)\n", 50 | "(7400, 8.110543e-05)\n", 51 | "(7600, 7.8959485e-05)\n", 52 | "(7800, 7.693277e-05)\n", 53 | "(8000, 7.498553e-05)\n", 54 | "(8200, 7.319725e-05)\n", 55 | "(8400, 7.144872e-05)\n", 56 | "(8600, 6.985915e-05)\n", 57 | "(8800, 6.822984e-05)\n", 58 | "(9000, 6.671975e-05)\n", 59 | "(9200, 6.52494e-05)\n", 60 | "(9400, 6.385853e-05)\n", 61 | "(9600, 6.254715e-05)\n", 62 | "(9800, 6.12755e-05)\n", 63 | "(10000, 6.000385e-05)\n", 64 | "('Accuracy:', 1.0)\n" 65 | ] 66 | } 67 | ], 68 | "source": [ 69 | "import numpy as np\n", 70 | "import matplotlib.pyplot as plt\n", 71 | "import tensorflow as tf\n", 72 | "INPUT_SIZE = 30\n", 73 | "LR = 0.1\n", 74 | "N_CLASSES = 3\n", 75 | "\n", 76 | "p = np.array([[-1,1,1,1,-1,\\\n", 77 | "1,-1,-1,-1,1,\\\n", 78 | "1,-1,-1,-1,1,\\\n", 79 | "1,-1,-1,-1,1,\\\n", 80 | "1,-1,-1,-1,1,\\\n", 81 | "-1,1,1,1,-1],\n", 82 | "[-1,1,1,-1,-1,\\\n", 83 | "-1,-1,1,-1,-1,\\\n", 84 | "-1,-1,1,-1,-1,\\\n", 85 | "-1,-1,1,-1,-1,\\\n", 86 | "-1,-1,1,-1,-1,\\\n", 87 | "-1,-1,1,-1,-1],\n", 88 | "[1,1,1,-1,-1,\\\n", 89 | "-1,-1,-1,1,-1,\\\n", 90 | "-1,-1,-1,1,-1,\\\n", 91 | "-1,1,1,-1,-1,\\\n", 92 | "-1,1,-1,-1,-1,\\\n", 93 | "-1,1,1,1,1]])\n", 94 | "labels = [[1, 0, 0], \n", 95 | " [0, 1, 0], \n", 96 | " [0, 0, 1]]\n", 97 | "X = tf.placeholder(tf.float32, [None, INPUT_SIZE])\n", 98 | "Y = tf.placeholder(tf.float32, [None, N_CLASSES])\n", 99 | "\n", 100 | "W = tf.Variable(tf.random_normal([INPUT_SIZE, N_CLASSES]))\n", 101 | "b = tf.Variable(tf.random_normal([N_CLASSES]))\n", 102 | "\n", 103 | "hypothesis = tf.nn.softmax(tf.matmul(X, W) + b)\n", 104 | "\n", 105 | "cost = tf.reduce_mean(-tf.reduce_sum(Y * tf.log(hypothesis), axis=1))\n", 106 | "optimizer = tf.train.GradientDescentOptimizer(learning_rate=LR).minimize(cost)\n", 107 | "\n", 108 | "sess = tf.Session()\n", 109 | "sess.run(tf.global_variables_initializer())\n", 110 | "\n", 111 | "print('Learning stared. It takes sometime.')\n", 112 | "for step in range(10001):\n", 113 | " cost_val, _ = sess.run([cost, optimizer], feed_dict={X: p, Y: labels})\n", 114 | " if step % 200 == 0:\n", 115 | " print(step, cost_val)\n", 116 | "\n", 117 | "correct_prediction = tf.equal(tf.argmax(hypothesis, 1), tf.argmax(Y, 1))\n", 118 | "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n", 119 | "print('Accuracy:', sess.run(accuracy, feed_dict={X: p, Y: labels}))" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 38, 125 | "metadata": {}, 126 | "outputs": [ 127 | { 128 | "name": "stdout", 129 | "output_type": "stream", 130 | "text": [ 131 | "('Label:', array([2]))\n", 132 | "('Prediction:', array([2]))\n" 133 | ] 134 | }, 135 | { 136 | "data": { 137 | "image/png": "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\n", 138 | "text/plain": [ 139 | "
" 140 | ] 141 | }, 142 | "metadata": {}, 143 | "output_type": "display_data" 144 | } 145 | ], 146 | "source": [ 147 | "import random\n", 148 | "r = random.randint(0, 2)\n", 149 | "print(\"Label:\", sess.run(tf.argmax(labels[r:r+1], 1)))\n", 150 | "print(\"Prediction:\", sess.run(tf.argmax(hypothesis, 1), feed_dict={X: p[r:r + 1]}))\n", 151 | "\n", 152 | "plt.imshow(p[r:r + 1].reshape(6, 5), cmap='Blues', interpolation='nearest')\n", 153 | "plt.show()" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": null, 159 | "metadata": {}, 160 | "outputs": [], 161 | "source": [] 162 | } 163 | ], 164 | "metadata": { 165 | "kernelspec": { 166 | "display_name": "Python 2", 167 | "language": "python", 168 | "name": "python2" 169 | }, 170 | "language_info": { 171 | "codemirror_mode": { 172 | "name": "ipython", 173 | "version": 2 174 | }, 175 | "file_extension": ".py", 176 | "mimetype": "text/x-python", 177 | "name": "python", 178 | "nbconvert_exporter": "python", 179 | "pygments_lexer": "ipython2", 180 | "version": "2.7.15" 181 | } 182 | }, 183 | "nbformat": 4, 184 | "nbformat_minor": 2 185 | } 186 | -------------------------------------------------------------------------------- /PCA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 调用sklearn包" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 169, 13 | "metadata": {}, 14 | "outputs": [ 15 | { 16 | "data": { 17 | "image/png": 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\n", 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\n", 28 | "text/plain": [ 29 | "
" 30 | ] 31 | }, 32 | "metadata": {}, 33 | "output_type": "display_data" 34 | } 35 | ], 36 | "source": [ 37 | "import numpy as np\n", 38 | "from PIL import Image\n", 39 | "import os,glob\n", 40 | "import matplotlib.pyplot as plt\n", 41 | "from sklearn.decomposition import PCA\n", 42 | "\n", 43 | "#原始图像\n", 44 | "image = Image.open('Lena.jpg')\n", 45 | "image = image.convert('L') \n", 46 | "plt.axis('off')\n", 47 | "plt.imshow(image)\n", 48 | "plt.title(\"Original Image\")\n", 49 | "plt.show()\n", 50 | "\n", 51 | "matrix = np.array(image)\n", 52 | "newData = np.zeros((64,64,64))\n", 53 | "\n", 54 | "for i in range(0,64):\n", 55 | " for j in range(0,64):\n", 56 | " newData[i][j] = matrix[8*i:8*i+8,8*j:8*j+8].reshape(64)\n", 57 | "\n", 58 | "for i in range(0,64):\n", 59 | " for j in range(0,64):\n", 60 | " newData[i][j] = newData[i][j] - np.mean(newData[i][j])\n", 61 | "Data = newData.reshape(4096,64)\n", 62 | "\n", 63 | "#提取主成分\n", 64 | "pca=PCA(n_components=1)\n", 65 | "DataPCA=pca.fit_transform(Data)\n", 66 | "transData = DataPCA.reshape(64,64)\n", 67 | "\n", 68 | "#显示图像\n", 69 | "plt.imshow(transData.reshape(64, 64), cmap='Greys', interpolation='nearest')\n", 70 | "plt.axis('off')\n", 71 | "plt.title(\"Compressed Image\")\n", 72 | "plt.show()" 73 | ] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "metadata": {}, 78 | "source": [ 79 | "# Oja's Algorithm - 初始化权重\n" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 242, 85 | "metadata": {}, 86 | "outputs": [ 87 | { 88 | "name": "stdout", 89 | "output_type": "stream", 90 | "text": [ 91 | "[[0.69070917]\n", 92 | " [0.33254268]\n", 93 | " [0.72693947]\n", 94 | " [0.31271685]\n", 95 | " [0.62893853]\n", 96 | " [0.2065456 ]\n", 97 | " [0.16301476]\n", 98 | " [0.71405137]\n", 99 | " [0.27657014]\n", 100 | " [0.00306254]\n", 101 | " [0.95372786]\n", 102 | " [0.88688973]\n", 103 | " [0.3418189 ]\n", 104 | " [0.92824619]\n", 105 | " [0.33627538]\n", 106 | " [0.6472369 ]\n", 107 | " [0.11786141]\n", 108 | " [0.17100996]\n", 109 | " [0.15466199]\n", 110 | " [0.91248679]\n", 111 | " [0.09996368]\n", 112 | " [0.0214006 ]\n", 113 | " [0.28147955]\n", 114 | " [0.75903988]\n", 115 | " [0.14733679]\n", 116 | " [0.98696695]\n", 117 | " [0.61619731]\n", 118 | " [0.28033101]\n", 119 | " [0.50776468]\n", 120 | " [0.14296834]\n", 121 | " [0.97147404]\n", 122 | " [0.74019118]\n", 123 | " [0.37144273]\n", 124 | " [0.84953106]\n", 125 | " [0.21943406]\n", 126 | " [0.22529043]\n", 127 | " [0.03340237]\n", 128 | " [0.15578467]\n", 129 | " [0.94553502]\n", 130 | " [0.29791836]\n", 131 | " [0.14471566]\n", 132 | " [0.60222993]\n", 133 | " [0.63788505]\n", 134 | " [0.71681054]\n", 135 | " [0.68135197]\n", 136 | " [0.62214892]\n", 137 | " [0.62995066]\n", 138 | " [0.31006403]\n", 139 | " [0.28328891]\n", 140 | " [0.05077775]\n", 141 | " [0.12901368]\n", 142 | " [0.30249916]\n", 143 | " [0.41601846]\n", 144 | " [0.15162099]\n", 145 | " [0.21754223]\n", 146 | " [0.84148277]\n", 147 | " [0.96597831]\n", 148 | " [0.17939288]\n", 149 | " [0.10815889]\n", 150 | " [0.3186345 ]\n", 151 | " [0.79009989]\n", 152 | " [0.8134045 ]\n", 153 | " [0.97022344]\n", 154 | " [0.59896992]]\n" 155 | ] 156 | } 157 | ], 158 | "source": [ 159 | "X = Data[0].reshape(64,1)\n", 160 | "R = np.random.rand(64,1)\n", 161 | "W = R\n", 162 | "print W" 163 | ] 164 | }, 165 | { 166 | "cell_type": "markdown", 167 | "metadata": {}, 168 | "source": [ 169 | "# Oja's Algorithm - 使权值收敛到1" 170 | ] 171 | }, 172 | { 173 | "cell_type": "code", 174 | "execution_count": 243, 175 | "metadata": {}, 176 | "outputs": [ 177 | { 178 | "name": "stdout", 179 | "output_type": "stream", 180 | "text": [ 181 | "4.358884929172928\n", 182 | "4.212741248053036\n", 183 | "3.949794322453993\n", 184 | "3.5140036154450005\n", 185 | "2.889864074992154\n", 186 | "2.1836006484924155\n", 187 | "1.6028506658290156\n", 188 | "1.2592688504081877\n", 189 | "1.1005048752219928\n", 190 | "1.037010546889255\n", 191 | "1.013337345900541\n", 192 | "1.0047662644582218\n", 193 | "1.0016979189943866\n", 194 | "1.0006041393102159\n", 195 | "1.0002148605470287\n", 196 | "1.0000764005142784\n", 197 | "1.000027164564565\n", 198 | "1.0000096581707831\n", 199 | "1.0000034338437926\n", 200 | "1.000001220852332\n", 201 | "1.0000004340544855\n", 202 | "1.0000001543208512\n", 203 | "1.0000000548661625\n", 204 | "1.000000019506725\n", 205 | "1.0000000069352804\n", 206 | "1.0000000024657194\n", 207 | "1.0000000008766439\n", 208 | "1.0000000003116756\n", 209 | "1.000000000110811\n", 210 | "1.000000000039397\n", 211 | "1.0000000000140068\n", 212 | "1.0000000000049798\n", 213 | "1.0000000000017706\n", 214 | "1.0000000000006295\n", 215 | "1.0000000000002236\n", 216 | "1.0000000000000795\n", 217 | "1.0000000000000282\n", 218 | "1.00000000000001\n", 219 | "1.0000000000000036\n", 220 | "1.0000000000000013\n", 221 | "1.0000000000000004\n", 222 | "1.0\n", 223 | "1.0000000000000002\n", 224 | "1.0\n", 225 | "1.0\n" 226 | ] 227 | } 228 | ], 229 | "source": [ 230 | "u = 0.001\n", 231 | "init = 0\n", 232 | "while 1:\n", 233 | " Y = np.matmul(W.reshape(1,64), X)\n", 234 | " W = W + u*( Y*X -Y*Y*W)\n", 235 | " print np.sqrt(sum(sum(W**2)))\n", 236 | " if init == np.sqrt(sum(sum(W**2))):\n", 237 | " break\n", 238 | " print np.sqrt(sum(sum(W**2)))\n", 239 | " init = np.sqrt(sum(sum(W**2)))" 240 | ] 241 | }, 242 | { 243 | "cell_type": "code", 244 | "execution_count": 244, 245 | "metadata": {}, 246 | "outputs": [ 247 | { 248 | "name": "stdout", 249 | "output_type": "stream", 250 | "text": [ 251 | "[[-0.09253779]\n", 252 | " [-0.09253779]\n", 253 | " [-0.09253779]\n", 254 | " [-0.04276957]\n", 255 | " [-0.09253779]\n", 256 | " [ 0.20607155]\n", 257 | " [-0.14230601]\n", 258 | " [ 0.00699866]\n", 259 | " [-0.09253779]\n", 260 | " [-0.09253779]\n", 261 | " [-0.09253779]\n", 262 | " [-0.04276957]\n", 263 | " [-0.09253779]\n", 264 | " [ 0.20607155]\n", 265 | " [-0.14230601]\n", 266 | " [ 0.00699866]\n", 267 | " [-0.09253779]\n", 268 | " [-0.09253779]\n", 269 | " [-0.09253779]\n", 270 | " [-0.04276957]\n", 271 | " [-0.09253779]\n", 272 | " [ 0.20607155]\n", 273 | " [-0.14230601]\n", 274 | " [ 0.00699866]\n", 275 | " [-0.09253779]\n", 276 | " [-0.09253779]\n", 277 | " [-0.09253779]\n", 278 | " [-0.04276957]\n", 279 | " [-0.09253779]\n", 280 | " [ 0.20607155]\n", 281 | " [-0.14230601]\n", 282 | " [ 0.00699866]\n", 283 | " [-0.09253779]\n", 284 | " [-0.09253779]\n", 285 | " [-0.09253779]\n", 286 | " [-0.04276957]\n", 287 | " [-0.09253779]\n", 288 | " [ 0.20607155]\n", 289 | " [-0.14230601]\n", 290 | " [ 0.00699866]\n", 291 | " [-0.19207424]\n", 292 | " [-0.19207424]\n", 293 | " [ 0.15630333]\n", 294 | " [ 0.25583977]\n", 295 | " [-0.04276957]\n", 296 | " [ 0.05676688]\n", 297 | " [ 0.1065351 ]\n", 298 | " [ 0.05676688]\n", 299 | " [ 0.00699866]\n", 300 | " [ 0.00699866]\n", 301 | " [-0.14230601]\n", 302 | " [ 0.1065351 ]\n", 303 | " [ 0.00699866]\n", 304 | " [-0.04276957]\n", 305 | " [ 0.05676688]\n", 306 | " [ 0.25583977]\n", 307 | " [ 0.1065351 ]\n", 308 | " [ 0.1065351 ]\n", 309 | " [ 0.25583977]\n", 310 | " [ 0.20607155]\n", 311 | " [ 0.1065351 ]\n", 312 | " [ 0.1065351 ]\n", 313 | " [ 0.20607155]\n", 314 | " [ 0.15630333]]\n" 315 | ] 316 | } 317 | ], 318 | "source": [ 319 | "print W" 320 | ] 321 | }, 322 | { 323 | "cell_type": "markdown", 324 | "metadata": {}, 325 | "source": [ 326 | "# 压缩图像" 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": 290, 332 | "metadata": {}, 333 | "outputs": [ 334 | { 335 | "name": "stdout", 336 | "output_type": "stream", 337 | "text": [ 338 | "[[-9.45730960e-106]\n", 339 | " [-2.84462298e-106]\n", 340 | " [-2.71212758e-106]\n", 341 | " ...\n", 342 | " [-1.38336810e-105]\n", 343 | " [ 1.23136452e-105]\n", 344 | " [ 2.73869986e-105]]\n" 345 | ] 346 | }, 347 | { 348 | "data": { 349 | "text/plain": [ 350 | "Text(0.5,1,'Compressed Image')" 351 | ] 352 | }, 353 | "execution_count": 290, 354 | "metadata": {}, 355 | "output_type": "execute_result" 356 | }, 357 | { 358 | "data": { 359 | "image/png": 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\n", 360 | "text/plain": [ 361 | "
" 362 | ] 363 | }, 364 | "metadata": {}, 365 | "output_type": "display_data" 366 | } 367 | ], 368 | "source": [ 369 | "myData = Data\n", 370 | "result = np.zeros((4096,1))\n", 371 | "for i in range(0,4096):\n", 372 | " pca = np.matmul(W.reshape(1,64),myData[i])\n", 373 | " result[i] = pca\n", 374 | "print result\n", 375 | "\n", 376 | "\n", 377 | "\n", 378 | "plt.subplot(1,2,1)\n", 379 | "plt.imshow(matrix.reshape(512,512), cmap='Greys', interpolation='nearest')\n", 380 | "plt.axis('off')\n", 381 | "plt.title(\"Original Image\")\n", 382 | "plt.subplot(1,2,2)\n", 383 | "plt.imshow(result.reshape(64, 64), cmap='Greys', interpolation='nearest')\n", 384 | "plt.axis('off')\n", 385 | "plt.title(\"Compressed Image\")" 386 | ] 387 | }, 388 | { 389 | "cell_type": "code", 390 | "execution_count": null, 391 | "metadata": {}, 392 | "outputs": [], 393 | "source": [] 394 | } 395 | ], 396 | "metadata": { 397 | "kernelspec": { 398 | "display_name": "Python 2", 399 | "language": "python", 400 | "name": "python2" 401 | }, 402 | "language_info": { 403 | "codemirror_mode": { 404 | "name": "ipython", 405 | "version": 2 406 | }, 407 | "file_extension": ".py", 408 | "mimetype": "text/x-python", 409 | "name": "python", 410 | "nbconvert_exporter": "python", 411 | "pygments_lexer": "ipython2", 412 | "version": "2.7.15" 413 | } 414 | }, 415 | "nbformat": 4, 416 | "nbformat_minor": 2 417 | } 418 | --------------------------------------------------------------------------------