├── .ipynb_checkpoints
    └── Autoencoders in Keras and Deep Learning-checkpoint.ipynb
├── Autoencoders in Keras and Deep Learning.ipynb
├── Denoising Autoencoder.ipynb
├── README.md
└── Variational Autoencoder.ipynb


/Denoising Autoencoder.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "ExecuteTime": {
  8 |      "end_time": "2020-10-28T08:29:40.046001Z",
  9 |      "start_time": "2020-10-28T08:29:38.644804Z"
 10 |     }
 11 |    },
 12 |    "outputs": [],
 13 |    "source": [
 14 |     "import numpy as np\n",
 15 |     "import matplotlib.pyplot as plt\n",
 16 |     "import pandas as pd\n",
 17 |     "import seaborn as sns\n",
 18 |     "import warnings\n",
 19 |     "warnings.filterwarnings('ignore')\n",
 20 |     "%matplotlib inline"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "markdown",
 25 |    "metadata": {},
 26 |    "source": [
 27 |     "# Loading Dataset"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 2,
 33 |    "metadata": {
 34 |     "ExecuteTime": {
 35 |      "end_time": "2020-10-28T08:29:43.799273Z",
 36 |      "start_time": "2020-10-28T08:29:40.592538Z"
 37 |     }
 38 |    },
 39 |    "outputs": [
 40 |     {
 41 |      "name": "stdout",
 42 |      "output_type": "stream",
 43 |      "text": [
 44 |       "Training data shapes: X=(60000, 28, 28), y=(60000,)\n",
 45 |       "Testing data shapes: X=(10000, 28, 28), y=(10000,)\n"
 46 |      ]
 47 |     },
 48 |     {
 49 |      "data": {
 50 |       "image/png": 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 51 |       "text/plain": [
 52 |        "<Figure size 432x288 with 5 Axes>"
 53 |       ]
 54 |      },
 55 |      "metadata": {},
 56 |      "output_type": "display_data"
 57 |     }
 58 |    ],
 59 |    "source": [
 60 |     "from tensorflow.keras.datasets import mnist\n",
 61 |     "\n",
 62 |     "(trainX, trainy), (testX, testy) = mnist.load_data()\n",
 63 |     "\n",
 64 |     "print('Training data shapes: X=%s, y=%s' % (trainX.shape, trainy.shape))\n",
 65 |     "print('Testing data shapes: X=%s, y=%s' % (testX.shape, testy.shape))\n",
 66 |     "\n",
 67 |     "for j in range(5):\n",
 68 |     "    i = np.random.randint(0, 10000)\n",
 69 |     "    plt.subplot(550 + 1 + j)\n",
 70 |     "    plt.imshow(trainX[i], cmap='gray')\n",
 71 |     "    plt.title(trainy[i])\n",
 72 |     "plt.show()"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "markdown",
 77 |    "metadata": {},
 78 |    "source": [
 79 |     "# Adding Noise"
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "code",
 84 |    "execution_count": 3,
 85 |    "metadata": {
 86 |     "ExecuteTime": {
 87 |      "end_time": "2020-10-28T08:29:46.107144Z",
 88 |      "start_time": "2020-10-28T08:29:46.074938Z"
 89 |     },
 90 |     "collapsed": true
 91 |    },
 92 |    "outputs": [],
 93 |    "source": [
 94 |     "# Adding Noise to the dataset\n",
 95 |     "def guassian_noise(image):\n",
 96 |     "    r,c= image.shape\n",
 97 |     "    mean = 0\n",
 98 |     "    var = 0.1\n",
 99 |     "    sigma = var**0.5\n",
100 |     "    gaussian = np.random.normal(mean,sigma,(r,c))\n",
101 |     "    gaussian = gaussian.reshape(r,c)\n",
102 |     "    noisy = image + gaussian\n",
103 |     "    return noisy\n",
104 |     "\n",
105 |     "def salt_and_pepper_noise(image):\n",
106 |     "    ratio = 0.9\n",
107 |     "    amount = 0.1\n",
108 |     "    noisy = np.copy(image)\n",
109 |     "    \n",
110 |     "    salt_count = np.ceil(amount * image.size * ratio)\n",
111 |     "    coords = [np.random.randint(0, i - 1, int(salt_count)) for i in image.shape]\n",
112 |     "    noisy[coords] = 1\n",
113 |     "\n",
114 |     "    pepper_count = np.ceil(amount* image.size * (1. - ratio))\n",
115 |     "    coords = [np.random.randint(0, i - 1, int(pepper_count)) for i in image.shape]\n",
116 |     "    noisy[coords] = 0\n",
117 |     "    return noisy\n",
118 |     "\n",
119 |     "def poisson_noise(image):\n",
120 |     "    vals = len(np.unique(image))\n",
121 |     "    vals = 2 ** np.ceil(np.log2(vals))\n",
122 |     "    noisy = np.random.poisson(image * vals) / float(vals)\n",
123 |     "    return noisy\n",
124 |     "\n",
125 |     "def speckle_noise(image):\n",
126 |     "    r,c = image.shape\n",
127 |     "    speckle = np.random.randn(r,c)\n",
128 |     "    speckle = speckle.reshape(r,c)        \n",
129 |     "    noisy = image + image * speckle\n",
130 |     "    return noisy    \n",
131 |     "\n",
132 |     "def add_noise(image):\n",
133 |     "    p = np.random.random()\n",
134 |     "    if p <= 0.25:\n",
135 |     "        #print(\"Guassian\")\n",
136 |     "        noisy = guassian_noise(image)\n",
137 |     "    elif p <= 0.5:\n",
138 |     "        #print(\"SnP\")\n",
139 |     "        noisy = salt_and_pepper_noise(image)\n",
140 |     "    elif p <= 0.75:\n",
141 |     "        #print(\"Poison\")\n",
142 |     "        noisy = poisson_noise(image)\n",
143 |     "    else:\n",
144 |     "        #print(\"speckle\")\n",
145 |     "        noisy = speckle_noise(image)\n",
146 |     "    return noisy"
147 |    ]
148 |   },
149 |   {
150 |    "cell_type": "code",
151 |    "execution_count": 4,
152 |    "metadata": {
153 |     "ExecuteTime": {
154 |      "end_time": "2020-10-28T08:29:48.228578Z",
155 |      "start_time": "2020-10-28T08:29:47.547729Z"
156 |     },
157 |     "scrolled": false
158 |    },
159 |    "outputs": [
160 |     {
161 |      "name": "stdout",
162 |      "output_type": "stream",
163 |      "text": [
164 |       "Corrupted Example Samples\n"
165 |      ]
166 |     },
167 |     {
168 |      "data": {
169 |       "image/png": 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aHLW6OAwYMCDutNNOudyzmBA6V8ceeywAV1xxRbZG7Ah5bn//+9+zbcop6lrR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\nD96Z3r4L7+o1AAAAAElFTkSuQmCC\n",
170 |       "text/plain": [
171 |        "<Figure size 432x288 with 9 Axes>"
172 |       ]
173 |      },
174 |      "metadata": {},
175 |      "output_type": "display_data"
176 |     }
177 |    ],
178 |    "source": [
179 |     "print (\"Corrupted Example Samples\")\n",
180 |     "for j in range(9):\n",
181 |     "    i = np.random.randint(0, 10000)\n",
182 |     "    plt.subplot(330 + 1 + j)\n",
183 |     "    noisy = add_noise(trainX[i]/255)\n",
184 |     "    plt.imshow(noisy, cmap='gray')\n",
185 |     "plt.show()"
186 |    ]
187 |   },
188 |   {
189 |    "cell_type": "code",
190 |    "execution_count": 5,
191 |    "metadata": {
192 |     "ExecuteTime": {
193 |      "end_time": "2020-10-28T08:29:54.713271Z",
194 |      "start_time": "2020-10-28T08:29:49.433671Z"
195 |     }
196 |    },
197 |    "outputs": [
198 |     {
199 |      "name": "stdout",
200 |      "output_type": "stream",
201 |      "text": [
202 |       "(60000, 28, 28, 1) (60000, 28, 28, 1) (10000, 28, 28, 1) (10000, 28, 28, 1)\n"
203 |      ]
204 |     }
205 |    ],
206 |    "source": [
207 |     "train_clean = [image/255 for image in trainX]\n",
208 |     "test_clean = [image/255 for image in testX]\n",
209 |     "\n",
210 |     "train_noisy = [add_noise(image/255) for image in trainX]\n",
211 |     "test_noisy = [add_noise(image/255) for image in testX]\n",
212 |     "\n",
213 |     "train_clean = np.reshape(train_clean, (60000, 28, 28, 1))\n",
214 |     "test_clean = np.reshape(test_clean, (10000, 28, 28, 1))\n",
215 |     "\n",
216 |     "train_noisy = np.reshape(train_noisy, (60000, 28, 28, 1))\n",
217 |     "test_noisy = np.reshape(test_noisy, (10000, 28, 28, 1))\n",
218 |     "\n",
219 |     "print (train_clean.shape, train_noisy.shape, test_clean.shape, test_noisy.shape)"
220 |    ]
221 |   },
222 |   {
223 |    "cell_type": "markdown",
224 |    "metadata": {},
225 |    "source": [
226 |     "# Denoiser"
227 |    ]
228 |   },
229 |   {
230 |    "cell_type": "code",
231 |    "execution_count": 6,
232 |    "metadata": {
233 |     "ExecuteTime": {
234 |      "end_time": "2020-10-28T08:30:07.331867Z",
235 |      "start_time": "2020-10-28T08:30:07.199155Z"
236 |     }
237 |    },
238 |    "outputs": [],
239 |    "source": [
240 |     "import tensorflow\n",
241 |     "\n",
242 |     "input_data = tensorflow.keras.layers.Input(shape=(28, 28, 1))\n",
243 |     "\n",
244 |     "encoder = tensorflow.keras.layers.Conv2D(64, (5,5), activation='relu')(input_data)\n",
245 |     "encoder = tensorflow.keras.layers.MaxPooling2D((2,2))(encoder)\n",
246 |     "\n",
247 |     "encoder = tensorflow.keras.layers.Conv2D(128, (3,3), activation='relu')(encoder)\n",
248 |     "encoder = tensorflow.keras.layers.MaxPooling2D((2,2))(encoder)\n",
249 |     "\n",
250 |     "encoder = tensorflow.keras.layers.Conv2D(256, (3,3), activation='relu')(encoder)\n",
251 |     "encoder = tensorflow.keras.layers.MaxPooling2D((2,2))(encoder)\n",
252 |     "\n",
253 |     "decoder = tensorflow.keras.layers.Conv2DTranspose(256, (3,3), activation='relu')(encoder)\n",
254 |     "\n",
255 |     "decoder = tensorflow.keras.layers.Conv2DTranspose(128, (3,3), activation='relu')(decoder)\n",
256 |     "decoder = tensorflow.keras.layers.UpSampling2D((2,2))(decoder)\n",
257 |     "\n",
258 |     "decoder = tensorflow.keras.layers.Conv2DTranspose(64, (3,3), activation='relu')(decoder)\n",
259 |     "decoder = tensorflow.keras.layers.UpSampling2D((2,2))(decoder)\n",
260 |     "\n",
261 |     "decoded = tensorflow.keras.layers.Conv2DTranspose(1, (5,5), activation='relu')(decoder)"
262 |    ]
263 |   },
264 |   {
265 |    "cell_type": "code",
266 |    "execution_count": 7,
267 |    "metadata": {
268 |     "ExecuteTime": {
269 |      "end_time": "2020-10-28T08:30:24.844388Z",
270 |      "start_time": "2020-10-28T08:30:24.790552Z"
271 |     }
272 |    },
273 |    "outputs": [
274 |     {
275 |      "name": "stdout",
276 |      "output_type": "stream",
277 |      "text": [
278 |       "Model: \"functional_1\"\n",
279 |       "_________________________________________________________________\n",
280 |       "Layer (type)                 Output Shape              Param #   \n",
281 |       "=================================================================\n",
282 |       "input_1 (InputLayer)         [(None, 28, 28, 1)]       0         \n",
283 |       "_________________________________________________________________\n",
284 |       "conv2d (Conv2D)              (None, 24, 24, 64)        1664      \n",
285 |       "_________________________________________________________________\n",
286 |       "max_pooling2d (MaxPooling2D) (None, 12, 12, 64)        0         \n",
287 |       "_________________________________________________________________\n",
288 |       "conv2d_1 (Conv2D)            (None, 10, 10, 128)       73856     \n",
289 |       "_________________________________________________________________\n",
290 |       "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 128)         0         \n",
291 |       "_________________________________________________________________\n",
292 |       "conv2d_2 (Conv2D)            (None, 3, 3, 256)         295168    \n",
293 |       "_________________________________________________________________\n",
294 |       "max_pooling2d_2 (MaxPooling2 (None, 1, 1, 256)         0         \n",
295 |       "_________________________________________________________________\n",
296 |       "conv2d_transpose (Conv2DTran (None, 3, 3, 256)         590080    \n",
297 |       "_________________________________________________________________\n",
298 |       "conv2d_transpose_1 (Conv2DTr (None, 5, 5, 128)         295040    \n",
299 |       "_________________________________________________________________\n",
300 |       "up_sampling2d (UpSampling2D) (None, 10, 10, 128)       0         \n",
301 |       "_________________________________________________________________\n",
302 |       "conv2d_transpose_2 (Conv2DTr (None, 12, 12, 64)        73792     \n",
303 |       "_________________________________________________________________\n",
304 |       "up_sampling2d_1 (UpSampling2 (None, 24, 24, 64)        0         \n",
305 |       "_________________________________________________________________\n",
306 |       "conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1)         1601      \n",
307 |       "=================================================================\n",
308 |       "Total params: 1,331,201\n",
309 |       "Trainable params: 1,331,201\n",
310 |       "Non-trainable params: 0\n",
311 |       "_________________________________________________________________\n"
312 |      ]
313 |     }
314 |    ],
315 |    "source": [
316 |     "autoencoder = tensorflow.keras.models.Model(inputs=input_data, outputs=decoded)\n",
317 |     "autoencoder.compile(loss='mse', optimizer='adam')\n",
318 |     "autoencoder.summary()"
319 |    ]
320 |   },
321 |   {
322 |    "cell_type": "code",
323 |    "execution_count": 9,
324 |    "metadata": {
325 |     "ExecuteTime": {
326 |      "end_time": "2020-10-28T09:10:23.361619Z",
327 |      "start_time": "2020-10-28T08:30:59.149637Z"
328 |     }
329 |    },
330 |    "outputs": [
331 |     {
332 |      "name": "stdout",
333 |      "output_type": "stream",
334 |      "text": [
335 |       "Epoch 1/30\n",
336 |       "938/938 [==============================] - 84s 90ms/step - loss: 0.0244 - val_loss: 0.0135\n",
337 |       "Epoch 2/30\n",
338 |       "938/938 [==============================] - 75s 80ms/step - loss: 0.0119 - val_loss: 0.0107\n",
339 |       "Epoch 3/30\n",
340 |       "938/938 [==============================] - 78s 84ms/step - loss: 0.0100 - val_loss: 0.0095\n",
341 |       "Epoch 4/30\n",
342 |       "938/938 [==============================] - 80s 85ms/step - loss: 0.0090 - val_loss: 0.0088\n",
343 |       "Epoch 5/30\n",
344 |       "938/938 [==============================] - 80s 85ms/step - loss: 0.0084 - val_loss: 0.0087\n",
345 |       "Epoch 6/30\n",
346 |       "938/938 [==============================] - 80s 85ms/step - loss: 0.0080 - val_loss: 0.0083\n",
347 |       "Epoch 7/30\n",
348 |       "938/938 [==============================] - 80s 85ms/step - loss: 0.0076 - val_loss: 0.0078\n",
349 |       "Epoch 8/30\n",
350 |       "938/938 [==============================] - 80s 85ms/step - loss: 0.0073 - val_loss: 0.0078\n",
351 |       "Epoch 9/30\n",
352 |       "938/938 [==============================] - 78s 84ms/step - loss: 0.0071 - val_loss: 0.0079\n",
353 |       "Epoch 10/30\n",
354 |       "938/938 [==============================] - 79s 84ms/step - loss: 0.0069 - val_loss: 0.0076\n",
355 |       "Epoch 11/30\n",
356 |       "938/938 [==============================] - 79s 84ms/step - loss: 0.0068 - val_loss: 0.0073\n",
357 |       "Epoch 12/30\n",
358 |       "938/938 [==============================] - 79s 84ms/step - loss: 0.0066 - val_loss: 0.0074\n",
359 |       "Epoch 13/30\n",
360 |       "938/938 [==============================] - 78s 84ms/step - loss: 0.0065 - val_loss: 0.0074\n",
361 |       "Epoch 14/30\n",
362 |       "938/938 [==============================] - 79s 84ms/step - loss: 0.0064 - val_loss: 0.0074\n",
363 |       "Epoch 15/30\n",
364 |       "938/938 [==============================] - 79s 84ms/step - loss: 0.0063 - val_loss: 0.0070\n",
365 |       "Epoch 16/30\n",
366 |       "938/938 [==============================] - 79s 85ms/step - loss: 0.0062 - val_loss: 0.0072\n",
367 |       "Epoch 17/30\n",
368 |       "938/938 [==============================] - 79s 85ms/step - loss: 0.0061 - val_loss: 0.0071\n",
369 |       "Epoch 18/30\n",
370 |       "938/938 [==============================] - 79s 84ms/step - loss: 0.0060 - val_loss: 0.0071\n",
371 |       "Epoch 19/30\n",
372 |       "938/938 [==============================] - 79s 84ms/step - loss: 0.0059 - val_loss: 0.0071\n",
373 |       "Epoch 20/30\n",
374 |       "938/938 [==============================] - 78s 84ms/step - loss: 0.0059 - val_loss: 0.0069\n",
375 |       "Epoch 21/30\n",
376 |       "938/938 [==============================] - 75s 80ms/step - loss: 0.0058 - val_loss: 0.0070\n",
377 |       "Epoch 22/30\n",
378 |       "938/938 [==============================] - 76s 81ms/step - loss: 0.0057 - val_loss: 0.0070\n",
379 |       "Epoch 23/30\n",
380 |       "938/938 [==============================] - 77s 83ms/step - loss: 0.0057 - val_loss: 0.0069\n",
381 |       "Epoch 24/30\n",
382 |       "938/938 [==============================] - 84s 90ms/step - loss: 0.0056 - val_loss: 0.0071\n",
383 |       "Epoch 25/30\n",
384 |       "938/938 [==============================] - 78s 83ms/step - loss: 0.0056 - val_loss: 0.0069\n",
385 |       "Epoch 26/30\n",
386 |       "938/938 [==============================] - 76s 81ms/step - loss: 0.0056 - val_loss: 0.0072\n",
387 |       "Epoch 27/30\n",
388 |       "938/938 [==============================] - 75s 80ms/step - loss: 0.0055 - val_loss: 0.0068\n",
389 |       "Epoch 28/30\n",
390 |       "938/938 [==============================] - 76s 81ms/step - loss: 0.0055 - val_loss: 0.0070\n",
391 |       "Epoch 29/30\n",
392 |       "938/938 [==============================] - 80s 85ms/step - loss: 0.0054 - val_loss: 0.0071\n",
393 |       "Epoch 30/30\n",
394 |       "938/938 [==============================] - 80s 86ms/step - loss: 0.0054 - val_loss: 0.0069\n"
395 |      ]
396 |     },
397 |     {
398 |      "data": {
399 |       "text/plain": [
400 |        "<tensorflow.python.keras.callbacks.History at 0x1521d0630>"
401 |       ]
402 |      },
403 |      "execution_count": 9,
404 |      "metadata": {},
405 |      "output_type": "execute_result"
406 |     }
407 |    ],
408 |    "source": [
409 |     "autoencoder.fit(train_noisy, train_clean, epochs=30, batch_size=64, validation_data=(test_noisy, test_clean))"
410 |    ]
411 |   },
412 |   {
413 |    "cell_type": "code",
414 |    "execution_count": 38,
415 |    "metadata": {
416 |     "ExecuteTime": {
417 |      "end_time": "2020-10-28T09:19:01.250939Z",
418 |      "start_time": "2020-10-28T09:18:59.808270Z"
419 |     }
420 |    },
421 |    "outputs": [
422 |     {
423 |      "name": "stdout",
424 |      "output_type": "stream",
425 |      "text": [
426 |       "Noisy test images\n"
427 |      ]
428 |     },
429 |     {
430 |      "data": {
431 |       "image/png": 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C0vBUR1MapDRPSKFC0k4t2267LZDS7hQ6ZCuFq1an3mr2Laqq8gqBUEC+tVHqmNIqFcIE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\n6TiOU5D/D0oUGI/WvBQLAAAAAElFTkSuQmCC\n",
432 |       "text/plain": [
433 |        "<Figure size 432x288 with 9 Axes>"
434 |       ]
435 |      },
436 |      "metadata": {},
437 |      "output_type": "display_data"
438 |     },
439 |     {
440 |      "name": "stdout",
441 |      "output_type": "stream",
442 |      "text": [
443 |       "Cleaned Version(Denoising Autoencoder):) \n"
444 |      ]
445 |     },
446 |     {
447 |      "data": {
448 |       "image/png": 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449 |       "text/plain": [
450 |        "<Figure size 432x288 with 9 Axes>"
451 |       ]
452 |      },
453 |      "metadata": {},
454 |      "output_type": "display_data"
455 |     }
456 |    ],
457 |    "source": [
458 |     "offset=92\n",
459 |     "# print (\"Real Test Images\")\n",
460 |     "# # Real Images\n",
461 |     "# for i in range(9):\n",
462 |     "#     plt.subplot(330 + 1 + i)\n",
463 |     "#     plt.imshow(test_clean[i+offset,:,:, -1], cmap='gray')\n",
464 |     "# plt.show()\n",
465 |     "\n",
466 |     "print(\"Noisy test images\")\n",
467 |     "for i in range(9):\n",
468 |     "    plt.subplot(330 + 1 + i)\n",
469 |     "    plt.imshow(test_noisy[i+offset,:,:, -1], cmap='gray')\n",
470 |     "plt.show()\n",
471 |     "\n",
472 |     "# Reconstructed Images\n",
473 |     "print (\"Cleaned Version(Denoising Autoencoder):) \")\n",
474 |     "for i in range(9):\n",
475 |     "    plt.subplot(330 + 1 + i)\n",
476 |     "    output = autoencoder.predict(np.array([test_noisy[i+offset]]))\n",
477 |     "    op_image = np.reshape(output[0]*255, (28, 28))\n",
478 |     "    plt.imshow(op_image, cmap='gray')\n",
479 |     "plt.show()"
480 |    ]
481 |   },
482 |   {
483 |    "cell_type": "code",
484 |    "execution_count": null,
485 |    "metadata": {
486 |     "collapsed": true
487 |    },
488 |    "outputs": [],
489 |    "source": []
490 |   }
491 |  ],
492 |  "metadata": {
493 |   "kernelspec": {
494 |    "display_name": "Python 3",
495 |    "language": "python",
496 |    "name": "python3"
497 |   },
498 |   "language_info": {
499 |    "codemirror_mode": {
500 |     "name": "ipython",
501 |     "version": 3
502 |    },
503 |    "file_extension": ".py",
504 |    "mimetype": "text/x-python",
505 |    "name": "python",
506 |    "nbconvert_exporter": "python",
507 |    "pygments_lexer": "ipython3",
508 |    "version": "3.6.9"
509 |   }
510 |  },
511 |  "nbformat": 4,
512 |  "nbformat_minor": 2
513 | }
514 | 


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/README.md:
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1 | # Autoencoders with Keras
2 | 
3 | ## This repository has working notebooks on the following types of autoencoders
4 | - Standard AutoEncoder
5 | - Denoising AutoEncoder
6 | - Variational AutoEncoder
7 | 


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