├── Categorical VAE.ipynb
├── LICENSE
├── README.md
├── gumbel_softmax_vae_v2.ipynb
└── notebook.json


/Categorical VAE.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Categorical VAE with Gumbel-Softmax\n",
  8 |     "\n",
  9 |     "Partial implementation of the paper [Categorical Reparameterization with Gumbel-Softmax](https://arxiv.org/abs/1611.01144) "
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "markdown",
 14 |    "metadata": {},
 15 |    "source": [
 16 |     "A categorical VAE with discrete latent variables. Tensorflow version is 0.10.0."
 17 |    ]
 18 |   },
 19 |   {
 20 |    "cell_type": "markdown",
 21 |    "metadata": {},
 22 |    "source": [
 23 |     "# 1. Imports and Helper Functions"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "code",
 28 |    "execution_count": 1,
 29 |    "metadata": {
 30 |     "collapsed": false
 31 |    },
 32 |    "outputs": [],
 33 |    "source": [
 34 |     "import tensorflow as tf\n",
 35 |     "from tensorflow.examples.tutorials.mnist import input_data\n",
 36 |     "import matplotlib.pyplot as plt\n",
 37 |     "import numpy as np\n",
 38 |     "%matplotlib inline\n",
 39 |     "slim=tf.contrib.slim\n",
 40 |     "Bernoulli = tf.contrib.distributions.Bernoulli"
 41 |    ]
 42 |   },
 43 |   {
 44 |    "cell_type": "code",
 45 |    "execution_count": 2,
 46 |    "metadata": {
 47 |     "collapsed": true
 48 |    },
 49 |    "outputs": [],
 50 |    "source": [
 51 |     "def sample_gumbel(shape, eps=1e-20): \n",
 52 |     "  \"\"\"Sample from Gumbel(0, 1)\"\"\"\n",
 53 |     "  U = tf.random_uniform(shape,minval=0,maxval=1)\n",
 54 |     "  return -tf.log(-tf.log(U + eps) + eps)\n",
 55 |     "\n",
 56 |     "def gumbel_softmax_sample(logits, temperature): \n",
 57 |     "  \"\"\" Draw a sample from the Gumbel-Softmax distribution\"\"\"\n",
 58 |     "  y = logits + sample_gumbel(tf.shape(logits))\n",
 59 |     "  return tf.nn.softmax( y / temperature)\n",
 60 |     "\n",
 61 |     "def gumbel_softmax(logits, temperature, hard=False):\n",
 62 |     "  \"\"\"Sample from the Gumbel-Softmax distribution and optionally discretize.\n",
 63 |     "  Args:\n",
 64 |     "    logits: [batch_size, n_class] unnormalized log-probs\n",
 65 |     "    temperature: non-negative scalar\n",
 66 |     "    hard: if True, take argmax, but differentiate w.r.t. soft sample y\n",
 67 |     "  Returns:\n",
 68 |     "    [batch_size, n_class] sample from the Gumbel-Softmax distribution.\n",
 69 |     "    If hard=True, then the returned sample will be one-hot, otherwise it will\n",
 70 |     "    be a probabilitiy distribution that sums to 1 across classes\n",
 71 |     "  \"\"\"\n",
 72 |     "  y = gumbel_softmax_sample(logits, temperature)\n",
 73 |     "  if hard:\n",
 74 |     "    k = tf.shape(logits)[-1]\n",
 75 |     "    #y_hard = tf.cast(tf.one_hot(tf.argmax(y,1),k), y.dtype)\n",
 76 |     "    y_hard = tf.cast(tf.equal(y,tf.reduce_max(y,1,keep_dims=True)),y.dtype)\n",
 77 |     "    y = tf.stop_gradient(y_hard - y) + y\n",
 78 |     "  return y"
 79 |    ]
 80 |   },
 81 |   {
 82 |    "cell_type": "markdown",
 83 |    "metadata": {},
 84 |    "source": [
 85 |     "# 2. Build Model"
 86 |    ]
 87 |   },
 88 |   {
 89 |    "cell_type": "code",
 90 |    "execution_count": 3,
 91 |    "metadata": {
 92 |     "collapsed": true
 93 |    },
 94 |    "outputs": [],
 95 |    "source": [
 96 |     "K=10 # number of classes\n",
 97 |     "N=30 # number of categorical distributions"
 98 |    ]
 99 |   },
100 |   {
101 |    "cell_type": "code",
102 |    "execution_count": 4,
103 |    "metadata": {
104 |     "collapsed": false
105 |    },
106 |    "outputs": [],
107 |    "source": [
108 |     "# input image x (shape=(batch_size,784))\n",
109 |     "x = tf.placeholder(tf.float32,[None,784])\n",
110 |     "# variational posterior q(y|x), i.e. the encoder (shape=(batch_size,200))\n",
111 |     "net = slim.stack(x,slim.fully_connected,[512,256])\n",
112 |     "# unnormalized logits for N separate K-categorical distributions (shape=(batch_size*N,K))\n",
113 |     "logits_y = tf.reshape(slim.fully_connected(net,K*N,activation_fn=None),[-1,K])\n",
114 |     "q_y = tf.nn.softmax(logits_y)\n",
115 |     "log_q_y = tf.log(q_y+1e-20)\n",
116 |     "# temperature\n",
117 |     "tau = tf.Variable(5.0,name=\"temperature\")\n",
118 |     "# sample and reshape back (shape=(batch_size,N,K))\n",
119 |     "# set hard=True for ST Gumbel-Softmax\n",
120 |     "y = tf.reshape(gumbel_softmax(logits_y,tau,hard=False),[-1,N,K])\n",
121 |     "# generative model p(x|y), i.e. the decoder (shape=(batch_size,200))\n",
122 |     "net = slim.stack(slim.flatten(y),slim.fully_connected,[256,512])\n",
123 |     "logits_x = slim.fully_connected(net,784,activation_fn=None)\n",
124 |     "# (shape=(batch_size,784))\n",
125 |     "p_x = Bernoulli(logits=logits_x)"
126 |    ]
127 |   },
128 |   {
129 |    "cell_type": "code",
130 |    "execution_count": 5,
131 |    "metadata": {
132 |     "collapsed": false
133 |    },
134 |    "outputs": [],
135 |    "source": [
136 |     "# loss and train ops\n",
137 |     "kl_tmp = tf.reshape(q_y*(log_q_y-tf.log(1.0/K)),[-1,N,K])\n",
138 |     "KL = tf.reduce_sum(kl_tmp,[1,2])\n",
139 |     "elbo=tf.reduce_sum(p_x.log_prob(x),1) - KL"
140 |    ]
141 |   },
142 |   {
143 |    "cell_type": "code",
144 |    "execution_count": 6,
145 |    "metadata": {
146 |     "collapsed": false
147 |    },
148 |    "outputs": [],
149 |    "source": [
150 |     "loss=tf.reduce_mean(-elbo)\n",
151 |     "lr=tf.constant(0.001)\n",
152 |     "train_op=tf.train.AdamOptimizer(learning_rate=lr).minimize(loss,var_list=slim.get_model_variables())\n",
153 |     "init_op=tf.initialize_all_variables()"
154 |    ]
155 |   },
156 |   {
157 |    "cell_type": "markdown",
158 |    "metadata": {
159 |     "collapsed": true
160 |    },
161 |    "source": [
162 |     "# 3. Train"
163 |    ]
164 |   },
165 |   {
166 |    "cell_type": "code",
167 |    "execution_count": 7,
168 |    "metadata": {
169 |     "collapsed": false
170 |    },
171 |    "outputs": [
172 |     {
173 |      "name": "stdout",
174 |      "output_type": "stream",
175 |      "text": [
176 |       "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
177 |       "Extracting /tmp/train-images-idx3-ubyte.gz\n",
178 |       "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
179 |       "Extracting /tmp/train-labels-idx1-ubyte.gz\n",
180 |       "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
181 |       "Extracting /tmp/t10k-images-idx3-ubyte.gz\n",
182 |       "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
183 |       "Extracting /tmp/t10k-labels-idx1-ubyte.gz\n"
184 |      ]
185 |     }
186 |    ],
187 |    "source": [
188 |     "# get data\n",
189 |     "data = input_data.read_data_sets('/tmp/', one_hot=True).train "
190 |    ]
191 |   },
192 |   {
193 |    "cell_type": "code",
194 |    "execution_count": 8,
195 |    "metadata": {
196 |     "collapsed": true
197 |    },
198 |    "outputs": [],
199 |    "source": [
200 |     "BATCH_SIZE=100\n",
201 |     "NUM_ITERS=50000\n",
202 |     "tau0=1.0 # initial temperature\n",
203 |     "np_temp=tau0\n",
204 |     "np_lr=0.001\n",
205 |     "ANNEAL_RATE=0.00003\n",
206 |     "MIN_TEMP=0.5"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "code",
211 |    "execution_count": 9,
212 |    "metadata": {
213 |     "collapsed": false
214 |    },
215 |    "outputs": [
216 |     {
217 |      "name": "stdout",
218 |      "output_type": "stream",
219 |      "text": [
220 |       "Step 1, ELBO: -543.696\n",
221 |       "Step 5001, ELBO: -108.782\n",
222 |       "Step 10001, ELBO: -103.602\n",
223 |       "Step 15001, ELBO: -98.898\n",
224 |       "Step 20001, ELBO: -103.085\n",
225 |       "Step 25001, ELBO: -103.185\n",
226 |       "Step 30001, ELBO: -102.382\n",
227 |       "Step 35001, ELBO: -98.935\n",
228 |       "Step 40001, ELBO: -106.222\n",
229 |       "Step 45001, ELBO: -99.148\n"
230 |      ]
231 |     }
232 |    ],
233 |    "source": [
234 |     "dat=[]\n",
235 |     "sess=tf.InteractiveSession()\n",
236 |     "sess.run(init_op)\n",
237 |     "for i in range(1,NUM_ITERS):\n",
238 |     "  np_x,np_y=data.next_batch(BATCH_SIZE)\n",
239 |     "  _,np_loss=sess.run([train_op,loss],{\n",
240 |     "      x:np_x,\n",
241 |     "      tau:np_temp,\n",
242 |     "      lr:np_lr\n",
243 |     "    })\n",
244 |     "  if i % 100 == 1:\n",
245 |     "    dat.append([i,np_temp,np_loss])\n",
246 |     "  if i % 1000 == 1:\n",
247 |     "    np_temp=np.maximum(tau0*np.exp(-ANNEAL_RATE*i),MIN_TEMP)\n",
248 |     "    np_lr*=0.9\n",
249 |     "  if i % 5000 == 1:\n",
250 |     "    print('Step %d, ELBO: %0.3f' % (i,-np_loss))"
251 |    ]
252 |   },
253 |   {
254 |    "cell_type": "markdown",
255 |    "metadata": {},
256 |    "source": [
257 |     "## save to animation"
258 |    ]
259 |   },
260 |   {
261 |    "cell_type": "code",
262 |    "execution_count": 10,
263 |    "metadata": {
264 |     "collapsed": false
265 |    },
266 |    "outputs": [],
267 |    "source": [
268 |     "np_x1,_=data.next_batch(100)\n",
269 |     "np_x2,np_y1 = sess.run([p_x.mean(),y],{x:np_x1})"
270 |    ]
271 |   },
272 |   {
273 |    "cell_type": "code",
274 |    "execution_count": 11,
275 |    "metadata": {
276 |     "collapsed": true
277 |    },
278 |    "outputs": [],
279 |    "source": [
280 |     "import matplotlib.animation as animation"
281 |    ]
282 |   },
283 |   {
284 |    "cell_type": "code",
285 |    "execution_count": 12,
286 |    "metadata": {
287 |     "collapsed": false
288 |    },
289 |    "outputs": [],
290 |    "source": [
291 |     "def save_anim(data,figsize,filename):\n",
292 |     "  fig=plt.figure(figsize=(figsize[1]/10.0,figsize[0]/10.0))\n",
293 |     "  im = plt.imshow(data[0].reshape(figsize),cmap=plt.cm.gray,interpolation='none')\n",
294 |     "  plt.gca().set_axis_off()\n",
295 |     "  #fig.tight_layout()\n",
296 |     "  fig.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=None, hspace=None)\n",
297 |     "  def updatefig(t):\n",
298 |     "    im.set_array(data[t].reshape(figsize))\n",
299 |     "    return im,\n",
300 |     "  anim=animation.FuncAnimation(fig, updatefig, frames=100, interval=50, blit=True, repeat=True)\n",
301 |     "  Writer = animation.writers['imagemagick']\n",
302 |     "  writer = Writer(fps=1, metadata=dict(artist='Me'), bitrate=1800)\n",
303 |     "  anim.save(filename, writer=writer)\n",
304 |     "  return"
305 |    ]
306 |   },
307 |   {
308 |    "cell_type": "code",
309 |    "execution_count": 13,
310 |    "metadata": {
311 |     "collapsed": false
312 |    },
313 |    "outputs": [],
314 |    "source": [
315 |     "# save_anim(np_x1,(28,28),'x0.gif')\n",
316 |     "# save_anim(np_y1,(N,K),'y.gif')\n",
317 |     "# save_anim(np_x2,(28,28),'x1.gif')"
318 |    ]
319 |   },
320 |   {
321 |    "cell_type": "markdown",
322 |    "metadata": {},
323 |    "source": [
324 |     "# 4. Plot Training Curves"
325 |    ]
326 |   },
327 |   {
328 |    "cell_type": "code",
329 |    "execution_count": 14,
330 |    "metadata": {
331 |     "collapsed": true
332 |    },
333 |    "outputs": [],
334 |    "source": [
335 |     "dat=np.array(dat).T"
336 |    ]
337 |   },
338 |   {
339 |    "cell_type": "code",
340 |    "execution_count": 15,
341 |    "metadata": {
342 |     "collapsed": false
343 |    },
344 |    "outputs": [
345 |     {
346 |      "data": {
347 |       "text/plain": [
348 |        "<matplotlib.text.Text at 0x1412400b8>"
349 |       ]
350 |      },
351 |      "execution_count": 15,
352 |      "metadata": {},
353 |      "output_type": "execute_result"
354 |     },
355 |     {
356 |      "data": {
357 |       "image/png": 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FGAaMdPdR7v4+8DNgCSEJqcmuwL/c/RF3n+7uLwCVQP/GCTd/LVpAy5Zhat8+LFOyIs2R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\nYII=\n",
358 |       "text/plain": [
359 |        "<matplotlib.figure.Figure at 0x1326f6c88>"
360 |       ]
361 |      },
362 |      "metadata": {},
363 |      "output_type": "display_data"
364 |     }
365 |    ],
366 |    "source": [
367 |     "f,axarr=plt.subplots(1,2)\n",
368 |     "axarr[0].plot(dat[0],dat[1])\n",
369 |     "axarr[0].set_ylabel('Temperature')\n",
370 |     "axarr[1].plot(dat[0],dat[2])\n",
371 |     "axarr[1].set_ylabel('-ELBO')"
372 |    ]
373 |   },
374 |   {
375 |    "cell_type": "markdown",
376 |    "metadata": {},
377 |    "source": [
378 |     "# 5. Unconditional Generation\n",
379 |     "\n",
380 |     "This consists of sampling from the prior $p_\\theta(y)$ and passing it through the generative model."
381 |    ]
382 |   },
383 |   {
384 |    "cell_type": "code",
385 |    "execution_count": 16,
386 |    "metadata": {
387 |     "collapsed": false
388 |    },
389 |    "outputs": [],
390 |    "source": [
391 |     "M=100*N\n",
392 |     "np_y = np.zeros((M,K))\n",
393 |     "np_y[range(M),np.random.choice(K,M)] = 1\n",
394 |     "np_y = np.reshape(np_y,[100,N,K])"
395 |    ]
396 |   },
397 |   {
398 |    "cell_type": "code",
399 |    "execution_count": 17,
400 |    "metadata": {
401 |     "collapsed": false
402 |    },
403 |    "outputs": [],
404 |    "source": [
405 |     "x_p=p_x.mean()\n",
406 |     "np_x= sess.run(x_p,{y:np_y})"
407 |    ]
408 |   },
409 |   {
410 |    "cell_type": "code",
411 |    "execution_count": 18,
412 |    "metadata": {
413 |     "collapsed": false
414 |    },
415 |    "outputs": [],
416 |    "source": [
417 |     "np_y = np_y.reshape((10,10,N,K))\n",
418 |     "np_y = np.concatenate(np.split(np_y,10,axis=0),axis=3)\n",
419 |     "np_y = np.concatenate(np.split(np_y,10,axis=1),axis=2)\n",
420 |     "y_img = np.squeeze(np_y)"
421 |    ]
422 |   },
423 |   {
424 |    "cell_type": "code",
425 |    "execution_count": 19,
426 |    "metadata": {
427 |     "collapsed": false
428 |    },
429 |    "outputs": [],
430 |    "source": [
431 |     "np_x = np_x.reshape((10,10,28,28))\n",
432 |     "# split into 10 (1,10,28,28) images, concat along columns -> 1,10,28,280\n",
433 |     "np_x = np.concatenate(np.split(np_x,10,axis=0),axis=3)\n",
434 |     "# split into 10 (1,1,28,280) images, concat along rows -> 1,1,280,280\n",
435 |     "np_x = np.concatenate(np.split(np_x,10,axis=1),axis=2)\n",
436 |     "x_img = np.squeeze(np_x)"
437 |    ]
438 |   },
439 |   {
440 |    "cell_type": "code",
441 |    "execution_count": 26,
442 |    "metadata": {
443 |     "collapsed": false
444 |    },
445 |    "outputs": [
446 |     {
447 |      "data": {
448 |       "text/plain": [
449 |        "<matplotlib.text.Text at 0x136ac3a58>"
450 |       ]
451 |      },
452 |      "execution_count": 26,
453 |      "metadata": {},
454 |      "output_type": "execute_result"
455 |     },
456 |     {
457 |      "data": {
458 |       "image/png": 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ed85ZvkUeT+vc3zuNo4tp3759K40LAACGQnIHAAAAYMAkdwAAAAAGTHIHAAAA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SWUXfXT2B0+fDgAJ598cqO6SDnTvXv3EOlW1F4eHqVQ7sgP4yc/+QkQM9PN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wcAAAAgMc0dAAAAgMRO+1hWzTb/ka8IP/Lxg95jz/woRc9zMNMrv2d6jff69yNf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\nAABAYpo7AAAAAIlp7gAAAAAk9v+rzGfwhCN0lwAAAABJRU5ErkJggg==\n",
459 |       "text/plain": [
460 |        "<matplotlib.figure.Figure at 0x136a3b9b0>"
461 |       ]
462 |      },
463 |      "metadata": {},
464 |      "output_type": "display_data"
465 |     }
466 |    ],
467 |    "source": [
468 |     "f,axarr=plt.subplots(1,2,figsize=(15,15))\n",
469 |     "# samples\n",
470 |     "axarr[0].matshow(y_img,cmap=plt.cm.gray)\n",
471 |     "axarr[0].set_title('Z Samples')\n",
472 |     "# reconstruction\n",
473 |     "axarr[1].imshow(x_img,cmap=plt.cm.gray,interpolation='none')\n",
474 |     "axarr[1].set_title('Generated Images')"
475 |    ]
476 |   },
477 |   {
478 |    "cell_type": "code",
479 |    "execution_count": 31,
480 |    "metadata": {
481 |     "collapsed": false
482 |    },
483 |    "outputs": [],
484 |    "source": [
485 |     "f.tight_layout()\n",
486 |     "f.savefig('/Users/ericjang/Desktop/gumbel_softmax/code.png')"
487 |    ]
488 |   }
489 |  ],
490 |  "metadata": {
491 |   "kernelspec": {
492 |    "display_name": "Python 3",
493 |    "language": "python",
494 |    "name": "python3"
495 |   },
496 |   "language_info": {
497 |    "codemirror_mode": {
498 |     "name": "ipython",
499 |     "version": 3
500 |    },
501 |    "file_extension": ".py",
502 |    "mimetype": "text/x-python",
503 |    "name": "python",
504 |    "nbconvert_exporter": "python",
505 |    "pygments_lexer": "ipython3",
506 |    "version": "3.5.2"
507 |   }
508 |  },
509 |  "nbformat": 4,
510 |  "nbformat_minor": 1
511 | }
512 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2016 Eric Jang
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # gumbel-softmax
2 | categorical variational autoencoder using the Gumbel-Softmax estimator. Paper is here: [https://arxiv.org/abs/1611.01144](https://arxiv.org/abs/1611.01144)
3 | 


--------------------------------------------------------------------------------
/gumbel_softmax_vae_v2.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Gumbel Softmax / Concrete VAE with BayesFlow\n",
  8 |     "\n",
  9 |     "Implements a categorical VAE using the technique introduced in [The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables (Maddison et al. 2016)](https://arxiv.org/abs/1611.00712) and [Categorical Reparameterization with Gumbel-Softmax (Jang et al. 2016)](https://arxiv.org/abs/1611.01144). The VAE architecture shown here are a bit different than the models presented in the papers, this one has 1 stochastic 20x10-ary layer with 2-layer deterministic encoder/decoders and a fixed prior.\n",
 10 |     "\n",
 11 |     "17 Feb 2017"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": null,
 17 |    "metadata": {
 18 |     "collapsed": true
 19 |    },
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "import tensorflow as tf\n",
 23 |     "import numpy as np\n",
 24 |     "import matplotlib.pyplot as plt\n",
 25 |     "from tensorflow.examples.tutorials.mnist import input_data"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": null,
 31 |    "metadata": {
 32 |     "collapsed": true
 33 |    },
 34 |    "outputs": [],
 35 |    "source": [
 36 |     "slim=tf.contrib.slim\n",
 37 |     "Bernoulli = tf.contrib.distributions.Bernoulli\n",
 38 |     "OneHotCategorical = tf.contrib.distributions.OneHotCategorical\n",
 39 |     "RelaxedOneHotCategorical = tf.contrib.distributions.RelaxedOneHotCategorical"
 40 |    ]
 41 |   },
 42 |   {
 43 |    "cell_type": "code",
 44 |    "execution_count": null,
 45 |    "metadata": {
 46 |     "collapsed": true
 47 |    },
 48 |    "outputs": [],
 49 |    "source": [
 50 |     "# black-on-white MNIST (harder to learn than white-on-black MNIST)\n",
 51 |     "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)"
 52 |    ]
 53 |   },
 54 |   {
 55 |    "cell_type": "code",
 56 |    "execution_count": null,
 57 |    "metadata": {
 58 |     "collapsed": true
 59 |    },
 60 |    "outputs": [],
 61 |    "source": [
 62 |     "batch_size=100\n",
 63 |     "tau0=1.0 # initial temperature\n",
 64 |     "K=10 # number of classes\n",
 65 |     "N=200//K # number of categorical distributions\n",
 66 |     "straight_through=False # if True, use Straight-through Gumbel-Softmax\n",
 67 |     "kl_type='relaxed' # choose between ('relaxed', 'categorical')\n",
 68 |     "learn_temp=False "
 69 |    ]
 70 |   },
 71 |   {
 72 |    "cell_type": "code",
 73 |    "execution_count": null,
 74 |    "metadata": {
 75 |     "collapsed": true
 76 |    },
 77 |    "outputs": [],
 78 |    "source": [
 79 |     "x=tf.placeholder(tf.float32, shape=(batch_size,784), name='x')\n",
 80 |     "net = tf.cast(tf.random_uniform(tf.shape(x)) < x, x.dtype) # dynamic binarization\n",
 81 |     "net = slim.stack(net,slim.fully_connected,[512,256])\n",
 82 |     "logits_y = tf.reshape(slim.fully_connected(net,K*N,activation_fn=None),[-1,N,K])\n",
 83 |     "tau = tf.Variable(tau0,name=\"temperature\",trainable=learn_temp)\n",
 84 |     "q_y = RelaxedOneHotCategorical(tau,logits_y)\n",
 85 |     "y = q_y.sample()\n",
 86 |     "if straight_through:\n",
 87 |     "  y_hard = tf.cast(tf.one_hot(tf.argmax(y,-1),K), y.dtype)\n",
 88 |     "  y = tf.stop_gradient(y_hard - y) + y\n",
 89 |     "net = slim.flatten(y)\n",
 90 |     "net = slim.stack(net,slim.fully_connected,[256,512])\n",
 91 |     "logits_x = slim.fully_connected(net,784,activation_fn=None)\n",
 92 |     "p_x = Bernoulli(logits=logits_x)\n",
 93 |     "x_mean = p_x.mean()"
 94 |    ]
 95 |   },
 96 |   {
 97 |    "cell_type": "code",
 98 |    "execution_count": null,
 99 |    "metadata": {
100 |     "collapsed": true
101 |    },
102 |    "outputs": [],
103 |    "source": [
104 |     "recons = tf.reduce_sum(p_x.log_prob(x),1)\n",
105 |     "logits_py = tf.ones_like(logits_y) * 1./K\n",
106 |     "\n",
107 |     "if kl_type=='categorical' or straight_through:\n",
108 |     "  # Analytical KL with Categorical prior\n",
109 |     "  p_cat_y = OneHotCategorical(logits=logits_py)\n",
110 |     "  q_cat_y = OneHotCategorical(logits=logits_y)\n",
111 |     "  KL_qp = tf.contrib.distributions.kl(q_cat_y, p_cat_y)\n",
112 |     "else:\n",
113 |     "  # Monte Carlo KL with Relaxed prior\n",
114 |     "  p_y = RelaxedOneHotCategorical(tau,logits=logits_py)\n",
115 |     "  KL_qp = q_y.log_prob(y) - p_y.log_prob(y)\n"
116 |    ]
117 |   },
118 |   {
119 |    "cell_type": "code",
120 |    "execution_count": null,
121 |    "metadata": {
122 |     "collapsed": true
123 |    },
124 |    "outputs": [],
125 |    "source": [
126 |     "KL = tf.reduce_sum(KL_qp,1)\n",
127 |     "mean_recons = tf.reduce_mean(recons)\n",
128 |     "mean_KL = tf.reduce_mean(KL)\n",
129 |     "loss = -tf.reduce_mean(recons-KL)\n"
130 |    ]
131 |   },
132 |   {
133 |    "cell_type": "code",
134 |    "execution_count": null,
135 |    "metadata": {
136 |     "collapsed": true
137 |    },
138 |    "outputs": [],
139 |    "source": [
140 |     "train_op=tf.train.AdamOptimizer(learning_rate=3e-4).minimize(loss)"
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "code",
145 |    "execution_count": null,
146 |    "metadata": {
147 |     "collapsed": true
148 |    },
149 |    "outputs": [],
150 |    "source": [
151 |     "data = []\n",
152 |     "with tf.train.MonitoredSession() as sess:\n",
153 |     "  for i in range(1,50000):\n",
154 |     "    batch = mnist.train.next_batch(batch_size)\n",
155 |     "    res = sess.run([train_op, loss, tau, mean_recons, mean_KL], {x : batch[0]})\n",
156 |     "    if i % 100 == 1:\n",
157 |     "      data.append([i] + res[1:])\n",
158 |     "    if i % 1000 == 1:\n",
159 |     "      print('Step %d, Loss: %0.3f' % (i,res[1]))\n",
160 |     "  # end training - do an eval\n",
161 |     "  batch = mnist.test.next_batch(batch_size)\n",
162 |     "  np_x = sess.run(x_mean, {x : batch[0]})"
163 |    ]
164 |   },
165 |   {
166 |    "cell_type": "code",
167 |    "execution_count": null,
168 |    "metadata": {
169 |     "collapsed": true
170 |    },
171 |    "outputs": [],
172 |    "source": [
173 |     "data = np.array(data).T"
174 |    ]
175 |   },
176 |   {
177 |    "cell_type": "code",
178 |    "execution_count": null,
179 |    "metadata": {
180 |     "collapsed": true
181 |    },
182 |    "outputs": [],
183 |    "source": [
184 |     "f,axarr=plt.subplots(1,4,figsize=(18,6))\n",
185 |     "axarr[0].plot(data[0],data[1])\n",
186 |     "axarr[0].set_title('Loss')\n",
187 |     "\n",
188 |     "axarr[1].plot(data[0],data[2])\n",
189 |     "axarr[1].set_title('Temperature')\n",
190 |     "\n",
191 |     "axarr[2].plot(data[0],data[3])\n",
192 |     "axarr[2].set_title('Recons')\n",
193 |     "\n",
194 |     "axarr[3].plot(data[0],data[4])\n",
195 |     "axarr[3].set_title('KL')"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": null,
201 |    "metadata": {
202 |     "collapsed": true
203 |    },
204 |    "outputs": [],
205 |    "source": [
206 |     "tmp = np.reshape(np_x,(-1,280,28)) # (10,280,28)\n",
207 |     "img = np.hstack([tmp[i] for i in range(10)])\n",
208 |     "plt.imshow(img)\n",
209 |     "plt.grid('off')"
210 |    ]
211 |   }
212 |  ],
213 |  "metadata": {
214 |   "kernelspec": {
215 |    "display_name": "Python 2",
216 |    "language": "python",
217 |    "name": "python2"
218 |   },
219 |   "language_info": {
220 |    "codemirror_mode": {
221 |     "name": "ipython",
222 |     "version": 2
223 |    },
224 |    "file_extension": ".py",
225 |    "mimetype": "text/x-python",
226 |    "name": "python",
227 |    "nbconvert_exporter": "python",
228 |    "pygments_lexer": "ipython2",
229 |    "version": "2.7.10"
230 |   }
231 |  },
232 |  "nbformat": 4,
233 |  "nbformat_minor": 0
234 | }
235 | 


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/notebook.json:
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1 | // Jupyter 2 space indent (move this file to ~/.jupyter/nbconfig/notebook.json
2 | // if it does not already exist)
3 | {
4 |   "CodeCell": {
5 |     "cm_config": {
6 |       "indentUnit": 2
7 |     }
8 |   }
9 | }


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