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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"from gumbel_softmax import GumbelSoftmax, GumbelSoftmaxLayer\n", | |
"import theano.tensor as T\n", | |
"import numpy as np" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"### Simple demo\n", | |
"* Sample from gumbel-softmax\n", | |
"* Average over samples" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"temperature = 0.01\n", | |
"logits = np.linspace(-2,2,10).reshape([1,-1])\n", | |
"gumbel_softmax = GumbelSoftmax(t=temperature)(logits)\n", | |
"softmax = T.nnet.softmax(logits)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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hwUTDQF5etPwg8CXgO8BnpZRHhBAfEEJ8oLzZh4E7hRAvAv8L+DUp5UX3b4Ur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\nSok1V3uj02ffzn0ouhOpOVY9rr9EHneCqmOszFlW17RDaG6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4AAAB\nWklEQVR3gKhHFcLrOPQnwGtSyt/pdjwAQogNQojB8ucp4AfwavddQ0r561LKCSnlTXiX5k9IKT/a\nzZgAhBBpIURf+fMM8IN4m1VdQ0p5GTgnhPAbfj8CHOliSEE+jPdGvB44AzwghEiVX4ePAOFG83QQ\nIcTG8p+TwI/SoAzV0b6Mss6Bok7+nWEQQvwF8C5gRAhxDvhNKeVnuhzW24CPAAeFEH4/91+TUn6l\nizFtBv6s3M5YAT4vpXy8i/HUYr2U78aAvy53ANSAz0opv9bdkAD4l8BnywupN1gHB/bKb3SPAOth\nHwEp5XNCiC8ALwF2+c8/6m5UAHxBCDECWMA/l1Iu1rth70BQjx49erzFWT+jcXr06NGjRyR6ibxH\njx493uL0EnmPHj16vMXpJfIePXr0eIvTS+Q9evTo8Ranl8h79OjR4y1OL5H36NGjx1ucXiLv0aNH\nj7c4/x83uXYzYOmcBAAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ed5916990>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"text/plain": [ | |
"<matplotlib.legend.Legend at 0x7f1ecdef1cd0>" | |
] | |
}, | |
"execution_count": 3, | |
"metadata": {}, | |
"output_type": "execute_result" | |
}, | |
{ | |
"data": { | |
"image/png": 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jRLxeD31vFTt+q4e/v2f1PEnw1kVjjDFe9OqqVwk60pUefTxP7p6yFrwxRZz9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\no2SAT5Ik6VEywCdJkvQo/wK/pNVwS+gjpQAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ed0efec90>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"import matplotlib.pyplot as plt\n", | |
"%matplotlib inline\n", | |
"plt.title('gumbel-softmax samples')\n", | |
"for i in range(100):\n", | |
" plt.plot(range(10),gumbel_softmax.eval()[0],marker='o',alpha=0.25)\n", | |
"plt.ylim(0,1)\n", | |
"plt.show()\n", | |
"\n", | |
"plt.title('average over samples')\n", | |
"plt.plot(range(10),np.mean([gumbel_softmax.eval()[0] for _ in range(500)],axis=0),\n", | |
" marker='o',label='gumbel-softmax average')\n", | |
"\n", | |
"plt.plot(softmax.eval()[0],marker='+',label='regular softmax')\n", | |
"plt.legend(loc='best')" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Autoencoder with gumbel-softmax\n", | |
"\n", | |
"* We do not use any bayesian regularization, simply optimizer by backprop\n", | |
"* Hidden layer contains 256 units, split into 32 blocks of 8 variables\n", | |
"* Gumbel-softmax is computed over each block" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"from sklearn.datasets import load_digits\n", | |
"X = load_digits().data" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"import lasagne\n", | |
"from lasagne.layers import *\n", | |
"import theano\n", | |
"\n", | |
"#graph inputs and shareds\n", | |
"input_var = T.matrix()\n", | |
"temp = theano.shared(np.float32(1),'temperature',allow_downcast=True)\n", | |
"\n", | |
"#architecture: encoder\n", | |
"nn = l_in = InputLayer((None,64),input_var)\n", | |
"nn = DenseLayer(nn,64,nonlinearity=T.tanh)\n", | |
"nn = DenseLayer(nn,32,nonlinearity=T.tanh)\n", | |
"\n", | |
"#bottleneck\n", | |
"nn = DenseLayer(nn,32,nonlinearity=None)\n", | |
"nn = reshape(nn,(-1,4)) #reshape so that softmax would be applied over blocks of 4\n", | |
"nn = GumbelSoftmaxLayer(nn,t=temp)\n", | |
"nn = bottleneck = reshape(nn,(-1,32))\n", | |
"\n", | |
"#decoder\n", | |
"nn = DenseLayer(nn,32,nonlinearity=T.tanh)\n", | |
"nn = DenseLayer(nn,64,nonlinearity=T.tanh)\n", | |
"nn = DenseLayer(nn,64,nonlinearity=None)\n", | |
"\n", | |
"#loss and updates\n", | |
"loss = T.mean((get_output(nn)-input_var)**2)\n", | |
"updates = lasagne.updates.adam(loss,get_all_params(nn))\n", | |
"\n", | |
"#compile\n", | |
"train_step = theano.function([input_var],loss,updates=updates)\n", | |
"evaluate = theano.function([input_var],loss)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## Training loop\n", | |
"* We gradually reduce temperature from 1 to 0.01 over time" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"59.574 21.860 18.880 18.789 18.798 18.799 18.787 18.781 18.785 18.788 18.795 18.734 18.597 18.257 17.493 16.118 14.711 13.833 13.153 12.481 12.001 11.529 11.209 10.837 10.617 10.364 10.235 10.119 10.030 9.859 9.737 9.595 9.517 9.449 9.299 9.255 9.103 9.013 8.953 8.844 8.782 8.682 8.669 8.557 8.490 8.455 8.370 8.381 8.304 8.252 8.252 8.256 8.106 8.123 8.152 8.084 8.102 8.023 8.122 7.962 7.910 7.892 7.797 7.838 7.832 7.792 7.822 7.746 7.834 8.035 7.887 7.724 7.795 7.878 7.844 7.828 7.926 7.716 7.731 7.875 7.873 7.908 7.724 7.731 7.751 8.296 8.249 8.217 7.919 7.703 8.030 7.780 7.850 7.794 7.644 7.702 7.613 7.767 7.998 7.826\n" | |
] | |
} | |
], | |
"source": [ | |
"for i,t in enumerate(np.logspace(0,-2,10000)):\n", | |
" sample = X[np.random.choice(len(X),32)]\n", | |
" temp.set_value(t)\n", | |
" mse = train_step(sample)\n", | |
" if i %100 ==0:\n", | |
" print '%.3f'%evaluate(X)," | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"#functions for visualization\n", | |
"get_sample = theano.function([input_var],get_output(nn))\n", | |
"get_sample_hard = theano.function([input_var],get_output(nn,hard_max=True))\n", | |
"get_code = theano.function([input_var],get_output(bottleneck,hard_max=False))\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
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pVkdGRlquMzo6SqPR+YHcduq0s5+IqGQ8g6qqn1FVv/tt27a1rNMNrcafmS3X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\nkQAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ebef401d0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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GnfOTPJDkpmWOZ2OSq5PckuTmJG9ZYp29klyfZEtT5+xljmtNkhuTfHoZNbYm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\nRK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ecc4136d0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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ATULdoqyoXQAz3kSb1bdK+mYzEgGaiLpFWVG7AGa83M2q7VmS3ijpO81LB0iL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\nwvr/kaL0Uqh7mMMAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ebe99da90>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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ASotiFQAAAKXVsFi1fZntp23f34kBAamQXVQV2QWAFxTZs3q5pEXtHgjQBmQXVUV2ASDX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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ebe6b5e10>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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A6G+40wBOlfRMRKxtRjJAk1C3KCtqF8CYN9xm9WxJ32tGIkATUbcoK2oXwJiX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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ebe34eed0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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JfLykyzvVEKBDmuZttVodWihUMVZ77713J8M3zd1KpTK0UKiilWq1qmnTpg0t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\nAAAKi2IVAAAAhUWxCgAAgML6/1lOlZ14133GAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ebe05ff10>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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JMtByXTLo65eklddoKX9njz766KLHWYi5XodVq1a1UieD1tsgywySU5KB8xon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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ebe31a750>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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sSpIkqbNGXqwmOSvJN5PcluSda2jn4iT3JFnTWfiTbE1ydZKbk9yU5LxVtnNU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\n/wM+Kz5qL9MwRAAAAABJRU5ErkJggg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ebe2a9850>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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aPrHdbUJ75TqzGhFvSEozaTnQIuQtyorcxQT3j5Juj4gP2Z4kaXq7G4T2Gk03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\ngg==\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ebe698b90>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
}, | |
{ | |
"data": { | |
"image/png": 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C2Z6h2kD/NPcfBTqAukVZUbuYzGzPsn297dW2V9k+tts5obty7UqKiOcl7d3m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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x7f1ebeda8f10>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"for i in range(10):\n", | |
" X_sample = X[np.random.randint(len(X)),None,:]\n", | |
" plt.figure(figsize=[12,4])\n", | |
" plt.subplot(1,4,1)\n", | |
" plt.title(\"original\")\n", | |
" plt.imshow(X_sample.reshape([8,8]),interpolation='none',cmap='gray')\n", | |
" plt.subplot(1,4,2)\n", | |
" plt.title(\"gumbel\")\n", | |
" plt.imshow(get_sample(X_sample).reshape([8,8]),interpolation='none',cmap='gray')\n", | |
" plt.subplot(1,4,3)\n", | |
" plt.title(\"hard-max\")\n", | |
" plt.imshow(get_sample_hard(X_sample).reshape([8,8]),interpolation='none',cmap='gray')\n", | |
" plt.subplot(1,4,4)\n", | |
" plt.title(\"code\")\n", | |
" plt.imshow(get_code(X_sample).reshape(8,4),interpolation='none',cmap='gray')\n", | |
" plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": null, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python [Root]", | |
"language": "python", | |
"name": "Python [Root]" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 2 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython2", | |
"version": "2.7.12" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 0 | |
} |
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# -*- coding: utf-8 -*- | |
""" | |
a bunch of lasagne code implementing gumbel softmax | |
https://arxiv.org/abs/1611.01144 | |
""" | |
import numpy as np | |
import theano | |
import theano.tensor as T | |
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams | |
from lasagne.random import get_rng | |
from lasagne.layers import Layer | |
class GumbelSoftmax: | |
""" | |
A gumbel-softmax nonlinearity with gumbel(0,1) noize | |
In short, it's a quasi-one-hot nonlinearity that "samples" from softmax | |
categorical distribution. | |
Explaination and motivation: https://arxiv.org/abs/1611.01144 | |
Code mostly follows http://blog.evjang.com/2016/11/tutorial-categorical-variational.html | |
Softmax normalizes over the LAST axis (works exactly as T.nnet.softmax for 2d). | |
:param t: temperature of sampling. Lower means more spike-like sampling. Can be symbolic. | |
:param eps: a small number used for numerical stability | |
:returns: a callable that can (and should) be used as a nonlinearity | |
""" | |
def __init__(self, | |
t=0.1, | |
eps=1e-20): | |
assert t != 0 | |
self.temperature=t | |
self.eps=eps | |
self._srng = RandomStreams(get_rng().randint(1, 2147462579)) | |
def __call__(self,logits): | |
"""computes a gumbel softmax sample""" | |
#sample from Gumbel(0, 1) | |
uniform = self._srng.uniform(logits.shape,low=0,high=1) | |
gumbel = -T.log(-T.log(uniform + self.eps) + self.eps) | |
#draw a sample from the Gumbel-Softmax distribution | |
return T.nnet.softmax((logits + gumbel) / self.temperature) | |
def onehot_argmax(logits): | |
"""computes a hard one-hot vector encoding maximum""" | |
return T.extra_ops.to_one_hot(T.argmax(logits,-1),logits.shape[-1]) | |
class GumbelSoftmaxLayer(Layer): | |
""" | |
lasagne.layers.GumbelSoftmaxLayer(incoming,**kwargs) | |
A layer that just applies a GumbelSoftmax nonlinearity. | |
In short, it's a quasi-one-hot nonlinearity that "samples" from softmax | |
categorical distribution. | |
If you provide "hard_max=True" in lasagne.layers.get_output | |
it will instead compute one-hot of aт argmax. | |
Softmax normalizes over the LAST axis (works exactly as T.nnet.softmax for 2d). | |
Explaination and motivation: https://arxiv.org/abs/1611.01144 | |
Code mostly follows http://blog.evjang.com/2016/11/tutorial-categorical-variational.html | |
Parameters | |
---------- | |
incoming : a :class:`Layer` instance or a tuple | |
The layer feeding into this layer, or the expected input shape | |
t: temperature of sampling. Lower means more spike-like sampling. Can be symbolic (e.g. shared) | |
eps: a small number used for numerical stability | |
""" | |
def __init__(self, incoming, t=0.1, eps=1e-20, **kwargs): | |
super(GumbelSoftmaxLayer, self).__init__(incoming, **kwargs) | |
self.gumbel_softmax = GumbelSoftmax(t=t,eps=eps) | |
def get_output_for(self, input, hard_max=False, **kwargs): | |
if hard_max: | |
return onehot_argmax(input) | |
else: | |
return self.gumbel_softmax(input) |
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