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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Adversarial Variational Bayes toy example\n",
"[Ben Poole](http://cs.stanford.edu/~poole)<br/>\n",
"January 26, 2017 <br/>\n",
"\n",
"This notebook implements the toy example from:<br/>\n",
"[Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks](https://arxiv.org/abs/1701.04722)<br/>\n",
"Lars Mescheder, Sebastian Nowozin, Andreas Geiger\n",
"\n",
"See this [blog post](http://www.inference.vc/variational-inference-with-implicit-models-part-ii-amortised-inference-2/) for another derivaiton and implementation of this approach. \n",
"\n",
"## Variational inference\n",
"Given samples $x$ from an unobserved data density $q(x)$, we are interested in building a latent-variable generative model $p(x, z)$ such that $p(x)$ is close to $q(x)$. This is a difficult problem as evaluating the density under the model, $p(x)$ requires evaluating an intractable integral over the unobserved latent variable $z$: $p(x)=\\int \\text{d}z \\;p(x, z)$.\n",
"\n",
"Variational inference bypasses this problem by optimizing a lower bound on the log-likelihood of the data:\n",
"$$\\log p(x) \\ge -\\text{KL}\\left(q(z|x) \\| p(z)\\right) + \\mathbb{E}_{q(z|x)}\\left[\\log p(x|z)\\right]$$\n",
"This lower bound depends on an additional distribution, $q(z|x)$ which is known as the _variational distribution_ and acts as an approximation to the true posterior $p(z|x)$. \n",
"To evaluate and learn using the variational lower bound, we need to be able to draw samples from the _variational distribution_ and to evaluate the density of a sample from the _variational distribution_. This typically restricts the family of variational distributions to those with known or tractable densities (e.g. factorial Gaussian, normalizing flows).\n",
"\n",
"## Variational inference with intractable densities\n",
"For many models we care about, such as models that exhibit [explaining away](http://www.inference.vc/variational-inference-with-implicit-models-part-ii-amortised-inference-2/), the true posterior distribution is complex and multimodal. Appropriately training these models with variational inference thus requires a rich variational family. One way of generating such complex variatonal families is to use neural networks! Here we will use an implicit posterior to generate samples $z=z_\\phi(x, \\epsilon$) where $\\epsilon \\sim \\mathcal{N}(0, I)$ and $x$ is the observed data. If $z_\\phi$ is a complex neural network, then we can use it to draw samples from any distribution. However, we no longer know how to evaluate the density of $z$, $q(z|x)$ and thus can't compute the $\\text{KL}(q(z|x) \\| p(z))$ term in the variational lower bound.\n",
"\n",
"The approach taken by adversarial variational bayes is to introduce a discriminator that can approximate the intractable $\\text{KL}$ term in the variatonal lower bound. This discriminator is trained to classify between samples $(x, z_\\text{prior})$ where $z_\\text{prior} \\sim p(z)$ and $(x, z_\\phi(x, \\epsilon))$ where $z_\\phi$ is our neural network that approximates the posterior. They show that at convergence, the optimal disriminator $T^*(x, z)$ is equal to $\\log q(z|x) - \\log p(z)$, the missing intractable term needed to compute the $\\text{KL}$ term! In practice, we don't have access to the optimal discriminator so we'll plug in our current best estimate, $T(x, z)$.\n",
"\n",
"The objective optimized by the generative model, $p(x, z)$ and inference network, $z_\\phi$ is:\n",
"$$\\underset{p, \\phi}{\\max} \\mathbb{E}_{x\\sim q(x)}\\left[\\mathbb{E}_\\epsilon\\left[-T(x, z_\\phi(x, \\epsilon)) + \\log p(x\\;|\\;z_\\phi(x, \\epsilon)\\right]\\right]$$\n",
"and the discriminator is trained to minimize the normal GAN loss, but using $z$ sampled from the prior or posterior:\n",
"$$\\underset{T}{\\min}\\mathbb{E}_{x\\sim q(x)}\\left[\\mathbb{E}_{\\epsilon}\\left[\\log \\sigma\\left(T(x, z_\\phi(x, \\epsilon)\\right)\\right] + \n",
"\\mathbb{E}_{z \\sim p(z)}\\left[\\log\\left(1 - \\sigma\\left(T(x, z)\\right)\\right)\\right]\\right]\n",
"$$\n",
"\n",
"## Implementation details\n",
"We trained an AVB model on the dataset of 4 points used in the paper. Each datapoint is a 4-dimensional binary vector with one non-zero component. For all the inference, generative, and discriminator networks we used neural networks with 2 hidden layers of 256 hidden units.\n",
"\n",
"Send questions/comments/bugs to [@poolio](https://twitter.com/poolio)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"import tensorflow as tf\n",
"slim = tf.contrib.slim\n",
"ds = tf.contrib.distributions\n",
"st = tf.contrib.bayesflow.stochastic_tensor\n",
"graph_replace = tf.contrib.graph_editor.graph_replace"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parameters"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"params = {\n",
" 'batch_size': 512,\n",
" 'latent_dim': 2, # dimensionality of latent space\n",
" 'eps_dim': 4, # dimensionality of epsilon, used in inference net, z_phi(x, eps)\n",
" 'input_dim': 4, # dimensionality of input (also the number of unique datapoints)\n",
" 'n_layer_disc': 2, # number of hidden layers in discriminator\n",
" 'n_hidden_disc': 256, # number of hidden units in discriminator\n",
" 'n_layer_gen': 2,\n",
" 'n_hidden_gen': 256,\n",
" 'n_layer_inf': 2,\n",
" 'n_hidden_inf': 256,\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create dataset of one-hot vectors"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f157e93c9d0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f159b3d2090>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"points_per_class = params['batch_size'] / params['input_dim']\n",
"labels = np.concatenate([[i] * points_per_class for i in xrange(params['input_dim'])])\n",
"np_data = np.eye(params['input_dim'], dtype=np.float32)[labels]\n",
"imshow(np_data, interpolation='nearest', aspect='auto', cmap=cm.gray); colorbar()\n",
"xticks(range(params['input_dim']))\n",
"xlabel('class')\n",
"ylabel('datapoint')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Network definitions"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def standard_normal(shape, **kwargs):\n",
" \"\"\"Create a standard Normal StochasticTensor.\"\"\"\n",
" return st.StochasticTensor(\n",
" ds.MultivariateNormalDiag(mu=tf.zeros(shape), diag_stdev=tf.ones(shape), **kwargs))\n",
"\n",
"def generative_network(batch_size, latent_dim, input_dim, n_layer, n_hidden, eps=1e-6):\n",
" with tf.variable_scope(\"generative\"):\n",
" z = standard_normal([batch_size, latent_dim], name=\"p_z\")\n",
" h = slim.repeat(z.value(), n_layer, slim.fully_connected, n_hidden, activation_fn=tf.nn.relu)\n",
" # BUG: BernoulliSigmoidP gives NaNs when log_p is large, so we constrain\n",
" # probabilities to be in (eps, 1-eps) and use Bernoulli\n",
" p = eps + (1-2 * eps) * slim.fully_connected(h, input_dim, activation_fn=tf.nn.sigmoid)\n",
" x = st.StochasticTensor(ds.Bernoulli(p=p, name=\"p_x\"))\n",
" return [x, z]\n",
"\n",
"def inference_network(x, latent_dim, n_layer, n_hidden, eps_dim):\n",
" eps = standard_normal([x.get_shape().as_list()[0], eps_dim], name=\"eps\").value()\n",
" h = tf.concat_v2([x, eps], 1)\n",
" with tf.variable_scope(\"inference\"):\n",
" h = slim.repeat(h, n_layer, slim.fully_connected, n_hidden, activation_fn=tf.nn.relu)\n",
" z = slim.fully_connected(h, latent_dim, activation_fn=None, scope=\"q_z\")\n",
" return z\n",
"\n",
"def data_network(x, z, n_layers=2, n_hidden=256, activation_fn=None):\n",
" \"\"\"Approximate log data density.\"\"\"\n",
" h = tf.concat_v2([x, z], 1)\n",
" with tf.variable_scope('discriminator'):\n",
" h = slim.repeat(h, n_layers, slim.fully_connected, n_hidden, activation_fn=tf.nn.relu)\n",
" log_d = slim.fully_connected(h, 1, activation_fn=activation_fn)\n",
" return tf.squeeze(log_d, squeeze_dims=[1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Construct model and training ops"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tf.reset_default_graph()\n",
"\n",
"x = tf.constant(np_data)\n",
"p_x, p_z = generative_network(params['batch_size'], params['latent_dim'], params['input_dim'],\n",
" params['n_layer_gen'], params['n_hidden_gen'])\n",
"q_z = inference_network(x, params['latent_dim'], params['n_layer_inf'], params['n_hidden_inf'],\n",
" params['eps_dim'])\n",
"\n",
"# Discriminator classifies between (x, z_prior) and (x, z_posterior)\n",
"# where z_prior ~ p(z), and z_posterior = q(z, eps) with eps ~ N(0, I)\n",
"log_d_prior = data_network(x, p_z.value(), n_layers=params['n_layer_disc'],\n",
" n_hidden=params['n_hidden_disc'])\n",
"log_d_posterior = graph_replace(log_d_prior, {p_z.value(): q_z})\n",
"disc_loss = tf.reduce_mean(\n",
" tf.nn.sigmoid_cross_entropy_with_logits(log_d_posterior, tf.ones_like(log_d_posterior)) +\n",
" tf.nn.sigmoid_cross_entropy_with_logits(log_d_prior, tf.zeros_like(log_d_prior)))\n",
"\n",
"# Compute log p(x|z) with z ~ p(z), used as a placeholder\n",
"recon_likelihood_prior = p_x.distribution.log_prob(x)\n",
"# Compute log p(x|z) with z = q(x, eps)\n",
"# This is the same as the above expression, but with z replaced by a sample from q instead of p\n",
"recon_likelihood = tf.reduce_sum(graph_replace(recon_likelihood_prior, {p_z.value(): q_z}), [1])\n",
"\n",
"# Generator tries to maximize reconstruction log-likelihood while minimizing the discriminator output\n",
"gen_loss = tf.reduce_mean(log_d_posterior) - tf.reduce_mean(recon_likelihood)\n",
"\n",
"qvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, \"inference\")\n",
"pvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, \"generative\")\n",
"dvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, \"discriminator\")\n",
"opt = tf.train.AdamOptimizer(1e-3, beta1=0.5)#, epsilon=1e-3)\n",
"train_gen_op = opt.minimize(gen_loss, var_list=qvars + pvars)\n",
"train_disc_op = opt.minimize(disc_loss, var_list=dvars)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sess = tf.InteractiveSession()\n",
"sess.run(tf.global_variables_initializer())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Train model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 10000/10000 [00:56<00:00, 175.94it/s]\n"
]
}
],
"source": [
"from tqdm import tqdm\n",
"fs = []\n",
"for i in tqdm(xrange(10000)):\n",
" f, _, _ = sess.run([[gen_loss, disc_loss], train_gen_op, train_disc_op])\n",
" fs.append(f)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-3.0, 3.0, -3.5, 3.5)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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w9ZHUUd4Ye4N0cICKWqFttBgKDWOYOnOVOabj+515Eh0vNa7E+Wrt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\niL8/TnDzNlL84ciivlxl8cI83sSjvznEHXuYRYPydAnVVAndEHfs4U9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fX0RWZGZON1l6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9bX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\nZ17w/wPo03KeK6BVWgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1519257590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"figure(figsize=(5,5), facecolor='w')\n",
"n_viz = 10\n",
"xx = np.vstack([sess.run(q_z) for _ in range(n_viz)])\n",
"ll = np.tile(labels, (n_viz))\n",
"scatter(xx[:, 0], xx[:, 1], c=cm.Set1(ll.astype(float)/params['input_dim']/2.0),\n",
" edgecolor='none', alpha=0.5)\n",
"xlim(-3, 3); ylim(-3.5, 3.5)\n",
"axis('off')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
@artsobolev
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What does making p(x) "close" to p(z) even mean? Those are distributions over different spaces, how can one compare them?

@poolio
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poolio commented Jan 29, 2017

That's a typo, should be p(x) close to q(x)

@JulienSiems
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Thanks for the great notebook! I have two questions though:

  • I don't understand why you make the the output of the generative network a stochastic tensor. In the paper in figure 1 the eps just gets added/multiplied. Shouldn't it be sufficient as an output?
  • Shouldn't the weights of q and p be updated separately? The paper states two different loss terms for them in the algorithm. Or is that possible because of proposition 2?

@poolio
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poolio commented Mar 7, 2017

  • The StochasticTensor in the generative model is used to keep track of the sample x ~ p(x|z) and the density p(x|z) so we can evaluate it when computing the log probability of the data given the sampled latent state, z.

  • In practice we don't have acess to T* and use the current discriminator T as a replacement. T does not directly depend on the parameters of p, so d/dp -T(x, z) is 0, and the gradients are identical to the gradients using the separate losses in the paper.

@JulienSiems
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Makes sense! Thank you for taking the time to reply!

@clarken92
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Thank you for your interesting post. Please forgive me if I ask a dumb question. How could I find "stochastic_tensor" module on tensorflow? I use the version 1.8 installed with pip but it says there is no such module. Thank you.

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