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xiangze/bayesian_logistic_regression_klqp.ipynb

Created July 18, 2017 17:00
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bayesian_logistic_regression_klqp.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"import edward as ed\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"%matplotlib inline\n",
"from edward.models import Bernoulli, Normal"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'1.3.1'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ed.__version__"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def build_toy_dataset(N, noise_std=0.1):\n",
" D = 1\n",
" X = np.linspace(-6, 6, num=N)\n",
" y = np.tanh(X) + np.random.normal(0, noise_std, size=N)\n",
" y[y < 0.5] = 0\n",
" y[y >= 0.5] = 1\n",
" X = (X - 4.0) / 4.0\n",
" X = X.reshape((N, D))\n",
" return X, y\n",
"\n",
"ed.set_seed(42)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DATA"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"N = 40 # number of data points\n",
"D = 1 # number of features\n",
"X_train, y_train = build_toy_dataset(N)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_test, y_test = build_toy_dataset(N)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MODEL"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X = tf.placeholder(tf.float32, [N, D])\n",
"w = Normal(loc=tf.zeros(D), scale=3.0 * tf.ones(D))\n",
"b = Normal(loc=tf.zeros([]), scale=3.0 * tf.ones([]))\n",
"y = Bernoulli(logits=ed.dot(X, w) + b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# INFERENCE"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"qw_loc = tf.Variable(tf.random_normal([D]))\n",
"qw_scale = tf.nn.softplus(tf.Variable(tf.random_normal([D])))\n",
"qb_loc = tf.Variable(tf.random_normal([]) + 10)\n",
"qb_scale = tf.nn.softplus(tf.Variable(tf.random_normal([])))\n",
"\n",
"qw = Normal(loc=qw_loc, scale=qw_scale)\n",
"qb = Normal(loc=qb_loc, scale=qb_scale)\n",
"\n",
"inference = ed.KLqp({w: qw, b: qb}, data={X: X_train, y: y_train})\n",
"inference.initialize(n_print=10, n_iter=600)\n",
"\n",
"tf.global_variables_initializer().run()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_post= ed.copy(y, {w: qw, b: qb})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean squared error on test data:\n"
]
},
{
"ename": "ValueError",
"evalue": "Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor(\"Cast:0\", shape=(), dtype=float32)'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-11-ad88f2648ab3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Mean squared error on test data:\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0med\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'mean_squared_error'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_post\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m/home/xiangze/anaconda2/lib/python2.7/site-packages/edward/criticisms/evaluate.pyc\u001b[0m in \u001b[0;36mevaluate\u001b[1;34m(metrics, data, n_samples, output_key)\u001b[0m\n\u001b[0;32m 120\u001b[0m \u001b[0mtensor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 121\u001b[0m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtensor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 122\u001b[1;33m \u001b[0my_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_n\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pred\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 123\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 124\u001b[0m \u001b[1;31m# Evaluate y_true (according to y_pred if supervised) for all metrics.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/xiangze/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/math_ops.pyc\u001b[0m in \u001b[0;36mbinary_op_wrapper\u001b[1;34m(x, y)\u001b[0m\n\u001b[0;32m 818\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 819\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msparse_tensor\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSparseTensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 820\u001b[1;33m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconvert_to_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbase_dtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"y\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 821\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 822\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/xiangze/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc\u001b[0m in \u001b[0;36mconvert_to_tensor\u001b[1;34m(value, dtype, name, preferred_dtype)\u001b[0m\n\u001b[0;32m 637\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 638\u001b[0m \u001b[0mpreferred_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpreferred_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 639\u001b[1;33m as_ref=False)\n\u001b[0m\u001b[0;32m 640\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/xiangze/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc\u001b[0m in \u001b[0;36minternal_convert_to_tensor\u001b[1;34m(value, dtype, name, as_ref, preferred_dtype)\u001b[0m\n\u001b[0;32m 702\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 703\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 704\u001b[1;33m \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconversion_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mas_ref\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mas_ref\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 705\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 706\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mret\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m/home/xiangze/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.pyc\u001b[0m in \u001b[0;36m_TensorTensorConversionFunction\u001b[1;34m(t, dtype, name, as_ref)\u001b[0m\n\u001b[0;32m 575\u001b[0m raise ValueError(\n\u001b[0;32m 576\u001b[0m \u001b[1;34m\"Tensor conversion requested dtype %s for Tensor with dtype %s: %r\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 577\u001b[1;33m % (dtype.name, t.dtype.name, str(t)))\n\u001b[0m\u001b[0;32m 578\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 579\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'Tensor(\"Cast:0\", shape=(), dtype=float32)'"
]
}
],
"source": [
"print(\"Mean squared error on test data:\")\n",
"print(ed.evaluate('mean_squared_error', data={X: X_test, y_post: y_test}))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Set up figure.\n",
"fig = plt.figure(figsize=(8, 8), facecolor='white')\n",
"ax = fig.add_subplot(111, frameon=False)\n",
"plt.ion()\n",
"plt.show(block=False)\n",
"\n",
"# Build samples from inferred posterior.\n",
"n_samples = 50\n",
"inputs = np.linspace(-5, 3, num=400, dtype=np.float32).reshape((400, 1))\n",
"probs = tf.stack([tf.sigmoid(ed.dot(inputs, qw.sample()) + qb.sample())\n",
" for _ in range(n_samples)])\n",
"\n",
"for t in range(inference.n_iter):\n",
" info_dict = inference.update()\n",
" inference.print_progress(info_dict)\n",
"\n",
" if t % inference.n_print == 0:\n",
" outputs = probs.eval()\n",
"\n",
" # Plot data and functions\n",
" plt.cla()\n",
" ax.plot(X_train[:], y_train, 'bx')\n",
" for s in range(n_samples):\n",
" ax.plot(inputs[:], outputs[s], alpha=0.2)\n",
"\n",
" ax.set_xlim([-5, 3])\n",
" ax.set_ylim([-0.5, 1.5])\n",
" plt.draw()\n",
" plt.pause(1.0 / 60.0)"
]
}
],
"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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