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@taku-y
Last active June 7, 2016 13:43
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Bayesian Neural Network in PyMC3\n",
"(c) 2016 by Thomas Wiecki"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sys, os\n",
"sys.path.insert(0, os.path.expanduser('~/work/git/github/taku-y/pymc3/'))\n",
"\n",
"%matplotlib inline\n",
"import pymc3 as pm\n",
"import theano.tensor as T\n",
"import theano\n",
"import sklearn\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import datasets\n",
"from sklearn.preprocessing import scale\n",
"from sklearn.cross_validation import train_test_split\n",
"from sklearn.datasets import make_moons, make_circles, make_classification\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#X, Y = datasets.make_blobs(n_samples=200, n_features=2, centers=2, cluster_std=3.0, \n",
"# center_box=(-5.0, 5.0), shuffle=True, random_state=None)\n",
"\n",
"X, Y = make_moons(noise=0.2, random_state=0, n_samples=1000)\n",
"X = scale(X)\n",
"X = X.astype('float32')\n",
"Y = Y.astype('float32')\n",
"X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f9f584bcc88>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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gtXUXNm++3ZB14VQDvrSueZPFSxDaIFEmVEll7VFpwE8mLWR/P2elK60pswUpxsdHsHfv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\nw+W6B62tYmumt/dxNDRUyt4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H+/fvV/kFYQTxAWkUwFZYLIcQCsWzbAEB2d/JWTktLTtk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\n/wm1fixcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9f584bccc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X[Y==0, 0], X[Y==0, 1])\n",
"plt.scatter(X[Y==1, 0], X[Y==1, 1], color='r')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def Ordered(name, var, model=None):\n",
" order = T.constant(list(range(var.tag.test_value.shape[1])))\n",
" return pm.Potential(\n",
" name,\n",
" T.switch(T.eq(T.argsort(T.sum(var, axis=0)), order), 0, -np.inf),\n",
" model=model\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Turn inputs and outputs into shared variables so that we can change them later\n",
"import theano.tensor as tt\n",
"\n",
"# ann_input = tt.matrix()\n",
"# ann_input.tag.test_value = X_train\n",
"# ann_output = tt.vector()\n",
"# ann_output.tag.test_value = Y_train\n",
"\n",
"ann_input = theano.shared(X_train)\n",
"ann_output = theano.shared(Y_train)\n",
"\n",
"n_hidden = 5\n",
"\n",
"# Initialize random but sorted starting weights.\n",
"init_1 = np.random.randn(X.shape[1], n_hidden)\n",
"init_1 = init_1[:, np.argsort(init_1.sum(axis=0))]\n",
"init_2 = np.random.randn(n_hidden, n_hidden)\n",
"init_2 = init_2[:, np.argsort(init_2.sum(axis=0))]\n",
"init_out = np.random.randn(n_hidden)\n",
"init_out = init_out[np.argsort(init_out)]\n",
"\n",
" \n",
"with pm.Model() as neural_network:\n",
" # Weights from input to hidden layer\n",
" weights_in_1 = pm.Normal('w_in_1', 0, sd=1, shape=(X.shape[1], n_hidden), \n",
" testval=init_1)\n",
" \n",
" # Weights from 1st to 2nd layer\n",
" weights_1_2 = pm.Normal('w_1_2', 0, sd=1, shape=(n_hidden, n_hidden), \n",
" testval=init_2)\n",
" \n",
" # Weights from hidden layer to output\n",
" weights_2_out = pm.Normal('w_2_out', 0, sd=1, shape=(n_hidden,), \n",
" testval=init_out)\n",
"\n",
" # Build neural-network\n",
" a1 = T.dot(ann_input, weights_in_1)\n",
" act_1 = T.tanh(a1)\n",
" a2 = T.dot(act_1, weights_1_2)\n",
" act_2 = T.tanh(a2)\n",
" act_out = T.dot(act_2, weights_2_out)\n",
" \n",
" out = pm.Bernoulli('out', \n",
" T.nnet.sigmoid(act_out),\n",
" observed=ann_output)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimation with ADVI"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"minibatch_tensors = [ann_input, ann_output]\n",
"minibatch_RVs = [out]\n",
"\n",
"def create_minibatch(data):\n",
" rng = np.random.RandomState(0)\n",
" \n",
" while True:\n",
" ixs = rng.randint(len(data), size=100)\n",
" yield data[ixs]\n",
"\n",
"minibatches = [\n",
" create_minibatch(X_train), \n",
" create_minibatch(Y_train),\n",
"]\n",
"\n",
"total_size = len(Y_train)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Iteration 0 [0%]: ELBO = -191.23\n",
"Iteration 4000 [10%]: ELBO = -182.2\n",
"Iteration 8000 [20%]: ELBO = -122.79\n",
"Iteration 12000 [30%]: ELBO = -80.09\n",
"Iteration 16000 [40%]: ELBO = -142.56\n",
"Iteration 20000 [50%]: ELBO = -107.72\n",
"Iteration 24000 [60%]: ELBO = -139.28\n",
"Iteration 28000 [70%]: ELBO = -82.98\n",
"Iteration 32000 [80%]: ELBO = -107.29\n",
"Iteration 36000 [90%]: ELBO = -108.81\n",
"Finished [100%]: ELBO = -102.11\n"
]
},
{
"data": {
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bicUuIoP3Xc9N6GcwFLR53Gz1Ktr6U2Ssj0FMRAAWTI7U6bhm7XU0S24nk4hd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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9f040edf98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"with neural_network:\n",
" # Run advi_minibatch\n",
" advi_fit = pm.variational.advi_minibatch(\n",
" n=40000, minibatch_tensors=minibatch_tensors, \n",
" minibatch_RVs=minibatch_RVs, minibatches=minibatches, \n",
" total_size=total_size, learning_rate=1e-2, epsilon=1.0, \n",
" n_mcsamples=1\n",
" )\n",
"plt.plot(advi_fit.elbo_vals)\n",
"trace_advi = pm.variational.sample_vp(advi_fit, 500, neural_network)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f9efc496d68>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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w64whhkNRJnFDrsM3a+MIObKznXjkkUlBkSgvf0dSJJSERBxCfHdHA/63pMxy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\nRZkQQgixCRRlQgghxCZQlAkhhBCb8P8BmKSRvshks9AAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9efd3ef0b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Replace shared variables with testing set\n",
"# (note that using this trick we could be streaming ADVI for big data)\n",
"ann_input.set_value(X_test)\n",
"ann_output.set_value(Y_test)\n",
"\n",
"# Creater posterior predictive samples\n",
"ppc = pm.sample_ppc(trace_advi, model=neural_network, samples=500)\n",
"pred = ppc['out'].mean(axis=0) > 0.5\n",
"\n",
"plt.scatter(X_test[Y_test==0, 0], X_test[Y_test==0, 1])\n",
"plt.scatter(X_test[Y_test==1, 0], X_test[Y_test==1, 1], color='r')\n",
"plt.title('Predicted labels in testing set')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x7f9efc461ef0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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5EUJijWGJXi0tLVi+fDnWrFmDAQMGGHVZQiyDSV6EkFhjiKXc2dmJ5cuXY968eZg+fbqq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\nLmp45osJT+MAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9efd3bd908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(X_test[pred==0, 0], X_test[pred==0, 1])\n",
"plt.scatter(X_test[pred==1, 0], X_test[pred==1, 1], color='r')\n",
"plt.title('Predicted labels in testing set')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy = 95.6%\n"
]
},
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9efc45d518>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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CL4Ts+z73338/Dz/8MF1dXWzatIkNGzbQ29tbPyafz/Od73yHH//4x6RSKTKZ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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9efd3bd898>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print('Accuracy = {}%'.format((Y_test == pred).mean() * 100))\n",
"sns.regplot(ppc['out'].mean(axis=0), Y_test, logistic=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lets look at what the classifier has learned"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"grid = np.mgrid[-3:3:100j,-3:3:100j]\n",
"grid_2d = grid.reshape(2, -1).T.astype('float32')\n",
"dummy_out = np.ones(grid_2d.shape[0], dtype=np.int8)\n",
"\n",
"ann_input.set_value(grid_2d)\n",
"ann_output.set_value(dummy_out)\n",
"# Creater posterior predictive samples\n",
"ppc = pm.sample_ppc(trace_advi, model=neural_network, samples=5000)\n",
"pred_grid = ppc['out'].mean(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f9ee45402b0>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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i7wEv3gIn3ALLX0UoNme5GbLzXKiKBlXTIGduAqhACqfBR04A4KDrOhhxEYmb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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9efc32b390>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cmap = sns.diverging_palette(145, 280, s=85, l=25, as_cmap=True)\n",
"# plt.contourf(*grid, pred_grid.reshape(100, 100), cmap=cmap)\n",
"plt.contourf(grid[0], grid[1], pred_grid.reshape(100, 100), cmap=cmap)\n",
"plt.scatter(X_test[pred==0, 0], X_test[pred==0, 1], alpha=0.5)\n",
"plt.scatter(X_test[pred==1, 0], X_test[pred==1, 1], color='r', alpha=0.5)\n",
"#plt.title('Predicted labels in testing set')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
},
"widgets": {
"state": {},
"version": "1.1.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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