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@fhuszar
Created January 19, 2017 14:13
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using gpu device 2: Tesla K80 (CNMeM is disabled, CuDNN 4007)\n"
]
}
],
"source": [
"import os\n",
"os.environ['THEANO_FLAGS']=\"device=gpu2\"\n",
"\n",
"%matplotlib inline\n",
"from matplotlib import pylab as plt\n",
"import theano\n",
"from theano import tensor as T\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#create a regular grid in weight space for visualisation\n",
"xmin = -5\n",
"xmax = 5\n",
"xrange = np.linspace(xmin,xmax,300)\n",
"x = np.repeat(xrange[:,None],300,axis=1)\n",
"x = np.concatenate([[x.flatten()],[x.T.flatten()]])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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wCHyy2Fbeih9wcg0g9u24LFwXGDfe2bxOcI+1gh/x/MQq8yuAkRIew7QIizNdapbbwHPx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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f91898d0cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"prior_variance = 2\n",
"logprior = -(x**2).sum(axis=0)/2/prior_variance\n",
"plt.contourf(xrange, xrange, logprior.reshape(300,300), cmap='gray');\n",
"plt.axis('square');\n",
"plt.xlim([xmin,xmax])\n",
"plt.ylim([xmin,xmax]);"
]
},
{
"cell_type": "code",
"execution_count": 329,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#let the likelihood be a simple exponential distribution where the \n",
"#mean is parametrised by the two hidden variables x_1 and x_2\n",
"\n",
"def likelihood(x, y, beta_0=3, beta_1=1):\n",
" beta = beta_0 + (beta_1*(x**3).clip(0,np.Inf).sum(axis=0))\n",
" return -np.log(beta) - y/beta\n",
" \n",
"y = [None]*5\n",
"y[0] = 0\n",
"y[1] = 5\n",
"y[2] = 8\n",
"y[3] = 12\n",
"y[4] = 50\n",
"\n",
"llh = [None]*5\n",
"llh[0] = likelihood(x, y[0]) \n",
"llh[1] = likelihood(x, y[1]) \n",
"llh[2] = likelihood(x, y[2]) \n",
"llh[3] = likelihood(x, y[3]) \n",
"llh[4] = likelihood(x, y[4]) "
]
},
{
"cell_type": "code",
"execution_count": 331,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f9160361f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#plotting posteriors for different observed y values\n",
"plt.subplots(figsize=(20,4))\n",
"for i in range(5):\n",
" plt.subplot(1,5,i+1)\n",
" plt.contourf(xrange, xrange, np.exp(logprior + llh[i]).reshape(300,300), cmap='gray')\n",
" plt.title('y={}'.format(y[i]))\n",
" plt.axis('square');\n",
" plt.xlim([xmin,xmax])\n",
" plt.ylim([xmin,xmax])\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"## Fitting an approximate posterior\n",
"\n",
"This part is for the actual GAN stuff. Here we define the generator and the discriminator networks in Lasagne, and code up the two loss functions in theano."
]
},
{
"cell_type": "code",
"execution_count": 369,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from lasagne.utils import floatX\n",
"\n",
"from lasagne.layers import (\n",
" InputLayer,\n",
" DenseLayer,\n",
" NonlinearityLayer,\n",
" ElemwiseSumLayer,\n",
" ReshapeLayer,\n",
")\n",
"from lasagne.nonlinearities import sigmoid\n",
"\n",
"#defines a 'generator' network with two inputs, noise z and observation y\n",
"\n",
"def build_G(input_z_var=None, input_y_var=None, num_z = 3):\n",
" \n",
" input_y_layer = InputLayer(input_var=input_y_var, shape=(None, 1))\n",
" \n",
" input_z_layer = InputLayer(input_var=input_z_var, shape=(None, num_z))\n",
"\n",
" network1 = DenseLayer(incoming = input_y_layer, num_units=10)\n",
" \n",
" network1 = DenseLayer(incoming = network1, num_units=20)\n",
" \n",
" network2 = DenseLayer(incoming = input_z_layer, num_units=20)\n",
"\n",
" network = ElemwiseSumLayer(incomings=(network1,network2))\n",
" \n",
" network = DenseLayer(incoming=network, num_units=10)\n",
" \n",
" network = DenseLayer(incoming=network, num_units=20)\n",
" \n",
" network = DenseLayer(incoming = network, num_units=2, nonlinearity=None)\n",
" \n",
" return network\n",
"\n",
"#defines the 'discriminator network'\n",
"def build_D(input_x_var=None, input_y_var=None):\n",
"\n",
" input_x_layer = InputLayer(input_var=input_x_var, shape = (None, 2))\n",
" \n",
" input_y_layer = InputLayer(input_var=input_y_var, shape = (None, 1))\n",
" \n",
" network1 = DenseLayer(incoming = input_x_layer, num_units=10)\n",
" \n",
" network1 = DenseLayer(incoming = network1, num_units=20)\n",
" \n",
" network2 = DenseLayer(incoming = input_y_layer, num_units=10)\n",
" \n",
" network2 = DenseLayer(incoming = network2, num_units=20)\n",
"\n",
" network = ElemwiseSumLayer(incomings=(network1, network2))\n",
" \n",
" network = DenseLayer(incoming = network, num_units=10)\n",
" \n",
" network = DenseLayer(incoming = network, num_units=20)\n",
"\n",
" network = DenseLayer(incoming = network, num_units=1, nonlinearity=None)\n",
" \n",
" normalised = NonlinearityLayer(incoming = network, nonlinearity = sigmoid)\n",
" \n",
" return { 'unnorm':network, 'norm':normalised }"
]
},
{
"cell_type": "code",
"execution_count": 370,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from lasagne.layers import get_output, get_all_params\n",
"from theano.printing import debugprint\n",
"from lasagne.updates import adam\n",
"from theano.tensor.shared_randomstreams import RandomStreams\n",
"\n",
"#variables for latent, observed and GAN noise variables\n",
"x_var = T.matrix('hidden variable')\n",
"y_var = T.vector('observation')\n",
"z_var = T.matrix('GAN noise')\n",
"\n",
"#theano variables for things like batchsize, learning rate, etc.\n",
"prior_variance_var = T.scalar('prior variance')\n",
"learningrate_var = T.scalar('learning rate')\n",
"\n",
"#random numbers for sampling from the prior or from the GAN\n",
"srng = RandomStreams(seed=1337)\n",
"z_rnd = srng.normal((y_var.shape[0],3))\n",
"prior_rnd = srng.normal((y_var.shape[0],2))\n",
"\n",
"#instantiating the G and D networks\n",
"generator = build_G(input_z_var=z_var, input_y_var=y_var.dimshuffle(0,'x'))\n",
"\n",
"#these expressions are random samples from the generator and the prior, respectively\n",
"samples_from_generator = theano.clone(get_output(generator), replace={z_var: z_rnd})\n",
"samples_from_prior = prior_rnd*T.sqrt(prior_variance_var)\n",
"\n",
"evaluate_generator = theano.function(\n",
" [z_var, y_var],\n",
" get_output(generator),\n",
" allow_input_downcast=True\n",
")\n",
"\n",
"sample_generator = theano.function(\n",
" [y_var],\n",
" samples_from_generator,\n",
" allow_input_downcast=True,\n",
")\n",
"\n",
"sample_prior = theano.function(\n",
" [y_var, prior_variance_var],\n",
" samples_from_prior,\n",
" allow_input_downcast=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 371,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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wJjswKvSaFGy1kStYaeSAML/gTU32wpzHzzcepZ4Ak4lvS1GO4gpViq0oyjyG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\ndeS6dTp6xQqNuuceXTppUsG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"text/plain": [
"<matplotlib.figure.Figure at 0x7f9160634dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#showing q when initialised, without any training\n",
"\n",
"y_test = np.array(y)\n",
"N_samples = 100\n",
"samples = sample_generator(np.repeat(y_test,100))\n",
"samples = samples.reshape(y_test.shape[0], N_samples, 2)\n",
"\n",
"#plotting posteriors for different observed y values\n",
"plt.subplots(figsize=(20,4))\n",
"for i in range(5):\n",
" plt.subplot(1,5,i+1)\n",
" plt.contourf(xrange, xrange, np.exp(logprior + llh[i]).reshape(300,300), cmap='gray')\n",
" plt.title('y={}'.format(y[i]))\n",
" plt.axis('square');\n",
" plt.plot(samples[i,:,0],samples[i,:,1],'r.')\n",
" plt.xlim([xmin,xmax])\n",
" plt.ylim([xmin,xmax])"
]
},
{
"cell_type": "code",
"execution_count": 373,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#compiling theano functions for D\n",
"\n",
"discriminator = build_D(input_x_var=x_var, input_y_var=y_var.dimshuffle(0,'x'))\n",
"\n",
"#discriminator output for synthetic samples, both normalised and unnormalised (after/before sigmoid)\n",
"D_of_G = theano.clone(get_output(discriminator['norm']), replace={x_var:samples_from_generator})\n",
"s_of_G = theano.clone(get_output(discriminator['unnorm']), replace={x_var:samples_from_generator})\n",
"\n",
"#discriminator output for real samples from the prior\n",
"D_of_prior = theano.clone(get_output(discriminator['norm']), replace={x_var:samples_from_prior})\n",
"\n",
"#loss of discriminator - simple binary cross-entropy loss\n",
"loss_D = -T.log(D_of_G).mean() - T.log(1-D_of_prior).mean()\n",
"\n",
"evaluate_discriminator = theano.function(\n",
" [x_var, y_var],\n",
" get_output([discriminator['unnorm'],discriminator['norm']]),\n",
" allow_input_downcast = True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 374,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#log likelihood for each synthetic w sampled from the generator\n",
"beta_0 = 3\n",
"beta_1 = 1\n",
"beta_expr = (beta_0 + beta_1*(T.nnet.relu(samples_from_generator)**3).sum(axis=1))\n",
"log_likelihood = (- T.log(beta_expr) - y_var/beta_expr)\n",
"\n",
"#loss for G is the sum of unnormalised discriminator output and the negative log likelihood\n",
"loss_G = s_of_G.mean() - log_likelihood.mean()"
]
},
{
"cell_type": "code",
"execution_count": 375,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#this is to evaluate the log-likelihood of an arbitrary set of x\n",
"beta_expr_for_x = (beta_0 + beta_1*(T.nnet.relu(x_var)**3).sum(axis=1)).dimshuffle(0,'x')\n",
"log_likelihood_for_x = - T.log(beta_expr_for_x) - y_var.dimshuffle('x',0)/beta_expr_for_x\n",
"\n",
"evaluate_loglikelihood = theano.function(\n",
" [x_var, y_var],\n",
" log_likelihood_for_x,\n",
" allow_input_downcast = True\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 376,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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NjQgzHRwVFxdr69atxq7vBWoTbnLzxjebscMS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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f917f608ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#testing if numpy and theano give the same likelihoods\n",
"\n",
"llh_theano = evaluate_loglikelihood(x.T, y_test)\n",
"plt.subplots(figsize=(20,8))\n",
"for i in range(5):\n",
" plt.subplot(2,5,i+1)\n",
" plt.contourf(xrange, xrange, llh[i].reshape(300,300), cmap='gray')\n",
" plt.title('y={}'.format(y[i]))\n",
" plt.axis('square');\n",
" plt.xlim([xmin,xmax])\n",
" plt.ylim([xmin,xmax])\n",
"\n",
" plt.subplot(2,5,5+i+1)\n",
" plt.contourf(xrange, xrange, llh_theano[:,i].reshape(300,300), cmap='gray')\n",
" plt.title('y={}'.format(y[i]))\n",
" plt.axis('square');\n",
" plt.xlim([xmin,xmax])\n",
" plt.ylim([xmin,xmax])\n",
" \n",
" assert np.allclose(llh_theano[:,i], llh[i])"
]
},
{
"cell_type": "code",
"execution_count": 377,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#compiling theano functions for training D:\n",
"\n",
"params_D = get_all_params(discriminator['norm'], trainable=True)\n",
"\n",
"updates_D = adam(\n",
" loss_D,\n",
" params_D,\n",
" learning_rate = learningrate_var\n",
")\n",
"\n",
"train_D = theano.function(\n",
" [y_var, prior_variance_var, learningrate_var],\n",
" loss_D,\n",
" updates = updates_D,\n",
" allow_input_downcast = True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 378,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"100 loops, best of 3: 3.4 ms per loop\n",
"0.19694069027900696\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.14136524498462677\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.09865792840719223\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.11149921268224716\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.10951042920351028\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.08253919333219528\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.13307051360607147\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.05772372707724571\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.10247500240802765\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.07597250491380692\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.09699824452400208\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.0701509416103363\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.1528717577457428\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.0788218304514885\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.13194730877876282\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.10677020251750946\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.0921526849269867\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.10467558354139328\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.07934151589870453\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.09821976721286774\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.0667971521615982\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.11635079979896545\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.06328829377889633\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.10881220549345016\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08699171245098114\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.13692587614059448\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08007560670375824\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.11251413822174072\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.0892680287361145\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08364537358283997\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.10010819137096405\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.10426244139671326\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.10919444262981415\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08194600045681\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.10860898345708847\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08114002645015717\n",
"100 loops, best of 3: 3.41 ms per loop\n",
"0.09767437726259232\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.09254080057144165\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.1189381554722786\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.10169433057308197\n",
"100 loops, best of 3: 3.41 ms per loop\n",
"0.07744131982326508\n",
"100 loops, best of 3: 3.41 ms per loop\n",
"0.0952213853597641\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.1086280569434166\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.09853222221136093\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.09496443718671799\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.10971258580684662\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.11001060158014297\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08959390223026276\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.112617626786232\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08670667558908463\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.15436330437660217\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08161386847496033\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.06193019077181816\n",
"100 loops, best of 3: 3.41 ms per loop\n",
"0.08402884006500244\n",
"100 loops, best of 3: 3.41 ms per loop\n",
"0.10323703289031982\n",
"100 loops, best of 3: 3.41 ms per loop\n",
"0.06650666892528534\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.09534305334091187\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.12728597223758698\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.06952452659606934\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.07130463421344757\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.07190843671560287\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.1303069293498993\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.09161616116762161\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.10217218846082687\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.07702568173408508\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.09167127311229706\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.0757702887058258\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.07446838915348053\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.09271244704723358\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.04827922582626343\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08601496368646622\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.12544262409210205\n",
"100 loops, best of 3: 3.41 ms per loop\n",
"0.14718058705329895\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.09025590121746063\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.11514679342508316\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.10335472226142883\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.07618217915296555\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08741478621959686\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08474110066890717\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.09554368257522583\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.10536107420921326\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.1152733713388443\n",
"100 loops, best of 3: 3.41 ms per loop\n",
"0.09492850303649902\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.0977790355682373\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.09554367512464523\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.1088155061006546\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08191279321908951\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.10399693250656128\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.09627046436071396\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.11592172086238861\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.0883626937866211\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.07343368977308273\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.09297041594982147\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.07670484483242035\n",
"100 loops, best of 3: 3.41 ms per loop\n",
"0.07446391880512238\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08786191791296005\n",
"100 loops, best of 3: 3.39 ms per loop\n",
"0.07787670940160751\n",
"100 loops, best of 3: 3.4 ms per loop\n",
"0.08764428645372391\n",
"100 loops, best of 3: 3.41 ms per loop\n",
"0.12690404057502747\n",
"100 loops, best of 3: 3.41 ms per loop\n",
"0.11945220828056335\n"
]
}
],
"source": [
"#pretrain the discriminator for randomly initialised q to get training starting quicker\n",
"\n",
"learning_rate = 0.001\n",
"for i in range(100):\n",
" %timeit -n 100 train_D(np.repeat(y_test,100), prior_variance, learning_rate)\n",
" print(train_D(np.repeat(y_test,100), prior_variance, 0))"
]
},
{
"cell_type": "code",
"execution_count": 379,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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SRgnCOpx+lnT6+IR1MFZPwCzc8CU26z244bMi7EuybXgB2vQSziEZ9UkQToHqABKEdZDh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\niIiIKMj/BxCzHQlSvqI2AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f91603e2160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#plotting discriminators for different observed y values\n",
"\n",
"N_samples = 100\n",
"q_samples = sample_generator(np.repeat(y_test,100))\n",
"q_samples = q_samples.reshape(y_test.shape[0], N_samples, 2)\n",
"\n",
"prior_samples = sample_prior(np.repeat(y_test,100), prior_variance)\n",
"prior_samples = prior_samples.reshape(y_test.shape[0], N_samples, 2)\n",
"\n",
"plt.subplots(figsize=(20,4))\n",
"for i in range(5):\n",
" foobar = evaluate_discriminator(x.T, y[i]*np.ones(90000))[0]\n",
" plt.subplot(1,5,i+1)\n",
" plt.contourf(xrange, xrange, foobar[:,0].reshape(300,300).T, cmap='gray')\n",
" plt.title('y={}'.format(y[i]))\n",
" plt.axis('square');\n",
" plt.plot(q_samples[i,:,0],q_samples[i,:,1],'r.')\n",
" plt.plot(prior_samples[i,:,0],prior_samples[i,:,1],'b.')\n",
"\n",
" plt.xlim([xmin,xmax])\n",
" plt.ylim([xmin,xmax])\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 395,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#compile theano functions for training q/G\n",
"\n",
"params_G = get_all_params(generator, trainable=True)\n",
"\n",
"updates_G = adam(\n",
" loss_G,\n",
" params_G,\n",
" learning_rate = learningrate_var\n",
")\n",
"\n",
"train_G = theano.function(\n",
" [y_var, learningrate_var],\n",
" loss_G,\n",
" updates = updates_G,\n",
" allow_input_downcast = True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"30 loops, best of 3: 9.1 ms per loop\n",
"3.8934788703918457\n",
"30 loops, best of 3: 8.69 ms per loop\n",
"3.8773000240325928\n",
"30 loops, best of 3: 8.75 ms per loop\n",
"3.8893651962280273\n",
"30 loops, best of 3: 8.77 ms per loop\n",
"3.8807172775268555\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8837335109710693\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.877973794937134\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.86313796043396\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8852791786193848\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.8729286193847656\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.885035276412964\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.903658390045166\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8744828701019287\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.871873617172241\n",
"30 loops, best of 3: 8.69 ms per loop\n",
"3.8924527168273926\n",
"30 loops, best of 3: 8.76 ms per loop\n",
"3.8612115383148193\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8839640617370605\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8873133659362793\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8517329692840576\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8821141719818115\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8925321102142334\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.867460012435913\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8918638229370117\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.870687246322632\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.908451557159424\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8801705837249756\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.9134109020233154\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.8801865577697754\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.866286277770996\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.9016053676605225\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.898961067199707\n",
"30 loops, best of 3: 8.69 ms per loop\n",
"3.883258819580078\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.9020607471466064\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.9063167572021484\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.8989450931549072\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8729023933410645\n",
"30 loops, best of 3: 8.69 ms per loop\n",
"3.8729913234710693\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.862938404083252\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8830225467681885\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8885653018951416\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8658607006073\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.889392614364624\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.881735324859619\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.8869848251342773\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8652169704437256\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8780994415283203\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.890516519546509\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8860788345336914\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.8859333992004395\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.898801326751709\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.880398750305176\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.87237811088562\n",
"30 loops, best of 3: 8.75 ms per loop\n",
"3.881225824356079\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.8715293407440186\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.876944065093994\n",
"30 loops, best of 3: 8.77 ms per loop\n",
"3.8509128093719482\n",
"30 loops, best of 3: 8.75 ms per loop\n",
"3.880810260772705\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.882941961288452\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.883906364440918\n",
"30 loops, best of 3: 8.81 ms per loop\n",
"3.8855979442596436\n",
"30 loops, best of 3: 8.76 ms per loop\n",
"3.8785171508789062\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.880937337875366\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.878472328186035\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.8850808143615723\n",
"30 loops, best of 3: 8.8 ms per loop\n",
"3.8855202198028564\n",
"30 loops, best of 3: 8.76 ms per loop\n",
"3.8863680362701416\n",
"30 loops, best of 3: 8.77 ms per loop\n",
"3.886876106262207\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.88616943359375\n",
"30 loops, best of 3: 8.69 ms per loop\n",
"3.8983988761901855\n",
"30 loops, best of 3: 8.78 ms per loop\n",
"3.8751485347747803\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.8577959537506104\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.876068115234375\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.8546059131622314\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8862576484680176\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.88470458984375\n",
"30 loops, best of 3: 8.79 ms per loop\n",
"3.887838125228882\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.8689849376678467\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.8711392879486084\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.8804335594177246\n",
"30 loops, best of 3: 8.67 ms per loop\n",
"3.8920836448669434\n",
"30 loops, best of 3: 8.67 ms per loop\n",
"3.892789840698242\n",
"30 loops, best of 3: 8.67 ms per loop\n",
"3.888348340988159\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.8799614906311035\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8841328620910645\n",
"30 loops, best of 3: 8.69 ms per loop\n",
"3.8624138832092285\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8916773796081543\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.8862411975860596\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8781495094299316\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.890317916870117\n",
"30 loops, best of 3: 8.69 ms per loop\n",
"3.8756942749023438\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8703925609588623\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.8863513469696045\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.8682327270507812\n",
"30 loops, best of 3: 8.83 ms per loop\n",
"3.8881001472473145\n",
"30 loops, best of 3: 8.82 ms per loop\n",
"3.8764615058898926\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8779637813568115\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.876533031463623\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8622970581054688\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.869492769241333\n",
"30 loops, best of 3: 8.75 ms per loop\n",
"3.887963056564331\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.895472764968872\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.860532522201538\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8698225021362305\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.884481906890869\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.8808579444885254\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.871746301651001\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.8766367435455322\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.88895845413208\n",
"30 loops, best of 3: 8.69 ms per loop\n",
"3.8810150623321533\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.855424165725708\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.8868539333343506\n",
"30 loops, best of 3: 8.69 ms per loop\n",
"3.886591911315918\n",
"30 loops, best of 3: 8.69 ms per loop\n",
"3.884516477584839\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.874213695526123\n",
"30 loops, best of 3: 8.69 ms per loop\n",
"3.886925220489502\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8728060722351074\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8876192569732666\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.8769757747650146\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.9017841815948486\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.8743062019348145\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.877429962158203\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.881195068359375\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.892887592315674\n",
"30 loops, best of 3: 8.75 ms per loop\n",
"3.8899240493774414\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.8925483226776123\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8812615871429443\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.877384901046753\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8855507373809814\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8793814182281494\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8787147998809814\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8817646503448486\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.8665785789489746\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.883277654647827\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.892141819000244\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8894741535186768\n",
"30 loops, best of 3: 8.74 ms per loop\n",
"3.8893561363220215\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.884796619415283\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8821284770965576\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.889864921569824\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8764522075653076\n",
"30 loops, best of 3: 8.79 ms per loop\n",
"3.8619635105133057\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.8533847332000732\n",
"30 loops, best of 3: 8.67 ms per loop\n",
"3.878660202026367\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.868276357650757\n",
"30 loops, best of 3: 8.76 ms per loop\n",
"3.8837966918945312\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.879452705383301\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8642213344573975\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.8765127658843994\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.889464855194092\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.870368003845215\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.86913800239563\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.8921239376068115\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8765978813171387\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.895301580429077\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.88694167137146\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.8910231590270996\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.886777639389038\n",
"30 loops, best of 3: 8.76 ms per loop\n",
"3.8940670490264893\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.872864246368408\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.873847484588623\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.8761234283447266\n",
"30 loops, best of 3: 8.72 ms per loop\n",
"3.891298294067383\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.8673882484436035\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.879309892654419\n",
"30 loops, best of 3: 8.98 ms per loop\n",
"3.866856813430786\n",
"30 loops, best of 3: 8.81 ms per loop\n",
"3.8803822994232178\n",
"30 loops, best of 3: 8.75 ms per loop\n",
"3.8640642166137695\n",
"30 loops, best of 3: 8.78 ms per loop\n",
"3.848623275756836\n",
"30 loops, best of 3: 8.77 ms per loop\n",
"3.8745429515838623\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.8850719928741455\n",
"30 loops, best of 3: 8.8 ms per loop\n",
"3.8940069675445557\n",
"30 loops, best of 3: 8.86 ms per loop\n",
"3.8629977703094482\n",
"30 loops, best of 3: 9.38 ms per loop\n",
"3.885540246963501\n",
"30 loops, best of 3: 8.98 ms per loop\n",
"3.8905491828918457\n",
"30 loops, best of 3: 8.73 ms per loop\n",
"3.8607044219970703\n",
"30 loops, best of 3: 8.81 ms per loop\n",
"3.8908426761627197\n",
"30 loops, best of 3: 8.79 ms per loop\n",
"3.8708953857421875\n",
"30 loops, best of 3: 8.76 ms per loop\n",
"3.8738954067230225\n",
"30 loops, best of 3: 8.8 ms per loop\n",
"3.879502534866333\n",
"30 loops, best of 3: 8.76 ms per loop\n",
"3.878369092941284\n",
"30 loops, best of 3: 8.8 ms per loop\n",
"3.86116886138916\n",
"30 loops, best of 3: 8.79 ms per loop\n",
"3.855984687805176\n",
"30 loops, best of 3: 8.71 ms per loop\n",
"3.887941837310791\n",
"30 loops, best of 3: 8.7 ms per loop\n",
"3.878481864929199\n",
"30 loops, best of 3: 8.68 ms per loop\n",
"3.8624348640441895\n"
]
}
],
"source": [
"#main training loop - will need more iterations, or run this cell several times\n",
"\n",
"learning_rate = 0.0003\n",
"for i in range(300):\n",
" %timeit -n 30 train_D(np.repeat(y_test,500), prior_variance, learning_rate)\n",
" print(train_G(np.repeat(y_test,500), learning_rate))"
]
},
{
"cell_type": "code",
"execution_count": 393,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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Hte7/SY4cOVJ9u8hp877YKbSx7xZjylNZpj9RmpAGYlqkbUYetcdW1sApr9Y+VLagZugC\nsaWCqubAZpHjkOvf5NhfNe5zdjbLOdbSNWRMi7YhbQ9iyFkIemoP+czbNgU0Wy2yT6IU9UbfLC/K\nsVxUla/nbrXAmva0f6eQYtHRKzUT0kzX66ialr9z1OlPQprhGFXTjkhvhurtab1XHlMLIc10pb/r\n5t8f6d7r1eGzi3TcFlVVUNNyIbbZTt+1p32xmZBmZy2FNa18j9YIaWBxmzuQYxRZvU53EtYQWa/t\ncidjBlE1BTcpCW9SinWMllFNUKNYW0zUJ/GLaum7lFZrWFPjNg9hkUWRSxHSjMOomnYN/UrvZYrB\nSCOCFiWsIaIciwa3JOr3mrRdke7Nwpt2VBHUKNxIKV+Rp1iMRxufz5gBrJAGyio92sYT+2OENUTS\ne3iaUtxgZhY1hjcptRvgtPJgK3xQo4AjJSHNoiKMqhn788lHSDO+VjofzC5XcJMzoGmlMBTWEEGO\ntll7m6x526eJHt6ktPHccy2MJ3RQo8DLo+a3SqVU73ZHMVZYo/2WM0abENLEIaxhXkbRTCesYUy5\nA9SU2gw9WlHLejeuiTGEDmrIa3PhXEMBVcM21mDosEZIU05NIU2vFH6UskgBJqTZmTbLGEq1zaHX\nvGJxUYObFkKbFh5qCWo6Nq2YjlKUldiO1hZXjkZAU1aNIU2vbU7hR27RQprap1psps0ypKEC1NoL\n1d6sv6ZGOXZeCT4eQQ1bRJgq1WNhV1qE9Wqoi3YYVwtPiphdtJBmjbAG5meUW1zbHZuhrw3RR9u4\nVpYnqGGqsQIbxeFsZgldNu9LYU29tmsXJdpqzt9lVA0sLmpIA9CCWa+XY4c40+4FYwU4ApvysgU1\nir92rT+2pYutIYq5nopG7bIdk87bzcc3V1vtpX2UMlSnxaia9rU0WgUQokZQ4hiMuabL2AFO5PVs\nau8nLRXUKALJSXFYPyN2ypt1/y46yqZUO+wpIIUclglp9u3bpyBckJFwlKRtjmPIfR4luBhjvRuj\nbPKaO6hRhPVt0vHPUXwNWcApFqlV6euvtlGf2p8WMZmRNNCuzUWs4GYxNey3KMHF2j2lx1E2NfeT\nZgpqhDNsZ9kn5YrDthhVE8c8bauFaY29qrkTwla5QhpP7hdnVA1DWjvXem+vLX//KMHF0IFNFGP3\nkxbd7zsGNQoudlJTAVbTtsIyIoU0wGxqHEnT2pufYCw9BzY9fecIo2yGDGyiBN9DhzWT7ouT/uyk\nk06a+jtOzLpFdGfZAk+B2CbHdVz2f38UyoypxaezPRWOxBKhqKW8w4cPH/8Zy1B9h16upysrK8d/\nchDUsDAhDcQTZfFgYD65O8y9dIxLsg8Zi7CmL2MGNjmDhe1EuJ6W+J65w5n1BDV0Q0FK6xY5x4eY\n3qrtDcOoGsbU4qgaGJOwpj9jjrIZIrBpJawpGc6st9TruUsZY12cRT6z5+Kj5+/ObCwqDEDtoqyv\nQJ8sCt6vsdayKb1+Tc3X1KEfiIUJasYq6Jb53LV/K7SYn30GeWlTpDT+mw2IQ4EHMHkUR01BwZiB\nTathTS19pdGCmghP2nNtwxCBzaRt3WnEgsLtAfYFTLd7925tBBrU81tkcqq90BvS5n1lPy1P6LpR\njn2x0++IeN6OEdiUHF0jrNnZoEFNhHAmpXLbUaLY2W5bx9qfCjpmZfrTMLRJ1quh88Gw1neGFXx5\nbLcfIxZ5JW23L2osiBletOtS5MBxrMBGv2J4xYOaaEVa6e2ZJayJtk/GMOQTfEXsuIQ1dSjZJo3Y\ngTiMsilvrKkKY1j2PIpcEEdiVM241u/7KOfo0NeZEmGNUTXbKxLURC3KhtquzUVJ1P1B3UqeV7mL\namFNOQIQNovc6chh/WJ+rX/XkhR+5Y1dhJRW4vyZVBC3vh9npc3GEC1cHDKwKb3Q8BjmCWv27t07\n6ILCWYIaBdhWY+8TxdsDPM2fX4m1j4Q18RlVE1dLnaJlbO4gbddhqm2frW3vkJ3A3gq/WQqZ3Ptj\n7NE1Ob7P5m0f6pxZ/znCmmN6a7M1iDLaZsg2knMkirY93cJBjYKLZSnc6qTtt610WJNS+0FybQFB\nJDlDilpH3gwd2JgKtVGp9XxyFyNDHi/nBsxm7NE2Qo/FRJ0CNVdQo0ADWrLdKJ/NYYLrXz5C2r4N\nOWJk0mdG7IxNMvQQa0/qtxqjyGI6Regx2mo9xhhtM1Q7iRpuLCri99kxqKmxOKlxm4E85g0AZv37\nPa07NUSQIqxp3xiBzCxqmmMvrGmP/bscYc0x2mp9hpwGOdRn1XQ/rdGgr+cuofWCqXWKNXIY+hyK\nNNqmVBsaYppSL1OhWhQ1hJlHxKdnESgAiU5Yc4y2WqdWA5tl7qdR2vQs32PIByhVBjXCGeYlEGpT\nlGO6th0tXpvWfycLDfephVBmmuhhzVj7XgFIdFEKO1hUa4FN9PvprCJ9jyqCmhaLn9Im7bOohZCn\n6iwi4vkyxputhtwP3grVh5aDmUkidcrW6+049ED4BWw2ZOhY+rOi3k9rdeLYGzDN7t27j/+QR659\n6ZgwhiNHjmz4iWrIbRtjP2j/bVpZWTn+06OI33vszq7RCnkJafLrfZ/2/v1bcvjw4UFfe1/ysxa9\nn0Y6n6P0h0YdUaPDP7whpjEsyhN11qv5XBhiZM2Y+0dbbUuEzkgEEZ8EDr2YMPlEKjqAOrQyHSri\n/bRGgwQ1ApmYIhZbEbeJsja/Tcnxr4MpizCMscIaQcP87DOG4lxr29CBTamwJqXxR4cuY1LgNOT9\nOGtQI5DpR21rVSgm49p83WglACg9qkaoRQ5Ga2wU9Sng2jY5XvEomBmac64fQwU2QwZDtVnfLxj6\nHrxwUCOUAUpqIbAR1syule9RE0V/fUyFGp8iOY4ei8pWz7+oIXkU6497yfPe29QmG+u+O1NQI5Rh\nkpaKROJynm1vzP3juNRLsT+ZQqFvrRbBreqtqGz9/Nx8X3I9nqz06Jfc7UoIt7gdgxohTbuiFllR\nt4tx1H4+jPHKbtiOkKZeQx27VgvC1r5XrvOhtyKqxqmErZ27sxDcbK9kUNlbCBrVqG99on5GO0Cf\ntPv61FSUjCF6EeD49av0gpa1Lvq5bCFZy1TCHkOaSSYdq9rO2dxKjq4ZY90aAdFGghqap6CsV+3H\nzkia2dR+nCOroQhhe0MfQ0Xh+CYd8yHOg5qmKPRSzGmP21vfLmo5d0uIvhhwTdeWSAQ1nVIYEd3a\nObo+7HDewmwENG1wHPsQ6Tj3VlBFHlUjpJmP0KZMYGOUy3gENR1S7FKDSaNRWngTVAty7n/HMp+o\nxUYtonXyxzieCsNhRG+rvYU10WiHy4t2PR/aUG+JoixBTUcURIxhp/NukelBAht4QPSij/k5pu2p\n7ZgKa8YhpMlPaLP8iBijasYhqOlAyWJWocw0s5wby67hYjHr4RlNE0NtRR+zc2zb0MJxbDGsifx9\nhDTl9RraRFjDpsXrSWmCmg7UVMzWsp1MN0RAM+l3RTt3LCRMCS0Uf0w35vFVJM6up3ZY6xuhaqP9\nDa/H0GaZkTFG1QxPUANkES0oGVOrIY1jPI6eisKeOc4xOA6TeRpejpBmfD2FNgKXeghqgKUsUrxP\neqMTi6tp1Nya2rZ3aIrF8bTeSZ+kl0JRu1pOlNE1LRWZvbS9mkQ5zyMS8gxLUEPTFINlRNyvNYYV\nsJ4iEqiB0TV5CGli62mUDTGdOPYGMIwSIxcUxX05cuTI8R/6YxHhMlZWVo7/wBgUiyyipWvW0EX4\n4cOHtbvKuE8/YJlz1z6cjxE1wFQlC2rTnuiRTkpsYz413bt3r/ODKhhdsDgBTd1aGWUTeQpT5G0b\nmqCmI6aGMAvnCJsZTbMcxXcdau50w1DGbic1F3BCmraYAkhppj4RRo8FXBRDT2symiY/+zQWU5rq\n0mtnO2fh6Fxv39jtJEdI4zyF5Qkdh2FEDXRKMAZlKARYlOlPRDR2QDMEbQ+IRlADHYkQzhj50a8I\n5x8As4sS0tQ85Yl2mf5ESaY+sbDoBbeicOOUptb3R+vfDygn0pP0oTr9pj2xnb179zZbgDpfYfl7\nwKL/XvubnaCGEBTZ+UQOZqKHe7WzfwFYVqsBzdiMCmqT4CE/a+AcI6jpjEKuTZHDmR45DmXZv7RO\nocxYejn3FNfk1Ov5JFApS1ADlaoxnKlpWznGMaMXvXa0l2W/tSNySFOiIHTukpPzidwENTRJcdkX\nx/sBUUfNOUYARDN0cW36U9t6DGuMqilHUEMIUYtL8uulYO/lew7NfqUXkUc30B7nG+SxsrLSZWBD\nfoKaDglFYBhCBQCITWFNCdHPqZyju4yqKUNQAwwud4AROXxc+66bX5UuxIF4onWsS41y0KkGKC/a\nPYW6CGqAJuzevfv4TzTTQplSYU3EfQC18HR9dvZTG0x7GpZ1amhR7gcAHigIarqlkGNsRpQcYz8A\nMJaaQhqFGzUSaLMoQQ0wGiHFMfYDxBOlc11TIU1dnFvQp7FHdUW5v0YnqAEIoKd1ezjm8OHDnhAH\npzNJq4Q04xq7UGZY7iUsQlDTqVxFYc5iMOrvoiyjSejJWjizPqAR1sSlmKVFzmugBP2ZvE4eewMA\noGWzdFzW/o6nrEyyd+9eT2TJQkgDfSvZz9CHyUtQ0yEjGIjmyJEjRkEVsHv37hDtPcI2DM1Tpfop\naGmNcxqgHqY+AUBGQhogGiFNPEONPjAaD+okqOlMj0+2gTb0cP0S8gC5CWmAlGJNTRIg7kxQQxg9\nFGFMZtqTfQCRRCxsI24T8TlvYHzaIYsoHtQcOXJEAR6E40BEuQOKGs9zIQ0AuSkOAepVZDHhSYXS\n5j9TmAAA1Mmw9dhaDGkiTdvIZd++faa8MogW20/rso2oWRs5M+vT7Hn/PtAega19wFY67ZTk/Gpf\niyEN0J6dAv/e71dLBTW5whZhTXn2MdEIKACG56lq24Q0EIs2yaLmDmpKjYQRJNQrasEddbtgjXOU\naXp/igTMT0EITCKgr9NMa9QMFaIcOXJE4VKAEIxotHMgqsjF7t69e60Nw0SRz1u2Z50aGM/atXNl\nZWXidXTMe+6OQc3QRb6wBtpWsn0LJQHoTc6QZq0oEfwA81h01M60gGQI6z932jZs/vMhg5vir+de\nhGIrn1r2ZYntrOW70yeBNDvxhBXYSakCx8itNjiO4xv7GJj2lNfevXuP/5RW5PXcOawV2YqZ+AQi\nzEp7ZlGuM8Cy1oefLRQvpQuFMZ90A5SU49pWerRNyBE16+mcL86+IxIhDczPqBooQ9uazcrKyvEf\nymkhOGQyYedyarpWrx9tk+O4hw9qUhI49EARD/m5dgJMV1MBsN5aETB0eCKwgfmMHdIIAMe1bHAT\nduoTyxnyTV2wE0Hc8LRNAOYhhAF6EGHx4lmut1WMqAHqNVRIU1MwUXqf1LQv2FmtT/1rNPbTz1ks\nu43Op41q3B9rI1tqOF9zMTKA2vTUPpnfLKNsBDUNUqQBUbgeQXuMvBifIrA9ucMo7bRvLYWbOQP1\nmq6dghoWFrkAi7xtPTHlaXjO/TbV+NQfKKNEoaGoh3xqCgN6UttxEdQARQhpAKiBIHQ+Qh1gvZZG\n70QiqAHIIEIwZTQNQNuMpmmbgrd+EUZtOI+2inBc5iWoYSEKQrYzdGgx9vkYIaShfZ76QznaF0Bc\nPV6jqwhqFEGzG7tghd7aa2/fF4Bx1PhEGHqijc7PPpvu5LE3gPoIg2CytdBmuzYi2GFRhjLnp4NI\n7xaZ9qTdwFZR2oW+wlZRjs28qhhRQxxCGrbTWwgx7fv2th+AYaysrIy+nsjYn19S5KH1tRYazE+h\nDZNFvkaXIKgBWMBOYczu3burD2yWDWYFu/nouOdXW+HbckDCOJxTkEdt9xPqED6oqb3QaUnJoivn\n71YcjmOsthr9eA8V2OTeDzl+n+s3UelUM01vT2yBdnio05bwQQ2zi16wTlPrdtOvRQKImkbYaJMQ\nS86RDzry9en9ldzOWSIT/FOKoIaZKNziiXRMagkgcmj9u0Y6rzhGkZJXbZ3qmgpqIA/X/TrUdj8p\nYehztadRj6GDmtYLopwUV/TIeZ9X1P0ZdbugNCHNOFovBJY5rxSmEJNwrz2hgxpiqGVtmjUCvmH1\ntL97+q4ur9pxAAAgAElEQVTEoOOVlyKTGuUK7JZ9a5j2Uw8hb3naA6UJatiWJ9mx9Xx8hv7urYc0\nPZ9L9KG2TrVCazwtBqTOJyCaRe7Li1yfa73+CWoAOlcypGk94CqpxWKR2dTaqaScRc+JZUfRrBkr\n6Oz5Otjzd48uWvDvXGnTyWNvwDQ697MrVWR5ws4s1s6Tlttsy98tejuPvn3UIVqnejtCGqaZdm5M\nO79znUs1tR+AVoQNamifAqwtR44cGSzQGPLcqSGk0ZYA8qjxyXSr4V6Nx4L2CS4ZiqlPTFR74VdD\ncd2i2s+b3jhecSlQ8tGphsVpPxDbmP0FfZWyBDVQsR4DKaNpgFkpMvNqdeTGGkUHsB33lHrVeP8K\nO/Wph3UvovKUnWUMOQWK+FxP5qdYpJTDhw+PvQkwE9dB2Jl20rawQQ2zqbUIqnW7I4kchpQKa4ym\nqUeOY+U6wTI8+XyAgGZnCp6ttCF4gPawkWtmeaY+sYHCCFiW68jidHzITUhDbVwHYWfaSfsENRzX\nWnFlRMS4cp9PrZ2fLTpy5IjjRAiefB6TO6SxX2EYhw8fFrIG4tqXn326s9BBjUK7TUMVcgpGFlXT\ntSfKeR5lO2rm6VgeNXf+ci52qMibnbYXh2Oh7bKzsdvJ2J+/qNoWFLZGDSml9sITRWMMudaqcTxp\nXa2dHqBNNQeetRLQxKQtMBZBTcUUr1vZJzCbnG1FuyMKHepjFHyzE5LG0fOx0GaZ1djtZOzP70nY\noKamqQe1a2WUi2KxPY7p9iLsnwjbUDudnjyENMco+KAu2mxc7iuMKfQaNbRDSNOvRY+NYxpf6WPU\nQ2AvpCEnBd98tL/JxihOez0WpdqsgKFNvbaTXglqOtdCMdzCd2jdvG8DGvOY9hAOEIMOVz6KkuHY\n1+TW67VQsBqba91WLbTVmhYUFtRQXMmiW0hTl1kCG8d0NmPvp7E/H9hI0UcOilOIqYWQhPmEDGo8\n0d5ZjiJpiEJLMcckk86LeUfdMB7HaXk6XPkoLFmENriVKU/DEazG5r6yVa9tdUxhFxOGnSgW6xbx\n+AmJGYLOTj4tdaaXGY49ZNFX07BxiEhIw7z0G/oUckQNbYhYiEMLemlbvXxPqEUrIY2iZyujaeCY\nlh4AUDdBDUUosKhNT6NplmmfQ7XtVq8hCpN8dKaH1UpIQwy9XguNpmFeEdpKhG3okalPABVpNcBY\nr9XvqKNDCaULv9YCGu1wK6Fne1prt0PRFohEUNMpb2IC5lW6bbd87VAc5qUzPQzFHiW4Hpal3bYj\nQluJsA29MvWJKrVc0DG8WqY9tXzet/zdyEtIMwzFXh+0p+EMMe1Ju12cthBfb6GRoKZCkQuayNsG\nxNT6daO3jgX1a7XY0xY3soBwW1ptt72K0FYibEMJtbQVQQ1ABVoNM1r9XpThiWd5tXRgqU+rRd8s\nrCUVm3vLsOzv2QhqyEbBRY1qmfbUoh6uGT0XJswuSpEVZTuA2Wm37YnQd4iwDb0LGdRYsLI+9imU\n02L7avE7UZYncFvlfEqv2OvL0O1J0VeGdrs89xaiChnUlKQ4ANbUMJom9zVr0d/n2jk/hUk+OtIA\nGwlplhfx3hKh7xBhG+gsqFFolDH0fnUcoW7aMMSi4KMkRV9+2iy07+SxNwDmMVSBp5AkghbPwxa/\n0yQKk3wiPvGEmg3ZplwL8y8kLKTJI+K9JUJ7ibANHNPNiJrNxUEvxUJLhDTkFH3ak/OwXjo5+UTs\nSLdG0QdAT2rpW3QR1Ch4ymktPHGuEIHzEPohKGFIRtPUzfUij4iFeoT2EmEbeEDzQU1rBU9r32cW\nQhp60vJ52PJ3I7+IHenWKPooRcGXn/YKfWk6qFEUlNXS/m3pu7CzqNOenIcAAOVEfAgQIdiMsA1s\n1GxQo+Bpg+MIbei5LedeSLIHETvSJSzzhNx5xbyGalcKPoDlNfnWp54LgpY4jvRkiPN90c/QFhez\nVkgrWojINAqoh/aaR8SHABH6CBG2ga1OWF1dXZ32H2+55ZYhtwVCOvvss8fehC20TdA2IaKI7TIl\nbRO0TYhpWtvcNqgBAAAAYDjNrlEDAAAAUBtBDQAAAEAQghoAAACAIAQ1AAAAAEEIagAAAACCENQA\nAAAABCGoAQAAAAhCUAMAAAAQhKAGAAAAIAhBDQAAAEAQghoAAACAIAQ1AAAAAEEIagAAAACCENQA\nAAAABCGoAQAAAAhCUAMAAAAQhKAGAAAAIAhBDQAAAEAQghoAAACAIAQ1AAAAAEEIagAAAACCENQA\nAAAABCGoAQAAAAhCUAMAAAAQhKAGAAAAIAhBDQAAAEAQghoAAACAIAQ1AAAAAEEIagAAAACCENQA\nAAAABCGoAQAAAAji5LE3gOV985vfTJdffnk6dOhQ+spXvpJ+8Ad/MP38z/98evnLX55OOeWUsTcP\nuvT7v//76X3ve9/E/7Zv37507bXXDrxFwJobb7wxvfOd70yf/exn0z333JO+7/u+Lz3pSU9KL3vZ\ny9KTnvSksTcPmnb77benD37wg+nWW29Nn/3sZ9PXv/71dP7556c3v/nNE//+9ddfnw4dOpQ++clP\npi9+8Yvp5JNPTmeeeWZ6znOek371V381nXTSSQN/A2jTIn3X++67L/3N3/xN+sAHPpC+9KUvpdNO\nOy2df/756dJLL00Pf/jDS29y0wQ1lbvnnnvSC17wgrSyspJ+6qd+Kp111lnp05/+dHrLW96SPvWp\nT6WrrroqnXDCCWNvJnTphBNOSC960YvSwx72sA1/ftppp420RcDVV1+dLrvssnTqqaemZz7zmen7\nv//701133ZWuu+66dOONN6Yrr7wy/cRP/MTYmwnNOnToUHrb296WTjnllHTGGWekb3zjG1P/7n33\n3Zde8YpXpAc96EHpqU99arrgggvSfffdl2644Yb02te+Nn34wx9OV1555YBbD22bp+96//33p4sv\nvjjddNNN6eyzz07Pfvaz0+c///n0rne9K33sYx9L7373u9NDHvKQoTa9OYKayl155ZXp8OHD6ZJL\nLkmvetWrjv/5H/3RH6W/+7u/S+9973vTc5/73BG3EPr2whe+MD32sY8dezOA//OWt7wlnXLKKema\na65Jj3/844//+c0335xe+MIXpiuuuEJQAwU9+9nPTueff37at29f+tKXvpR+5md+ZurfPfHEE9Nv\n//Zvpxe84AUbCsff+Z3fSS9+8YvTv/3bv6V/+qd/Ss961rOG2HTowqx912uuuSbddNNN6cILL0yv\ne93rjv/52972tvT6178+XXXVVRvqU+ZjjZrMPv7xj6f9+/en1772tRP/+0033ZT279+f/uzP/izL\n5733ve9ND3/4w9PLX/7yDX9+6aWXppNOOim95z3vyfI5ULuh2yYwm6Hb5t13353OOOOMDSFNSimd\nd9556UEPelC6++67s3wO1GLoNviEJzwh7d+/f6YpSyeffHK6+OKLtzzdP+WUU9Kv/dqvpdXV1fTx\nj388y3ZBNNH7ru95z3vSiSeemH7rt35rw5+/+MUvTqeddlq65pprRtmuVghqMjvnnHPSmWeema69\n9tr0ne98Z8t/v/rqq9MJJ5yQnv/85y/9WXfccUe6++6701Oe8pQta9E88pGPTAcPHkyf+cxn0n33\n3bf0Z0Hthmyb6/3Lv/xLuuKKK9Lb3/72dNNNN6X7778/6++H2g3dNs8999x05513prvuumvDn998\n883p29/+djrvvPOyfA7UYqz747JWV1dTSskaNTQrct/13nvvTZ/97GfTmWeemU4//fQN/+3kk09O\nT33qU9N//ud/pqNHj2bdtp6Y+lTARRddlP78z/88HTp0aMNQzG9961vp0KFD6YlPfGLat29fSunY\nPN3bb7995t99/vnnp/3796eU0vET/4wzzpj4d3ft2pU+/elPp7vuuis94QlPWPTrQDOGapvrveY1\nrzn+v1dXV9OePXvSG9/4xol/F3o1ZNt87Wtfm175ylem5z73uVvWqHnGM56Rfvd3fzffF4NKjHF/\nXNb73//+dMIJJ5iqSNOi9l3vvPPOdP/9929bh6Z0rF5d+9/MR1BTwC/+4i+mN7zhDemaa67Z0KD+\n4R/+Id17773pec973vE/u/7669P73//+mX/3D/3QDx1vJN/61rdSSmnLcNA1a3++9vegd0O1zZRS\netrTnpae9axnpR/7sR9Lp556avryl7+c3vOe96QrrrgiveQlL0nXXnttetSjHpXni0Hlhmybp59+\nerrwwgvTX/3VX6Wrr776+J8/7nGPS7/0S7809Z4KLRuyDebw/ve/P91www3p3HPPTc94xjOy/m6I\nJGrfVR1anqCmgEc+8pHpggsuSP/8z/+cvvzlLx8fDnb11VenBz3oQennfu7njv/d173udRsWXwLK\nGbJtPuc5z9nw/x/zmMekV73qVemUU05Jl19+eXrXu95lgTX4P0O2zcsuuyxdc8016Vd+5VfSxRdf\nnH7gB34g3Xnnnen1r399uvTSS9Mf/MEfpBe+8IVLfyeoSU1914985CPpD//wD9NjH/vY9Bd/8Rej\nbQcMQd+1X9aoKeSiiy5K3/3ud48vonT77ben2267LT3rWc/K9rRup6Ryp6QTejRE29zO8573vLS6\nupo+8YlPFP8sqMkQbfPzn/98uuaaa9I555yTXv3qV6fHPOYx6aSTTkp79uxJb3rTm9JjH/vY9Jd/\n+Zfpf//3f7N8HtRk7PvjLD7+8Y+nSy65JD3iEY9I73jHO7asjQEtGrttTuq7qkPLM6KmkB//8R9P\nj3/849P73ve+dMkllxxf7Gn98LSUlptLuDbf784775z4d48ePZpOPPHELW+2gJ4N0Ta3c+qpp6aU\nUvr2t78934ZD44Zom5/73OdSSimdffbZW/7eySefnJ785CenD37wg+muu+5KZ5555hLfBuoz9v1x\nJ5/85CfTxRdfnB760Iemd7zjHfq3dGPstjmp73rGGWekE088cds6NKVkfZolCGoKuuiii9Ib3/jG\ndOONN6Zrr7027dq1a0vncJm5hHv27EmPfvSj0yc+8Yl03333bXjz03//93+nW2+9NT3xiU/c8kYo\n6F3ptrmdT3/60ymlY+thABuVbpsPfvCDU0opfe1rX5v4d9f+/KEPfegimw/VG/P+uJ1PfepT6WUv\ne1l68IMfnN7+9renPXv2LPX7oDbR+q7f+73fm574xCemz3zmMxumZKWU0ne+85300Y9+ND360Y8W\n1CxjlWLuvvvu1YMHD64+/elPX92/f//q2972tuyf8YY3vGH1rLPOWn3Tm9604c9f/epXr+7fv3/1\n6quvzv6ZULvSbfNrX/va6he/+MUtf/6Vr3xl9cILL1zdv3//6vXXX5/1M6EFpdvmV77yldUf+ZEf\nWT333HNXjxw5suG/fexjH1s9cODA6gUXXJD1M6EmQ/Rd1/vCF76wetZZZ62+4hWvmPp3PvWpT62e\nc845q+edd97q5z73uaLbA1FF7Lv+/d///epZZ521+nu/93sb/vyKK65YPeuss1Yvv/zyrNvYmxNW\nV1dXxw6LWvbKV74yXXfddenkk09ON954YzrttNOy/v577rkn/fIv/3JaWVlJT3/609P+/fvTv//7\nv6ebb745/eRP/mS66qqr0gknnJD1M6EFJdvm7bffnp773OempzzlKWn//v3pYQ97WPrSl76Ubrjh\nhvSNb3wjXXTRRelP/uRPsn0etKT0ffOtb31ruvzyy9NDHvKQ9LM/+7Pp9NNPT3feeWe67rrr0urq\nanrzm9+c/t//+39ZPxNqUroN/sd//Ee68sor0wknnJDuueee9KEPfSg95jGPSU972tNSSsdGjF98\n8cUppZS+/vWvpwsuuCB985vfTD/90z+dfviHf3jL73vc4x6XLrzwwqzbCBFF67vef//96SUveUm6\n6aab0pOf/OR0zjnnpJWVlXTDDTeks846K7373e9OD3nIQ7JtY28ENYUdOnQo/eZv/mY6//zz05vf\n/OYin/HNb34zXX755enQoUPpq1/9ajr99NPTL/zCL6Tf+I3fMO0JpijZNr/61a+mN7zhDekzn/lM\n+sIXvpDuvffe9LCHPSwdPHgwPf/5z0/PfOYzs34etGSI++Z1112X/vZv/zbddttt6X/+53/SIx7x\niPSUpzwlvfSlL00/+qM/WuQzoRal2+DNN9+cXvSiF0397+eee2565zvfmVJK6Ytf/GI6//zzt/19\n6/8+tCxi3/W+++5Lb33rW9O1116bvvzlL6dHPepR6YILLkiXXnqphYSXZI2awm699daJiz3l9PCH\nPzxddtll6bLLLiv2GdCakm3zUY96VHrNa16T/fdCD4a4b15wwQXpggsuKPb7oWal2+B55513fGHv\nnTzucY+b+e9C6yL2XU855ZT0qle9ymu7CzCipqB77703nX/++el7vud70vXXX28KEgShbUJM2iaM\nSxuEmLTN/hhRU8Att9ySPvrRj6Z//dd/Tf/1X/+V/viP/1hjggC0TYhJ24RxaYMQk7bZL0FNAR/5\nyEfSX//1X6dTTz01/fqv/3p6/vOfP/YmAUnbhKi0TRiXNggxaZv92nbq0y233DLktkBIZ5999tib\nsIW2CdomRBSxXaakbYK2CTFNa5s7jqh59rOfnX1joBb/+I//OPYmTPXSl7507E2AbR05cqTY7z50\n6FCx372sSy65ZOxNgIlWVlaK/v4PfehDRX//sv70T/907E2gEyXvf+sdPXp0pr8X+Z6Z0rFXskOP\n7rjjjqn/bceg5uDBg1k3hvhuvfXWsTcBaMDu3bsH66wCO9u7d29KqXxgA73bvXt3Sql8YLNr166Z\nwxqgLtaoYYudwjlBTgxrnQDa1ErAMVRnFZjd3r17hTUwgPV9tVL3QWENtElQw9wmBTnCG8hr1iCu\nlgCkp9E1+/btG3sTKOzw4cNjb8LSjK6BYZV8cCGsgfYIashic3gjuIFhTAt0IoYiRtfQilnCuFrC\nnJ5G11gHoz3bre8QVakHF8IaaIughiIENzCuzQFOpHCkp9E19GtamBMxwDG6hlrNE75FCnVKPbgQ\n1kA7BDUMYn1wI7SB4UULboQ19GpzgBMpuOlpdA39mRbqjBnglAhshDXQBkENg1sLbQQ2MJ4hFjic\nZRuENfQuWnAjrKE3kwKcocOb3PdDYQ3UT1DDaIyygRjGDG2ENbDR+uBmrNBGWEPvNoc3QwQ3uUfX\nCGugbieOvQGQ0rHQZqfXggPl7d69e/BXv3vVPEy2b9++4z9DW1u3BjgW3Kz9lJbznrhr165svwsY\nlqCGUAQ2EMPQgY2wBrY3RmAjrIGthghshDWAoIaQBDYQw5CBjbAGdjZ0YCOsgclKj7JxT4S+CWoI\nTVgDMQwV2OiYwmyGDGyENbC9UoHNGNORgRgENYRndA3EMUSnUacUZjdUYCOsgZ2VDGyAvnjrE9U4\nePCgt0NBEKXf1uRtUDCftbCm5Juian8blLU66lXb24vWwpqcb4tyX4S+CGqoytrIGoGNpyu9iNwp\ny/0q0Um/P/L3h4j27dsnrKE5i4RsEcKdPXv2CGuAhQhqqJLRNfRiu0AuSmetZMexxk7pGK9SZhgl\nA5CcSo+uEdZQg2nhztABTu7RNTXeF4H5CWqolrCG3k0KccbqvJUeXQMR7BTCRQtySo+ugRpNCnCG\nCG9yj64B2mYxYapmkWHYaG2x37GmxpX4XNP8qMXawr7rf8ZWahssLkxLdu3adfynpFyLDbsvQvsE\nNVRPWAOTjRXaCGvgARGCG2ENzG6I0EZYA+xEUEMThDWwvaFDGx1ImGys0CbC6B6oTcnQRlgDbEdQ\nQzOENTCboQKb3J+hQ0prhg5tSnyOUTX0okRgI6wBphHU0BRhDcxuiFE2whqYzVCBjbAGlpM7sMkR\n1gDt2fGtTwcOHBhiO1jQbbfdNvYmhONtUDA/r9iGGEq/WnvtM7wNCpazFtbkeGPUsm+Ecp+F9ng9\nd+VyBGkthj3CGphfyVds5+xEGlVDD0oHNrnDmhpG1Ri5UL+Ir7fetWuXsAbITlDD1LCn9gBHWAOL\nKRXY6ETC/EoGNkbWUJtZw7ahA51co2uWDWuAdghqmGpzgFN7cNMaowraETW8EKxAHKUCG2ENLZoU\n6AwRgOQYXbNMWOO+De0Q1DCzGoMbo2qowU6h25idrtydPp1IWE6JYEVYQw82hzelgpsco2uMrAEE\nNSxsLbiJHtgIa6jdpCBnyLAj91So1sOaGtbqYD4rKytjb8IGghVY3vrgpkQosuzomkXDmtbvsdAL\nQQ1LWz/SJnpoA63YHN4M0SnT+aNX24VvY4U4uadCCX/oWanQJtdCw0B/Thx7A2jLgQMHQr7S/eDB\ng2NvAhS1e/fu4z+lPyfS74Gx7d27d8PP0NYCm2i/C2q1Z8+erG8IW5sKtei2LMI9FuonqKGIiIGN\nsIZelA5shDUw3RjBjYAF8ssZ2IwR1gB1E9RQVLSwBnpScpSNkAVmM1RokyusEfrARrkCm2XCmkW4\nT0PdBDUUF2l0jVE19KpEYJPj9+lI0pPSgY2wBsoZM6wxqgb6YzFhBnPgwAGLDcPIvG4bxrcW1pRY\niNiiwNMJhtsw5j1nLTBZZsHhRRcYXuQtUO7RUC8jahhUhNE1RtXQuyEWHZ5HpG2BIZUaYZNjRIxR\nNUS1flrv5p+hLDvCZehpUEB9BDWMYuywBrAoMEQx1hujoDVDhjdjTEda5DPdo6FOpj4xmjGnQh08\neDDdeuuto3x2Lm687RljePLaebTsZy87vNrwbDgW2OSaDpVjCpRpVLRgfX8p931mmalQi06BAvpg\nRA2jMrIGHjDmUG4LA0MMOUfWmL4EG5W6ty46umaRKVAWFoY+GFHD6CwyDNsr+TRw8+eMOapl7M/P\nRXFch8gjRXIuNrzsqBijamhVrhGlaxZZ7BdgGkENIYwR1rQw/Yn+lA5tTGGiF/MEamMFFTmnQgGT\n5QxsFglrTIECJjH1iTBMg4L5lBrCPeYUJtOniGjfvn1bfoaSYyqUUV6wszHfiDjvFCjTn6B9O46o\n8SrjthhBspFRNbQi9xDuZUbGGFVDDzaHHyVH3Yw9ssb0J3qy7D3MFCggByNqOnPw4MEtP5EYVQPL\nGfOJIPSs9GibZUfWGFUDs1v2XrrIiBejaoD1BDWEC26ENbC8sd/iNNa/hQhKBTY53wg1L0EPPRo6\nrAFYYzFhtlgLa3qZEmT6E63KMR3KNCZY3Fq4EWXaUO9TmITAsUW91wx5H7SwMLBGUMNU60fXDB1k\neGU35DPWm5yEPHBMzoBk7PVqoJR5grSh7y2L3s9Kr1djPRxol6CGmYwxykZYsz1PButWSydzLLVt\nL+wk5+iaZcKa3kfV0IZJfaDS94yoYQ3QJmvUMJcIa9hAC9a/WrvUa7YnfebQ/1agCBvlWutljPVq\nrFNDZEPcS4e4p827qDDQJkENcxty0eGhFhYWQBFF6eBGcALjGzvwGPvzobRo99GSCwtbtBjaJKhh\nYcINKK9ER9PoGBhfjrBkzLdAQS2GGLGam1E1gKCGpQwR1nhdN+R/OjhkWCMYgslqHNlS4zZDSnkD\nm2ijaoD2WEyYpXm9NQwr10K7FuyF8S27uO+iCwtbVJheuYfG40129TKysxxBDVm0ENa08B3ox9rT\nPJ3EeHRa6jVWsSA0gWG1FrJ4sxRjmXTf1A/Kw9Qnsik5Dcr0J5hs2WHcQ01lMo2JGuzdu3fLz1CW\nmVKkUwzzyzEVat5/P8/0J+vUUKuVlZUNPyxGUENWFhiG4dW4UCLUYsjgZuj1X6w3A+M88ICeCGwW\nI6ghu1JhjVE1sL0aFgiO+jkwq6FH2kQk4KE1Q95rLCpMrwQ289lxjRrFcd1uu+22sTcBGNCi8+5b\nm68Ppa2FNbk7nYuuV7PoosLAMcvcB0vdQ3ft2pWOHj2a/ffCmNbuVb0/9NiJxYQbtzloGyq4qXVh\n3pq220iDuMYOPKKGLlG3C5ZRIrCxuDAwLwsKU5uVlRVhzTYENZ1ZH9yUDm1qCj0gp1lCtIiBxbxB\niuAFHmBEC9TPfQ2GJayZzho1HTtw4EB1U9tq216YZm0B4FILARtxBcPL2dlcZB2YRT7fejOw0RDr\nvVmnBh7gIcdkghqKBjbeAgWzKRHYCGtgeJ4MAsB8hDVbmfrEcQcOHLD4MIxsLVwZa+h1xGHfEbcJ\nttPDNCjr6NCySPedHhYUdi2pQ+kRmKZBbWREDRuYWgQx5BphU3pUzby/3ygfepGjsznU9Cdgq0Xu\nV+5xtOzw4cMTf3Jq/SHHPIyoYYvcI2tyLyps5A89yfFUL9KTQehJDyNrauI6WI8eAg9vaaIV68Oa\nHKNujKw5xogaJjKyBuIYusPaQwcZeIAFhYnmyJEjW37YmUWKGVuJUTa9EtQwVc6wpqZFhWvaVvoh\nPIE6LftUUIgCx4wR2Jj+BItZNqwxGlVQww6MrIE4lun86TjCeAzhhnyMsIE6LDu6pvewxho17Mia\nMBBHxPVmIm4T9MzaOPRg7b4zxKL57nFluV7Ft8wDh8OHDxsdugAjagAqs2indJ5/ZwQO5LVMJ1cH\nF6YTokB5KysrW37mYd2a+RlRw0xyjKrJ+fYno3ygXp5OAmNw3Ylr2YcDR44cqfIBw6xvftq1a1c6\nevToAFsEs1sLa2Z9ELHIyJqe3wC1Y1BjYdX4cr76GqiDsAOAVky6n80bvEQKa9yj6ck8gY1pULMz\noqYBm8O0UsGNUSwAOzPXvh5DP6WzdgzMbn3QMWsAUyqsEbzAzkqNful1VI2gpkFrwY2RNtC2RTqO\ntXY2a91uYlsfmvTYCVzWvn37rDvAIOZZODjSyBpm4zoS36yjYGYZXWNUzWwENQ0T2ADAbOada9+a\nHkKXWdYCYRh79uxZ6N8JYWAcm+8PgpbyvPWpAznXGTpw4MBS/96aR5BXyQ6rzjA9Kj01adEgSKeY\nlqETNlkAAAUUSURBVNxxxx1Tf3Yyy+hKIzChrMOHD28b7u90L239wUAOgppOCEgAYDYtrCPT68gg\n6jdLYCOIgRiWCWvm0cJ9eV6mPnUk5+uxAdazhgyt6XXxQohiLayZNk1qp2lQpknVw+iKmGYdybnd\nmjPb3UutVbM9QU1nhDUAMBthTVuOHj069iawzq5du2b6e3fcccfCYU1OHkjQm3nWpRG65GfqE3Nb\ndp0aIK95O6meMAIwtqNHj275mcZC0DC+ndalmfbfepy2lIOgpkPWqwGA2ehgwnC2C2ymhTXbjXIx\nAgbyM1VtGIIaAIABmU4F25s3rAGGNS2smTfEEfpMZ42aTlmrBgBmY60aGN7Ro0dnXscmorHWtNmz\nZ091gZaRi/HMcs+zLk1ZghoAyMjToZh0JqmteO3FtIWCU5oc1kxaXNgbniCv9eHZvA8qJgU4OR54\n9PbQxNQnAKB5Oy2CCIzjjjvu2DZE87YuGNd2I57cV8sR1AAA3Vg0sDE0H8rqdcTTdiOKIAr3wOEJ\najrm7U8AAEQxa1gzT6jjzU+Qx7SwxqiaMgQ1VOvAgQNjbwIAldKxhJgmhTCmP0Fd3GOXJ6gBAAAA\ntmUK1HAENQBAlzzxAwAiEtQAAABh9LqwMLTMGjfzEdQAAAAABCGoAWjA7t27x94EAAAgA0ENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIIQ1AAAAAAEIagBAAAACEJQAwAAABCEoAYAAAAgCEENAAAA\nQBCCGgAAAIAgBDUAAEDVjhw5MvYmAGRzwurq6uq0/3jLLbcMuS0Q0tlnnz32JmyhbYK2CRFFbJcp\naZugbUJM09rmtkENAAAAAMMx9QkAAAAgCEENAAAAQBCCGgAAAIAgBDUAAAAAQQhqAAAAAIL4/1kf\n1vXzvxraAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9178206cc0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#plotting approximate density ratios for different observed y values\n",
"\n",
"sns.set_context('poster')\n",
"N_samples = 1000\n",
"q_samples = sample_generator(np.repeat(y_test, N_samples))\n",
"q_samples = q_samples.reshape(y_test.shape[0], N_samples, 2)\n",
"\n",
"prior_samples = sample_prior(np.repeat(y_test, N_samples), prior_variance)\n",
"prior_samples = prior_samples.reshape(y_test.shape[0], N_samples, 2)\n",
"\n",
"plt.subplots(figsize=(20,8))\n",
"for i in range(5):\n",
" s_values = evaluate_discriminator(x.T, y[i]*np.ones(90000))[0]\n",
" plt.subplot(2,5,i+1)\n",
" plt.contourf(xrange, xrange, s_values.reshape(300,300).T, cmap='gray')\n",
"# plt.title('y={}'.format(y[i]))\n",
" plt.axis('square');\n",
"# plt.plot(prior_samples[i,:,0],prior_samples[i,:,1],'b.')\n",
"# plt.plot(q_samples[i,:,0],q_samples[i,:,1],'r.')\n",
" plt.xlim([xmin,xmax])\n",
" plt.ylim([xmin,xmax])\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
"\n",
" \n",
" plt.subplot(2,5,5+i+1)\n",
" plt.contourf(xrange, xrange, llh_theano[:,i].reshape(300,300), cmap='gray')\n",
" plt.title('y={}'.format(y[i]))\n",
" plt.axis('square');\n",
" plt.xlim([xmin,xmax])\n",
" plt.ylim([xmin,xmax])\n",
" plt.xticks([])\n",
" plt.yticks([])"
]
},
{
"cell_type": "code",
"execution_count": 394,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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1x3J1jSRqhBBCCDErKKVgTEG5xshVOSGEEGIiSrnBEh8NDZ361RpFJx9MOdLQ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\nHniZlwdeZk50Dqe3nc5JzSd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Sqs8lbQ0G7ynvZLGVTVO4ZdwksZdoilZVvznKRiklI7zF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\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4vAJitcTOK61V8AgeStV0eOxwehxQcAqavAoVUIfTDJLxvwY2qGgffhv1CJ6Q64FRJQp+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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9160361240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#approximate posteriors for different observed y values\n",
"\n",
"sns.set_style('whitegrid')\n",
"sns.set_context('poster')\n",
"\n",
"plt.subplots(figsize=(20,8))\n",
"N_samples = 1000\n",
"q_samples = sample_generator(np.repeat(y_test, N_samples))\n",
"q_samples = q_samples.reshape(y_test.shape[0], N_samples, 2)\n",
"\n",
"for i in range(5):\n",
" foobar = evaluate_discriminator(x.T, y[i]*np.ones(90000))[0]\n",
" plt.subplot(2,5,i+1)\n",
" sns.kdeplot(q_samples[i,:,:], cmap='Greens')\n",
" plt.axis('square');\n",
" plt.title('q(x|y={})'.format(y[i]))\n",
" plt.xlim([xmin,xmax])\n",
" plt.ylim([xmin,xmax])\n",
" plt.xticks([])\n",
" plt.yticks([]);\n",
" \n",
" plt.subplot(2,5,5+i+1)\n",
" plt.contour(xrange, xrange, np.exp(logprior+llh[i]).reshape(300,300).T, cmap='Greens', linewidth=2)\n",
" plt.axis('square');\n",
" plt.title('p(x|y={})'.format(y[i]))\n",
" plt.xlim([xmin,xmax])\n",
" plt.ylim([xmin,xmax])\n",
" plt.xticks([])\n",
" plt.yticks([]);"
]
}
],
"metadata": {
"anaconda-cloud": {},
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
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