From d311f86bd755a58078f367fcad74eb872f87be5e Mon Sep 17 00:00:00 2001 From: Zhenwen Dai Date: Wed, 29 May 2019 16:07:17 +0100 Subject: [PATCH] Updated the VAE and GP regression notebook. --- examples/notebooks/gp_regression.ipynb | 195 ++++++++++++++++-- .../notebooks/variational_auto_encoder.ipynb | 133 ++++++------ .../modules/gp_modules/svgp_regression.py | 3 + 3 files changed, 255 insertions(+), 76 deletions(-) diff --git a/examples/notebooks/gp_regression.ipynb b/examples/notebooks/gp_regression.ipynb index be50626..107148a 100644 --- a/examples/notebooks/gp_regression.ipynb +++ b/examples/notebooks/gp_regression.ipynb @@ -6,7 +6,7 @@ "source": [ "# Gaussian Process Regression\n", "\n", - "**Zhenwen Dai (2018-11-2)**" + "**Zhenwen Dai (2019-05-29)**" ] }, { @@ -68,7 +68,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -159,16 +159,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iteration 11 loss: -13.523289192527265\n", - "Iteration 21 loss: -16.077990179961076\n", - "Iteration 31 loss: -16.784414553096843\n", - "Iteration 41 loss: -16.820970924702017\n", - "Iteration 51 loss: -16.859865329532193\n", - "Iteration 61 loss: -16.895666914166453\n", - "Iteration 71 loss: -16.899409131167452\n", - "Iteration 81 loss: -16.901728290347176\n", - "Iteration 91 loss: -16.903122097339737\n", - "Iteration 100 loss: -16.903135093930537" + "Iteration 10 loss: -13.09287954321266\t\t\t\t\n", + "Iteration 20 loss: -15.971970034359586\t\t\t\t\n", + "Iteration 30 loss: -16.725359053995163\t\t\t\t\n", + "Iteration 40 loss: -16.835084442759314\t\t\t\t\n", + "Iteration 50 loss: -16.850332113428053\t\t\t\t\n", + "Iteration 60 loss: -16.893812683762203\t\t\t\t\n", + "Iteration 70 loss: -16.900137667771077\t\t\t\t\n", + "Iteration 80 loss: -16.901158761459012\t\t\t\t\n", + "Iteration 90 loss: -16.903085976668137\t\t\t\t\n", + "Iteration 100 loss: -16.903135093930537\t\t\t\t\n" ] } ], @@ -303,7 +303,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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" ] @@ -408,7 +408,149 @@ "source": [ "## Gaussian process with a mean function\n", "\n", - "TBA" + "In the previous example, we created an GP regression model without a mean function (the mean of GP is zero). It is very easy to extend a GP model with a mean field. First, we create a mean function in MXNet (a neural network). For simplicity, we create a 1D linear function as the mean function." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "mean_func = mx.gluon.nn.Dense(1, in_units=1, flatten=False)\n", + "mean_func.initialize(mx.init.Xavier(magnitude=3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create the GP regression model in a similar way as above. The difference is \n", + "1. We create a wrapper of the mean function in model definition ```m.mean_func```.\n", + "2. We evaluate the mean function with the input of our GP model, which results into the mean of the GP.\n", + "3. We pass the resulting mean into the mean argument of the GP module." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "m = Model()\n", + "m.N = Variable()\n", + "m.X = Variable(shape=(m.N, 1))\n", + "m.mean_func = MXFusionGluonFunction(mean_func, num_outputs=1, broadcastable=True)\n", + "m.mean = m.mean_func(m.X)\n", + "m.noise_var = Variable(shape=(1,), transformation=PositiveTransformation(), initial_value=0.01)\n", + "m.kernel = RBF(input_dim=1, variance=1, lengthscale=1)\n", + "m.Y = GPRegression.define_variable(X=m.X, kernel=m.kernel, noise_var=m.noise_var, mean=m.mean, shape=(m.N, 1))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 10 loss: -6.288699675985622\t\t\t\t\n", + "Iteration 20 loss: -13.938366520031717\t\t\t\t\n", + "Iteration 30 loss: -16.238146742572965\t\t\t\t\n", + "Iteration 40 loss: -16.214515784955303\t\t\t\t\n", + "Iteration 50 loss: -16.302410205174386\t\t\t\t\n", + "Iteration 60 loss: -16.423765889507315\t\t\t\t\n", + "Iteration 70 loss: -16.512277794947106\t\t\t\t\n", + "Iteration 80 loss: -16.5757306621185\t\t\t\t\t\t\n", + "Iteration 90 loss: -16.6410597628529\t\t\t\t\t\t\n", + "Iteration 100 loss: -16.702913078848557\t\t\t\t\n" + ] + } + ], + "source": [ + "import mxnet as mx\n", + "from mxfusion.inference import GradBasedInference, MAP\n", + "\n", + "infr = GradBasedInference(inference_algorithm=MAP(model=m, observed=[m.X, m.Y]))\n", + "infr.run(X=mx.nd.array(X, dtype='float64'), Y=mx.nd.array(Y, dtype='float64'), \n", + " max_iter=100, learning_rate=0.05, verbose=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "from mxfusion.inference import TransferInference, ModulePredictionAlgorithm\n", + "infr_pred = TransferInference(ModulePredictionAlgorithm(model=m, observed=[m.X], target_variables=[m.Y]), \n", + " infr_params=infr.params)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "xt = np.linspace(-5,5,100)[:, None]\n", + "res = infr_pred.run(X=mx.nd.array(xt, dtype='float64'))[0]\n", + "f_mean, f_var = res[0].asnumpy()[0], res[1].asnumpy()[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot(xt, f_mean[:,0], 'b-', label='mean')\n", + "plot(xt, f_mean[:,0]-2*np.sqrt(f_var), 'b--', label='2 x std')\n", + "plot(xt, f_mean[:,0]+2*np.sqrt(f_var), 'b--')\n", + "plot(X, Y, 'rx', label='data points')\n", + "ylabel('F')\n", + "xlabel('X')\n", + "_=legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The effect of the mean function is not noticable, because there is no linear trend in our data. We can plot the values of the estimated parameters of the linear mean function." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The weight is 0.021969 and the bias is 0.079038.\n" + ] + } + ], + "source": [ + "print(\"The weight is %f and the bias is %f.\" %(infr.params[m.mean_func.parameters['dense1_weight']].asnumpy(), \n", + " infr.params[m.mean_func.parameters['dense1_bias']].asscalar()))" ] }, { @@ -417,7 +559,26 @@ "source": [ "## Variational sparse Gaussian process regression\n", "\n", - "TBA" + "In MXFusion, we also have variational sparse GP implemented as a module. A sparse GP model can be created in a similar way as the plain GP model. " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "from mxfusion import Model, Variable\n", + "from mxfusion.components.variables import PositiveTransformation\n", + "from mxfusion.components.distributions.gp.kernels import RBF\n", + "from mxfusion.modules.gp_modules import SparseGPRegression\n", + "\n", + "m = Model()\n", + "m.N = Variable()\n", + "m.X = Variable(shape=(m.N, 1))\n", + "m.noise_var = Variable(shape=(1,), transformation=PositiveTransformation(), initial_value=0.01)\n", + "m.kernel = RBF(input_dim=1, variance=1, lengthscale=1)\n", + "m.Y = SparseGPRegression.define_variable(X=m.X, kernel=m.kernel, noise_var=m.noise_var, shape=(m.N, 1), num_inducing=50)" ] } ], @@ -437,7 +598,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.3" + "version": "3.6.0" } }, "nbformat": 4, diff --git a/examples/notebooks/variational_auto_encoder.ipynb b/examples/notebooks/variational_auto_encoder.ipynb index 8c0295e..3e0efdc 100644 --- a/examples/notebooks/variational_auto_encoder.ipynb +++ b/examples/notebooks/variational_auto_encoder.ipynb @@ -6,7 +6,25 @@ "source": [ "# Variational Auto-Encoder (VAE)\n", "\n", - "### Zhenwen Dai (2019-04-23)" + "### Zhenwen Dai (2019-05-29)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Variational auto-encoder (VAE) is a latent variable model that uses a latent variable to generate data represented in vector form. Consider a latent variable $x$ and an observed variable $y$. The plain VAE is defined as\n", + "\\begin{align}\n", + "p(x) =& \\mathcal{N}(0, I) \\\\\n", + "p(y|x) =& \\mathcal{N}(f(x), \\sigma^2I)\n", + "\\end{align}\n", + "where $f$ is the deep neural network (DNN), often referred to as the decoder network.\n", + "\n", + "The variational posterior of VAE is defined as \n", + "\\begin{align}\n", + "q(x) = \\mathcal{N}\\left(g_{\\mu}(y), \\sigma^2_x I)\\right)\n", + "\\end{align}\n", + "where $g_{\\mu}$ is the encoder networks that generate the mean of the variational posterior of $x$. For simplicity, we assume that all the data points share the same variance in the variational posteior. This can be extended by generating the variance also from the encoder network." ] }, { @@ -59,7 +77,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Model Defintion" + "## Model Defintion\n", + "\n", + "We first define that the encoder and decoder DNN with MXNet Gluon blocks. Both DNNs have two hidden layers with tanh non-linearity." ] }, { @@ -102,16 +122,10 @@ ] }, { - "cell_type": "code", - "execution_count": 7, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from mxfusion.components.variables.var_trans import PositiveTransformation\n", - "from mxfusion import Variable, Model, Posterior\n", - "from mxfusion.components.functions import MXFusionGluonFunction\n", - "from mxfusion.components.distributions import Normal\n", - "from mxfusion.components.functions.operators import broadcast_to" + "Then, we define the model of VAE in MXFusion. Note that for simplicity in implementation, we use scalar normal distributions defined for individual entries of a Matrix instead of multivariate normal distributions with diagonal covariance matrices." ] }, { @@ -123,19 +137,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Model (95c68)\n", - "Variable (b8df3) = BroadcastToOperator(data=Variable noise_var (873b0))\n", - "Variable (36f25) = BroadcastToOperator(data=Variable (399cc))\n", - "Variable (6a234) = BroadcastToOperator(data=Variable (2fe44))\n", - "Variable x (1696c) ~ Normal(mean=Variable (6a234), variance=Variable (36f25))\n", - "Variable f (0d26d) = GluonFunctionEvaluation(decoder_input_0=Variable x (1696c), decoder_dense0_weight=Variable (89315), decoder_dense0_bias=Variable (41eac), decoder_dense1_weight=Variable (b69fe), decoder_dense1_bias=Variable (8e4e6), decoder_dense2_weight=Variable (a99ff), decoder_dense2_bias=Variable (f0361))\n", - "Variable y (5f5c3) ~ Normal(mean=Variable f (0d26d), variance=Variable (b8df3))\n" + "Model (37a04)\n", + "Variable (b92c2) = BroadcastToOperator(data=Variable noise_var (a50d4))\n", + "Variable (39c2c) = BroadcastToOperator(data=Variable (e1aad))\n", + "Variable (b7150) = BroadcastToOperator(data=Variable (a57d4))\n", + "Variable x (53056) ~ Normal(mean=Variable (b7150), variance=Variable (39c2c))\n", + "Variable f (ad606) = GluonFunctionEvaluation(decoder_input_0=Variable x (53056), decoder_dense0_weight=Variable (b9b70), decoder_dense0_bias=Variable (d95aa), decoder_dense1_weight=Variable (73dc2), decoder_dense1_bias=Variable (b85dd), decoder_dense2_weight=Variable (7a61c), decoder_dense2_bias=Variable (eba91))\n", + "Variable y (23bca) ~ Normal(mean=Variable f (ad606), variance=Variable (b92c2))\n" ] } ], "source": [ - "m = mf.models.Model()\n", - "m.N = mf.components.Variable()\n", + "from mxfusion.components.variables.var_trans import PositiveTransformation\n", + "from mxfusion import Variable, Model, Posterior\n", + "from mxfusion.components.functions import MXFusionGluonFunction\n", + "from mxfusion.components.distributions import Normal\n", + "from mxfusion.components.functions.operators import broadcast_to\n", + "\n", + "m = Model()\n", + "m.N = Variable()\n", "m.decoder = MXFusionGluonFunction(decoder, num_outputs=1,broadcastable=True)\n", "m.x = Normal.define_variable(mean=broadcast_to(mx.nd.array([0]), (m.N, Q)),\n", " variance=broadcast_to(mx.nd.array([1]), (m.N, Q)), shape=(m.N, Q))\n", @@ -146,6 +166,13 @@ "print(m)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We also define the variational posterior following the equation above." + ] + }, { "cell_type": "code", "execution_count": 9, @@ -155,10 +182,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Posterior (63197)\n", - "Variable x_mean (09eba) = GluonFunctionEvaluation(encoder_input_0=Variable y (5f5c3), encoder_dense0_weight=Variable (81ec2), encoder_dense0_bias=Variable (aa736), encoder_dense1_weight=Variable (3c4ae), encoder_dense1_bias=Variable (1bab5), encoder_dense2_weight=Variable (7b531), encoder_dense2_bias=Variable (84731))\n", - "Variable (f88b7) = BroadcastToOperator(data=Variable x_var (fc12e))\n", - "Variable x (1696c) ~ Normal(mean=Variable x_mean (09eba), variance=Variable (f88b7))\n" + "Posterior (4ec05)\n", + "Variable x_mean (86d22) = GluonFunctionEvaluation(encoder_input_0=Variable y (23bca), encoder_dense0_weight=Variable (51b3d), encoder_dense0_bias=Variable (c0092), encoder_dense1_weight=Variable (ad9ef), encoder_dense1_bias=Variable (83db0), encoder_dense2_weight=Variable (78b82), encoder_dense2_bias=Variable (b856d))\n", + "Variable (6dc84) = BroadcastToOperator(data=Variable x_var (19d07))\n", + "Variable x (53056) ~ Normal(mean=Variable x_mean (86d22), variance=Variable (6dc84))\n" ] } ], @@ -175,16 +202,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Variational Inference" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "from mxfusion.inference import BatchInferenceLoop, StochasticVariationalInference, GradBasedInference" + "## Variational Inference\n", + "\n", + "Variational inference is done via creating an inference object and passing in the stochastic variational inference algorithm." ] }, { @@ -193,18 +213,18 @@ "metadata": {}, "outputs": [], "source": [ + "from mxfusion.inference import BatchInferenceLoop, StochasticVariationalInference, GradBasedInference\n", + "\n", "observed = [m.y]\n", "alg = StochasticVariationalInference(num_samples=3, model=m, posterior=q, observed=observed)\n", "infr = GradBasedInference(inference_algorithm=alg, grad_loop=BatchInferenceLoop())" ] }, { - "cell_type": "code", - "execution_count": 12, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "infr.initialize(y=mx.nd.array(Y))" + "SVI is a gradient-based algorithm. We can run the algorithm by providing the data and specifying the parameters for the gradient optimizer (the default gradient optimizer is Adam)." ] }, { @@ -218,16 +238,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iteration 201 loss: 1715.0395507812555\n", - "Iteration 401 loss: 599.86877441406255\n", - "Iteration 601 loss: 177.60995483398438\n", - "Iteration 801 loss: -75.347778320312555\n", - "Iteration 1001 loss: -213.82623291015625\n", - "Iteration 1201 loss: -332.34564208984375\n", - "Iteration 1401 loss: -305.57965087890625\n", - "Iteration 1601 loss: -577.47900390625585\n", - "Iteration 1801 loss: -669.97760009765625\n", - "Iteration 2000 loss: -753.83203125234385" + "Iteration 200 loss: 1720.556396484375\t\t\t\t\t\n", + "Iteration 400 loss: 601.11962890625\t\t\t\t\t\t\t\n", + "Iteration 600 loss: 168.620849609375\t\t\t\t\t\t\n", + "Iteration 800 loss: -48.67474365234375\t\t\t\t\t\n", + "Iteration 1000 loss: -207.34835815429688\t\t\t\t\n", + "Iteration 1200 loss: -354.17742919921875\t\t\t\t\n", + "Iteration 1400 loss: -356.26409912109375\t\t\t\t\n", + "Iteration 1600 loss: -561.263427734375\t\t\t\t\t\t\n", + "Iteration 1800 loss: -697.8665161132812\t\t\t\t\t\n", + "Iteration 2000 loss: -753.83203125\t\t\t\t8\t\t\t\t\t\n" ] } ], @@ -239,30 +259,25 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Plot the training data in the latent space" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "from mxfusion.inference import TransferInference" + "## Plot the training data in the latent space\n", + "\n", + "Finally, we may be interested in visualizing the latent space of our dataset. We can do that by calling encoder network." ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ + "from mxfusion.inference import TransferInference\n", + "\n", "q_x_mean = q.encoder.gluon_block(mx.nd.array(Y)).asnumpy()" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { diff --git a/mxfusion/modules/gp_modules/svgp_regression.py b/mxfusion/modules/gp_modules/svgp_regression.py index dad23b1..bfbda08 100644 --- a/mxfusion/modules/gp_modules/svgp_regression.py +++ b/mxfusion/modules/gp_modules/svgp_regression.py @@ -224,6 +224,9 @@ def compute(self, F, variables): kern = self.model.kernel kern_params = kern.fetch_parameters(variables) + X, Z, noise_var, mu, S_W, S_diag, kern_params = arrays_as_samples( + F, [X, Z, noise_var, mu, S_W, S_diag, kern_params]) + S = F.linalg.syrk(S_W) + make_diagonal(F, S_diag) Kuu = kern.K(F, Z, **kern_params)