VAE_with_latent_subspaces_categorical.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "VAE_with_latent_subspaces_categorical.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dauparas/tensorflow_examples/blob/master/VAE_with_latent_subspaces_categorical.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pXQG7VtQVk6Z",
        "colab_type": "text"
      },
      "source": [
        "Variational Autoencoder Model (VAE) with latent subspaces based on:\n",
        "https://arxiv.org/pdf/1812.06190.pdf"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bL6BBloIUobY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Step 1: import dependencies\n",
        "from tensorflow.keras import layers\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "from keras import regularizers\n",
        "import time\n",
        "from __future__ import division\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_probability as tfp\n",
        "tfd = tfp.distributions\n",
        "\n",
        "%matplotlib inline\n",
        "plt.style.use('dark_background')\n",
        "\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZcBeT-LQNdG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets.samples_generator import make_blobs\n",
        "X, U = make_blobs(n_samples=500, centers=5, n_features=20,random_state=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klhGu6mFqR9p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t = np.random.uniform(0.0, 1.0, 500)\n",
        "u = np.random.uniform(0.0, 1.0, 500)\n",
        "s = np.random.uniform(0.0, 1.0, 500)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCVP7ZrrqT56",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X1 = 10.0*np.cos(t)[:,np.newaxis]\n",
        "X2 = 10.0*np.sin(t)[:,np.newaxis]\n",
        "X3 = 10.0*(0.5*t)[:,np.newaxis]\n",
        "\n",
        "# X4 = u[:,np.newaxis]\n",
        "# X5 = (u*u)[:,np.newaxis]\n",
        "# X6 = np.sin(0.5*u)[:,np.newaxis]\n",
        "\n",
        "# X7 = (s*s*s)[:,np.newaxis]\n",
        "# X8 = (s+0.2)[:,np.newaxis]\n",
        "# X9 = 3.2*np.cos(0.5*s)[:,np.newaxis]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VRIdRrmEqV-v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# X = np.concatenate((X, X1, X2, X3, X4, X5, X6, X7, X8, X9), axis=-1) #data\n",
        "X = np.concatenate((X, X1, X2, X3), axis=-1) #data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVIEs95PUdAI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d7dc0245-2f1c-457f-979a-13de62e70b3a"
      },
      "source": [
        "X.shape"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 23)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4E1cgAP9UtB_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Reconstruction loss\n",
        "def x_given_w_z(w, z, output_size, training_phase, reg):\n",
        "  with tf.variable_scope('M/x_given_w_z'):\n",
        "    act = tf.nn.leaky_relu\n",
        "    \n",
        "    h = tf.concat((w,z), axis=-1)\n",
        "    h = tf.layers.dense(h, 8, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.batch_normalization(h, training=training_phase)\n",
        "    h = tf.layers.dense(h, 16, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 32, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.batch_normalization(h, training=training_phase)\n",
        "    h = tf.layers.dense(h, 64, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 128, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.batch_normalization(h, training=training_phase)\n",
        "    h = tf.layers.dense(h, 256, act, kernel_regularizer=reg)\n",
        "    loc = tf.layers.dense(h, output_size)\n",
        "    #log_variance = tf.layers.dense(x, latent_size)\n",
        "    #scale = tf.nn.softplus(log_variance)\n",
        "    scale = 0.01*tf.ones(tf.shape(loc))\n",
        "    return tfd.MultivariateNormalDiag(loc, scale)\n",
        "\n",
        "\n",
        "#KL term for w\n",
        "def w_given_x_y(x, y, latent_size, training_phase, reg): #+\n",
        "  with tf.variable_scope('M/w_given_x_y'):\n",
        "    act = tf.nn.leaky_relu\n",
        "    h = tf.concat((x,y), axis=-1)\n",
        "    \n",
        "    h = tf.layers.dense(h, 256, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 128, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 64, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 32, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 16, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 8, act, kernel_regularizer=reg)\n",
        "    \n",
        "    loc = tf.layers.dense(h,latent_size)\n",
        "    log_variance = tf.layers.dense(h, latent_size)\n",
        "    scale = tf.nn.softplus(log_variance)\n",
        "    #scale = 0.01*tf.ones(tf.shape(loc))\n",
        "    return tfd.MultivariateNormalDiag(loc, scale)\n",
        "\n",
        "# def w_given_y(y, y_size, training_phase, reg):\n",
        "#   with tf.variable_scope('M/w_given_y'):\n",
        "#     act = tf.nn.leaky_relu\n",
        "#     h = y\n",
        "    \n",
        "#     h = tf.layers.dense(h, 256, act, kernel_regularizer=reg)\n",
        "#     h = tf.layers.dense(h, 128, act, kernel_regularizer=reg)\n",
        "#     h = tf.layers.dense(h, 64, act, kernel_regularizer=reg)\n",
        "#     h = tf.layers.dense(h, 32, act, kernel_regularizer=reg)\n",
        "#     h = tf.layers.dense(h, 16, act, kernel_regularizer=reg)\n",
        "#     h = tf.layers.dense(h, 8, act, kernel_regularizer=reg)\n",
        "    \n",
        "#     loc = tf.layers.dense(h, y_size)\n",
        "#     log_variance = tf.layers.dense(h, y_size)\n",
        "#     scale = tf.nn.softplus(log_variance)\n",
        "#     return tfd.MultivariateNormalDiag(loc, scale)\n",
        "\n",
        "def w_given_y(y, y_size, training_phase, reg):\n",
        "  with tf.variable_scope('M/w_given_y'):\n",
        "    act = tf.nn.leaky_relu\n",
        "    h = y\n",
        "    \n",
        "    h = tf.layers.dense(h, 256, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 128, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 64, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 32, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 16, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 8, act, kernel_regularizer=reg)\n",
        "    \n",
        "    loc = tf.layers.dense(h, y_size)\n",
        "    log_variance = tf.layers.dense(h, y_size)\n",
        "    scale = tf.nn.softplus(log_variance)\n",
        "    return tfd.MultivariateNormalDiag(loc, scale)\n",
        "  \n",
        "  \n",
        "  \n",
        "#KL term for z\n",
        "def z_given_x(x, latent_size, training_phase, reg): #+\n",
        "  with tf.variable_scope('M/z_given_x'):\n",
        "    act = tf.nn.leaky_relu\n",
        "    h = x\n",
        "    \n",
        "    h = tf.layers.dense(h, 256, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 128, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 64, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 32, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 16, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 8, act, kernel_regularizer=reg)\n",
        "    \n",
        "    loc = tf.layers.dense(h,latent_size)\n",
        "    log_variance = tf.layers.dense(h, latent_size)\n",
        "    scale = tf.nn.softplus(log_variance)\n",
        "#     scale = 0.01*tf.ones(tf.shape(loc))\n",
        "    return tfd.MultivariateNormalDiag(loc, scale)\n",
        "\n",
        "def z_given(latent_size):\n",
        "  with tf.variable_scope('M/z_given'):\n",
        "    loc = tf.zeros(latent_size)\n",
        "    scale = 0.1*tf.ones(tf.shape(loc))\n",
        "    return tfd.MultivariateNormalDiag(loc, scale)\n",
        "\n",
        "\n",
        "# #For the Mutual information term\n",
        "# def y_given_z(z, y_size, training_phase, reg, reuse=None):\n",
        "#   with tf.variable_scope('N', reuse=reuse):\n",
        "#     act = tf.nn.leaky_relu\n",
        "#     h = z\n",
        "    \n",
        "#     h = tf.layers.dense(h, 8, act, kernel_regularizer=reg)\n",
        "#     h = tf.layers.dense(h, 16, act, kernel_regularizer=reg)\n",
        "#     h = tf.layers.dense(h, 32, act, kernel_regularizer=reg)\n",
        "#     h = tf.layers.dense(h, 64, act, kernel_regularizer=reg)\n",
        "#     h = tf.layers.dense(h, 128, act, kernel_regularizer=reg)\n",
        "#     h = tf.layers.dense(h, 256, act, kernel_regularizer=reg)\n",
        "    \n",
        "    \n",
        "#     loc = tf.layers.dense(h, y_size)\n",
        "#     log_variance = tf.layers.dense(h, y_size)\n",
        "#     scale = tf.nn.softplus(log_variance)\n",
        "#     #scale = 0.01*tf.ones(tf.shape(loc))\n",
        "#     return tfd.MultivariateNormalDiag(loc, scale)\n",
        "  \n",
        "  \n",
        "def y_given_z(z, y_size, training_phase, reg, reuse=None):\n",
        "  with tf.variable_scope('N', reuse=reuse):\n",
        "    act = tf.nn.leaky_relu\n",
        "    h = z\n",
        "    \n",
        "    h = tf.layers.dense(h, 8, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 16, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 32, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 64, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 128, act, kernel_regularizer=reg)\n",
        "    h = tf.layers.dense(h, 256, act, kernel_regularizer=reg)\n",
        "    \n",
        "    \n",
        "    loc = tf.layers.dense(h, y_size)\n",
        "    loc = tf.nn.softmax(loc, axis=-1)\n",
        "    return tfd.Categorical(probs=loc)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6cz7pGYjU6Mo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Connect encoder and decoder and define the loss function\n",
        "tf.reset_default_graph()\n",
        "\n",
        "x_in = tf.placeholder(tf.float32, shape=[None, X.shape[1]], name='x_in')\n",
        "x_out = tf.placeholder(tf.float32, shape=[None, X.shape[1]], name='x_out')\n",
        "u_in = tf.placeholder(tf.int32, shape=[None, 1], name='u_in')\n",
        "training_phase = tf.placeholder(tf.bool, None, name='training_phase')\n",
        "reg = regularizers.l1_l2(l1=0.0, l2=0.0000001)\n",
        "\n",
        "beta = np.array([20.0, 0.1, 0.001, 10.0, 1.0])\n",
        "#beta= np.array([100000.0, 1.0, 0.02, 10.0, 1.0])\n",
        "y_size = 5 \n",
        "\n",
        "z_latent_size = 2\n",
        "w_latent_size = 2\n",
        "\n",
        "M_learning_rate = 5e-4\n",
        "N_learning_rate = 5e-4\n",
        "\n",
        "u_one_hot = tf.one_hot(u_in[:,0], y_size, axis=-1)\n",
        "\n",
        "#KL_w\n",
        "wIy = w_given_y(u_one_hot, w_latent_size, training_phase, reg)\n",
        "wIxy = w_given_x_y(x_in, u_one_hot, w_latent_size, training_phase, reg)\n",
        "wIxy_sample = wIxy.sample()\n",
        "wIxy_mean = wIxy.mean()\n",
        "kl_w = tf.reduce_mean(wIxy.log_prob(wIxy_sample)- wIy.log_prob(wIxy_sample))\n",
        "#kl_w = tf.reduce_mean(tfd.kl_divergence(wIxy, wIy)) #analytical\n",
        "\n",
        "#KL_z\n",
        "zI = z_given(z_latent_size)\n",
        "zIx = z_given_x(x_in, z_latent_size, training_phase, reg)\n",
        "zIx_sample = zIx.sample()\n",
        "zIx_mean = zIx.mean()\n",
        "kl_z = tf.reduce_mean(zIx.log_prob(zIx_sample)- zI.log_prob(zIx_sample))\n",
        "#kl_z = tf.reduce_mean(tfd.kl_divergence(zIx, zI)) #analytical\n",
        "\n",
        "#Reconstruction\n",
        "xIwz = x_given_w_z(wIxy_sample, zIx_sample, X.shape[1], training_phase, reg)\n",
        "rec_out = xIwz.mean()\n",
        "rec_loss = tf.losses.mean_squared_error(x_out, rec_out)\n",
        "\n",
        "M1 = beta[0]*rec_loss + beta[1]*kl_w + beta[2]*kl_z\n",
        "\n",
        "yIz = y_given_z(zIx_sample, y_size, training_phase, reg, reuse=False)\n",
        "ent2 = -tf.reduce_mean(yIz.entropy())\n",
        "M2 = beta[3]*ent2\n",
        "\n",
        "M_reg = tf.losses.get_regularization_loss(scope='M/')\n",
        "\n",
        "M_cost = M1 + M2 + M_reg\n",
        "\n",
        "M_optimizer = tf.train.AdamOptimizer(M_learning_rate)\n",
        "\n",
        "M_train_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='M/')\n",
        "M_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='M/')\n",
        "\n",
        "with tf.control_dependencies(M_update_ops):\n",
        "  M_train = M_optimizer.minimize(M_cost, var_list=M_train_vars,\n",
        "                                       name='train_M')\n",
        "\n",
        "  \n",
        "yIz_N = y_given_z(zIx_sample, y_size, training_phase, reg, reuse=True)\n",
        "#logN = tf.reduce_mean(-yIz_N.log_prob(u_in))\n",
        "logN = -tf.reduce_sum(yIz_N.log_prob(u_in[:,0]))\n",
        "N = beta[4]*logN\n",
        "\n",
        "N_reg = tf.losses.get_regularization_loss(scope='N')\n",
        "\n",
        "N_cost = N + N_reg\n",
        "\n",
        "\n",
        "\n",
        "N_optimizer = tf.train.AdamOptimizer(N_learning_rate)\n",
        "\n",
        "N_train_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='N')\n",
        "N_update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='N')\n",
        "\n",
        "with tf.control_dependencies(N_update_ops):\n",
        "  N_train = N_optimizer.minimize(N_cost, var_list=N_train_vars,\n",
        "                                       name='train_N')\n",
        "\n",
        "# Saver to save session for reuse\n",
        "saver = tf.train.Saver()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oVaO0RxsY7Ht",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Helper function\n",
        "def batch_generator(features, x, u, batch_size):\n",
        "  \"\"\"Function to create python generator to shuffle and split features into batches along the first dimension.\"\"\"\n",
        "  idx = np.arange(features.shape[0])\n",
        "  np.random.shuffle(idx)\n",
        "  for start_idx in range(0, features.shape[0], batch_size):\n",
        "    end_idx = min(start_idx + batch_size, features.shape[0])\n",
        "    part = idx[start_idx:end_idx]\n",
        "    yield features[part,:], x[part,:] , u[part, :]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gm1vfi3hYzuK",
        "colab_type": "code",
        "outputId": "29406cdb-2d50-4a09-d17e-dcb472e36e89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "n_epochs = 15000\n",
        "batch_size = X.shape[0]\n",
        "start = time.time()\n",
        "rec_out_plt = []\n",
        "kl_w_plt= []\n",
        "kl_z_plt = []\n",
        "epoch_plt = []\n",
        "M2_plt = []\n",
        "N_loss_plt = []\n",
        "with tf.Session() as sess:\n",
        "  sess.run(tf.global_variables_initializer())\n",
        "  \n",
        "  \n",
        "  for i in range(n_epochs):\n",
        "    gen = batch_generator(X, X, U[:,np.newaxis], batch_size) #create batch generator\n",
        "    M_loss = 0\n",
        "    N_loss = 0\n",
        "    rec_loss_ = 0\n",
        "    kl_w_ = 0\n",
        "    kl_z_ = 0\n",
        "    M1_ = 0\n",
        "    M2_ = 0\n",
        "    ent2_ = 0\n",
        "    logN_ = 0\n",
        "    \n",
        "    for j in range(np.int(X.shape[0]/batch_size)):\n",
        "      x_in_batch, x_out_batch, u_batch = gen.__next__()\n",
        "      \n",
        "      \n",
        "      _, M_loss_, rec_loss__, kl_w__, kl_z__, M1__, ent2__, M2__= sess.run([M_train, M_cost, rec_loss, kl_w, kl_z, M1, ent2, M2], feed_dict={x_in: x_in_batch, x_out: x_out_batch, u_in: u_batch, training_phase:True})\n",
        "      M_loss += M_loss_\n",
        "      rec_loss_ += rec_loss__\n",
        "      kl_w_ += kl_w__\n",
        "      kl_z_ += kl_z__\n",
        "      M1_ += M1__\n",
        "      M2_ += M2__\n",
        "      \n",
        "      _, N_loss_, logN__= sess.run([N_train, N_cost, logN], feed_dict={x_in: x_in_batch, x_out: x_out_batch, u_in: u_batch, training_phase:True})\n",
        "      N_loss += N_loss_\n",
        "      logN_ += logN__\n",
        "      \n",
        "      \n",
        "    if (i+1)% 100 == 0 or i == 0:\n",
        "      wIxy_mean_, zIx_mean_, rec_out_= sess.run([wIxy_mean, zIx_mean, rec_out], feed_dict ={x_in:X, x_out:X, u_in:U[:,np.newaxis], training_phase:False})\n",
        "      rec_out_plt.append(rec_loss_)\n",
        "      kl_w_plt.append(kl_w_/10.0)\n",
        "      kl_z_plt.append(kl_z_/10.0)\n",
        "      M2_plt.append(M2_/1.0)\n",
        "      N_loss_plt.append(N_loss)\n",
        "      epoch_plt.append(i)\n",
        "      \n",
        "      plt.figure(1) \n",
        "      plt.plot(epoch_plt[-10:], rec_out_plt[-10:])\n",
        "      plt.plot(epoch_plt[-10:], kl_w_plt[-10:])\n",
        "      plt.plot(epoch_plt[-10:], kl_z_plt[-10:])\n",
        "      plt.plot(epoch_plt[-10:], M2_plt[-10:])\n",
        "      \n",
        "      plt.figure(2)\n",
        "      plt.scatter(wIxy_mean_[:,0],wIxy_mean_[:,1], c=U, s=6.0, cmap='viridis');\n",
        "      plt.xlabel('w1');\n",
        "      plt.ylabel('w2');\n",
        "      \n",
        "      plt.figure(3)\n",
        "      plt.scatter(zIx_mean_[:,0],zIx_mean_[:,1], c=t, s=6.0, cmap='viridis');\n",
        "      plt.xlabel('z1');\n",
        "      plt.ylabel('z2');\n",
        "      plt.show()\n",
        "      \n",
        "      end = time.time()\n",
        "      print('epoch: {0}, M_loss: {1:.1f}, M1_loss: {2:.1f}, M2_loss: {3:.1f}, N_loss: {4:.2f}'.format((i+1), M_loss/(1+np.int(X.shape[0]/batch_size)), M1_/(1+np.int(X.shape[0]/batch_size)), M2_/(1+np.int(X.shape[0]/batch_size)), N_loss_/(1+np.int(X.shape[0]/batch_size))))\n",
        "      print('epoch: {0}, rec_loss: {1:.5f}, kl_w: {2:.2f}, kl_z: {3:.2f}'.format((i+1), rec_loss_/(1+np.int(X.shape[0]/batch_size)), kl_w_/(1+np.int(X.shape[0]/batch_size)), kl_z_/(1+np.int(X.shape[0]/batch_size))))\n",
        "      start = time.time()"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGEHwAcCI/wMRE9PxKaX9vAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEKCAYAAADuEgmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPAkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEKCAYAAADuEgmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNCRJkqRuk0lDkiRJ6rb/D5+qVLuRdvwpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "epoch: 1, M_loss: 346.1, M1_loss: 354.1, M2_loss: -8.0, N_loss: 403.66\n",
            "epoch: 1, rec_loss: 17.69966, kl_w: 0.92, kl_z: 61.23\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngoFPROQg/j/9toKI7L7wwAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEKCAYAAADuEgmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl8VPW9//HXOWe2TPZ9hbDvqxDA\nBXEpUmoFq15LbSsu1Wtbba/WVq9tr22vvbf6u7W3q9dSrPtS7SK0briVqIABAoQlkIQAISH7vs1y\nzvf3xySThKCOkGSS8Hk+HudBZubM5HMGmPec73Y0QCGEEEKEQA93AUIIIUYOCQ0hhBAhk9AQQggR\nMgkNIYQQIZPQEEIIETIJDSGEECGT0BBCCBEyCQ0hhBAhk9AQQggRMlu4Cxho1dXVHD16NNxlCCHE\niJKdnU1KSson7jfqQuPo0aPk5OSEuwwhhBhR8vLyQtpPmqeEEEKETEJDCCFEyCQ0hBBChExCQwgh\nRMgkNIQQQoRMQkMIIUTIJDSEEEKETEJDiBFu5vWz+dI7X2X5bz6L4TTCXY4Y5SQ0hBjBDKfB/NsW\nYHfbSZqZTNYFY0/rdWZeP5tr37iOZf99CZqhDXCVYjSR0BBiBDO9Jp4mD5bPQtM1WsubP/E59igH\nl/1uJVe9fC2Z52Vhj3Iw/9ZzcMW6yFiSScaSzCGoXIxUo24ZESHOKgpeuXED4y+bSM2+auoK6z7x\nKVOunEry7BQMh8F5P1zKn1e9gL/Tj93Q0XSNtqq2IShcjFQSGkKMcG2Vbex9ck/I+3fUtaNMC8uv\n0VHXgeWzeOXmvzP+sglU76qisbhhEKsVI52EhhBnmcOvlqDbdKLHxlD4wn4Amo82sXtdfpgrEyOB\nhIYQo4gj2sElDy8nZkwMWx/awrG3j5xyv+KNRUNbmBg1pCNciFFk0hVTSJyWhCs+gnPvO19GQokB\nJ6EhxCjSeqIFy7Sw/BY2l42v5N7ArLVzcCVEEJkWedqvqxka9kj7wBUqRixpnhJiFDn2zlF04z0m\nfG4S6TkZaIbG3FvmM+fmeQDs+FUeB186QFRGNJf8z2ewue1s/sE71O6t+cjXjEyN5HOPr8IZ42Tv\nk7vZ9aj0fZzN5ExDiFHmyJul7H1yD8pSmD4TTdewOW3YnDamXDUNgLm3zCN2fBxR6VEsufd8orOi\nP/L1si8djyPKgW7TmXHd7KE6DDFMhTU01q9fT1VVFQUFBad8fNmyZTQ2NpKfn09+fj4//OEPh7hC\nIUam6l1V5P3vNlCgGzpKKfydfo6+XYo9ykFLRaAZyzRN4ibEccWzX2D2jXNP+Vo1BdUoS+Fr91Gz\nt3qIj0QMN2Ftnnr88cf5zW9+w5NPPvmR++Tm5nLFFVcMYVVCjGzJc1M45+sLcadGAgoAy2+x/Zfb\nWPjtxcxeO5f22nZ0W+A7o6Zp6IbO5FVTKPjj7n6vV1NQzSs3bSQ6M5ryreVDeShiGApraOTm5pKd\nnR3OEoQYFexRDvztPhZ9ZwmTV09Bs+lomoZSgdDAUsy5aR42V+C/fFR6FJoWGFmlLIXpNbFF2vnq\n1hvxd/p5/bZXqO81u7yxpIHGEpn0J0ZAR/i5557Lrl27qKio4O6772b//v3hLkmIoaeBM8aJp8kT\nvCsi2c3Cb+cw9qJx6Dad1ooWHNFOdLuBUgplqeBzdYdBRKIbIBgk3X/uWrcDu9vBzC/PRtM07BF2\n5tw4j3fveWtoj1GMCMM6NHbu3El2djZtbW2sXLmSv/3tb0yZMqXffrfccgu33norAElJSUNdphCD\nyhZh43N/XEXMmBgqth7n7e+8iT3Kwapnv4Aj2hE8Y3AnR3JiewVpC9LRDZ3qgipix8URER8ROOtA\noZTqOcNQigPP7aOz3oO3xYuyLNB0UFB74KNHU4mz27AePdXS0kJbW2DxtFdffRW73U5iYmK//dat\nW0dOTg45OTnU1tYOdZlCDKqUualEprjRbTrpizKJSHYTnRmNzWULBgAE5lLsenQnlt8CDVLnpeGK\nc/U0UXVRKhAeylRMuWoaOXcuZtFd56KsQFNV0YaDFP314FAfphghhnVopKamBn/OyclB13Xq6j55\nFU8hRpOG4noALL+J5bMwPX5aKprxd/h6AkApPI2djFk2Fl0PdGx3B0p330b3bV+7D2UpvG1egEA/\nhwaGw0DTNSZdMYXVL1yNPcoRngMWw1pYm6eeffZZLrroIpKSkigrK+P+++/Hbg/MOn300Ue55ppr\n+PrXv47f76ejo4M1a9aEs1whwqKjtoPa/bWknpOGbteZf9sC/B1+bBF2UOD3+NENnYhEN7O+Moeq\nnSdIX5wZGDjVaxURywxcc8PutqNpGs4YJ+3VbRjOSHztvuD9mqFhOA3iJsZRs7tniK2m93Sci7OX\nRveYvFEiLy+PnJyccJchxMDQYPzyCcy+aR6x2bEoS1F7oBZPYycZ52aiGz2NBZqmYZkWW/7rPRpK\nGph69XQmXT4ZtEBgHN9cRub5Wdicge+KSiksv4Wv3UdbZRsx2TGBx5QCTcPT5OGVGzbQeqKV9JwM\nLv6fz6CU4q1/e4PqXVXhekfEIAn1s3NYd4QLcTZb8O1FTF41BcNpoEwVmFthaCRMTQCLPk1QSiks\ny0I3dM799wtQpoXWNalP13UMm0HW0jHUH6wjKiMKV1xEoEnKbqDH6DijncEmLEUggOyRdrIvHc++\npwuY87We4bqzb5zLW99+I4zvjAinYd2nIcTZKmFaIlO/MDW4fIfNZaOzvgNnnAubw4bhNIL7dn/Y\nB/swDA3DaUO36fg9/mBHuG7TSZqZzP5n91FTUAWqa9itpfp0liszMPtbmYqagkDzVPWuKnztPnzt\nPjnLOMvJmYYQw5C3yQO9RkZ5WjyUf3Cc2PFxwSapoo0HmXzF1OA+3R3evnYfjshAJ3ZreQt7nyxg\n6U+WBR+fcuVUItOign0UllKUvFKM5TOJmxjP7j/swuYyaC1vofFwIwD5/7eD2n01KEtx/L2yoXob\nxDAkoSHEMNR6opV37n6TMcvGUvrGYWr2VBM9JoaZX52NZVk0H2mi5WgLptfEsBuoXl2TNXurSZ2X\nBkDxxkMceeMws746m/jJCaCgYttxJl0xtac5ylLk/Xwr/k7/RxekoGzzscE+bDECSGgIMUydyKvg\nRF5F8HbagnSUqTDsBu5kN1EZUYFhspqGt82LzWVDN3RS5qSy/5kCZn55Ngu+mYPlU/zjhg0kz06h\ntaKFjroOJqycjG7XUZai4PHdHx8YQvQifRpCjBD+dl9weRBfhx93r4sq2SPsweYm3dCY8eXZGE4b\nhtPGtH+ZjjIV1buqaK9uR5kKvWttKr/HT+uJ1nAdkhiBJDSEGCFKNx1mzx/yqdxegaZrZC7JAro6\nwvWeUVS63cDmtAUD5ui7R8i6YAyL7zmPpJnJALz34820VrRQseU4RzYdDtsxiZFHmqeEGCkU7Ht6\nL3NvPScYCr1Xsu09/Lb7Mcu0sDwmF/70YmwuGxNXTuRPn3uOo2+WcvTN0nAejRih5ExDiBFkwsqJ\nGA6jz7IgEJil3bOciAdfmxfLH7hWeGevlXE1Qw9O7osdH8fqP13F6j9dRez4uCE/FjEyyZmGECPI\n9DUzgZ7htf52H4V/PkBTaRMV28rxNHaiTIUrIYLxl02g/lAdGUsy0W2BUVKlmw7T2dAJwMJv5RA9\nJgYNjYX/tkgm7ImQSGgIMYLYInpWtrX8Fhu/+jKt5S199olIdjPlyqk0lTZStbOSxd87D90WmAxo\nd9vI+c5iPE1e2qraUD4LC2irkM5wERoJDSFGkPzf7WDpAxdhekw23fFav8AAWPHISqLSo7F8Jn6P\nn/3PFrD4u+eBBsmzUnHFubBMi/3P7SX/0Z1oGhS+eCAMRyNGIgkNIUaQY+8e5dllTwZWmj1pqVHd\nprPi0c8RnRUT7ASPTI3i4EsHiEh0M/uGuUQkRgRGWmngiHay6/92hudAxIgloSHECKPMUy9MnTgj\nibgJ8cH+jsbiBg6/WgyAM9YVmJuha5heP9V7qtmzPn8oyxajhISGEKNE87FmlBlY6tzs9PPGN14N\nzvTe99Qekuek4Ip38f6PN8uig+K0SWgIMUp4GjvZ+OW/kTwnhcodlX2WBumo6+DVmzaGsToxWkho\nCDGKtFW10bZJJu2JwSOT+4QQQoRMQkMIIUTIJDSEEEKETEJDCCFEyCQ0hBBChExCQwghRMgkNIQQ\nQoRMQkMIIUTIJDSEEEKETEJDCCFEyCQ0hBBChExCQwghRMgkNIQQQoRMQkMIIUTIJDSEEEKETEJD\nCCFEyCQ0hBBChExCQwghRMgkNIQQQoRMQkMIIUTIJDSEEEKELKyhsX79eqqqqigoKPjIfX75y19S\nVFTE7t27mT9//hBWJ4QQ4mRhDY3HH3+cz372sx/5+MqVK5k8eTKTJ0/m1ltv5ZFHHhnC6oQQQpws\nrKGRm5tLfX39Rz6+evVqnnzySQC2bdtGXFwcaWlpQ1WeEEKIkwzrPo3MzEzKysqCt48fP05mZmYY\nKxJCiLObLdwFDIRbbrmFW2+9FYCkpKQwVyOEEKPXsD7TKC8vZ8yYMcHbWVlZlJeX99tv3bp15OTk\nkJOTQ21t7VCWKIQQZ5VhHRobNmzg+uuvB2Dx4sU0NTVRWVkZ5qqEEOLsFdbmqWeffZaLLrqIpKQk\nysrKuP/++7Hb7QA8+uijvPLKK3zuc5+juLiY9vZ2brzxxnCWK4QQZ72whsZ11133ifvcfvvtQ1CJ\nEEKIUAzr5ikhhBDDi4SGEEKIkEloCCGECJmEhhBCiJBJaAghhAiZhIYQQoiQSWgIIYQImYSGEEKI\nkEloCCGECJmEhhBCiJBJaAghhAiZhIYQQoiQSWgIIYQImYSGEEKIkEloCCGECJmEhhBCiJBJaAgh\nhAiZhIYQQoiQSWgIIYQImYSGEEKIkEloCCGECJmEhhBCiJBJaAghhAiZhIYQQoiQSWgIIYQImYSG\nEEKIkEloCCGECJmEhhBCiJBJaAghhAjZJ4bGZZddxk033UR2dnaf+2+88cZBK0oIIcTw9LGh8dOf\n/pTvf//7zJ49m7feeovbb789+Fjvn4UQQpwdPjY0rrjiCi655BLuvPNOFixYwMqVK3n44YcB0DRt\nSAoUQggxfHxsaNhsNkzTBKCpqYkrrriCmJgY/vSnP+FwOIakQCGEEMPHx4ZGSUkJF154YfC2ZVl8\n7Wtf4+DBg0yfPn3QixNCCDH8qI/aXC6Xcrlc6qmnnlJf+9rX1NSpU4OPZWRkfOTzwrnl5eWFvQbZ\nZJNNtpG2hfrZ+bFnGp2dnXR2drJ+/XrS09P59a9/TUlJCS+99BLXXHPNxz1VCCHEKKQRSI9PpOs6\nOTk5XHzxxdx22210dHQMyyaqvLw8cnJywl2GEEKMKKF+dtpCebE333yTyMhItmzZQm5uLjk5OdTU\n1JxxkUIIIUaWkGaE79mzB6/Xy6xZs5gzZw6zZs3C5XINdm1h43TH43THExGdQlRcVrjLEUKIYSOk\n0LjrrrtYtmwZV111FXV1dfzxj3+ksbHxjH/5ihUrKCwspKioiHvuuaff42vXrqW6upr8/Hzy8/O5\n+eabz/h3fpKkrPmcs/y7nHPZ95h3yZ3MuvDrjJm+HADdsDNm2nKyZ12OKyp50GsRQojhJqTmqW9+\n85ssXbqUBQsWcOTIER577DFyc3PP6Bfrus5vf/tbli9fzvHjx8nLy2PDhg0cOHCgz34vvPACd9xx\nxxn9rk8jdVwOumHvc19S5lzKDmxi/JzVpGTnoGkamZOXcWTfq1QcemfIahNCjHyazYayLLCscJdy\nWkIKDZfLxcMPP8yOHTuCk/3O1KJFiyguLqa0tBSA559/ntWrV/cLjaFWfXQ7MYnj0PTAW6OUyYmS\n97E5IolNnoSmacHZ8NkzLsPydVBZujWcJQshRgj3nFmkrP0KyjSp/N3v8Rw5Gu6SPrWQmqd+/vOf\n8+GHHw5YYABkZmZSVlYWvH38+HEyMzP77Xf11Veze/duXnzxRbKyTt2/cMstt5CXl0deXh5JSUln\nVFdN2U6qy/KBwFIplmlSf2If0xZfj9MdB4BSgQFnum5jwryrSBu/JLC/buCOSWXMtOUkZc07ozqE\nECOf5nBAryWX4lYsR7PZ0J1OYi9ZFsbKTl9IZxrhsnHjRp577jm8Xi+33norTzzxBJdeemm//dat\nW8e6deuAwLCxM2X0ap4ybA7mf+ZudMOOrhvBwFBKBc86ksecQ31lIfMuuRObIwJQWKYfZZnUVRR0\nvZJGRHQyps+Dz9OKUibpE5eSNn4xVUc+pKJ48xnXLYQYPhJWf56Yiy7EbGqm/Of/i9XSSkdRMfbk\npECQ6AYR06bQUXgo3KV+KmELjfLycsaMGRO8nZWVRXl5eZ996uvrgz//4Q9/4KGHHhqS2hoqC0nM\nmA26gaZp6IYNTQuclGmaFgwOCISHz9fBlIVfxOaI6Gq60tA0Hac7PrjfzAtuISZpApqm4/e2c/DD\npxk/5wo0TWPc7M+jgLHTl+P3trPvvd/T2VY3JMcqhBgcMRddiKbr6JFuImfPouWDrTS8/Hc6DxWT\n/JU1uGdMI2LqZCp/++iIaqYK20WY8vLymDx5MuPGjcNut7NmzRo2bNjQZ5+0tLTgz6tWrRqC/o7A\naWRN2U5Kdv0VT3sDyrIC/RtdYdEdGL3DIyFtOjFJE4N9HUpZdLbVU3diH4bNxbhZnyc2eRJ6VwgZ\ndhfxaX0nRmZNuQib3YXTHUfGpAuJSZqA050wyMcrhBgs3uPlWJ0eADzHuprilaKj8CC6y4VmGKAU\ntsQEImZMJ+bCC9Ajhv9UhrCdaZimye23387rr7+OYRg89thj7N+/nx//+Mds376djRs38q1vfYtV\nq1bh9/upr6/nhhtuGLR6krLmMXnhFzF9Hop3vkhC+nS8Hc3BfozukNB6hcfJy8P3brpyumOZd8m3\n6WitJSo286THLZwRcaBUYDEXZWH6vV2Pa0QnjiMlewGgsTf3/2htKONslZRo4PcrGptG5kgTcfY6\n8etHiJgxDV9VNb4TlT0PWBb1L/+duJWX4auqxpacRNylF4OmEblgPid+8evwFR2CkJcRGSlOdxmR\nBSv+HVdkAkopLNOLbti7gkHvc1ZxcvNUt5ND5eOe4+tsxeZ0B1/bssyen00/oNANO5ZlUnbgDY4f\nfPv03owR7qv/Es3//b8UlIKrbjzBG++2f6rnf+mqaD6zNIJHHm9i+27PIFUpxKfjmjiBlBuvR7Pb\nwDDQdB30rv//nZ0cvecHn/gaRkw0RkwM3vIKOMXn0ekY0GVEzgYtDcewOyMJ9EcYaJqOsnxdidq/\nP6N3OPSmlNX1Z/+w6N7X7orq91rBfXSDmmM7Scqag2WZ1FXsHZwDHgG+fWscLmfgvb9tbeynCo0l\nC1w8+v9SiHTrXP35aFJmHsbrHVXfj8QQcmRmkHLT9Si/n6o/PI6/pva0XyvhmisxoqP6fX4opeg4\nVBxSLenf/iYA7Xv2UvP0c6ddy+kIW5/GcFOU9zwl+X+mouQ9lLICZxzK4sO//5ja47uxTB+WZUKf\nv2gVbKpSSkEwMBSNVQf7nHFYltmnT6Q7XEyfl7bGEyjLRFkW7c2VHN7zN3a88SB5r/wnHS3V/WpN\nHbeYCfOuImKUz0r/yz9aaWuzaGu3+NurrcH7V17iprlkIicKxjNz6qkvBhYfq2N1ZYTToWGXr0fi\nDCRcfSX2pCTsKSnEX77yjF7LX13br6nb8vlAKRyZGTiy+k896C1i+tTgsF33nFnYks9smsGnJaHR\nRSmTmrJ8PF2jljRNQ1kmpr+Dou3PcWTv3zl24A3qKwvpbKuntbGcoh1/oubYDizTFxh+qwfOUHTd\nICp+TJ/XL9z6BId3/S342oEzGZPKox/S2nwi0NEORESnsvjzP2HivKuwTG+/OhPSZzJ+zirSxi9m\n1oW30d15Pxr91/82sPzacpauKuPJP7UE73/wh0lEunWSEgzu/kZ8n+fExercfnMsmgZPv9hM0WEv\nt95dRVu7nGWI0+evb8Dy+VB+E399PZrdFpiDATjHZRN7yUXYEkMbuFLzzPMor7fnC6RpgmmCpmFP\nTCDlputP+TwjJpqYZRfgb2xG+f1YPh+azUbm975D/BWXD8hxhkK+f52k+tgOEjPn4I5Jo7RgIxA4\nK6g8vOWU+9cc20FneyNZU5aBpqMsBVi0NpYTlzIZpRR+TxtTFq7BZu87MkLTDTImnt9zu1eTV3za\ndGwON35v3yYZm8Pdta+O3RnN/M/cxe63f4ll+QfwXRg+tu3sBOC8hU4aWxSXLXOTkmzD67UwDI05\nMxzEROssX+ZmyQIXn7vUTfYYO5YF190W6Hz8zX+n8NlLIvnKNypRKhAs2Vk2fvXTFDo9ihu+VcmJ\nqoGbuCpGn7o//RlfZSXK58dbVcXY//5PNE2j7m8bSFj1eTRdJ/Yzl3Dshz8OBMDHUF4vTe/mErPs\nAnSHA91mQ3X1aQAYUVHEXnYpzf98D+Xp6ovTNDK+8230yEhQiqpH1xN5znxizl+CZhhEL8mhYeM/\nBvttACQ0+omMzSQmcTy6YWPi3CupO76HTxorUHbgdRyuaJLHzANNp+boTpLHngOAZfqpKdtJ2oTz\n0HQj+JyTO8277+uh8Hs7+v2umrJ8EjPnEJ86FU3TcETEEZUwlubaw2d87MPVn9alcdXlUUBguR7D\n6Hnfpk92ULItm/i4nvdW0zT8fovzF7m44YsxRETofH55JIvmu8hKt/Hkb1Ox2zW0rtd76D+S+Oo3\nq8J0dGIkUF4vTW8G1plL/deb0e2BCcAxSy8AS6E5bOho6C4nVls70RechyMrg+a3/4mvuu9lJCKm\nTSH20osCYdHVNNW2Zy9R8+YEJv0ZBvErluMck0X1+icA0AwDIyYGTdexPB6M6Ghat28nKuccUIq2\nXXuG7L2Q5qmTuNzxQGDUlM3uDoxsCEF0wlgMmxPDsOOOTUM37MGJgakTzkU7aTZ5sI+k67ZSCr+v\nnYbKQjrbGziw9QlOFVbK8lO49Qm8HU34fZ0oFegHGW2iIjXe/Wsm1fsmsOqzUcHZ973/OiwrMGQ5\nLtYIPt4dwJqm8YdnmvGb4PMrNA3KK/3c9fVA57qhB/YzTUVTswznFaFrP3AQy+PF8nho27WbzsOl\nWF4vTe/8E6utHfecWSSsupzoxYtI++Zt/Z6vuVzBzxVN0+goPITq6AgEhqahGQaazYYtvqfpVfn9\nNL6+CeX34604Qfu+/XgOH+H4fz3EiV/9jroX/zJkxy9nGiepO7GP5NoFRMePpaxwE8oKrdmirPAt\npixcg1IWVUe3ExU/Bk0LTN7RdVvfkVRKsfONB5n/mbuxOSK6OtEVlYe30VxbQlNNCUr1/73pE89n\n3KzP09FWS0HuI7ijU2ltON6vCWs0+PLVMSyY68IdoWOaVp9RhX6/wlJgM8Bh19A0+p2xebwWzS0W\n511exucvi+TNze0cr/Cz6Z/tnDMn0Ez4YX4nO3Z38vc32viv+xLZ8HobW3d0huNwxQjSsvk9fOUV\nYOh09hrt5BiThXP8OHS3O/Dhr+t9JusZMdFEzp9H9NLzA3O0lALTpHVbHknXXRsMEqUUVmdnnyAw\n4mKxp6bSsi2P+r9tRHkD/Z22uDiszqH9NyuhcRJl+TnwwWOf+nl15bvZVrkfpSw0TSdrykU4I+Iw\nLV8gNNBAA9Pvx9fZgrezBZ+3DcPuCozPVorMKcvImHg+HW21OFwx2OwReD0t7H9/PR0tVYybdTm6\nYSMiMpGE1OmcOPz+ILwDw8ORMh+GAaal+pxBWJaips5ParItuA7cycMWNU0jwqVTlj+eQ4c9fLiz\nk1/9IdA2PG+WEw2FacF7Wzv49fpGDm0ZR6Rb5/ab45hy7hEqq6V/Q3y8zpK+zcFRSxaRePWVoKB1\nx07a9x3AkZlOW/5uEr6wCtfkiTgyMoL7a5qG5fXSWXqElBu+EmhtsKxA2GgaSteJuXgZrokTaNmy\nFdfECbgmjAfTxPJ4aHj578SvupyYC84DTaPm6edp3z00TVQSGgPIMn0AKEw6WmtwRSZi0w0s08f+\nLY/R2VZPZFwGTdVFgGL/+39g4jnXEps0vusDUEe3O4mMzQh+EDoj4sieuZLCrY8HXjMqCRS0t4y+\nJqneIlwaSoGuBea89O7vSUsJ/LM9eY5M7/s0TcNuhxlTnMyY4uQzyyKZkHOE1CQbdruO5lekphgk\nxht9xp+lpRhU1ZgDNV9KnCXcs2aid42mil6yCF9VNXUv/JnUf70Zrav/o9+/V6WImDQRzTBQpkln\nSQmu8RMCp9CahnvmdHSHg9hLLsJf3xAYYdnVfAUQOXsWutMZ+P2zZw5ZaEifxiCJT5nS086uGzhc\nsXja66mv2IvpD3zr7Wyro6LoXSzTG1wQ0TL9eDubg3NFlDLpbK3B5oikofIgdeUFHPjgMZpqSsJ5\neIMuPtboGoWodTX1anR9Eetz5nGq2fm97+/eNyvdRmPRRObNdtLQaNLQZLLx9Vb2HfTyh2eaqKnz\nk1/QybbXxlKWP56sDPk+JXro7giSrvsiSV9ZExjBdJLm3PdQfn/gTFfXsSUm4hiTGTxz6D0no1vH\noWI8R48Fn+OaMIGOomI8FRW0bPkwuJ+m69jiYkEpPCWHaXz1DQCa3t0cGHrr9dK6ZdvgvwldJDQG\nSW35nuCEPk3TmDB3VfDCTr01Vh+ipe4olumjvOifbH/9v9j11sOUFb5N7fHdlO7ZyNF9rzLj3BvJ\nmHwhiRmzcUaO/oUMn/lLCxtfb+VgsZfyE35aWi10/dRNUb0HGJzq/m6Rbh2HXSMuVic50cazj6ST\nkmRw53/UkjarlHmzXNgMjcR4nTVXRg/dwYphL+GqK4laMJ+o+fNI/Jer+j3eebCIY//535jNzVge\nD/66Ojr2FwbPCrr7LbsppVCmibcyMGpP6zqDsKelYE9KInpJDvUb/kHj2++i/P7AGYVSdBSVYHUE\nRlW2vL+FY/c/wLH/+E80p+MTJwUOFPk6NUgO5T1LanUx4+eswrA70Q0HNrsTn6fvfAplmex7f12/\n55cdeL3PbYc7Dl03sAg0WY32CwSjAAAYA0lEQVR2Xq/iy98I/IeKitS481/j+eFdCRg9I2tPGRjd\nTr59qmVbDCMQINW1gT6MHXs6WTDHhabBjt3SIS56aIbRdQ0MHfeM6UTMnE7Hvr6rbluNTRx/4EHs\nKcl4T1Rii41BmSaarqN8ftoLC4mcNTM4JtKeloo9vufCbsrvR3e5gs1ctvg4Gl7+O5puEH3+EjBN\n2gv6LitktbaSdN0Xiewarlvz5DO0F+wb1PdCQmMQVR/bTkL6DGJTJnGi5H18nrbTfq3inS8y6Zx/\nwdveyInDHwxglcPfuDF2vnd7fJ9RUr0vgnWqNcBOPiMBsBRYpsJuDzznhQ3NHCrxBfe7/MsVrF4R\nSckRH3m7ZIFD0aP+ry9jT0vFkZqK7nSQ8tUvc/Te/gsLKq8XX00tiVetRnPY6SwpxZGRjvdEJe7p\n07oXpQv0uSUEhtRqmoYyTRreeJOEz14W7BSPnDcXW0ICtc88R/M/N2O2tQcn+9lTU0n+6pdQPh+2\nxER0pxOlFK7JkyQ0RjKlLA5sfTykfZ3ueKYvuQHdZufgh8/Q2VobuPpf19DbxqqDbH/1gUGsdvia\nPMGOroGu94ygshQYek9wWEqh0dOHYVmq3+iqze+3UVFlcsVlkTz+QjN3/rDvonMdHYrn/9aKECcz\nm1tofH0TydetAc2G2dbzBdCIiyVy/jw8R49hREeR/OU1wc7v7n+ErohxwTMVLAvL6lp7rq0dNA3P\nsTIixo0LNmd1LyliREcTtXgRHQcKodfw/8Rrr8aRGRiN5Sk7jhEZifL7aN3a0xcyWCQ0homxMz6L\nOzYNTdOZtvirOCLisEwfBf/8De3NZ/ds5Vfeaqeo1MesaQ6UgoYmi/t+WsvaL0azeEEEyoKvfPME\nS5e4+foNsSgL1j/bxM9+1cCaL0Txg39LBA3WPdPMCxIK4jS179pDfUQE9vQ0mv+ZG7ize3kPd9dE\n4F5nv4GHu7652Gy079mLY+wYjEg3aDqe8uP4yk/gnjkDZ0Y6TW+/i2vKpMBKBZ0e9Eg3oIhanEPC\nlVegOjspf/BhzJYWrLa2wBBdpfAcPkLDK6+TePVq4ldfQc3jTwX7PQaDXE9jmBg747NkTL4QDQ2F\nwui6nkdF8WaOFPw93OUNC7etjeX8HBdLz40gIc7g8FEfV66toKXNoq4+8M0tLlbHZtOorev5VjZr\nmgOvTwWbohwOjZfWp7HknAh+8nAdv1nfFJbjESOX5nCg2e0or5fsh34a6Lc4aQCG5fGiO+ygaTS/\nu5nGN94i877vYosODLIIfOgDeiBofLW1VPzP/+LIyEBzu0lYdTn2xISea214PLTu3IWvsoq23QWk\nfeNWbDHR1P31ZeJWXIY9IR7L76dp09s0vvbGpz4muZ7GCFN2YBN+bzuGzUlETAoJadMBbVSvKfVp\n/d8TTeze5+GKFVFEunUmZtuJiNA5UtYzuOBUV/jbW9h3teDVKyJZdq6bqEid//lRMr9/qlmutSFC\n5sweS9o3b0MzdOpe/jvNue8Ts/T8YFNU99mFr7ycE7/6XfB5qbfdghHV6zoaWtd3dqWwfH58lVVE\nzp9HwhdWgQLNYe87VNcwiFowP3CFv3lzscXGoDudJF17Dd6KE6jYGLAszJaWU5U9YCQ0hgmlTCqK\nNwduaDrxqVPxdbbQ2ng8vIUNM3sLvTS3BIKhutbP4aO+T3hGf8fK/eh6YN2pxkYTn08CQ3yCriGx\n7nlzSbr2KnRnYIRT7MXLAqteApgmfo8nsBCh30/9xlf6vIQjM73fUHDv8XJa8naAZdH64XZSbr4h\nOHrK6uwEpzMYQv6GRmyJCWi6jhEVuGCcsiys9g6qfv8YsZ+5GH99Ay0fbB3Ut0JCYzhSFg2VBz55\nv7NQS6vFzAuPMmuag937PHg8n/4Df9vOTq792gkWnePiqRdbZPa3+Fj29DTSv/XNQFB0rSnVPUS2\nY99+3HPnBFafNU2aXttE8+b3Tvk6nUUlRJ4zrycE6huo+Pkv++zTkvseERPHA1D74p+JnDePiCmT\nQNNofPNt4i69GM3ppObZF7DFxuIcN5aW97dgNjdT/5eXB/eN6CKhIUacllaLLdvPbB7Fq2+38+rb\no2+hRzHwos9bgh7h6rowmxXYTJOGja/Q8sEWbAkJ6JMm0Hm4lJYtH/0tv+7Fv6BHRRIxaSIodcrr\nX3iOllG/8RU8x8vxlBymbXs+zvHjsNra8FXX9Bkd5QHa8ncNwhF/PAkNIYT4GJ6SUtSSRcFrePtr\na/AcLaMtf3dgUcEpk9DtduwpyWguFxHTsuksPYrV2nekntXRQdXvfk/X0gb9L9ak62R8987gyrjl\nDz2Mv6YWT+kRjNgYos8/F0/pEbwVJ4bq0E9JQkMIIT5G267d+JubMKKiaN+7v6cPAwIf/N0LalqK\nrHvvRuvq0yj7yX/3XHmvS+zyS4hZej7tBfuoe/Ev2JKTcE+bSsehIsyWFoyoSDTDwOr04EhNwV9T\nC4ZB5nfvROtanLA7TMJFQkMIIT6B5/CRU97fvPVD7BnpRC2Yjy0hAWWZ6HY7lseLPSkRb3lFcF8j\nNob4FcvR7HaichZiS0sjYnw2SikSDAN/QwOtO3YSlbMQb1UVHQeLAAJLi7jdwTCxpySHNTRkwUIh\nhDgNzuyxZD9wP9GLFqLZbGiGHugkN02U30/y9dfhmjQxuL8eHROcFKfZbUSMzwZdR7cFLtJmi4vD\n39hE+YM/p33XHmxJiQBYbW20fLAVZVl4K07Q2RUm4SKhIYQQpyFm2VJ0pzO49Ifl8dJZcpi2gn3o\nES4caWkk3/AVAIz4ODK+9XVQBC6+1H0Rp645GMqyUH4//oZGMu68g/jLP0vGnXcEl2Gve+mvHLnz\ne5z45W9Qfv8p6xkq0jwlhBCnoaOoGPesmYCi/cBBOvbtx1dbG5j41z0styMwys+RkhK49LPTjvL7\naf1wR+BKfJZF9VPPYERF429oxGxoALRAv4hlYcREY7Wd/kKng0FCQwghTkPrlm34q2vQnM7AgoJK\nBZqjTBNsgY9WW3ISmf/+XSp//Qjeyiqc2WNp27WHqEULgmcoMeedS+Xvfh94UU2jfU8B7rlzaNu1\nB9+J4XeFTgkNIYQ4TSdfK7yzuITm3Pdxz5yBPS01sAR6cjLpd32L6sefwnu8HCyL+M9/DufYMYHn\nHDna8wJKUfP0c/D0c0N5GJ+K9GkIIcQAatj4CuU/+x/8tXUovwl6YJnzlOuvCw7XbfjHq9Q8+wI1\nzzwfvHzrSCGhIYQQg6Di4V/StntP4NrhponV0WsVA6Vo37WH9t0FjLR1bKR5SgghBpquEzFlCi0f\n5uGrrsYWF0fDaSxXPhxJaAghxABLuu6LRM6ZBWjUvfQXGl/bFO6SBow0TwkhxABzjc8OzOFw2HGO\nzw53OQNKQkMIIQZYwz9eQ/n9WB0dNG9+P9zlDChpnhJCiAHWtnMXbbv2BDq5R1hH9yeR0BBCiMFg\n9b/08GggzVNCCCFCJqEhhBAiZGENjRUrVlBYWEhRURH33HNPv8cdDgfPP/88RUVFbN26lezs0TUK\nQQghRpqwhYau6/z2t79l5cqVzJgxgy996UtMnz69zz4333wzDQ0NTJ48mV/84hc8+OCDYapWCCEE\nhDE0Fi1aRHFxMaWlpfh8Pp5//nlWr17dZ5/Vq1fzxBNPAPDSSy9x6aWXhqNUIYQQXcIWGpmZmZSV\nlQVvHz9+nMzMzI/cxzRNmpqaSExM7Pdat9xyC3l5eeTl5ZGUlDS4hQshxFlsVHSEr1u3jpycHHJy\ncqitDd+1c4UQYrQLW2iUl5czZsyY4O2srCzKy8s/ch/DMIiNjaWurm5I6xRCCNEjbKGRl5fH5MmT\nGTduHHa7nTVr1rBhw4Y++2zYsIG1a9cCcM011/D222+Ho1QhhBBdwjYj3DRNbr/9dl5//XUMw+Cx\nxx5j//79/PjHP2b79u1s3LiR9evX89RTT1FUVER9fT1r1qwJV7lCCCEADRhVC6Pk5eWRk5MT7jKE\nEGJECfWzc1R0hAshhBgaEhpCCCFCJqEhhBAiZBIaQgghQiahIYQQImQSGkIIIUImoSGEECJkEhpC\nCCFCJqEhhBAiZBIaQgghQiahIYQQImQSGkIIIUImoSGEECJkEhpCCCFCJqEhhBAiZBIaYWZgQ8cI\ndxlCCBGSsF25T0A62UxnAQqLnWymifpwlySEEB9LzjTCaBzT0DUdQ7MxhskAaGjM4wIu5WpmseiU\nz0smgywmYnzEGYqBjQnMJJupaGiDVr8Q4uwjZxphVEclbhUJaCSRjpMIXLiJIwlN00hWGbiJop3W\n4HOmMo9MJqBQJJNOPu/1e92Z5JBEGgqFDRsl7BvCoxJCjGYSGmFUzF7GMAlN00BBPEnUU4OGhqUs\nFAoPnWjoTGIW0cQTRyIaGrqmE6liTvm6TlzomoFSFk4ihviohBCjmYTGELFhZyEX4SaaEvZSRjFJ\npNNMPVEqFh2dGeRQRyWg0NAwsTDxM5YpZDERvas1UdM0lFIcYvcpf1ch+cxSizDxy1mGEGJASWgM\nkTTGEkkMmqYxSc0mgVRiSUBDo4wSspiATbOTpNKBQDDYlT1wVtGr68nEj1/5KGEf1ZSf8ne10MgW\n3hiS4xJCnF0kNIZIMpl9bseRhKEZWMoCQMdAKQX0nEl00sGl2tVYykJDw0sne/mQeJL7vNYU5hJL\nIiXso56qoTkgIcRZSUJjiNiwBfouuujowZBwEQkoNC1wRtF9vxNXYN+u+23KwVTm4yYKUBgY+PCS\nwXhsmo256jze4a9Dd1BCiLOODLkdIsXsDXRuK4WmaT0bGilkdHVc95xpQN9gUUqhoxNJNLqmo6Ez\njmlYmMHmKx2dOJLCc4BCiLOChMYQaaCa93kVhUKpnq07OLp/7h0c3ZupzODrdO+jaRo6BjWcQNHz\nnJSuZrAUMpnKfKKIHfqDFUKMWhIaQ8hDBxqBIFAo/HgD4YEFXWFysu4zjO4AUUrhx4dXeVBYXMrV\nwTMNpRSxJBJDAjPJIYsJLOQimeAnhBgw0qcxxKooJ1Vl0kkbrbQQQSR1nKCdVqKJI4uJAP2aqnoH\nSguNgQmA9ARJTz9IBA6cqK7n6spAQ0dhIoQQZ0pCYwDpGLhw007LKR9PJ5sk0rCwiCCKCKJQKNxE\nBib0YQX37d1p3t0c1f1z9+ip3k1Vvfet5QTVHCdeJVNKIZYEhhBigEhoDBA7DpZwGTZsNFHPTjb3\n2yeaOAyMvh/2CrSu+3oPuz1Z7w703v0fJv6us4lAk9cRCgHYz/ZBPV4hxNlJQmOAxJGEgYGh2YhT\nSdhx4MPbZ58yikkmA4fqGkqr+s7w7g4F6Nsc1f8+BV1nJu/xKgY6etfwWz++wT1QIcRZTUJjgDRR\nH+jcVj7aae0TGBoamUzAgYNtvIkfHzHEM41ziCauz+t8VGc4dIeLhYVCUxaH2YcfL/7BPTQhhAiS\n0BggXjr5gNdwE00zDX0eG8sUJjAdDY0YEtjF+7TQiHmKj/vAalN+HDiDZyBeOrHjDI60MjRbcKQU\nBEIplTF48ciMcCHEoJLQGEA+vDRR1+/+CNyBFaQ0HZdyo6GRwyUnzaFQNKkGWmkilTF95mw4umaG\nKyzKOcIYNRFN00hSaThwMYW5JJOBjk4rTXzI213DeIUQYmDJPI0hUEohrTTRodo4wE7cRAdndkOg\n+ekoRWznHVppRAMsFTjnUPSaQY5OI7XB0VAKmM1iUsnC0AKd6ZHEkEx6+A5WCDGqyZnGEPDQwYe8\nFbyto+PFg10pTEy2sQkvHuZyPvEkARoVHOEoB8lkPNlqKgDtNFNDOftQpKhMWmhiPNP6nJUoFB20\nheMwhRBnAQmNMLCw2MomYoinhcbgiKd4krBpdixl0UkbHbRRzF7KKMFAp70rDKopp5pyIohkAtMx\nVeDKG8coopE6WmgM5+EJIUYxCY0wMfHTQE2f+45xiHFqOiY+qjgevN9Dxylfo4M2tvMucSRSwwk6\naR/UmoUQIiyhER8fzwsvvMC4ceM4cuQI1157LY2N/b8d+/1+CgoKADh27BirV68e6lKH1GEOcIzi\nYF9GKFpolDMLIcSQCUtH+L333stbb73FlClTeOutt7j33ntPuV9HRwfz589n/vz5oz4wuvnxhRwY\nQggx1MISGqtXr+aJJ54A4IknnuDKK68MRxlCCCE+pbCERmpqKpWVlQBUVlaSmpp6yv1cLhd5eXls\n2bLlrDnTEEKI4WzQ+jQ2bdpEWlpav/u///3v97vvoxbpy87OpqKigvHjx/P2229TUFDA4cOH++13\nyy23cOuttwKQlCRXrhNCiMGkhnorLCxUaWlpClBpaWmqsLDwE5/zxz/+UV199dWfuF9eXt6QH49s\nsskm20jfQv3sDEvz1IYNG1i7di0Aa9eu5eWXX+63T1xcHA6HA4DExETOP/989u/fP6R1CiGE6G/I\nEy0hIUG9+eab6tChQ2rTpk0qPj5eAWrBggVq3bp1ClDnnnuu2rNnj9q1a5fas2ePuummmwY0LWWT\nTTbZZOvZQv3s1Lp+GDXy8vLIyckJdxlCCDGihPrZOepCo7q6mqNHj4a7jNOSlJREbW1tuMsYFHJs\nI5Mc28hzuseVnZ1NSkpKSPuG/bRItk93ejgSNzm2kbnJsY28bbCPS5ZGF0IIETIJDSGEECEzgB+F\nuwjRY+fOneEuYdDIsY1Mcmwjz2Ae16jrCBdCCDF4pHlKCCFEyCQ0hqm77roLpRSJiYnhLmXA/OQn\nP2H37t3k5+fz+uuvk54+Oq5l/tBDD3HgwAF2797NX/7yF2JjY8Nd0oC55ppr2Lt3L6ZpsmDBgnCX\nMyBWrFhBYWEhRUVF3HPPPeEuZ8CsX7+eqqqq4DWIBlPYh4jJ1nfLyspSr732mjpy5IhKTEwMez0D\ntUVHRwd/vuOOO9QjjzwS9poGYlu+fLkyDEMB6mc/+5n62c9+FvaaBmqbNm2amjJlinrnnXfUggUL\nwl7PmW66rqvi4mI1fvx4Zbfb1a5du9T06dPDXtdAbEuXLlXz589XBQUFg/seIoadX/ziF3zve9/7\nyNV/R6qWlpbgz5GRkaPm+DZt2oRpmgBs3bqVrKysMFc0cAoLCzl06FC4yxgwixYtori4mNLSUnw+\nH88///youexCbm4u9fX1g/575Brhw8yqVasoLy9nz5494S5lUDzwwANcf/31NDU1cfHFF4e7nAF3\n00038cILL4S7DPERMjMzKSsrC94+fvw4ixcvDmNFI4+ERhh83LVG7rvvPi677LIwVDUwPu7YNmzY\nwA9+8AN+8IMfcO+993L77bfzox/9aOiLPA2fdFwA9913H36/n2eeeWaoyzsjoRybEL2FvS1OtsA2\na9YsVVVVpUpLS1Vpaany+Xzq6NGjKjU1Ney1DfQ2ZsyYQW97Hcpt7dq16oMPPlARERFhr2UwttHS\np7FkyRL12muvBW/fe++96t577w17XQO1ZWdnD8X/q/AfqGyn3kpLS0dVR/ikSZOCP99+++3qxRdf\nDHtNA7GtWLFC7du3TyUlJYW9lsHaRktoGIahSkpK1Lhx44Id4TNmzAh7XQO1SWic5dtoC42XXnpJ\nFRQUqN27d6sNGzaojIyMsNc0EFtRUZE6duyYys/PV/n5+aNmVBigrrzySlVWVqY6OztVZWVln2/p\nI3VbuXKlOnjwoCouLlb33Xdf2OsZqO3ZZ59VFRUVyuv1qrKyspCvQfRpN5kRLoQQImQy5FYIIUTI\nJDSEEEKETEJDCCFEyCQ0hBBChExCQwghRMgkNIQYYg888ADHjh3rsxaXECOFhIYQQ2zjxo0sWrQo\n3GUIcVokNIQYYHfffTd33HEHAA8//DBvvfUWABdffDFPP/0027Zto7KyMpwlCnHaJDSEGGC5ubks\nXboUgIULFxIVFYXNZmPp0qVs3rw5zNUJcWYkNIQYYDt27GDBggVER0fj8XjYsmULCxcuZOnSpeTm\n5oa7PCHOiCyNLsQA8/v9lJaWcsMNN/DBBx+wZ88eLr74YiZNmsSBAwfCXZ4QZ0TONIQYBLm5udx9\n991s3ryZ3NxcbrvtNvLz88NdlhBnTEJDiEGQm5tLeno6W7Zsobq6ms7OzmDT1IMPPkhZWRlut5uy\nsjLuv//+MFcrROhklVshhBAhkzMNIYQQIZPQEEIIETIJDSGEECGT0BBCCBEyCQ0hhBAhk9AQQggR\nMgkNIYQQIZPQEEIIEbL/D2uxjIy/zOXkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSpD7/D0fNly1rnKEAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "epoch: 100, M_loss: 6.9, M1_loss: 12.7, M2_loss: -5.8, N_loss: 230.90\n",
            "epoch: 100, rec_loss: 0.61653, kl_w: 3.41, kl_z: 44.83\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-125-d227fc266535>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     36\u001b[0m       \u001b[0mM2_\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mM2__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m       \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN_loss_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogN__\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mN_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mN_cost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogN\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mx_in\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx_in_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_out\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx_out_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mu_in\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mu_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraining_phase\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m       \u001b[0mN_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mN_loss_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m       \u001b[0mlogN_\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mlogN__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 950\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    951\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1171\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1173\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1174\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1348\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1350\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1351\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1354\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1355\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1356\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1357\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1358\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1339\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1340\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1341\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1343\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1427\u001b[0m     return tf_session.TF_SessionRun_wrapper(\n\u001b[1;32m   1428\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1429\u001b[0;31m         run_metadata)\n\u001b[0m\u001b[1;32m   1430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1431\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jgd8gA9xrXG6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "db1b6dc4-cb7b-4092-ec36-a677eb40288f"
      },
      "source": [
        "  plt.figure(2)\n",
        "  plt.scatter(wIxy_mean_[:,0],wIxy_mean_[:,1], c=U, s=6.0, cmap='viridis');\n",
        "  plt.xlabel('w1');\n",
        "  plt.ylabel('w2');"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEKCAYAAADuEgmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMgkNIYQQIZPQEEIIEbL/D2uxjIy/zOXkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXGBXpA_YzsB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "bf7bbd2c-4646-4281-e0ad-884e1afe6faf"
      },
      "source": [
        "  plt.figure(2)\n",
        "  plt.scatter(wIxy_mean_[:,0],wIxy_mean_[:,1], c=t, s=6.0, cmap='viridis');\n",
        "  plt.xlabel('w1');\n",
        "  plt.ylabel('w2');"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEKCAYAAADuEgmxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGkREpBuHBhER6cahQUREunFoEBGRbv8ALadnjqFROKYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPw-3s3HVgFO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "f9bb7ec5-5e4d-49e8-cca2-88ca3ced6514"
      },
      "source": [
        "plt.figure(3)\n",
        "plt.scatter(zIx_mean_[:,0],zIx_mean_[:,1], c=U, s=6.0, cmap='viridis');\n",
        "plt.xlabel('z1');\n",
        "plt.ylabel('z2');\n",
        "plt.show()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GisEnzwdVnqe",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "dc304d05-df65-4342-d34b-957cdef56145"
      },
      "source": [
        "plt.figure(3)\n",
        "plt.scatter(zIx_mean_[:,0],zIx_mean_[:,1], c=t, s=6.0, cmap='viridis');\n",
        "plt.xlabel('z1');\n",
        "plt.ylabel('z2');\n",
        "plt.show()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJEmSpD7/D0fNly1rnKEAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}