exp_colored_mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import logging\n",
    "import tensorflow as tf\n",
    "tf.get_logger().setLevel(logging.ERROR)\n",
    "import tensorflow.keras.backend as K\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras import activations\n",
    "from tensorflow.keras.models import Model,Sequential\n",
    "from tensorflow.keras.layers import Dense,Conv2D,BatchNormalization,Lambda,Activation,Input,Flatten,Conv2DTranspose,concatenate,Reshape,LeakyReLU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Set some options and read data based on selected options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#constants\n",
    "IMG_DIM = 28\n",
    "# manual options\n",
    "sensitive = 'color' # 'color' or 'class'\n",
    "prior_dist = 0 # 0: simple gaussian 1: guassian mixture Note: if prior generator model enabled, this option is not used\n",
    "mnist_type = 'balanced' # 'balanced' 'unbalanced\n",
    "data_path = \"./Colored_MNIST\"\n",
    "\n",
    "# automatically set variables based on selected options\n",
    "utility = 'class' if sensitive=='color' else 'color'\n",
    "prior_type = 'Isotropic' if prior_dist == 0 else 'Mixture'\n",
    "data_type = 'biased' if mnist_type=='unbalanced' else 'uniform'\n",
    "\n",
    "\n",
    "\n",
    "data = np.load(f\"{data_path}/colored_mnist_{mnist_type}.npz\")\n",
    "\n",
    "x_train = data['x_train']\n",
    "x_train = x_train.astype(np.float32) / 255.0\n",
    "s_train = data['s_train'] if(sensitive == 'color') else data['y_train']\n",
    "u_train = data['s_train'] if(sensitive == 'class') else data['y_train']\n",
    "\n",
    "x_test = data['x_test']\n",
    "x_test = x_test.astype(np.float32) / 255.0\n",
    "s_test = data['s_test'] if(sensitive == 'color') else data['y_test']\n",
    "u_test = data['s_test'] if(sensitive == 'class') else data['y_test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/razeghi/ICASSP'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "cwd = os.getcwd()\n",
    "cwd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# one hot S and U in order to ready them for the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot(labels, num_labels):\n",
    "    num_labels_data = labels.shape[0]\n",
    "    one_hot_encoding = np.zeros((num_labels_data,num_labels))\n",
    "    one_hot_encoding[np.arange(num_labels_data),labels] = 1\n",
    "    one_hot_encoding = np.reshape(one_hot_encoding, [-1, num_labels])\n",
    "    return one_hot_encoding\n",
    "\n",
    "DIM_S = 3 if(sensitive == 'color') else 10\n",
    "DIM_U = 10 if(sensitive == 'color') else 3\n",
    "\n",
    "u_train = one_hot(u_train, DIM_U).astype(np.float32)\n",
    "s_train = one_hot(s_train, DIM_S).astype(np.float32)\n",
    "u_test = one_hot(u_test, DIM_U).astype(np.float32)\n",
    "s_test = one_hot(s_test, DIM_S).astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sampling(args):\n",
    "    \"\"\"Reparameterization trick by sampling from an isotropic unit Gaussian.\n",
    "\n",
    "    # Arguments\n",
    "        args (tensor): mean and log of variance of Q(z|X)\n",
    "\n",
    "    # Returns\n",
    "        z (tensor): sampled latent vector\n",
    "    \"\"\"\n",
    "\n",
    "    z_mean, z_log_var = args\n",
    "    batch = K.shape(z_mean)[0]\n",
    "    dim = K.int_shape(z_mean)[1]\n",
    "    # by default, random_normal has mean = 0 and std = 1.0\n",
    "    epsilon = K.random_normal(shape=(batch, dim))\n",
    "    return z_mean + K.exp(0.5 * z_log_var) * epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_encoder(DIM_Z, input_x):\n",
    "    stride = 2\n",
    "#     input_x = Input( shape = [IMG_DIM,IMG_DIM,3], name=\"x\" )\n",
    "\n",
    "    #first hidden layer\n",
    "    x = Conv2D(64, 5, strides=stride, padding=\"same\", name=\"enc_h1\")(input_x)\n",
    "    x = BatchNormalization(name=\"enc_h1_normalized\")(x)\n",
    "    x = Activation(LeakyReLU(), name=\"enc_h1_activation\")(x)\n",
    "    #second hidden layer\n",
    "    x = Conv2D(128, 5, strides=stride, padding=\"same\", name=\"enc_h2\")(x)\n",
    "    x = BatchNormalization(name=\"enc_h2_normalized\")(x)\n",
    "    x = Activation(LeakyReLU(), name=\"enc_h2_activation\")(x)\n",
    "\n",
    "    shape = K.int_shape(x)\n",
    "    x = Flatten()(x)\n",
    "    x = Dense(DIM_Z*4, name=\"enc_dense_1\")(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Activation('tanh')(x)\n",
    "\n",
    "    z_mean = Dense(DIM_Z, name=\"z_mean\")(x)\n",
    "    z_log_sigma_sq = Dense(DIM_Z, name=\"z_sigma\")(x)\n",
    "    z = Lambda(sampling, output_shape=DIM_Z, name='z')([z_mean, z_log_sigma_sq])\n",
    "    \n",
    "#     prior_loss = K.mean(-0.5 * K.sum(1 + z_log_sigma_sq - K.square(z_mean) - K.exp(z_log_sigma_sq), axis=-1))\n",
    "    \n",
    "    encoder = Model(input_x, z, name = \"Encoder\")\n",
    "#     encoder.add_loss((alpha+beta) * prior_loss)\n",
    "    \n",
    "    return (encoder, z_mean, z_log_sigma_sq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_uncertainty_model(DIM_Z):\n",
    "    model = Sequential(name=\"Uncertainty_Decoder\")\n",
    "    model.add(Dense(DIM_Z * 4, input_dim=DIM_Z))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    \n",
    "#     model.add(Dense(DIM_Z * 4))\n",
    "#     model.add(BatchNormalization())\n",
    "#     model.add(LeakyReLU(alpha=0.2))\n",
    "    \n",
    "    model.add(Dense(DIM_S, activation='softmax', name=\"s_hat\"))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_utility_model(DIM_Z):\n",
    "    model = Sequential(name=\"Utility_Decoder\")\n",
    "    model.add(Dense(DIM_Z * 4, input_dim=DIM_Z))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "\n",
    "#         model.add(Dense(DIM_Z * 4))\n",
    "#         model.add(BatchNormalization())\n",
    "#         model.add(LeakyReLU(alpha=0.2))\n",
    "\n",
    "    model.add(Dense(DIM_U, activation='softmax', name=\"u_hat\"))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_z_discriminator(DIM_Z):\n",
    "    model = Sequential(name=\"Latent_Space_Discriminator\")\n",
    "    \n",
    "    model.add(Dense(512, input_dim=DIM_Z))\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    \n",
    "    model.add(Dense(256))\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    \n",
    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_utility_discriminator():\n",
    "    model = Sequential(name=\"Attribute_Class_Discriminator\")\n",
    "\n",
    "    model.add(Dense(DIM_U * 8, input_dim=DIM_U))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    \n",
    "    model.add(Dense(DIM_U * 8))\n",
    "    model.add(BatchNormalization())\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    \n",
    "    model.add(Dense(1, activation=\"sigmoid\"))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define VARIATIONAL NESTED LEAKAGE Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## utility and reconstruction losses\n",
    "\n",
    "def loss_u(u_true, u_pred):\n",
    "    return K.mean(K.sum(K.square(u_true-u_pred), axis=-1))\n",
    "\n",
    "bce = tf.keras.losses.BinaryCrossentropy()\n",
    "# Weighted cross-entropy loss\n",
    "def loss_wce(coef):\n",
    "    def loss(y, y_pred):\n",
    "         return coef * bce(y, y_pred)\n",
    "    return loss\n",
    "\n",
    "############################################################################\n",
    "def get_full_model(DIM_Z, beta, learning_rate=0.0001):\n",
    "    ########## Inputs\n",
    "    input_x = Input( shape=[IMG_DIM,IMG_DIM,3], name=\"x\" )\n",
    "    input_z = Input( shape = (DIM_Z,), name=\"z\" )\n",
    "\n",
    "    ########## Define AE: Encoder, Utility Decoder\n",
    "    encoder,z_mean,z_log_sigma_sq = get_encoder(DIM_Z, input_x)\n",
    "    utility_decoder = get_utility_model(DIM_Z)\n",
    "\n",
    "    z = encoder(input_x)\n",
    "    u_hat = utility_decoder(z)\n",
    "\n",
    "    autoencoder = Model(input_x, u_hat, name=\"Autoencoder\")\n",
    "    prior_loss = beta * K.mean(-0.5 * K.sum(1 + z_log_sigma_sq - K.square(z_mean) - K.exp(z_log_sigma_sq), axis=-1))\n",
    "    prior_loss = tf.identity(prior_loss, name=\"kl_loss\")\n",
    "    autoencoder.add_loss(prior_loss)\n",
    "    autoencoder.compile(loss=loss_u, optimizer=tf.keras.optimizers.Adam(lr=learning_rate*5))\n",
    "\n",
    "    ########## Define Latent Space Discriminator\n",
    "    z_discriminator = get_z_discriminator(DIM_Z)\n",
    "    z_discriminator.compile(loss=loss_wce(0.1*beta), optimizer=tf.keras.optimizers.Adam(lr=learning_rate))\n",
    "\n",
    "    ##########\n",
    "    z_discriminator.trainable = False\n",
    "    encoder_zdiscriminator = Model(input_x, z_discriminator(encoder(input_x)), name=\"encoder_zdiscriminator\")\n",
    "    encoder_zdiscriminator.compile(loss=loss_wce(-0.1*beta), optimizer=tf.keras.optimizers.Adam(lr=learning_rate))\n",
    "\n",
    "    ########## Define Utility Discriminator\n",
    "    u_dircriminator = get_utility_discriminator()\n",
    "    u_dircriminator.compile(loss=loss_wce(0.1), optimizer=tf.keras.optimizers.Adam(lr=learning_rate))\n",
    "\n",
    "    ##########\n",
    "    #u_dircriminator should train separately\n",
    "    u_dircriminator.trainable = False\n",
    "    decoder_udiscriminator = Model(input_z, u_dircriminator(utility_decoder(input_z)), name=\"decoder_udiscriminator\")\n",
    "    decoder_udiscriminator.compile(loss=loss_wce(-0.1), optimizer=tf.keras.optimizers.Adam(lr=learning_rate))\n",
    "    \n",
    "    return encoder, utility_decoder, autoencoder, z_discriminator, encoder_zdiscriminator, u_dircriminator, decoder_udiscriminator\n",
    "\n",
    "\n",
    "############################################################################\n",
    "# Attacker model: Encoder (Freezed) to S-Decoder\n",
    "############################################################################\n",
    "def get_attacker_ae(DIM_Z, encoder, learning_rate=0.001):\n",
    "    input_x = Input( shape=[IMG_DIM,IMG_DIM,3], name=\"x\" )\n",
    "    encoder.trainable = False\n",
    "    uncertainty_decoder = get_uncertainty_model(DIM_Z)\n",
    "    ae_attacker = Model(input_x, uncertainty_decoder(encoder(input_x)), name=\"Autoencoder_Attacker\")\n",
    "    ae_attacker.compile(loss=\"mse\", optimizer=tf.keras.optimizers.Adam(lr=learning_rate))\n",
    "    return ae_attacker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pre-training implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def pre_train_ae(dim_z, beta, force_train=False, max_itr=50, batch_size=1024, verbose=2):\n",
    "    info_str = f\"d_{dim_z}_beta_{beta}_u_{utility}_s_{sensitive}_{data_type}_{prior_type}\"\n",
    "    if not os.path.exists(f\"saved_models/mnist_pretrain_{info_str}.h5\") or force_train:\n",
    "        print(f\"Pre-Training with {info_str}\")\n",
    "        autoencoder.fit(x_train, u_train, batch_size=batch_size, epochs=max_itr, shuffle=True, verbose=verbose)\n",
    "        autoencoder.save_weights(f\"saved_models/mnist_pretrain_{info_str}.h5\")\n",
    "    else:\n",
    "        print(f\"Loading model from file with {info_str}\")\n",
    "        autoencoder.load_weights(f\"saved_models/mnist_pretrain_{info_str}.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(sum([np.sum(w) for w in encoder.get_weights()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Full model training loop "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.sampler import gaussian, gaussian_mixture\n",
    "\n",
    "def sample_prior(latent_dim, batch_size):\n",
    "    if prior_dist == 0:  \n",
    "        return gaussian(batch_size, latent_dim)\n",
    "    elif prior_dist == 1:\n",
    "        return gaussian_mixture(batch_size, latent_dim, num_labels=min(10, DIM_U))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "def main_train(dim_z, max_itr=1000, batch_size=1024, z_disc_enabled=False,u_disc_enabled=False,verbose=0):\n",
    "    ones = np.ones((batch_size, 1))\n",
    "    zeros = np.zeros((batch_size, 1))\n",
    "    start_time = time.time()\n",
    "    for epoch in range(max_itr):\n",
    "        start_time_epoch = time.time()\n",
    "        # ---------------------\n",
    "        #  1- Train the Encoder and the Utility Decoder\n",
    "        # ---------------------\n",
    "        # Select a random batch of images\n",
    "        idx = np.random.randint(0, x_train.shape[0], batch_size)\n",
    "        x = x_train[idx]\n",
    "        u = u_train[idx]\n",
    "        ae_loss = autoencoder.train_on_batch(x, u)\n",
    " \n",
    "        if z_disc_enabled:\n",
    "        # ---------------------\n",
    "        #  2- Train the Latent Space Discriminator\n",
    "        # ---------------------\n",
    "            latent_prior = sample_prior(dim_z, batch_size)\n",
    "\n",
    "            idx = np.random.randint(0, x_train.shape[0], batch_size)\n",
    "            x = x_train[idx]\n",
    "            latent_enc = encoder(x)\n",
    "\n",
    "            d_loss_prior = z_discriminator.train_on_batch(latent_prior, zeros)\n",
    "            d_loss_enc = z_discriminator.train_on_batch(latent_enc, ones)\n",
    "            dz_loss = d_loss_prior + d_loss_enc\n",
    "\n",
    "        # ---------------------\n",
    "        # 3- Train the Encoder Adversarially\n",
    "        # ---------------------\n",
    "\n",
    "            idx = np.random.randint(0, x_train.shape[0], batch_size)\n",
    "            x = x_train[idx]\n",
    "            edz_loss = encoder_zdiscriminator.train_on_batch(x, ones)\n",
    "            \n",
    "        else:\n",
    "            dz_loss = 0\n",
    "            edz_loss = 0\n",
    "\n",
    "        if u_disc_enabled:\n",
    "        # ---------------------\n",
    "        #  4- Train the Attribute_Class Discriminator \n",
    "        # ---------------------\n",
    "            \n",
    "            latent = sample_prior(dim_z, batch_size)\n",
    "            u_dec = utility_decoder(latent)\n",
    "\n",
    "            idx = np.random.randint(0, x_train.shape[0], batch_size)\n",
    "            u = u_train[idx]\n",
    "\n",
    "            d_loss_real = u_dircriminator.train_on_batch(u, ones)\n",
    "            d_loss_fake = u_dircriminator.train_on_batch(u_dec, zeros)\n",
    "            du_loss = d_loss_real + d_loss_fake\n",
    "\n",
    "        # ---------------------\n",
    "        #  5- Train the Utility Decoder Adversarially\n",
    "        # ---------------------  \n",
    "            latent = sample_prior(dim_z, batch_size)\n",
    "            udu_loss = decoder_udiscriminator.train_on_batch(latent, zeros)\n",
    "        else:\n",
    "            udu_loss = 0\n",
    "            du_loss = 0\n",
    "\n",
    "        # ---------------------\n",
    "        #  Print stats info\n",
    "        # ---------------------\n",
    "        if verbose != 0 and epoch % 100 == 0:\n",
    "            print(f\"{epoch}, ae:{ae_loss:.4f}, dz:{dz_loss:.5f}, edz:{edz_loss:.5f}, du:{du_loss:.4f}, udu:{udu_loss:.4f}\")\n",
    "            print(f\"One epoch execution time: {(time.time() - start_time_epoch):.5} seconds\")\n",
    "\n",
    "    total_time = (time.time() - start_time) / 60\n",
    "    print(f\"Total execution time for full model loop: {total_time} minutes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Experiment 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model from file with d_8_beta_1e-09_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1591, udu:-0.0845\n",
      "One epoch execution time: 9.0476 seconds\n",
      "100, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1480, udu:-0.0796\n",
      "One epoch execution time: 0.14121 seconds\n",
      "200, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1407, udu:-0.0965\n",
      "One epoch execution time: 0.13809 seconds\n",
      "300, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1382, udu:-0.0991\n",
      "One epoch execution time: 0.14145 seconds\n",
      "400, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1316, udu:-0.0965\n",
      "One epoch execution time: 0.14635 seconds\n",
      "Total execution time for full model loop: 1.3177427689234416 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.59\n",
      "Evaluate Attacker performance\n",
      "s acc:  12.47\n",
      "Loading model from file with d_8_beta_1e-08_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1511, udu:-0.0700\n",
      "One epoch execution time: 3.4097 seconds\n",
      "100, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1373, udu:-0.0599\n",
      "One epoch execution time: 0.14325 seconds\n",
      "200, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1324, udu:-0.0606\n",
      "One epoch execution time: 0.15572 seconds\n",
      "300, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1305, udu:-0.0656\n",
      "One epoch execution time: 0.14457 seconds\n",
      "400, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1300, udu:-0.0673\n",
      "One epoch execution time: 0.14417 seconds\n",
      "Total execution time for full model loop: 1.2540591438611348 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.61\n",
      "Evaluate Attacker performance\n",
      "s acc:  13.77\n",
      "Loading model from file with d_8_beta_1e-07_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1637, udu:-0.0611\n",
      "One epoch execution time: 4.0907 seconds\n",
      "100, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1380, udu:-0.0638\n",
      "One epoch execution time: 0.1397 seconds\n",
      "200, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1356, udu:-0.0697\n",
      "One epoch execution time: 0.1472 seconds\n",
      "300, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1344, udu:-0.0674\n",
      "One epoch execution time: 0.14304 seconds\n",
      "400, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1334, udu:-0.0660\n",
      "One epoch execution time: 0.14023 seconds\n",
      "Total execution time for full model loop: 1.2574063460032145 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.2\n",
      "Evaluate Attacker performance\n",
      "s acc:  12.76\n",
      "Loading model from file with d_8_beta_1e-06_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0001, dz:0.00000, edz:-0.00000, du:0.1470, udu:-0.0714\n",
      "One epoch execution time: 3.656 seconds\n",
      "100, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1377, udu:-0.0751\n",
      "One epoch execution time: 0.14967 seconds\n",
      "200, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1365, udu:-0.0880\n",
      "One epoch execution time: 0.14436 seconds\n",
      "300, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1351, udu:-0.0824\n",
      "One epoch execution time: 0.15236 seconds\n",
      "400, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1342, udu:-0.0793\n",
      "One epoch execution time: 0.14725 seconds\n",
      "Total execution time for full model loop: 1.2559456825256348 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  11.08\n",
      "Evaluate Attacker performance\n",
      "s acc:  17.48\n",
      "Loading model from file with d_8_beta_1e-05_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0002, dz:0.00000, edz:-0.00000, du:0.1476, udu:-0.0668\n",
      "One epoch execution time: 3.3969 seconds\n",
      "100, ae:0.0001, dz:0.00000, edz:-0.00000, du:0.1368, udu:-0.0639\n",
      "One epoch execution time: 0.14786 seconds\n",
      "200, ae:0.0002, dz:0.00000, edz:-0.00000, du:0.1357, udu:-0.0708\n",
      "One epoch execution time: 0.14166 seconds\n",
      "300, ae:0.0001, dz:0.00000, edz:-0.00000, du:0.1345, udu:-0.0706\n",
      "One epoch execution time: 0.1435 seconds\n",
      "400, ae:0.0001, dz:0.00000, edz:-0.00000, du:0.1343, udu:-0.0698\n",
      "One epoch execution time: 0.14636 seconds\n",
      "Total execution time for full model loop: 1.2729182521502176 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.86\n",
      "Evaluate Attacker performance\n",
      "s acc:  15.89\n",
      "Loading model from file with d_8_beta_0.0001_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0006, dz:0.00001, edz:-0.00001, du:0.1563, udu:-0.0802\n",
      "One epoch execution time: 3.4046 seconds\n",
      "100, ae:0.0006, dz:0.00001, edz:-0.00001, du:0.1428, udu:-0.0478\n",
      "One epoch execution time: 0.14224 seconds\n",
      "200, ae:0.0006, dz:0.00001, edz:-0.00001, du:0.1400, udu:-0.0582\n",
      "One epoch execution time: 0.14223 seconds\n",
      "300, ae:0.0007, dz:0.00001, edz:-0.00001, du:0.1366, udu:-0.0694\n",
      "One epoch execution time: 0.13633 seconds\n",
      "400, ae:0.0005, dz:0.00001, edz:-0.00001, du:0.1362, udu:-0.0754\n",
      "One epoch execution time: 0.13872 seconds\n",
      "Total execution time for full model loop: 1.24934610525767 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  11.03\n",
      "Evaluate Attacker performance\n",
      "s acc:  15.659999999999998\n",
      "Loading model from file with d_8_beta_0.0005_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0020, dz:0.00007, edz:-0.00003, du:0.1468, udu:-0.0760\n",
      "One epoch execution time: 3.3032 seconds\n",
      "100, ae:0.0018, dz:0.00007, edz:-0.00003, du:0.1405, udu:-0.0775\n",
      "One epoch execution time: 0.13745 seconds\n",
      "200, ae:0.0020, dz:0.00006, edz:-0.00003, du:0.1383, udu:-0.0826\n",
      "One epoch execution time: 0.14442 seconds\n",
      "300, ae:0.0018, dz:0.00006, edz:-0.00003, du:0.1380, udu:-0.0813\n",
      "One epoch execution time: 0.14816 seconds\n",
      "400, ae:0.0015, dz:0.00006, edz:-0.00003, du:0.1375, udu:-0.0764\n",
      "One epoch execution time: 0.14272 seconds\n",
      "Total execution time for full model loop: 1.2521699825922648 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.209999999999999\n",
      "Evaluate Attacker performance\n",
      "s acc:  13.020000000000001\n",
      "Loading model from file with d_8_beta_0.001_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0035, dz:0.00014, edz:-0.00007, du:0.1528, udu:-0.0741\n",
      "One epoch execution time: 3.2938 seconds\n",
      "100, ae:0.0033, dz:0.00013, edz:-0.00007, du:0.1397, udu:-0.0562\n",
      "One epoch execution time: 0.14475 seconds\n",
      "200, ae:0.0034, dz:0.00013, edz:-0.00006, du:0.1375, udu:-0.0619\n",
      "One epoch execution time: 0.14719 seconds\n",
      "300, ae:0.0031, dz:0.00013, edz:-0.00006, du:0.1380, udu:-0.0647\n",
      "One epoch execution time: 0.14797 seconds\n",
      "400, ae:0.0029, dz:0.00013, edz:-0.00006, du:0.1371, udu:-0.0645\n",
      "One epoch execution time: 0.14788 seconds\n",
      "Total execution time for full model loop: 1.2520255088806151 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.7\n",
      "Evaluate Attacker performance\n",
      "s acc:  13.819999999999999\n",
      "Loading model from file with d_8_beta_0.005_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0125, dz:0.00070, edz:-0.00038, du:0.1512, udu:-0.0678\n",
      "One epoch execution time: 3.3809 seconds\n",
      "100, ae:0.0120, dz:0.00069, edz:-0.00034, du:0.1395, udu:-0.0670\n",
      "One epoch execution time: 0.14897 seconds\n",
      "200, ae:0.0117, dz:0.00069, edz:-0.00034, du:0.1373, udu:-0.0685\n",
      "One epoch execution time: 0.14764 seconds\n",
      "300, ae:0.0126, dz:0.00069, edz:-0.00034, du:0.1370, udu:-0.0670\n",
      "One epoch execution time: 0.14162 seconds\n",
      "400, ae:0.0137, dz:0.00068, edz:-0.00034, du:0.1368, udu:-0.0670\n",
      "One epoch execution time: 0.14325 seconds\n",
      "Total execution time for full model loop: 1.241486648718516 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  9.89\n",
      "Evaluate Attacker performance\n",
      "s acc:  12.690000000000001\n",
      "Loading model from file with d_8_beta_0.01_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0, ae:0.0244, dz:0.00140, edz:-0.00068, du:0.1802, udu:-0.0665\n",
      "One epoch execution time: 3.88 seconds\n",
      "100, ae:0.0220, dz:0.00138, edz:-0.00068, du:0.1440, udu:-0.1151\n",
      "One epoch execution time: 0.14095 seconds\n",
      "200, ae:0.0248, dz:0.00138, edz:-0.00068, du:0.1401, udu:-0.1157\n",
      "One epoch execution time: 0.14075 seconds\n",
      "300, ae:0.0241, dz:0.00137, edz:-0.00067, du:0.1381, udu:-0.1014\n",
      "One epoch execution time: 0.13869 seconds\n",
      "400, ae:0.0225, dz:0.00136, edz:-0.00067, du:0.1371, udu:-0.0915\n",
      "One epoch execution time: 0.14108 seconds\n",
      "Total execution time for full model loop: 1.2425715525945027 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.73\n",
      "Evaluate Attacker performance\n",
      "s acc:  12.24\n",
      "Loading model from file with d_8_beta_0.05_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0954, dz:0.00703, edz:-0.00369, du:0.1659, udu:-0.0673\n",
      "One epoch execution time: 3.6221 seconds\n",
      "100, ae:0.0934, dz:0.00693, edz:-0.00344, du:0.1370, udu:-0.0747\n",
      "One epoch execution time: 0.138 seconds\n",
      "200, ae:0.1025, dz:0.00692, edz:-0.00344, du:0.1351, udu:-0.0774\n",
      "One epoch execution time: 0.14035 seconds\n",
      "300, ae:0.0992, dz:0.00691, edz:-0.00343, du:0.1364, udu:-0.0751\n",
      "One epoch execution time: 0.14779 seconds\n",
      "400, ae:0.0950, dz:0.00690, edz:-0.00343, du:0.1349, udu:-0.0725\n",
      "One epoch execution time: 0.14405 seconds\n",
      "Total execution time for full model loop: 1.2581148703893026 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.84\n",
      "Evaluate Attacker performance\n",
      "s acc:  11.27\n",
      "Loading model from file with d_8_beta_0.1_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.1684, dz:0.01406, edz:-0.00715, du:0.1830, udu:-0.0657\n",
      "One epoch execution time: 3.2827 seconds\n",
      "100, ae:0.1756, dz:0.01388, edz:-0.00687, du:0.1478, udu:-0.0540\n",
      "One epoch execution time: 0.14736 seconds\n",
      "200, ae:0.1802, dz:0.01382, edz:-0.00688, du:0.1450, udu:-0.0520\n",
      "One epoch execution time: 0.14738 seconds\n",
      "300, ae:0.1763, dz:0.01381, edz:-0.00686, du:0.1434, udu:-0.0509\n",
      "One epoch execution time: 0.14436 seconds\n",
      "400, ae:0.1709, dz:0.01380, edz:-0.00687, du:0.1413, udu:-0.0514\n",
      "One epoch execution time: 0.14493 seconds\n",
      "Total execution time for full model loop: 1.2328725496927897 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.23\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.96\n",
      "Loading model from file with d_8_beta_0.5_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.5983, dz:0.07084, edz:-0.03699, du:0.1707, udu:-0.0858\n",
      "One epoch execution time: 3.1065 seconds\n",
      "100, ae:0.5905, dz:0.06938, edz:-0.03456, du:0.1386, udu:-0.1029\n",
      "One epoch execution time: 0.14231 seconds\n",
      "200, ae:0.6069, dz:0.06938, edz:-0.03457, du:0.1358, udu:-0.0955\n",
      "One epoch execution time: 0.14506 seconds\n",
      "300, ae:0.6076, dz:0.06944, edz:-0.03460, du:0.1345, udu:-0.0870\n",
      "One epoch execution time: 0.14481 seconds\n",
      "400, ae:0.6080, dz:0.06933, edz:-0.03433, du:0.1327, udu:-0.0843\n",
      "One epoch execution time: 0.13675 seconds\n",
      "Total execution time for full model loop: 1.2441580732663473 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.190000000000001\n",
      "Evaluate Attacker performance\n",
      "s acc:  11.19\n",
      "Loading model from file with d_8_beta_1.0_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.6695, dz:0.14050, edz:-0.06672, du:0.1536, udu:-0.0634\n",
      "One epoch execution time: 4.1298 seconds\n",
      "100, ae:0.6711, dz:0.13866, edz:-0.06947, du:0.1376, udu:-0.0732\n",
      "One epoch execution time: 0.15069 seconds\n",
      "200, ae:0.6692, dz:0.13841, edz:-0.06971, du:0.1333, udu:-0.0837\n",
      "One epoch execution time: 0.14379 seconds\n",
      "300, ae:0.6685, dz:0.13867, edz:-0.06963, du:0.1304, udu:-0.0948\n",
      "One epoch execution time: 0.144 seconds\n",
      "400, ae:0.6706, dz:0.13883, edz:-0.06922, du:0.1278, udu:-0.0998\n",
      "One epoch execution time: 0.14512 seconds\n",
      "Total execution time for full model loop: 1.288160498936971 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.040000000000001\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.9\n",
      "Loading model from file with d_64_beta_1e-09_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1624, udu:-0.0668\n",
      "One epoch execution time: 3.4574 seconds\n",
      "100, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1360, udu:-0.0851\n",
      "One epoch execution time: 0.15963 seconds\n",
      "200, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1300, udu:-0.0863\n",
      "One epoch execution time: 0.17522 seconds\n",
      "300, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1261, udu:-0.0782\n",
      "One epoch execution time: 0.16801 seconds\n",
      "400, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1252, udu:-0.0721\n",
      "One epoch execution time: 0.16145 seconds\n",
      "Total execution time for full model loop: 1.4140459577242532 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  16.509999999999998\n",
      "Evaluate Attacker performance\n",
      "s acc:  25.91\n",
      "Loading model from file with d_64_beta_1e-08_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.2027, udu:-0.0773\n",
      "One epoch execution time: 3.321 seconds\n",
      "100, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1452, udu:-0.0819\n",
      "One epoch execution time: 0.16022 seconds\n",
      "200, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1360, udu:-0.0866\n",
      "One epoch execution time: 0.15965 seconds\n",
      "300, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1303, udu:-0.0977\n",
      "One epoch execution time: 0.15161 seconds\n",
      "400, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1285, udu:-0.0989\n",
      "One epoch execution time: 0.15306 seconds\n",
      "Total execution time for full model loop: 1.3862460573514304 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  18.33\n",
      "Evaluate Attacker performance\n",
      "s acc:  27.47\n",
      "Loading model from file with d_64_beta_1e-07_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1488, udu:-0.0703\n",
      "One epoch execution time: 3.2978 seconds\n",
      "100, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1350, udu:-0.0742\n",
      "One epoch execution time: 0.17062 seconds\n",
      "200, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1301, udu:-0.0752\n",
      "One epoch execution time: 0.15592 seconds\n",
      "300, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1286, udu:-0.0739\n",
      "One epoch execution time: 0.15463 seconds\n",
      "400, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1266, udu:-0.0750\n",
      "One epoch execution time: 0.16507 seconds\n",
      "Total execution time for full model loop: 1.3972167452176412 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  11.61\n",
      "Evaluate Attacker performance\n",
      "s acc:  24.19\n",
      "Loading model from file with d_64_beta_1e-06_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1453, udu:-0.0723\n",
      "One epoch execution time: 3.3348 seconds\n",
      "100, ae:0.0001, dz:0.00000, edz:-0.00000, du:0.1300, udu:-0.0788\n",
      "One epoch execution time: 0.15251 seconds\n",
      "200, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1243, udu:-0.0736\n",
      "One epoch execution time: 0.15526 seconds\n",
      "300, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1243, udu:-0.0710\n",
      "One epoch execution time: 0.14737 seconds\n",
      "400, ae:0.0000, dz:0.00000, edz:-0.00000, du:0.1243, udu:-0.0715\n",
      "One epoch execution time: 0.15981 seconds\n",
      "Total execution time for full model loop: 1.3308987657229105 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  16.17\n",
      "Evaluate Attacker performance\n",
      "s acc:  21.740000000000002\n",
      "Loading model from file with d_64_beta_1e-05_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0002, dz:0.00000, edz:-0.00000, du:0.1585, udu:-0.0760\n",
      "One epoch execution time: 4.2149 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100, ae:0.0003, dz:0.00000, edz:-0.00000, du:0.1339, udu:-0.0525\n",
      "One epoch execution time: 0.16577 seconds\n",
      "200, ae:0.0002, dz:0.00000, edz:-0.00000, du:0.1303, udu:-0.0607\n",
      "One epoch execution time: 0.15428 seconds\n",
      "300, ae:0.0002, dz:0.00000, edz:-0.00000, du:0.1278, udu:-0.0704\n",
      "One epoch execution time: 0.14692 seconds\n",
      "400, ae:0.0005, dz:0.00000, edz:-0.00000, du:0.1307, udu:-0.0718\n",
      "One epoch execution time: 0.16193 seconds\n",
      "Total execution time for full model loop: 1.3513233304023742 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  11.73\n",
      "Evaluate Attacker performance\n",
      "s acc:  21.13\n",
      "Loading model from file with d_64_beta_0.0001_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0006, dz:0.00001, edz:-0.00001, du:0.1505, udu:-0.0682\n",
      "One epoch execution time: 3.2915 seconds\n",
      "100, ae:0.0006, dz:0.00001, edz:-0.00001, du:0.1403, udu:-0.0874\n",
      "One epoch execution time: 0.15904 seconds\n",
      "200, ae:0.0006, dz:0.00001, edz:-0.00001, du:0.1340, udu:-0.0898\n",
      "One epoch execution time: 0.15883 seconds\n",
      "300, ae:0.0006, dz:0.00001, edz:-0.00001, du:0.1340, udu:-0.0903\n",
      "One epoch execution time: 0.15149 seconds\n",
      "400, ae:0.0005, dz:0.00001, edz:-0.00001, du:0.1386, udu:-0.0865\n",
      "One epoch execution time: 0.16697 seconds\n",
      "Total execution time for full model loop: 1.3575763463974 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.440000000000001\n",
      "Evaluate Attacker performance\n",
      "s acc:  15.42\n",
      "Loading model from file with d_64_beta_0.0005_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0015, dz:0.00007, edz:-0.00005, du:0.1879, udu:-0.0743\n",
      "One epoch execution time: 4.1482 seconds\n",
      "100, ae:0.0018, dz:0.00007, edz:-0.00003, du:0.1381, udu:-0.0807\n",
      "One epoch execution time: 0.16788 seconds\n",
      "200, ae:0.0016, dz:0.00007, edz:-0.00003, du:0.1318, udu:-0.0876\n",
      "One epoch execution time: 0.16545 seconds\n",
      "300, ae:0.0018, dz:0.00007, edz:-0.00003, du:0.1289, udu:-0.0788\n",
      "One epoch execution time: 0.17889 seconds\n",
      "400, ae:0.0017, dz:0.00007, edz:-0.00003, du:0.1288, udu:-0.0727\n",
      "One epoch execution time: 0.16789 seconds\n",
      "Total execution time for full model loop: 1.4078996499379477 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.8\n",
      "Evaluate Attacker performance\n",
      "s acc:  14.549999999999999\n",
      "Loading model from file with d_64_beta_0.001_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0029, dz:0.00014, edz:-0.00006, du:0.1515, udu:-0.0635\n",
      "One epoch execution time: 3.2693 seconds\n",
      "100, ae:0.0033, dz:0.00014, edz:-0.00007, du:0.1400, udu:-0.0528\n",
      "One epoch execution time: 0.15386 seconds\n",
      "200, ae:0.0028, dz:0.00014, edz:-0.00007, du:0.1386, udu:-0.0585\n",
      "One epoch execution time: 0.15445 seconds\n",
      "300, ae:0.0033, dz:0.00014, edz:-0.00007, du:0.1374, udu:-0.0652\n",
      "One epoch execution time: 0.14588 seconds\n",
      "400, ae:0.0029, dz:0.00014, edz:-0.00007, du:0.1373, udu:-0.0624\n",
      "One epoch execution time: 0.14803 seconds\n",
      "Total execution time for full model loop: 1.3291112820307414 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.76\n",
      "Evaluate Attacker performance\n",
      "s acc:  14.760000000000002\n",
      "Loading model from file with d_64_beta_0.005_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0119, dz:0.00073, edz:-0.00047, du:0.1479, udu:-0.0569\n",
      "One epoch execution time: 3.8741 seconds\n",
      "100, ae:0.0131, dz:0.00070, edz:-0.00035, du:0.1361, udu:-0.0620\n",
      "One epoch execution time: 0.1611 seconds\n",
      "200, ae:0.0126, dz:0.00070, edz:-0.00035, du:0.1349, udu:-0.0670\n",
      "One epoch execution time: 0.15847 seconds\n",
      "300, ae:0.0128, dz:0.00070, edz:-0.00035, du:0.1371, udu:-0.0695\n",
      "One epoch execution time: 0.15412 seconds\n",
      "400, ae:0.0123, dz:0.00069, edz:-0.00034, du:0.1351, udu:-0.0683\n",
      "One epoch execution time: 0.15704 seconds\n",
      "Total execution time for full model loop: 1.3819538474082946 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.73\n",
      "Evaluate Attacker performance\n",
      "s acc:  13.4\n",
      "Loading model from file with d_64_beta_0.01_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0232, dz:0.00143, edz:-0.00073, du:0.1625, udu:-0.0724\n",
      "One epoch execution time: 3.1154 seconds\n",
      "100, ae:0.0228, dz:0.00139, edz:-0.00069, du:0.1510, udu:-0.0726\n",
      "One epoch execution time: 0.1567 seconds\n",
      "200, ae:0.0226, dz:0.00139, edz:-0.00069, du:0.1454, udu:-0.0791\n",
      "One epoch execution time: 0.16558 seconds\n",
      "300, ae:0.0221, dz:0.00139, edz:-0.00069, du:0.1434, udu:-0.0896\n",
      "One epoch execution time: 0.16073 seconds\n",
      "400, ae:0.0220, dz:0.00139, edz:-0.00069, du:0.1385, udu:-0.0918\n",
      "One epoch execution time: 0.15579 seconds\n",
      "Total execution time for full model loop: 1.3745163520177206 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.39\n",
      "Evaluate Attacker performance\n",
      "s acc:  11.360000000000001\n",
      "Loading model from file with d_64_beta_0.05_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.0966, dz:0.00729, edz:-0.00357, du:0.1471, udu:-0.0743\n",
      "One epoch execution time: 3.3443 seconds\n",
      "100, ae:0.0952, dz:0.00696, edz:-0.00349, du:0.1383, udu:-0.0831\n",
      "One epoch execution time: 0.15207 seconds\n",
      "200, ae:0.1064, dz:0.00695, edz:-0.00348, du:0.1380, udu:-0.0820\n",
      "One epoch execution time: 0.1565 seconds\n",
      "300, ae:0.1107, dz:0.00697, edz:-0.00347, du:0.1370, udu:-0.0773\n",
      "One epoch execution time: 0.16279 seconds\n",
      "400, ae:0.1026, dz:0.00695, edz:-0.00348, du:0.1366, udu:-0.0777\n",
      "One epoch execution time: 0.16288 seconds\n",
      "Total execution time for full model loop: 1.3581474741299948 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.01\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.84\n",
      "Loading model from file with d_64_beta_0.1_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.1818, dz:0.01473, edz:-0.00694, du:0.1826, udu:-0.0703\n",
      "One epoch execution time: 3.3752 seconds\n",
      "100, ae:0.1790, dz:0.01392, edz:-0.00694, du:0.1404, udu:-0.0680\n",
      "One epoch execution time: 0.14985 seconds\n",
      "200, ae:0.1733, dz:0.01394, edz:-0.00698, du:0.1383, udu:-0.0651\n",
      "One epoch execution time: 0.15281 seconds\n",
      "300, ae:0.1830, dz:0.01391, edz:-0.00693, du:0.1368, udu:-0.0672\n",
      "One epoch execution time: 0.15368 seconds\n",
      "400, ae:0.1818, dz:0.01399, edz:-0.00693, du:0.1377, udu:-0.0679\n",
      "One epoch execution time: 0.1584 seconds\n",
      "Total execution time for full model loop: 1.3483068505922953 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.35\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.85\n",
      "Loading model from file with d_64_beta_0.5_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.6276, dz:0.07430, edz:-0.03992, du:0.1432, udu:-0.0721\n",
      "One epoch execution time: 3.2316 seconds\n",
      "100, ae:0.6089, dz:0.06979, edz:-0.03478, du:0.1369, udu:-0.0783\n",
      "One epoch execution time: 0.16971 seconds\n",
      "200, ae:0.5864, dz:0.06970, edz:-0.03487, du:0.1343, udu:-0.0761\n",
      "One epoch execution time: 0.16219 seconds\n",
      "300, ae:0.6151, dz:0.06971, edz:-0.03474, du:0.1341, udu:-0.0730\n",
      "One epoch execution time: 0.16409 seconds\n",
      "400, ae:0.6330, dz:0.06965, edz:-0.03493, du:0.1323, udu:-0.0732\n",
      "One epoch execution time: 0.16196 seconds\n",
      "Total execution time for full model loop: 1.3639655669530233 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.79\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.979999999999999\n",
      "Loading model from file with d_64_beta_1.0_u_color_s_class_uniform_Isotropic\n",
      "Full model training\n",
      "0, ae:0.6695, dz:0.14858, edz:-0.09048, du:0.1523, udu:-0.0708\n",
      "One epoch execution time: 4.5777 seconds\n",
      "100, ae:0.6781, dz:0.13961, edz:-0.06975, du:0.1388, udu:-0.0709\n",
      "One epoch execution time: 0.15897 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200, ae:0.6799, dz:0.13693, edz:-0.07037, du:0.1362, udu:-0.0687\n",
      "One epoch execution time: 0.16758 seconds\n",
      "300, ae:0.6859, dz:0.13967, edz:-0.06913, du:0.1338, udu:-0.0657\n",
      "One epoch execution time: 0.16145 seconds\n",
      "400, ae:0.6734, dz:0.13904, edz:-0.06943, du:0.1330, udu:-0.0644\n",
      "One epoch execution time: 0.15725 seconds\n",
      "Total execution time for full model loop: 1.410800278186798 minutes\n",
      "Evaluate performance\n",
      "Evaluate Mutual Information I(Z,S)\n",
      "Evaluate Mutual Information I(Z,U)\n",
      "Training attacker model\n",
      "Evaluate Attacker performance\n",
      "s acc:  10.77\n",
      "Evaluate Attacker performance\n",
      "s acc:  11.25\n"
     ]
    }
   ],
   "source": [
    "from utils.mine import MINE\n",
    "beta_list = [10.0**i for i in range(-9,-3)]\n",
    "beta_list.extend([0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0])\n",
    "DIM_Z = [8, 64]\n",
    "s_data_ratio = [0.01, 1.0]\n",
    "\n",
    "util_acc_tr = np.zeros((len(DIM_Z),len(beta_list)))\n",
    "util_acc_ts = np.zeros((len(DIM_Z),len(beta_list)))\n",
    "sens_acc_ts = np.zeros((len(DIM_Z),len(beta_list),len(s_data_ratio)))\n",
    "mi_s = np.zeros((len(DIM_Z),len(beta_list)))\n",
    "mi_u_ts = np.zeros((len(DIM_Z),len(beta_list)))\n",
    "mi_u_tr = np.zeros((len(DIM_Z),len(beta_list)))\n",
    "\n",
    "\n",
    "s_all = np.vstack((s_train, s_test))\n",
    "\n",
    "\n",
    "for i, dimz in enumerate(DIM_Z):\n",
    "    for j, beta in enumerate(beta_list):\n",
    "        encoder, utility_decoder, autoencoder, z_discriminator, encoder_zdiscriminator, u_dircriminator, decoder_udiscriminator = get_full_model(dimz, beta)\n",
    "        pre_train_ae(dimz, beta, force_train=False, max_itr=50, verbose=0)\n",
    "        print(\"Full model training\")\n",
    "        main_train(dimz, max_itr=500, z_disc_enabled=True, u_disc_enabled=True, verbose=1)\n",
    "        \n",
    "        print(\"Evaluate performance\")\n",
    "        u_test_hat = autoencoder.predict(x_test)\n",
    "        if \n",
    "        util_acc_ts[i][j] = np.mean(np.argmax(u_test_hat,axis=1)==np.argmax(u_test,axis=1)) * 100\n",
    "        u_train_hat = autoencoder.predict(x_train)\n",
    "        util_acc_tr[i][j] = np.mean(np.argmax(u_train_hat,axis=1)==np.argmax(u_train,axis=1)) * 100\n",
    "        \n",
    "        print(\"Evaluate Mutual Information I(Z,S)\")\n",
    "        z_test = encoder.predict(x_test)\n",
    "        z_train = encoder.predict(x_train)\n",
    "        mine = MINE(x_dim=dimz, y_dim=DIM_S)\n",
    "        z_all = np.vstack((z_train, z_test))\n",
    "        _, mi_s[i][j] = mine.fit(z_all, s_all, epochs=200, batch_size=1024, verbose=0)\n",
    "\n",
    "        print(\"Evaluate Mutual Information I(Z,U)\")\n",
    "        mine = MINE(x_dim=dimz, y_dim=DIM_U)\n",
    "        _, mi_u_ts[i][j] = mine.fit(z_test, u_test, epochs=200, batch_size=1024, verbose=0)\n",
    "        mine = MINE(x_dim=dimz, y_dim=DIM_U)\n",
    "        _, mi_u_tr[i][j] = mine.fit(z_train, u_train, epochs=200, batch_size=1024, verbose=0)\n",
    "        \n",
    "        \n",
    "        print(\"Training attacker model\")\n",
    "        for k, ratio in enumerate(s_data_ratio):\n",
    "            ae_attacker = get_attacker_ae(dimz, encoder, learning_rate=0.005)\n",
    "            ae_attacker.fit(x_train[:int(ratio*x_train.shape[0])], s_train[:int(ratio*s_train.shape[0])], batch_size=1024, epochs=20, shuffle=True, verbose=0)\n",
    "            print(\"Evaluate Attacker performance\")\n",
    "            s_test_hat = ae_attacker.predict(x_test)\n",
    "            sens_acc_ts[i][j][k] = np.mean(np.argmax(s_test_hat,axis=1)==np.argmax(s_test,axis=1)) * 100\n",
    "            print(\"s acc: \", sens_acc_ts[i][j][k])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-d6b84d80ce27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'font.size'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m16\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      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrcParams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'figure.dpi'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mlinestyles\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[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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mutil_acc_ts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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[1;32m      5\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m7\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;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 16})\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "linestyles = ['-', '--', '-.', ':']\n",
    "for i in range(util_acc_ts.shape[0]):\n",
    "    plt.figure(figsize=(9,7))\n",
    "#     plt.title(r'Accuracy with $\\beta$ $\\in$ [10e-9 1.0]' + f' - U={utility} S={sensitive}_{prior_type}_{data_type}_d={DIM_Z[i]}', fontsize=16)\n",
    "    plt.plot(util_acc_tr[i], label='Utility Acc., Train', linestyle=linestyles[1], c='b', linewidth=3)\n",
    "    plt.plot(util_acc_ts[i], label='Utility Acc., Test', linestyle=linestyles[0], c='r', linewidth=2)\n",
    "    for j in range(len(s_data_ratio)):\n",
    "        plt.plot(sens_acc_ts[i,:,j], label=f'Adversary Acc., Data ratio = {s_data_ratio[j]*100}%', linestyle='-', linewidth=2)\n",
    "        \n",
    "    plt.legend()\n",
    "    plt.grid()\n",
    "    plt.xticks(list(range(len(beta_list))), beta_list, fontsize=18)\n",
    "    plt.tick_params(labelrotation=90)\n",
    "    plt.xlabel(r'$\\beta$', fontsize=22)\n",
    "    plt.ylabel(r'Accuracy on $\\mathbf{U}$ and $\\mathbf{S}$', fontsize=16)\n",
    "    plt.savefig(f'./saved_figures/chart_colored_mnist_acc_u_{utility}_s_{sensitive}_{prior_type}_{data_type}_d_{DIM_Z[i]}.eps', format='eps', bbox_inches='tight')\n",
    "    plt.savefig(f'./saved_figures/chart_colored_mnist_acc_u_{utility}_s_{sensitive}_{prior_type}_{data_type}_d_{DIM_Z[i]}.png', format='png', bbox_inches='tight')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot mutual information between Z and S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4500x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 16})\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.figure(figsize=(15,8))\n",
    "# plt.title(r'Mutual Information with $\\beta$ $\\in$ [10e-9 1.0]' + f' - U is {utility} and S is {sensitive} - prior is {prior_type} - {data_type}', fontsize=20)\n",
    "linestyles = ['-', '--', '-.', ':']\n",
    "colors = ['b', 'r', 'g', 'c']\n",
    "for i in range(mi_s.shape[0]):\n",
    "    plt.plot(mi_s[i], label=f'Full Data ' + r'$d_z=$' + f'{DIM_Z[i]}', linestyle=linestyles[0], c=colors[i%len(colors)] ,linewidth=3)\n",
    "        \n",
    "# plt.xscale('linear')\n",
    "plt.xlabel(r'$\\beta$', fontsize=24)\n",
    "plt.xticks(list(range(len(beta_list))), beta_list, fontsize=18)\n",
    "plt.tick_params(labelrotation=90)\n",
    "\n",
    "plt.ylabel(r'$\\mathrm{I}(\\mathbf{S};\\mathbf{Z})$', fontsize=24)\n",
    "plt.legend(prop={'size': 24})\n",
    "plt.grid()\n",
    "plt.savefig(f'./saved_figures/mi_zs_colored_mnist_s_{sensitive}_{prior_type}_{data_type}.eps', format='eps', bbox_inches='tight')\n",
    "plt.savefig(f'./saved_figures/mi_zs_colored_mnist_s_{sensitive}_{prior_type}_{data_type}.png', format='png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot mutual information between Z and U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n",
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 4500x2400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 16})\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "plt.figure(figsize=(15,8))\n",
    "linestyles = ['-', '--', '-.', ':']\n",
    "colors = ['b', 'r', 'g', 'c']\n",
    "# plt.title(r'Mutual Information with $\\beta$ $\\in$ [10e-9 1.0]' + f' - U is {utility} and S is {sensitive} - prior is {prior_type} - {data_type}', fontsize=20)\n",
    "for i in range(mi_u_tr.shape[0]):\n",
    "    plt.plot(mi_u_tr[i], label=f'Train, ' + r'$d_z=$' + f'{DIM_Z[i]}', linestyle=linestyles[0],c=colors[i%len(colors)], linewidth=3)\n",
    "    plt.plot(mi_u_ts[i], label=f'Test, ' + r'$d_z=$' + f'{DIM_Z[i]}', linestyle=linestyles[1],c=colors[i%len(colors)], linewidth=3)\n",
    "        \n",
    "# plt.xscale('linear')\n",
    "plt.xlabel(r'$\\beta$', fontsize=24)\n",
    "plt.xticks(list(range(len(beta_list))), beta_list, fontsize=18)\n",
    "plt.tick_params(labelrotation=90)\n",
    "\n",
    "plt.ylabel(r'$\\mathrm{I}(\\mathbf{U};\\mathbf{Z})$', fontsize=24)\n",
    "plt.legend(prop={'size': 24})\n",
    "plt.grid()\n",
    "plt.savefig(f'./saved_figures/mi_zu_colored_mnist_u_is_{utility}_{prior_type}_{data_type}.eps', format='eps', bbox_inches='tight')\n",
    "plt.savefig(f'./saved_figures/mi_zu_colored_mnist_u_is_{utility}_{prior_type}_{data_type}.png', format='png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Save results to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio\n",
    "sio.savemat(f'./saved_data/acc_s_{sensitive}_u_{utility}_{prior_type}_{data_type}.mat', {'util_acc_ts':util_acc_tr, 'util_acc_tr':util_acc_tr, 'sens_acc_ts':sens_acc_ts})\n",
    "sio.savemat(f'./saved_data/mi_s_{sensitive}_u_{utility}_{prior_type}_{data_type}.mat', {'mi_s':mi_s, 'mi_u_ts':mi_u_ts, 'mi_u_tr':mi_u_tr})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load results from file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sensitive' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-891bb50d192c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mmat_contents\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0msio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloadmat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'./saved_data/acc_s_{sensitive}_u_{utility}_{prior_type}_{data_type}.mat'\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      9\u001b[0m \u001b[0mutil_acc_ts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmat_contents\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'util_acc_ts'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mutil_acc_tr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmat_contents\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'util_acc_tr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sensitive' is not defined"
     ]
    }
   ],
   "source": [
    "beta_list = [10.0**i for i in range(-9,-3)]\n",
    "beta_list.extend([0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0])\n",
    "DIM_Z = [8, 64]\n",
    "s_data_ratio = [0.01, 1.0]\n",
    "\n",
    "\n",
    "import scipy.io as sio\n",
    "mat_contents  = sio.loadmat(f'./saved_data/acc_s_{sensitive}_u_{utility}_{prior_type}_{data_type}.mat')\n",
    "util_acc_ts = mat_contents['util_acc_ts']\n",
    "util_acc_tr = mat_contents['util_acc_tr']\n",
    "sens_acc_ts = mat_contents['sens_acc_ts']\n",
    "\n",
    "\n",
    "mat_contents  = sio.loadmat(f'./saved_data/mi_s_{sensitive}_u_{utility}_{prior_type}_{data_type}.mat')\n",
    "mi_s = mat_contents['mi_s']\n",
    "mi_u_ts = mat_contents['mi_u_ts']\n",
    "mi_u_tr = mat_contents['mi_u_tr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow-23",
   "language": "python",
   "name": "tensorflow-23"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}