test_mmd.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  make dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8100, 30, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABI0AAAHiCAYAAABlUjwcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe0c00df9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = 3000\n",
    "sequence = 30\n",
    "\n",
    "def make_sin(T, noise=False, ampl=0.1, freq=1.0):\n",
    "    x = np.arange(-np.pi * 2, np.pi * 2, 0.25)\n",
    "    start_seq = np.random.randint(0, x.shape[0] - T, 1)[0]\n",
    "    sin = np.sin(x[start_seq: start_seq + T] * freq) * 0.5\n",
    "    noise_ = ampl * np.random.uniform(low=-1.0, high=1.0, size=len(sin))\n",
    "    return sin + noise_ if noise else sin\n",
    "\n",
    "# generate training dataset\n",
    "sin_x = []\n",
    "sin_y = []\n",
    "# make different sin_wave by label\n",
    "for y, freq in enumerate([0.3, 1.5, 3.0]):\n",
    "    for _ in range(samples):\n",
    "        ampl = np.random.uniform(low=0.01, high=0.1)\n",
    "        freq = np.random.uniform(low=freq-0.01, high=freq+0.01)\n",
    "        sin_x.append(make_sin(sequence, noise=False, ampl=ampl, freq=freq))\n",
    "        sin_y.append(y)\n",
    "sin_x = np.array(sin_x).reshape(samples * 3, sequence, 1)\n",
    "sin_y = np.array(sin_y)\n",
    "\n",
    "sin_samples = 15\n",
    "idxes = np.random.randint(0, sin_x.shape[0], sin_samples)\n",
    "plt.figure(figsize=(20, 8))\n",
    "# show sin wave\n",
    "for i in range(sin_samples):\n",
    "    plt.subplot(3, sin_samples / 3, i + 1)\n",
    "    plt.plot(sin_x[idxes[i]].squeeze())\n",
    "    plt.ylim(-0.8, 0.8)\n",
    "    plt.title(sin_y[idxes[i]])\n",
    "    plt.axis('off')\n",
    "    \n",
    "X_train, X_eval, y_train, y_eval = train_test_split(sin_x,\n",
    "                                                    sin_y,\n",
    "                                                    test_size=0.1,\n",
    "                                                    random_state=1234)\n",
    "\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sin_plot(sin_sample, c_sample):\n",
    "    plt.figure(figsize=(8, 1.5))\n",
    "    for i in range(sin_sample.shape[0]):\n",
    "        plt.subplot(1, 8, i + 1)\n",
    "        plt.axis('off')\n",
    "        plt.plot(sin_sample[i])\n",
    "        plt.title(c_sample[i])\n",
    "        plt.ylim(-1.0, 1.0)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MMD setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.backend.tensorflow_backend import set_session\n",
    "import tensorflow as tf\n",
    "from mmd import tf_initialize, sigma_optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MMD():\n",
    "    def __init__(self,\n",
    "                 seq_length,\n",
    "                 input_dim,\n",
    "                 latent_dim):\n",
    "        self.seq_length = seq_length\n",
    "        self.input_dim = input_dim\n",
    "        self.latent_dim = latent_dim\n",
    "        self.sigma = 0\n",
    "        self.dic_tf_sigma = None\n",
    "        self.that = 0\n",
    "        self.sess = None\n",
    "\n",
    "    def set_sigma(self, x_eval):\n",
    "        self.sigma, self.dic_tf_sigma, self.that, self.sess = tf_initialize(\n",
    "            x_eval,\n",
    "            self.seq_length,\n",
    "            self.input_dim)\n",
    "\n",
    "    def calc_mmd(self, eval_real, eval_gen):\n",
    "        eval_real = np.float32(eval_real)\n",
    "        eval_gen = np.float32(eval_gen)\n",
    "        # get MMD\n",
    "        mmd2, that_np = sigma_optimization(eval_real,\n",
    "                                           eval_gen,\n",
    "                                           self.sigma,\n",
    "                                           self.dic_tf_sigma,\n",
    "                                           self.that,\n",
    "                                           self.sess)\n",
    "        return mmd2, that_np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## build RCGAN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def DPSGD(sigma, l2norm_bound, learning_rate, total_examples):\n",
    "    import tensorflow as tf\n",
    "    from differential_privacy.dp_sgd.dp_optimizer import dp_optimizer\n",
    "    from differential_privacy.dp_sgd.dp_optimizer import sanitizer\n",
    "    from differential_privacy.privacy_accountant.tf import accountant\n",
    "\n",
    "    eps = tf.placeholder(tf.float32)\n",
    "    delta = tf.placeholder(tf.float32)\n",
    "\n",
    "    priv_accountant = accountant.GaussianMomentsAccountant(total_examples)\n",
    "    clip = True\n",
    "    batches_per_lot = 1\n",
    "\n",
    "    gaussian_sanitizer = sanitizer.AmortizedGaussianSanitizer(\n",
    "                    priv_accountant,\n",
    "                    [l2norm_bound, clip])\n",
    "\n",
    "    return dp_optimizer.DPGradientDescentOptimizer(learning_rate,\n",
    "                                                  [eps, delta],\n",
    "                                                  sanitizer=gaussian_sanitizer,\n",
    "                                                  sigma=sigma,\n",
    "                                                  batches_per_lot=batches_per_lot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class RCGAN():\n",
    "    def __init__(self,\n",
    "                 input_dim,\n",
    "                 seq_length,\n",
    "                 latent_dim,\n",
    "                 hidden_dim,\n",
    "                 embed_dim,\n",
    "                 batch_size,\n",
    "                 num_classes,\n",
    "                 summary=False,\n",
    "                 save_image=False,\n",
    "                 save_model=False,\n",
    "                 make_data=False):\n",
    "\n",
    "        self.input_dim = input_dim\n",
    "        self.seq_length = seq_length\n",
    "        self.latent_dim = latent_dim\n",
    "        self.hidden_dim = hidden_dim\n",
    "        self.embed_dim = embed_dim\n",
    "        self.batch_size = batch_size\n",
    "        self.num_classes = num_classes\n",
    "        self.save_image = save_image\n",
    "        self.save_model = save_model\n",
    "        self.make_data = make_data\n",
    "\n",
    "        # model instantiation\n",
    "        self.discriminator = self.build_discriminator()\n",
    "        self.generator = self.build_generator()\n",
    "        \n",
    "        # initialize MMD Class\n",
    "        self.my_mmd = MMD(self.seq_length, self.input_dim, self.latent_dim)\n",
    "        \n",
    "        # define input tenor shape\n",
    "        # we define batch size here for DP-SGD\n",
    "        x = Input(batch_shape=(self.batch_size, self.seq_length, self.input_dim))\n",
    "        z = Input(batch_shape=(self.batch_size, self.seq_length, self.latent_dim))\n",
    "        c = Input(batch_shape=(self.batch_size, 1), dtype='int32')\n",
    "        \n",
    "        self.set_trainable(self.generator, trainable=False)\n",
    "        \n",
    "        # discriminator takes real x and gererated gx\n",
    "        d_logit_real = self.discriminator([x, c])\n",
    "        gx = self.generator([z, c])\n",
    "        d_logit_fake = self.discriminator([gx, c])\n",
    "        \n",
    "        # get loss function\n",
    "        d_loss, g_loss = self.gan_loss(d_logit_real, d_logit_fake)\n",
    "        \n",
    "        # build traiable discriminator model\n",
    "        self.D_model = Model([x, z, c], [d_logit_real, d_logit_fake])\n",
    "        self.D_model.add_loss(d_loss)\n",
    "        self.D_model.compile(optimizer=D_OPTIMIZER, loss=None)\n",
    "        \n",
    "        # freeze discriminator parameter when training discriminator\n",
    "        self.set_trainable(self.generator, trainable=True)\n",
    "        self.set_trainable(self.discriminator, trainable=False)\n",
    "        \n",
    "        # build traiable generator model\n",
    "        self.G_model = Model([z, c], d_logit_fake)\n",
    "        self.G_model.add_loss(g_loss)\n",
    "        self.G_model.compile(optimizer=G_OPTIMIZER, loss=None)\n",
    "        \n",
    "    def gan_loss(self, d_logit_real, d_logit_fake):\n",
    "        \"\"\"\n",
    "        define loss function\n",
    "        \"\"\"\n",
    "        \n",
    "        d_loss_real = K.mean(K.binary_crossentropy(output=d_logit_real, \n",
    "                                                   target=K.ones_like(d_logit_real), \n",
    "                                                   from_logits=True))\n",
    "        d_loss_fake = K.mean(K.binary_crossentropy(output=d_logit_fake, \n",
    "                                                   target=K.zeros_like(d_logit_fake), \n",
    "                                                   from_logits=True))\n",
    "        \n",
    "        d_loss = d_loss_real + d_loss_fake\n",
    "        \n",
    "        g_loss = K.mean(K.binary_crossentropy(output=d_logit_fake, \n",
    "                                              target=K.ones_like(d_logit_fake), \n",
    "                                              from_logits=True))\n",
    "        \n",
    "        return d_loss, g_loss\n",
    "        \n",
    "\n",
    "    def build_generator(self):\n",
    "\n",
    "        # define sequential model\n",
    "        model = Sequential()\n",
    "\n",
    "        model.add(CuDNNLSTM(units=self.hidden_dim, \n",
    "                       return_sequences=True))\n",
    "        model.add(TimeDistributed(Dense(input_dim, activation='tanh')))\n",
    "\n",
    "        # define tenor variable\n",
    "        z = Input(batch_shape=(self.batch_size, self.seq_length, self.latent_dim))\n",
    "        c = Input(batch_shape=(self.batch_size, 1), dtype='int32')\n",
    "        c_emb = Flatten()(Embedding(self.num_classes, self.embed_dim)(c))\n",
    "        c_emb = RepeatVector(self.seq_length)(c_emb)\n",
    "#         inputs = multiply([z, c_emb])\n",
    "        inputs = concatenate([z, c_emb], axis=-1)\n",
    "\n",
    "        # define generator output\n",
    "        gx = model(inputs)\n",
    "\n",
    "        return Model([z, c], gx)\n",
    "\n",
    "    def build_discriminator(self):\n",
    "\n",
    "        # define sequential model\n",
    "        model = Sequential()\n",
    "\n",
    "        model.add(CuDNNLSTM(units=self.hidden_dim, \n",
    "                       return_sequences=True))\n",
    "#         model.add(TimeDistributed(Dense(1, activation='sigmoid')))  pass logit value to loss function\n",
    "        model.add(TimeDistributed(Dense(1)))\n",
    "\n",
    "        # define tenor variable \n",
    "        x = Input(batch_shape=(self.batch_size, self.seq_length, 1))\n",
    "        c = Input(batch_shape=(self.batch_size, 1), dtype='int32')\n",
    "        c_emb = Flatten()(Embedding(self.num_classes, self.embed_dim)(c))\n",
    "        c_emb = RepeatVector(self.seq_length)(c_emb)\n",
    "        \n",
    "#         inputs = multiply([x, c_emb])\n",
    "        inputs = concatenate([x, c_emb], axis=-1)\n",
    "    \n",
    "        # define discriminator output\n",
    "        validity = model(inputs)\n",
    "\n",
    "        return Model([x, c], validity)\n",
    "\n",
    "    def set_trainable(self, model, trainable=False):\n",
    "        model.trainable = trainable\n",
    "        for layer in model.layers:\n",
    "            layer.trainable = trainable\n",
    "        \n",
    "    def data_shuffle(self, x, y):\n",
    "        # shuffle x and y \n",
    "        rnd = np.random.randint(999)\n",
    "        for l in [x, y]:\n",
    "            np.random.seed(rnd)\n",
    "            np.random.shuffle(l)\n",
    "\n",
    "    def train(self, n_epochs, X_train, y_train, X_eval, y_eval):\n",
    "        \n",
    "        eval_iter = 10\n",
    "        self.my_mmd.set_sigma(X_eval[:eval_iter * self.batch_size])\n",
    "\n",
    "        for epoch in range(n_epochs):\n",
    "\n",
    "            self.data_shuffle(X_train, y_train)\n",
    "\n",
    "            for i in range(int(X_train.shape[0] / self.batch_size)):\n",
    "                \n",
    "                tr_x = X_train[i * self.batch_size: (i + 1) * self.batch_size]\n",
    "                tr_y = y_train[i * self.batch_size: (i + 1) * self.batch_size]\n",
    "\n",
    "                noise = np.random.normal(0, 1, (\n",
    "                    self.batch_size, self.seq_length, self.latent_dim))\n",
    "\n",
    "                d_loss_curr = self.D_model.train_on_batch([tr_x, noise, tr_y], None)\n",
    "                g_loss_curr = self.G_model.train_on_batch([noise, tr_y], None)\n",
    "            \n",
    "            if (epoch + 1)% 5 == 0:\n",
    "                \n",
    "                # prepare data for maximum mean discrepancy                \n",
    "                np.random.shuffle(X_eval)\n",
    "                \n",
    "                eval_x = []\n",
    "                eval_gx = []\n",
    "                for j in range(eval_iter):\n",
    "                    \n",
    "                    eval_x.extend(X_eval[j * self.batch_size: (j + 1) * self.batch_size])\n",
    "                    \n",
    "                    noise = np.random.normal(0, 1, (\n",
    "                        self.batch_size, self.seq_length, self.latent_dim))\n",
    "                    sample_c = np.random.randint(0, self.num_classes,\n",
    "                                                 self.batch_size)\n",
    "                    sample_gx = self.generator.predict([noise, sample_c])\n",
    "                    eval_gx.extend(sample_gx)\n",
    "                    \n",
    "                print(len(eval_x))\n",
    "                    \n",
    "                mmd2, that_np = self.my_mmd.calc_mmd(eval_x, eval_gx)\n",
    "                \n",
    "                # Plot the progress\n",
    "                print (\"epoch {} [D loss: {:.3f}] [G loss: {:.3f}] [MMD2: {:.3f}]\".format(epoch + 1,\n",
    "                                                                           np.mean(d_loss_curr),\n",
    "                                                                           np.mean(g_loss_curr),\n",
    "                                                                           0))\n",
    "                # Plot gererated samples from cureent generator\n",
    "                sin_plot(sample_gx[:8], sample_c[:8])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## set hyper parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "from keras.layers import Input, Dense, Reshape, CuDNNLSTM\n",
    "from keras.layers import concatenate, Flatten, Embedding, RepeatVector\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.models import Sequential, Model\n",
    "from keras.optimizers import RMSprop, Adam, SGD\n",
    "from keras.layers.wrappers import TimeDistributed\n",
    "from keras import backend as K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# from keras.backend.tensorflow_backend import set_session\n",
    "# import tensorflow as tf\n",
    "\n",
    "# config = tf.ConfigProto()\n",
    "# config.gpu_options.allow_growth = True\n",
    "# sess = tf.Session(config=config)\n",
    "# set_session(sess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# hyper parameter for training\n",
    "\n",
    "sigma = 100.0\n",
    "l2norm_bound = 1e-04\n",
    "learning_rate = 0.1\n",
    "total_examples = X_train.shape[0]\n",
    "\n",
    "input_dim = X_train.shape[2]\n",
    "seq_length = X_train.shape[1]\n",
    "latent_dim = 50\n",
    "hidden_dim = 100\n",
    "embed_dim = 10\n",
    "batch_size = 32\n",
    "n_epochs = 50\n",
    "num_classes = 3\n",
    "\n",
    "D_OPTIMIZER = DPSGD(sigma, l2norm_bound, learning_rate, total_examples)\n",
    "# D_OPTIMIZER = SGD(0.1)\n",
    "G_OPTIMIZER = Adam()\n",
    "\n",
    "rcgan = RCGAN(input_dim,\n",
    "              seq_length,\n",
    "              latent_dim,\n",
    "              hidden_dim,\n",
    "              embed_dim,\n",
    "              batch_size,\n",
    "              num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "InternalError",
     "evalue": "Failed to create session.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mInternalError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m<ipython-input-8-62ef587c83c9>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, n_epochs, X_train, y_train, X_eval, y_eval)\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    144\u001b[0m         \u001b[0meval_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 145\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmy_mmd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_sigma\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_eval\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0meval_iter\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch_size\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    146\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-efd39934639f>\u001b[0m in \u001b[0;36mset_sigma\u001b[0;34m(self, x_eval)\u001b[0m\n\u001b[1;32m     16\u001b[0m             \u001b[0mx_eval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseq_length\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m             self.input_dim)\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcalc_mmd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_real\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_gen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/mnt/sugimoto/gan/dev/notebooks/mmd.py\u001b[0m in \u001b[0;36mtf_initialize\u001b[0;34m(x_eval, seq_length, input_dim)\u001b[0m\n\u001b[1;32m    229\u001b[0m                     'eval_sample_PH': eval_sample_PH}\n\u001b[1;32m    230\u001b[0m     \u001b[0;31m# session start\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 231\u001b[0;31m     \u001b[0msess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\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    232\u001b[0m     \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_variables_initializer\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    233\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/bin/.pyenv/versions/anaconda3-5.0.1/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, target, graph, config)\u001b[0m\n\u001b[1;32m   1558\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1559\u001b[0m     \"\"\"\n\u001b[0;32m-> 1560\u001b[0;31m     \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1561\u001b[0m     \u001b[0;31m# NOTE(mrry): Create these on first `__enter__` to avoid a reference cycle.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1562\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_default_graph_context_manager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/bin/.pyenv/versions/anaconda3-5.0.1/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, target, graph, config)\u001b[0m\n\u001b[1;32m    631\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_with_new_api\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    632\u001b[0m         \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 633\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_NewSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_c_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    634\u001b[0m         \u001b[0;31m# pylint: enable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    635\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mInternalError\u001b[0m: Failed to create session."
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rcgan.train(n_epochs, X_train, y_train, X_eval, y_eval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## reconstruction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeYAAAEkCAYAAAD6s5JwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMQcEBAQEBPQR/j/BuwCcdPtB5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd6f547a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def sin_plot(sin_sample, c_sample):\n",
    "    plt.figure(figsize=(8, 4.5))\n",
    "    for i in range(sin_sample.shape[0]):\n",
    "        plt.subplot(4, 8, i + 1)\n",
    "        plt.axis('off')\n",
    "        plt.plot(sin_sample[i])\n",
    "        plt.title(c_sample[i])\n",
    "        plt.ylim(-1.0, 1.0)\n",
    "    plt.show()\n",
    "\n",
    "noise = np.random.normal(0, 1, (batch_size, seq_length, latent_dim))\n",
    "sample_c = np.sort(np.random.randint(0, num_classes, batch_size))\n",
    "sample_gx = rcgan.generator.predict([noise, sample_c])\n",
    "sin_plot(sample_gx, sample_c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# hyper parameter for training\n",
    "input_dim = X_train.shape[2]\n",
    "seq_length = X_train.shape[1]\n",
    "latent_dim = 50\n",
    "hidden_dim = 100\n",
    "embed_dim = 10\n",
    "batch_size = 32\n",
    "n_epochs = 50\n",
    "num_classes = 3\n",
    "\n",
    "sigma = 2.0\n",
    "D_OPTIMIZER = DPSGD(sigma, l2norm_bound, learning_rate, total_examples)\n",
    "# D_OPTIMIZER = SGD(0.1)\n",
    "G_OPTIMIZER = Adam()\n",
    "\n",
    "rcgan = RCGAN(input_dim,\n",
    "              seq_length,\n",
    "              latent_dim,\n",
    "              hidden_dim,\n",
    "              embed_dim,\n",
    "              batch_size,\n",
    "              num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 5 [D loss: 1.374] [G loss: 0.346] [MMD2: 0.027]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAACACAYAAAAiXOH2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCyGEEDlD4i2EEELkDIm3EEIIkTP+H4A+D53vT0uEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa77c5932b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 10 [D loss: 1.400] [G loss: 0.760] [MMD2: 0.020]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAACACAYAAAAiXOH2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKt6KoiiK0jH+C0rLig0lGmMGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa7508bbcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 15 [D loss: 1.392] [G loss: 0.971] [MMD2: 0.019]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAACACAYAAAAiXOH2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niqIoilIzVLwVRVEUpWb8N8tElseYXw/vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa750432a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 20 [D loss: 1.385] [G loss: 0.763] [MMD2: 0.019]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAACACAYAAAAiXOH2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa77c618b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 25 [D loss: 1.337] [G loss: 0.955] [MMD2: 0.019]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAACACAYAAAAiXOH2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1AwVb0VRFEWpGSreiqIoilIz/h+xttgn1RLNGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa74bfd62b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 30 [D loss: 1.379] [G loss: 0.688] [MMD2: 0.012]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAACACAYAAAAiXOH2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyCIxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa74bbb6240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 35 [D loss: 1.254] [G loss: 0.915] [MMD2: 0.009]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAACACAYAAAAiXOH2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQ8VbURRFUSqGireiKIqiVIz/BzqcgBenZ/KaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa74b70cac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 40 [D loss: 1.172] [G loss: 1.244] [MMD2: 0.010]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAACACAYAAAAiXOH2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa74ba76748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 45 [D loss: 1.316] [G loss: 1.122] [MMD2: 0.009]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAACACAYAAAAiXOH2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa751e57b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch 50 [D loss: 1.289] [G loss: 0.790] [MMD2: 0.008]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe8AAACACAYAAAAiXOH2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa74b281828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16min 56s, sys: 6min 9s, total: 23min 6s\n",
      "Wall time: 6min 58s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "rcgan.train(n_epochs, X_train, y_train, X_eval, y_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}