MNIST-DCGAN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Convolutional GAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Tensorflow & Download data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets(\"MNIST_data/\", reshape=False, validation_size=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_inputs(real_dim, z_dim):\n",
    "    inputs_real = tf.placeholder(tf.float32, (None, *real_dim), name='input_real')\n",
    "    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n",
    "    return inputs_real, inputs_z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def generator(z, output_dim, reuse=False, alpha=0.2, training=True):\n",
    "    with tf.variable_scope('generator', reuse=reuse):\n",
    "        \n",
    "        # First fully connected layer\n",
    "        x1 = tf.layers.dense(z, 2*2*512)\n",
    "        \n",
    "        # Reshape it to start the convolutional stack\n",
    "        x1 = tf.reshape(x1, (-1, 2, 2, 512))\n",
    "        x1 = tf.layers.batch_normalization(x1, training=training)\n",
    "        x1 = tf.maximum(alpha * x1, x1)\n",
    "        # 2x2x512 now\n",
    "        \n",
    "        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='valid')\n",
    "        x2 = tf.layers.batch_normalization(x2, training=training)\n",
    "        x2 = tf.maximum(alpha * x2, x2)\n",
    "        # 7x7x256 now\n",
    "        \n",
    "        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')\n",
    "        x3 = tf.layers.batch_normalization(x3, training=training)\n",
    "        x3 = tf.maximum(alpha * x3, x3)\n",
    "        # 14x14x128 now\n",
    "        \n",
    "        # Output layer\n",
    "        logits = tf.layers.conv2d_transpose(x3, output_dim, 5, strides=2, padding='same')\n",
    "        # 28x28x3 now\n",
    "        \n",
    "        out = tf.sigmoid(logits)\n",
    "        \n",
    "        return out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def discriminator(x, reuse=False, alpha=0.2):\n",
    "    with tf.variable_scope('discriminator', reuse=reuse):\n",
    "        \n",
    "        # Input layer is 28x28x1\n",
    "        x1 = tf.layers.conv2d(x, 64, 5, strides=2, padding='same')\n",
    "        relu1 = tf.maximum(alpha * x1, x1)\n",
    "        # 14x14x64\n",
    "        \n",
    "        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')\n",
    "        bn2 = tf.layers.batch_normalization(x2, training=True)\n",
    "        relu2 = tf.maximum(alpha * bn2, bn2)\n",
    "        # 7x7x128\n",
    "        \n",
    "        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')\n",
    "        bn3 = tf.layers.batch_normalization(x3, training=True)\n",
    "        relu3 = tf.maximum(alpha * bn3, bn3)\n",
    "        # 4x4x256\n",
    "\n",
    "        # Flatten it\n",
    "        flat = tf.reshape(relu3, (-1, 4*4*256))\n",
    "        logits = tf.layers.dense(flat, 1)\n",
    "        out = tf.sigmoid(logits)\n",
    "        \n",
    "        return out, logits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_loss(input_real, input_z, output_dim, alpha=0.2):\n",
    "    \"\"\"\n",
    "    Get the loss for the discriminator and generator\n",
    "    :param input_real: Images from the real dataset\n",
    "    :param input_z: Z input\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :return: A tuple of (discriminator loss, generator loss)\n",
    "    \"\"\"\n",
    "    g_model = generator(input_z, output_dim, alpha=alpha)\n",
    "    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)\n",
    "    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)\n",
    "\n",
    "    d_loss_real = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))\n",
    "    d_loss_fake = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))\n",
    "    g_loss = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))\n",
    "\n",
    "    d_loss = d_loss_real + d_loss_fake\n",
    "\n",
    "    return d_loss, g_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_opt(d_loss, g_loss, learning_rate, beta1):\n",
    "    \"\"\"\n",
    "    Get optimization operations\n",
    "    :param d_loss: Discriminator loss Tensor\n",
    "    :param g_loss: Generator loss Tensor\n",
    "    :param learning_rate: Learning Rate Placeholder\n",
    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
    "    :return: A tuple of (discriminator training operation, generator training operation)\n",
    "    \"\"\"\n",
    "    # Get weights and bias to update\n",
    "    t_vars = tf.trainable_variables()\n",
    "    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n",
    "    g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
    "\n",
    "    # Optimize\n",
    "    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
    "        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n",
    "        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n",
    "\n",
    "    return d_train_opt, g_train_opt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyper Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "real_size = (28,28,1)\n",
    "z_size = 100\n",
    "learning_rate = 0.0002\n",
    "batch_size = 128\n",
    "epochs = 10\n",
    "alpha = 0.2\n",
    "beta1 = 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "\n",
    "input_real, input_z = model_inputs(real_size, z_size)\n",
    "\n",
    "d_loss, g_loss = model_loss(input_real, input_z, real_size[2], alpha=alpha)\n",
    "\n",
    "d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### view_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def view_samples(epoch, samples):\n",
    "    \n",
    "    fig, axes = plt.subplots(figsize=(7, 7), nrows=4, ncols=4, sharey=True, sharex=True)\n",
    "    for ax, img in zip(axes.flatten(), samples[epoch]):\n",
    "        ax.xaxis.set_visible(False)\n",
    "        ax.yaxis.set_visible(False)\n",
    "        im = ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n",
    "    \n",
    "    return fig, axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10... Discriminator Loss: 0.0351... Generator Loss: 4.2114\n",
      "Epoch 1/10... Discriminator Loss: 0.1093... Generator Loss: 13.9183\n",
      "Epoch 1/10... Discriminator Loss: 0.0710... Generator Loss: 2.8001\n",
      "Epoch 1/10... Discriminator Loss: 0.0474... Generator Loss: 3.9060\n",
      "Epoch 1/10... Discriminator Loss: 0.0452... Generator Loss: 3.7032\n",
      "Epoch 1/10... Discriminator Loss: 0.0279... Generator Loss: 4.1513\n",
      "Epoch 1/10... Discriminator Loss: 0.0272... Generator Loss: 4.1735\n",
      "Epoch 1/10... Discriminator Loss: 0.1418... Generator Loss: 2.1591\n",
      "Epoch 1/10... Discriminator Loss: 0.7933... Generator Loss: 0.7653\n",
      "Epoch 1/10... Discriminator Loss: 0.1551... Generator Loss: 12.2330\n"
     ]
    },
    {
     "data": {
      "image/png": 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L3a677tr++8orrxxs9G233dbszTbbrNk33njj4PGf/OQnza72Cd9qq60Gz994\n442b/eMf//ge5/70pz9ttp32wx/+cPDa3//+95vtvuIe33zzzQfb4jN5/jbbbNNsB9e6deu6VatW\nrXgzqKWlpW6PPfZo/61fHDQ333xzs7fYYotm6xefzf7fZJNNmu2zPeQhvxxn7r3+oAc96B7Xqfrt\ngQ98YLP1i230uP3sPR/84Ac3ux8Ld7+vbdfv/tExD78sLS1169at6573vOe1Y1dddVWzfXHdcsst\ng2296aabBo/rF/vOvtAvP/vZz5ptn37ve9/ruq7rttxyy3ZMvzhWq2vrl4022mjwOl7f+WjbfSav\nr1/OO++8ucyV6h3mXPEdZt+PeYfd//73b3b1XPrEudL3p37yDxU/CNW1x/jE6+sTz3cOOVd8j846\nVyKphRBCmISZIpyu67rLLrus2ddff/3gOX5J77zzzmYbvnqOX2rP96/AH/zgB82+6667mu1fJX2U\n5b/zK2005DX6v/Tufr5htX8FGHr7HB73L0Lboz1PLr/88mZ/5zvfabaRp89mP/sMol/8i8y+u/32\n25vtX2L2Xe/3+93vfoPXrvpZbKN9WMlUlR89/pv0S9VW/WKfS3W+z2Ak57yTvl+q/lFt0Of+BW0b\nvf+YuW4feNx72bZ5YVRT+aR6h2krLdlvlURfvcOco/37rJq3RiCVT5xD3t+54jW1q3E5L58kwgkh\nhDAJ+eCEEEKYhJkltSpDyR+zDPsMO7U9x1BciczQtPq3hoB9G/zx2B/sb7jhhmb7w54ouykLVM9h\nHygvVNkk/lA6T7yH2I4q1Pcc/ej54jni+UOZL/4IbRJClQ2jb5Xjqgw0z7ctlR+n9ov3lirDqDpn\nTIaR/eKY9pz+Ov4g7A/8/kCuvCZe2/Hv/W2v/azEU/WNiTtKPCuhykCzLx0/jiupJMfKJ5UPh/y2\n9dZbt2PVO8z3oJiEoLxWzW3fhZVELfpkVhLhhBBCmIR8cEIIIUzCzJKaawPMLDFEMyyrsirMAqnC\nPkNTZTLvOxSmeo1bb7118J7KLKtXr262WVdD2SNdtzxTpcqRH8qeu/t954nyhxlyShVKl0MLALtu\nuS+qZ6sWEvpshul9X3ifKjOuWljntasFdzLm/Cn88t3vfrfZZhLZn86LSl6rMoYqv1SZfENyiH5x\nTZBjwXvutNNOzXa9ihKM467KfKoWWvoc3nde6BPnSjWWKym6yhasnl2q7Nn+XvrejEP/nf203Xbb\nNdt3WOW5AnucAAAgAElEQVQTr19lQzqG5uWTRDghhBAmIR+cEEIIkzCzpGYGRCWdmb1hNpKh20Mf\n+tDB44bxSmf77rtvsw844IBmGz725V0e9ahHDbb9gx/84OC1DaVdCGZY7WIxM20Md5U3fCavr7wz\nTyyzUd1bfyl5KQf4bFVWiz6y7IdjwMynPhw/8MAD2zH7TYnDLJyqtJBtcdGuY035ygw32+5x+2me\n+Az2s31b9bP+0i9VXSvHq/2/5557Nltpui/xctBBBw3e/8Mf/vBg281k8/7OkTvuuKPZa9eubbbl\nfawzp7TlHNGuFgXPij6xj3127+v7zLnl2PecSq7aYYcdmu37zD58xCMe0XVd1z3mMY8Z/P8f+tCH\nmm1/+LOAktc111zTbH9eeNjDHtbs6677ZWUaF/M7h6oMu1lJhBNCCGES8sEJIYQwCTPHRoZZZmwY\nEku1eNOw0xBQaejggw9utllNhxxySLMNK4866qiu65ZnV3jtl7zkJc026+IjH/lIs/uQtuu67owz\nzmi20p1tNGvPsLpazFotcFspSktKK0oGVUZX5YuqdLshtecrUSkfPPKRj+y6ruv23nvvduyss85q\nthk2a9asafbVV1/dbOU6n0lbv5g9VWV++XyLylKzflbVn47FKjOyaqvP5lxTgrQN++yzT7OPO+64\nruvqhb/9/++65f58//vf3+yjjz662Z/5zGearU+9vrUYt99++2b7/qhkrnnhO6wa7/pBqtpkvoeU\nq5Ufva8/Tey8887N7vu8ygR9+ctfPnjt//k//2ez9cknP/nJZvt+cj69/e1vb7ZVp32nVBmus5II\nJ4QQwiTkgxNCCGESZo6NzHIx7FNOMVvIsNkNwHqZ5e7/Vilg2223bbZh+eGHH95sw9Q+s6Ta3EjJ\nxXuaMbfLLrs0W9lDieDb3/52s+0PryljFoKtFLNUKglPOcDwusp2cZGZ1zGDx3754z/+42Ybdve1\noAzLjz322GZfcsklzXZMnXPOOc0260q5TP+b4aaMpF+UjBblC3F8+Pxj/FJtmFUtmvXZdtttt2b/\n7d/+bbP1Xd8vykpmXul//ak0qv+POOKIZvsOOPPMM5utHK4c7eLHK664otmL2DbCZ6zqqjk2qmxJ\n3xuON98/ymtm673+9a8fPL/vZ+U95S9t/a1U6lzRJ/rw9NNPb7bZo8ponq+vsvAzhBDCfZ58cEII\nIUzCzJKa2SQuBlPGcVGl4athoqGyYZzh68UXX9zs1772tYPtMSTuQ0zDc+UU76+MdOihhzb7ggsu\naPZFF13UbJ/b8LlasGaYXJXtnyf2YbUvvX2uZGCI7PMoqehHn9+svm9961vNduFaH5pbct2FjPrQ\nhWf7779/s/WFC9Wq3WI9rvTgOVWZ/3liX1ULP31+/eKcUjr0Okosjm+lPBdk7r777vdow1Atr7u3\n0fniwmulQceg8rUZbs4vsxBdBFqN03lR7VjqszsnnEMet36h16yyJZ/5zGc2u8oe7e+lFGe7qkWl\n++23X7OrWpJLS0uDtpKnO9S6IFs/r2SuJMIJIYQwCfnghBBCmISZJTWzZQx3lUgMQQ2nDTUNAf23\nShBDdZ+6brmkYIjbh+KVXGFoWslchrLKDy5Ysy3KV17HWlJVRtI80S9V5o2Si9JltQ2EGX5mTynB\nmHVkRqJt6GVN/519Yjai7VLGU9KxvWbPVH73uc2UrBYiz5MxfnEMOxeUBe0Xx6jHvc6jH/3owfY4\nd/p+rBYs2z+V7Ob1PMfxr+yn1KpP7Q+f2wXX80KfOJZsg33sOPSdpyylb6vdhJ/4xCc2W+naNvTX\n8aeAyifep6rjaNurrMCqbpvXV15XPpyVRDghhBAmIR+cEEIIkzCzpGbWhaFeFcZ5fK+99mq2obi1\nhJQ2jjnmmGZb1twsGSWtXhpwcZYynnKFtiW/DbfN0jjttNMG/63nKG8oDU6Rpab0YJ9XWT5myVSy\nWyVdKiWcffbZzX71q1892Ia+PpNjR+lOH1kf6nOf+1yzlX30r4tw7WfHi+NxaKFw1y1uEahttc+r\nWmriOfpCH+kXswA/+9nPNvvZz352s5WreslUecdaWsouzgszmVz86HMowZhhuH79+mY7j84777xm\nV7uYzguz6ezXandMpb+qXl31b5/61Kc2++Mf/3iz3/jGNzbbd0jf5/rJ+aHMXWX0Kvnbf84VM+zW\nrVvXbBfs6rd5ZXQmwgkhhDAJ+eCEEEKYhJkltb4uVtctl0jMNDIENdR0YaC7DBrGef4XvvCFZrtD\nnTWzvvKVrzS7lxRcjGi2jAvgzDozfFU68z5jssuUTJQFDEcXJd0ohVTZg8pcSlQ+m3Klu58OZdJ0\n3fK+dvGe/dsv8DNLR9nBBYBVzSZ9oaRjuxybtt3nrjKRpvBLlclUlbb3fGuWOaaVeHzOww47rNku\n7FNK6SWWJz3pSe2YEpOycLWA1YWc73vf+5qt3GTGnL7zuZXSq+0k5oW1xsxaVKKybc4V3xXWg3SB\npWPbeoAvfOELm33++ec3+9xzz212X0fO96P97TvXOWF7lbk/8IEPDLbRWnvWrqvkMudNJLUQQgj3\nefLBCSGEMAkzS2qG84ZxhqYeN5PNrBclD7MklFeUFN785jc326yOr3/9683+kz/5k67rlksxShTu\nildllRgCG84rl1U14Ty/+reLwiw1M/dsn+2wb22r4bUhuyG1/9b6UF/72teabZbSkUceeY9riLt/\nKkEpfVx66aXNVtZQMlSOqzLCbPuiMgalyh6sxlDlF+dXVb/P861JZ7afGXt/+qd/2nXd8vliX51y\nyinNdoGvi7mdr7fcckuzXditvKbvKn9VvpsX+qSqB2kbqjmubKycrJyun5/ylKc0W/nRedZLofrE\nhbNm1O65557Ndt74PrWNZhTaXv1T7YBajZFZSYQTQghhEvLBCSGEMAkzS2qGd9XiQcO7avdPQ3uz\nQMxiURbwuPcys+Qb3/hG13W/lHC6bnmGTlUO3Uwr5UCvrYxR1QSrFqxNscDQBWGGwkqK2vrRNukj\npRMXgSlDmPVkttM3v/nNZvcL19xNVNnPPnTrAbPOHCP6y8VsZtgp/Q7VD+u6aRbkOv6r+SL6qMp6\ndPzpa7nwwgub7TYPSiP94th99923HVO6U2p5z3ve0+znP//5zdbPZpd9/vOfb/aTn/zkZldStset\nI7aI2oOVTxwP+se5orzmOTvttFOzlQodVy5u3WOPPZqt/NxnrD3taU9rx5S5fSf6bnv605/e7DPO\nOKPZ+kRpVYl0TCbgvBbjJsIJIYQwCfnghBBCmIQV7fhpeGloX0lOhnde5znPeU6zzXR529ve1uzf\n//3fb7ahpFJPnzFlW4444ojBc2373/7t3zbb8Nm6W4b2huFj6mAtaksCcWFe5ZdKxrGtPrOSmrW0\nLGf+6U9/utmvetWrBtvTS12G7tZS+/f//t8P3vNFL3pRs1/3utc1+53vfGezzbxRyq1216wyCT1n\nntgPVaZPZTtuzHCz3ZV0ay25o446qtlKMr005hxVxnzJS17SbCUg64OZ+amPXHjqolWfT5lUWamS\nEudF5RPnSiUhaVuTTZnO/rTu4Kc+9almv+IVr2i2vnruc5/bdd3yftpnn32a7Zxw4enBBx/c7De8\n4Q3N7jN3u265VOlza/vc1Vj0+WYlEU4IIYRJmDnC8Yd0/xr2rxWjBHPFrWar7Q+/5sifeuqpzfYv\nWb/I/vjV/4XnX0j+NeNfUdVfWkZh/tVSlUExmvCvA/8KqOx5YgTiD+b+VVNtjOcP+K6fsL/8y85E\ngbVr1w62wciyf2b9Zj/Yh0a+O+644+Bz+CO4a3X6pJG7/1uvWW1UtSjsE8e27ajWqpnA4nzRv1XS\ngEk5Vg+27/o2GA16T+e3P4R7beeaY+fxj398sy3dYmV4/7J3PJissIjNC62+bLRYKQBGzj6v1e8t\nOeP7wXVHjkkTFJ71rGc1u3//VO8qoyorRFfjbO+99272mjVrmn3NNdc023d6NZ4S4YQQQvgXRT44\nIYQQJmFmSc0QUYlEDL8MNQ31HvvYxzbbXH4rOn/mM59pdv9jWtctD/u8V98ey51YTdq1PB5XLrON\nHvdZq2q21aZai5LRRBnC/rEdttXzq/3KL7nkksFzzPN/wQte0Gx9MdQX9qftUtJzk6pKlnFdQrW/\nuuVCKomj+oF0nlR+qRIZlLeU2qqSUuIzVOWKrBLc950JHMqMrol6xzve0Wx/oLbqsQkJliJSPrK6\ntP1R+XERKLFWSTWOT8+v+sdSXfpNSd/NBZXG9Gdv+7OE97dUzUknndRsq+m/973vHWyvUriyqG2s\npN55vcMS4YQQQpiEfHBCCCFMwoo2YDPMV8ayzIohvBkqhtmef/jhhzd79913b7ZViZVXzLbqJR3l\nBMNR76/kYtaK8p7ZPYbMhrv2gRlGykQ+6yI2lOq65WsdlDHN7jJcthSH2XhmQ+mXagO2Y445ptlK\ncJbI6aUen91Q3/63LJHZg5ZqcXMqK4ebqbPLLrt0Q1jSSBZV0dv+dAOsqvq2vlDGrNZ5VJWJzR5U\n1nO+9Nf0/krKZlW59ubhD394sz/60Y82+9BDD212tQ7K94HjwbGjNFitP1oJZjYqbekT2+z5ykzO\nOWX2KuvSatFKi/Zb/67QJ0qVjmulTf3t2ih/uvAnBcel8893mOO1er5ZSYQTQghhEvLBCSGEMAkr\n2oCtyqIxRFNaUsZyEZIZYGZMnHDCCc1++9vf3uz/9t/+W7O/9a1vNXv9+vVd1y0P2y0tYWkbM3OU\n0QxlrS5dbSo3pvxIVbpkniijKa3YbmUZZRZtJdNqwaTypuH73/3d3zVbGaDvFyWLqi3Knq9//eub\nrexgOZ3PfvazzbaKtNk8+tpsqCmy1KpFmlUWo7bzQj86zjxfOUQJ8g//8A+brdTVz0fniFKofjn6\n6KOb/ZrXvKbZlr9RXrMysdmGStNKpmYPLsoXPcqGlU/MqFTarKrJmwHmOHeh7cc+9rFmW+bGudL3\nj5K3/vEd+sIXvrDZL37xi5vtPHCDShfYuyDUd7TvLd9t81p0mwgnhBDCJOSDE0IIYRJWzRK+HnDA\nARsM562vpGxhmKpEo+xjCKh0YGaE52+77bbNrrJqegnAUPOss85qtgvgXLTlMxkym+GljOZxn7Va\nEFpVvL3rrru6VatWrd+wYcMBgyeM5IADDthgHxqiK135bGZ0KZ258ZPnKzeYGWYtOzMVlUj6TCAl\nUrPClDr1kfKqFcKVLJTIfCbbqHSjfFD56xe/+MWK/XLAAQdsWLdu3bLsIdvkGFKyMQPScek8qjKr\nHIvWHvReHu/795WvfGU7Zh+uW7eu2UpGZvop3TkvHTtWmrb+l/NCv4h++ed//ue5zBX7VZ84fivp\nTF859q1NJp5vrTklO33Y++TEE09sx5Rl9Ym16PSP898MWeec72uv7/mOp2rh56zvsEQ4IYQQJiEf\nnBBCCJMwc5aaMpdhsOG0mTCGlMo+SidmtChdGQKateEmbcoivXTgAqeTTz652WM2FPIcQ8rquFKT\nba/qqi0qA0cpUr/Y5/antu02ZLeunX2kf5W0zKBRMuizbMwo/Id/+IfBf6dcVG2Foc/tW/1iGyt/\nVbXf5onzRSnHNumjaoM4+6XKUvMZlIy9l8d7H5k5ZgaaY8p5XNXhqsr7VzJpVU9u0VQ+qTYu1D+O\nGRerKs35XJ5/9tlnN1vJWSmvt732m9/85mYr9TmHlcLGbKhWbUJXvcPmRSKcEEIIk5APTgghhEmY\nWVJTKjGMU6qoJA9xsWW1CK4qK18tlOvDfhcDjgkLq9C+Ol5lbFTnKzUsSrrRL2Ylie2onkFpoFqw\nqm2mos+sT3t54JRTTmnHXJhZ/Tslg6rfPL/aLmOMvxblF7MElfm8n36pxorPWe0sWy3ac576b3t5\nxvlixlIlOVY7t1al7ZXjxlD5ZV4+UsKqdn2t6qr5vGZ3VnKi5zuefUd6/f6abs0yZh5UVPJetZBz\n0e+wRDghhBAmIR+cEEIIk7CiHT8NmytZrKopZqbImMyIKrtlKLyvZJ6K6px5HZ9XHaJfhVlc9kkl\naVaZc8of1fN4fiUrDO2Q6i6QVT06r60EJVV59Or4rP6aJ2N2Yh2T0TjGp1JlKvnMvfRqqXzlm0qi\nrBZpVvO4khKr/h/zfCthJXOl2ql2zM8CVXah47+fQy6QrfqvGh9SvXuq44ueK4lwQgghTEI+OCGE\nECZhZknNrQeUCMyMMrwzQ2YodLz7dSqqemRD2SHzymapQuNZj1dtnycuxhvjF/vNcHnM7qRVZpJ4\nX7OC7u3aY5g1S3CMv6prrhRrkyk5eT/9Ve0EOiZLaMyYG8pYqxbPrmQeVWNkjPRUXWde87qaK9Xi\nySoDbdbMrSr7bihLcUyG6Bjm9Q6b1/xIhBNCCGES8sEJIYQwCTNLamaLVQv2quOGaGbDrCST7N7q\nlK0kDB8jXYw5vuism65bng2lFFPV5qoyo5QYxvil6v+hLCWljJVIarP28xh/LWrhpzXQqkyiMX04\na923qi+GMuXmVdNsXn6Z9ZxZqeaKzOqTMXN8TAZif7yan7Myr3fYvPyQCCeEEMIk5IMTQghhEmaW\n1Nx58Oabb252VUfJmkGGjmYxVTV7qnpQHt94442b3Us3ZkVVbfF4lUVT7fhZtXHM+Yti6623bnbl\nl6r/lTc9bl/4DD6bNdzsiy233LLZ/QI5/VItGK36zTHiDp5VlpHXqY5PkT241VZbNVu/jMlSq8au\n/VzJYT6zcoiyZr8QUb+48HfWtrhbaZUpOcZflV/mtfiw8kk1V6p32Jj3iW2uakya+dvPFce4Cz+r\ne1ZzpXonVdepzp/XXEmEE0IIYRLywQkhhDAJc8tSG1MPrdodc8yOmFVGiOFm356qLWN2+azuOSZr\nZMz5i8qGGuOXMbuTVv1cLQKrMq+sudefUy0qrPxS9fNQVs+vau+sfpwn1u2qar2NyeKqMgxnrd/n\njpG9Pyr5rfJLdc+qVlzV9t+UX/TJrG0Y45Mx7wexJls/RqrF8LPOlUW8w1ZCIpwQQgiTkA9OCCGE\nSZhZUltaWmr2dddd12yzGAwRq3DdzJhqt9AxWRKe31/fc/3/1bWlyswyfK1qLpnhUWUSLSpjbYxf\nlLnMKFJmcYdKz6/6rso0urc6ZdUulGO2GBhTJ8yxVvlFfL55sv322w+2SbuaL44t/WWZ+ypjbEy2\nUW9Xc2SMjFbtRCrVzsBj6vY5X8ymXAk77LBDs93hVvRJldFZ7RxaZbHat/eWvWbfLMInVeZglXXq\nfVfyDkuEE0IIYRLywQkhhDAJM+sIVXZTFfZVuLDJxWZVllQVfg9lTxjqKhdVGRjVoqmK6vmqTI4p\nFn5WGWD6xXZUZeINtcdkr1WLA4fYdNNNm209qyrbppIgKjy/qglX9c2iqCQ8pY4x86UaQ1VmXuWX\nIanzQQ96UDtmtuO8/FKV8f9N+aUap5WEXmVoDsn5XVf7pOq3ocxV32FK2/PyyZjFtYvwSSKcEEII\nk5APTgghhElYNctCxFWrVt3edd1193pimIU1GzZseMhKLhC/LIQV+SU+WQiZK/dNRvtlpg9OCCGE\n8OsSSS2EEMIk5IMTQghhEvLBCSGEMAn54IQQQpiEfHBCCCFMQj44IYQQJiEfnBBCCJOQD04IIYRJ\nyAcnhBDCJOSDE0IIYRLywQkhhDAJ+eCEEEKYhHxwQgghTEI+OCGEECZhpn1DV69evcFtTattjd1W\n121b77rrrl/euNgS1a1PPe751RbH/fnVtd2etbr2Stoy5nzbsHbt2m79+vV3rHSPj9WrVy/bY+Ln\nP//54HlV/3u82m67erZq6+9780u1xXXVb9WY8viYtlTnyzz8snr16g1LS0vdNddc045Vfqmeza2Q\nZ/XLLPNlzNbj85ov9zZG7n6+bbjzzjvnMld8V1XvMPt+1rky5rlmmStVHztuxlx7zPnVnJvXXJnp\ng7O0tNTtuuuu7b+vuuqqZtvoW265pdmbb755s7/73e82e4sttmi2e3ZvtNFGzf7Rj37U7K222qrZ\nP/3pT5vtfuz9+Q95yC+f3Wvb+T/+8Y+b7fnf//73m73xxhs3+yc/+clg222Lbfd8r6/j1q1b161a\ntWrFm0EtLS11e+21V/vvyy+/vNlOoqr/b7jhhmY/+MEPbrb9b1+47/222247eP5mm212j+P6UL/Y\nJ5XPv/e97zV7k002afYPf/jDZtvPVVts+zbbbNNsJ/I8/LK0tNStW7eue85zntOOXXHFFc3WL7fe\neutgW2+++ebB447dBz7wgc22T1evXj14/pBfHAuO2wc84AHNtp+9tsertmy55ZaDx/Vj5Xfn7EUX\nXTSXubJ27dr231deeWWz/djeeOONzfZ5r7/++mZXc8V3kuPN69zbXPFc+8x56LvKPtYnXrs637Z4\n/I477mi289w/AmadK5HUQgghTMJMEU7XLf+L4Dvf+U6z/YvNv5KUEbT9qvoX2J133tlsQzr/UjAM\nlv4vFKOtn/3sZ802AjE09i8C2+h1PO5f5LbdPvCvEu/lM82Tyy67rNnXXntts/2rzTbZz/aRf71U\n5ys3+FfQvUl5+q2KnryP17af7U/v6V9wtt0+qMaRzzRPjGr869g2OV8qv0g1X3wG+8JzHKNDfqn6\nzX422rSNlV8cU7bdueB9fY5F+EUFQMnTvqn6WEVDqrlin9gP9pv90J/vfXw/+U7ynKrt3t/zvY7j\nz/6urqM9K4lwQgghTEI+OCGEECZhZknN8M8Q0R/3lJyq8+9///s32/C7yvry+kowXrO/rz+47bTT\nTs1W3vAa4nFDUO+vXT1Hxaabbnqv5/w6VNl4tvV+97tfs+1n0Xeeb8iuX7yXPzJL3waf3X6+/fbb\n77WNXtuw3/uL2TbKF7bdZ/KH3nky63zRj+L5PptUfnGMDp3vj/1ew/GsX8TjY/zisyrlVRlxtq2S\nGGelyv5zPKxkroj9KdX5fXu8tolXVUZj1fZK0qvmc/Vzhff1p4lZSYQTQghhEvLBCSGEMAkzS2rm\np5s9YZhvWGZoaChrVoehu+Gdoay2WSBDcoBtcU2Q2RiG7dtvv32zb7vttmYbdor/tsoAqrLRFpUN\n5VoaM5QMu8f4pcrOqaSoKjtRyWxIVjDTSenStrgmwPO9Z7UQuWq7vtOupISVctNNNzXbzDzbVC0y\ndAzZt8ok1Xzxmvf2bPaD89L7ew0lHmUu/VL1beWXaq4vwi++w8xaHDNX7BPniu+wauGn/9Z+GJI8\nqwy+oSzDrqul0GrhZ5UlZ3v1g/cd89NBRSKcEEIIk5APTgghhEmYWVIz08iw0LDT8M5w0fO9jhlI\nlXS2Zs2aZu+///6D9z3ooIO6ruu6RzziEYNtef/7399sQ0pLe3jc0j1Kc3vssUezXcynrXyiTFLV\nJFoplrBQojJjxXbY5z6zcolyZSWd7bDDDs22vI7X6SXLxzzmMe2Yfj7llFOarRxoNoySmjKIfW5G\nogv6RBnE/lB6mCf6RSmiurfzRb9YBqaq5+X5lvmxXyxR0o9754uSytlnn91sMwmdu0qGPp/HLQHj\nPHJuOmarvlHCXwmVJOjc9L7OFd9JPlf1LlQO23rrrZttJq3X6f3/qEc9qh1TAnRRt2PcuWpJJOeQ\nPvR4VVrMvvE9UmXejSERTgghhEnIByeEEMIkzCypKWcY2pvVUS2UMhvC8LsK3QzdlVQMcZUDnvnM\nZ96jXYa3xx9/fLMNaT/0oQ81+6ijjmr2xz72sWa7MNBw+7//9//ebENm+8lnXVQtNftHX1SLaqtS\n7P5b5SelHv2ivGa/WFX8+c9/ftd1y0Nxw/gXvehFg9fQL094whOaffrppzdb6cistksvvbTZVQ29\narHsPBkzX2xHlenlv60ynJw7Vf0vJbVnP/vZXdctl5gcR45n2/XFL36x2XvvvXezL7roomY7RqrK\n5PrFMViV158X1ugbM1fE+VtlfSlv+Yxe0/fJLrvs0uyDDz6467rlsv3nP//5Zvve0lZecx5UNe2q\nZ6rqPlYZu7OSCCeEEMIk5IMTQghhEmaW1Azjqro+hsTVZlnbbbfd4HFDN+WwPffcs9mvfe1rm21I\n3Es2VQ0uw1vba/iqjHDEEUc02zD561//erPNvDJU977KCIta+Km0VPmlCunNDNMvSmr6VOlGv7zx\njW8cPL9fBKr85n1sr/28zz77NPuwww5rtmPK/vzGN77R7N12260bosoCW5TUaQbSmF1LfX7733Fp\nPzpfvKZZnW9/+9ubrV96H3g9+83rOV7cyK+XgLpu+RhUxrvwwgubvbS0NNiWauG4z6dMuBKUnCr5\nzvsqD5p9uuOOOzbbZ/eavgf0yUknndRs/dxLnkqb/U8FXdd1l1xySbOdH5/97GcH72+fmeHmnFd2\nq+rqrSQzTRLhhBBCmIR8cEIIIUzCzJKaspGSRCURiBkQLkIyY8nw1eMuXlMCGMq8UDpTlvNc27Lv\nvvsOttcMIyUF22g4bKbI1VdfPXjfKvtlpeiXahFdtVOfx6sFlkpDt956a7OPPvroZhumK3v1z28f\nKm3ZJ17j8MMPb7ZShjs2ViX5XWCo7OYzVfXh5olycTVfbF9VK8t2Oy/MMPP4ox/96GY7Fh/2sIc1\nux8byuTV1h/6xUxOx4u7Adsun8nrVLUVKxl4XtiXY+ZKtbjW69gPZgK6K/JjH/vYZjuHlLF6udpr\n+A7zPi56Niv0a1/7WrOrunE+R7XLctUHK5kriXBCCCFMQj44IYQQJmFmSc1sjGrXOLMklJ88f5tt\nthm8plKLIb1hfBUG9+GgWVTV4qWq9psSmaGuIbAZQ/vtt183hG0wlNWeJ1WWjM9W7SJoP3udrbba\nqtku3rMfH//4xw8eVy7pfVCVeTeTp5LdlNQe/vCHN7uSFZTjlCEuu+yywTaupOT6r0IpstqGQOnC\n+SPnUQYAACAASURBVKKcZE02ZS/7y3OUb6o6e/197f8qY857Kln7HMp1Sppmph166KHNNttN6clx\nVG1tsBKqzEGfxTnk+frErDMlf/vNWoNHHnlks30/DC0+dX7aloc+9KHN1ifOLWupmQ167rnnNlsZ\nVeyDqh7kSkiEE0IIYRLywQkhhDAJM8dJ7ohpeF5hdoPSjVkphoyiLGL9pv/8n/9zs5W6+hBTWUw5\nQSlAKcKsp5133rnZZnhcfPHFzTYbR4nGENfwtZL45okShn6pMkrsI88xjFbq8fghhxzS7NNOO63Z\nLvw0G6mXGLxGtQ2C/nIhp3KNGY7nnHNOs5U7zNRR1lCCU4aYIntQ2a7aNVXpwvliVpESjLZ9dNZZ\nZzX7d37nd5rtvOtlFX2ldOp4drGkC5+tA+aYco4oJdou7+V7pZK25oX3sl/F95bzScnTvvc6zhsl\n529+85vNfulLX9psF2T276VqKwFtf5Y49dRTm+2/9VndbkW5Wpnf7FKfSZ8kSy2EEMJ9nnxwQggh\nTMLMkpqZS9XiJRdmGroZalrW/Lrrrmu2oax1g4477rhmn3/++YN2nymi5GNYqLyhtGLIbOaM2xMo\nr1k/zJLsFfOqQ/SrMLx2t0VlkWq7AZ/f7R4M9Q3lPW6dJ+USz+mlG+UFpR0zlDxuqH/mmWc2W5+b\nkaN05ljTp9Xuq4uQbrpu+XxR9lS60C9VxpiZedbmqzIs9aNysLJKP3/NtPSezhf70PsoXep/MwPN\nrFKOUe5x3itBV1s0rAR9YhuUy6rdVp0r1jLzuNl0vjdcYO4YXr9+fbN7KdK5onTnIl7lfM/x3eY7\n2vHuMznnqnp/1QL6WUmEE0IIYRLywQkhhDAJK6qlNrSIrOvqHS7NVlGKqkJrw0RDQ8ut24a/+Iu/\nuEdblPre8573NHv//fdvtou2bJeS3l577dVsw2RrZVW7B1bl1udJlT1Y+WVoYWbXLQ/1vaYhtc9j\nP375y19utlLPiSeeeI97mhX2wQ9+cPCZvLYZPmZAKRkqNdkH2rZ9XhLNr6KqPVjtflvtKGmdPqU5\n/WL/Kmt/5CMfabbPf8ABB9zj38lXvvKVZiuBu+BRqdNsN8+pMvXGjMdFYBaX/eR9lcWqHVn1ie8K\n8bme9KQnNdstG5TJnvjEJ97jGkpYn/nMZwbvo0Su/FntLOt7y+d2Xnq+11kJiXBCCCFMQj44IYQQ\nJmFmSU0Jw5BYKcyMJkM9ZRbPt1aRiwMNX88+++xmuzjTrJ7+nN///d9vx1zUaSitvPb0pz+92coI\nPuunP/3pZlsPagxD9d7mjdJhtWBLX1Q16+xzM1mUGAy7lSCt2+R9r7jiiq7rum7t2rX3ONZ1yyUi\nj3t/ZUwXDCpHHHTQQc32+UQZrcqGmif6xfli/3iO7VaOcSxWcpV+UV50qwJloP5eLrZVatF24bUL\nSZWkzBLUj2aN6i9lcudFtSB3XnPH/lYqcjxUi8SrHTyt4+eCZWXGKmPNrL++PY5xs3idqx43K9E2\nmr3qu9CF19W4dM7rk5WQCCeEEMIk5IMTQghhEmaW1KqsLKUYw74qA0LpzFDW6xj6Kqkdc8wxzbau\n03Of+9yu65ZLd4auf/AHf9Bss84M+d/85jc3+8///M+breyn7FFtf+Cz2k8rWTT1q9AvykZVdokS\nkm01vK/OUW7QLy972cua7YLMpz3taV3XLfensqRSp9lQT37yk5vtAlPlTUP9quR7VQdqCr9Utemq\ne1dZQvpXycR/6wJL+8hx72LaAw888B73Uco54YQTmr399ts3Wz+fdNJJzX7Tm97UbCVA661Vi8Jd\nfKjEs4hF095L2bKS16qdYc1+9Bn32GOPZvdjv+u67r/8l//S7M9//vPNVlI+7LDDuq5b7hPfVfrE\nhar9u89rdN3y92O1qLPKUnQsOnaz8DOEEMJ9npkjHH9sqr6Yfhn9wc2/CKxsW5Xq8DpGGLbB3Pb+\nr5Jq33p/YK32X7eN/rXtj4Lm3Fc/rFebkS3qL+nKL1V/+heo7faHf3/U9d9WfjEp4JGPfOQ97jX0\n42jXLfeLZT6qTccsLeQaKv9SdIOpar3NFH6xf/yrtYq6PN+xaGkbn9MIyr/c3fTMPjJS7J+52rve\n69n/1WZ//iWuXVX9dr2IeF+p/hKfFSMD22/EZYRjm8U+NlFHv61bt67ZRqD634ikn6/3tslk1y2P\nBI1AnTcmE+y2227NVoFQeaiqlUsinBBCCPd58sEJIYQwCXNLGjAUN0w1/DIEVEazhIp4HaU5qwsb\n3vcVag0pldfMQ3cTN3/kswyI7TKfXhnNqrgyr1IQY/E5x/hFqhJCXlM/Vn6xWrDJGr104joN1++4\nZsMK3a63Ovnkk5utvONaBOXAqvyL8uGiZDSp/FIl0yhpKNMoP2krxzk3lXtMypDej8pHtsX7v/e9\n722263D0o+tM9JFSldWq7Q/n16JRsqsSn3x25SfPdyw5hq3W7gaBxx9//OC9hsoZVRuk+d583/ve\n12zHvgkJjnETNhxnlXy3CMk5EU4IIYRJyAcnhBDCJMwsqVkqQdlC6cZw0ewpQ1A3QVrWIEI6s5qO\nOOKIZit1Wc6kl3cMBQ1BzZU/8sgjm2121Qc+8IHBe7qpkRKEkp7SjWG4fbOoCsXbbrtts/VLleFi\npo4hteswxGfWL33F4a5b3u9m9my55ZZd1y2XI5Q13MjKrLqtt9662e7Z/rjHPa7ZQxu9dd3y8eWz\n6kf7ZlF+8RnMxnNMSN9XXbe8vxxn/lvX5CipmflkpW3XP/XymfNV39pvhx9+eLOVUZVv3MjNOWob\nzdRyvLiZmPjOmFdpG32iJO540FaeVLqyH8zKM7vV9j/1qU9ttpviOXd7nyhbm4moLO088GcEM3Cr\nTd+U7OwDpVDH67zKcyXCCSGEMAn54IQQQpiEmSU1M14M+atMG8NBzzfM9hyzIVzM9IUvfKHZH/7w\nhwf/bZ8Bo5xjppNywbOf/exm/9Ef/VGzDUFPP/30ZpuBZfVds2sMU6comyJKGFUmje1QuvKcaiGh\nPlVqcQOu17zmNc12nPQVuN2nXZnCCsIHH3xws//qr/6q2cplZ5xxRrPdyEopQ78o3UydpaYsUWWp\nKVeaSWRbzVTy2fSji20/+tGPNtsyN0OLRs26VALX/7vvvnuz//Iv/7LZxx57bLOVo90oTKnt0ksv\nbbbjSDmwKiUzL8b4RImq2kRPec32K60/61nParaZfmbJmnXbZ7jZN8qsZsNZrfqtb31rs30X+950\nnjmfqvJLiygxlAgnhBDCJOSDE0IIYRJmltTMqHABmhWElQWq8NiaUYapygtmeimXvP71r2+2oWEv\nF7zqVa9qxwyZzdIw/Ff+ede73tVsQ2NDTTNqzCZRuqmykBaF9bIM0fWLkqJ+0a6ypJQL9a+y59//\n/d8327pN/WJSr2dmjIsBTzvttGbb51alrjaDqhZEeh2lwSmwf8yQU7rSL1WFXrNDq42xzjvvvGYr\nY7361a9u9tB4cL54vW9/+9vNtm+V94477rhm618lJmsPOnacL9XmjPbHvNAnZrE69u1jpSXfFdVG\nh/bD3/zN3zTbBeave93rmm2f9P38ile8oh1zwbrvHrPR7EvP19+20WeqNmDUD/7blfgkEU4IIYRJ\nyAcnhBDCJKwoS82QstrISFlA+cm6S8or1WZU1mzyvpbW7uU4szHe8pa3NNsFjoaxZswZSleZGWaE\nGZr6rIuQAn4Vyo/2j77QR1VbrQNl9kq1zYF+tO/soz40V/L5h3/4h2Y7Liq5yPZWfqlKt1fPWkkJ\n88TadNUixjHzxewur1nVuDv33HObrR9d2NdLSGb6mUmlNOscdRFqVbOu2ujOsWl/6JdqU8N54di0\nPdX4sZ3OISV6bcen17/mmmuarZSnXNVLrWbIvvvd7262fTmmBprPUW2ZUtX1qzYDXAmJcEIIIUxC\nPjghhBAmYWZJTQnLcLHKbqikGP9tldFVhdmG7kPhqyXulc7GhIVKFLbL+1fbL1QLCRedddN1y+s6\n2bdSLc5VxvE5q93/9LV9UWW19GF9vwD07v+u6hN9uxK/VGNw0QsMu66eL+LzVNtJVJmfFcotle96\n+dQtOcyCquS9akdO0S/VTrjVc1TzaF4+qjJqpdqRVZTuxb7y3yo/eo7vs94nLpw1i9HrVbY4npTR\nqvPHzJWVkAgnhBDCJOSDE0IIYRJmltTMeDEDQirJxeP+2ypzSKp7Gbr3YZ8LzczqqNpiyC+GoFLt\nElhJdvPK8PhVVDtLVmF3lY1SZQ5Vsky1gEwZoMespzFjpzqn8kslGVTl1Kfwiwsgx2QxVn5Rjhsj\ny1bZlkpIvZSjjFZlMtqfynVSbfFQHa/6f9EZnpVPKpmpem9Y0656lmpHVn0yNJ7dyXZM1l41xqu5\nUrV30XMlEU4IIYRJyAcnhBDCJMwsqVkDqirB73FDxyqjyTCxkm4qqW2oNla1GG5WqntWzzRr2+eJ\nO3Xa/1ItnrSt1QKy6tmq6wxlHlbnzto/i/DLonxkzcCqP8csaq3my5hsI7FWYV8fTwmrkmPHLJId\n45dZF94uYnGuPvF5q515q8xBs8vGvGcqnwztNDrr+7FizNgfc7zKGJ2VRDghhBAmIR+cEEIIkzCz\npGZNJcPOqgZaFf5XmRdVmFhlrgxl/lQh/KzM2pbq/EUtKhT9Ukmd1WLLqsbSSvwydJ1KUpu1fxbh\nl0X5yEymar6M8UtV76pizHzpswCVyVciQc/LL7OeMyv6pFo4PMYnVQZsxZj3Yu8Ta94tYq7Menxe\nmYOJcEIIIUxCPjghhBAmYWZJbauttmq2JfGVS6osNcPXqh5WddyMEMO7oRpiZuK4eLCqK2SWiPf0\nOtX5Hvf8KstlUbgTa5UNNcYv1bPN6pfNNtus2f1iQ2tYVf1T3VPJovKLbfG4u5VWWUmLktS23nrr\nZrv1g/1f7UhqW2f1i32kX9zVsl/Auckmm7RjLuyedb64zYfPVM3HMfOlWqy8Eqpdi6sstar9jquq\nTqH9Zv94fNNNN212/w5zrriId15zZcy4WcQOxolwQgghTEI+OCGEECZhZknNekBVpllVe2jMoq8x\n9ciquk79OVXNqmqB05i6QmNC+6pu0RS7f95xxx2D7ZjVL9U5s/rFml19PyoRjfFL1W+zjruqXtbU\nNe7GjKcqI2lWv1Q1sdxaoO8XpZ4qM27MfKnmXdWW6vii54sZnT5LlQlYHa8y2cZI2rPMlcrfK5kr\nY2qyLWKuJMIJIYQwCfnghBBCmISZJbU1a9Y023LzMlQCveuWh4ljsjCqukWG7h7vQ0CPmTEyptZW\nVe9Nquwa71vJEYvKWNtll12afdVVVw2e45YBs/qlygCrjg9lSZnVU2XAVHJE5RfPqfzi8an9stNO\nOzXbkvPVVg5VRlS1c2g1psdkG/Vt8Fi1m+6YMvdj6rcp01RtrPxSbVcxK86Va6+9dvBeSo9Kjj6v\nizOrd5Vzq3qHDb3zqt1zx0ibs77DbGM1V/TtSuZKIpwQQgiTkA9OCCGESZhZUqtC3zHlqz1umDgm\nY6e6/lC2hbKQckV1n6rMeJXJMevCtCkWflZ1usaUEh/jlyp7pZLDhjLP9IsLDKs+HNp64u7nS7Vo\nsvLpFH6pMrEqmUQ8PiQdd924+VLRX99FumY7VllYtr2SdSpZbEzm3bxqIVZU47SSaqssSqU2s3fH\nXF/uba4o71WZaZVPqrZXbVn0OywRTgghhEnIByeEEMIkrJqlhtSqVatu77ruuns9MczCmg0bNjxk\nJReIXxbCivwSnyyEzJX7JqP9MtMHJ4QQQvh1iaQWQghhEvLBCSGEMAn54IQQQpiEfHBCCCFMQj44\nIYQQJiEfnBBCCJOQD04IIYRJyAcnhBDCJOSDE0IIYRLywQkhhDAJ+eCEEEKYhHxwQgghTEI+OCGE\nECYhH5wQQgiTkA9OCCGESRjeNL5g9erVG9zz+uc///ngee7p7V7bHnePbPfgdj9zj1d7dg9dpzq3\nunbVljHH7Q+Pu5e9+9HLnnvu2a1fv/6OlW4qtXr16mWbGlV+ueuuu5ptH3l81r6YxS9Vv7nX+phr\nz2OM3P1827B27doV+2X16tUblpaWumuvvbYd0y/uQ+VYsa2/iflyX/bLnXfeOZe5Yt+PeYfNyye+\nB37dd9h9zSezzpWZPjhLS0vdwx72sPbfV1xxRbN14i233NLszTffvNk333xzszfbbLNm//jHP272\nJpts0uwf/OAHzX7IQ375PP/0T//U7Ac96EH3uM4WW2wxeO4DH/jAZv/whz8cbKPnb7rppvd6vm33\nmTx/q622arbOWrduXbdq1aoV7z64tLTU7bXXXu2/r7zyymY7aG666aZm20fXX399sx/84Ac3+0c/\n+lGz7Wf94rN5vn3Rn+897beNNtqo2fbbmLZ4fMstt2y2frQt3//+95u9zTbbNNsJNQ+/LC0tdevW\nreue+9zntmOVX2699dbBtuqvar7Yd/aF86Uao/359ttPf/rTZvuCrK5tf2688caD99SPXn/MmLIN\nF1xwwVzmyu67797+e8w7rPJJ9R6o3jOON8en77z+OvrkJz/5SbPtD69djX19MmauVO88fbKSuTLT\nB6fruu7yyy9vti8qnWUH+RfEz372s2b74tVZnu9fEw5Ir+PE7f9Sd1B7bc/1nKrt/uXp+R6/8847\nB497HSMIz5knl112WbP9q9pnti98HtsqDkT7XL/cfvvtg+f4nL/4xS+6rlvuWwe/1/Ma+sX2er79\nX/nR8+0Dj2vPE19o1133y3lpJGFbfR77Qqrz7XPnS9//d6c/v5oXD3jAAwav4Uem8rm+9q/sWefd\nIvziO2yMT3zG6h3mXNEnzv3vfe97za6i3b6f/Xe++P04eR+vbRu9zqx97xz1Otqzkt9wQgghTEI+\nOCGEECZhZkmt+gFLu/pxTAyzPd/reI4h4P3ud7/B4/35Xk+d2HBRHdJr+G8N521X9RyGmp5vqK5M\nMU+qfh7jl6rPq2QHz/Hf3v/+9/+V53s9df0qyWSMXyq8jnJQ9aOrUsU8qX7AFZ/NsVKdU/ml8qNj\nznP6H4jV7bVvu+22wWvoF487/r2/P0Trl+oHe6/v/FUSWgm+B2yndvWOEc+pxn71Y7vn6/P+fMej\n/lYSru7p8conVRv1ySLmSiKcEEIIk5APTgghhEmYWVIzLdAsBsO1SiIxlDWTwjCuWqth+Or5Hh+6\njxkYhsZVqFkdN6SsMuk8x7ZrVxlDK+WGG25othlK9qf3rvyibDHGL1XGmOm6PfaPWT1ez/5XSrDt\nY/xStb3y0aL88t3vfrfZZhtVkt+sfqlkZ5/NuWbKbi+ZeI077rhj8N/pZ9OiPd972pZqjFSSjfYi\n/HLjjTc2e4xPlKiqd4vPZX/aJ/a95w9JnvaNbawy4JRCq0y6ql+r8bSId1ginBBCCJOQD04IIYRJ\nmFlSM3QzFDMcVcIwHDUU8zrKL1VGxrbbbttsM5y0+3Bw3333bcdcpFYtwvP+ZubYFmUqQ2DDauU9\nn9XrVJkiK8XV0Mov+qLKkjEEr7JjfDb94gpkpZaddtrpHvfSL17j/PPPb7aLVn0mq1T4TPqrqijg\n+T6rz+c588Q2KYfYn1VGmed7nSrTUb/vuOOOzd5vv/2a7TztV9wfeOCBg2059dRTmz1m9bq+cH7Z\nlmuuuabZPp/Xr/xSZbXNiplvlU/sJ/vbNlg5o8rccpzvvPPOzd5jjz0G77X99tt3Xdd1j370owev\n8bnPfa7Zzgnfp0qGvoeqyhUu3q7mSvV+n5VEOCGEECYhH5wQQgiTMLOkprRkloShZlVx1gwPMyaq\nzBVDd/+tIa5h6uGHH951Xddtt9127djXvva1Zpsl4jlKbYbJVcaadrWIzONVf8wTs4UMo80WqhaH\nenxMnbJKOvQ6vTTQdV135JFHdl3XdVtvvXU7Zu03Cwkqy1xwwQXNVrqz2KX313fKCvrd56sWWc6T\nar7Y7qodttXzlc70xVARyK5b7ru999672X1h0SqT6bjjjmu2ks173/veZj/rWc9q9ic+8YlmOy8t\ncvntb397sO1V9uAiJGizvsb4pKoCr3+U1pXoV69e3Wz95k8Bys+9T7y/8rD/TsnrU5/6VLN/+7d/\nu9mXXnpps/Wzc85MymqujFnAPIZEOCGEECYhH5wQQgiTMLOkpmxRbdxj6GaIZiaKmStVjSTDR7PU\n/vzP/3zwmv05ykvPeMYzmq1Eo1xU1TVSlvjOd77TbDNzqsWOlQS3KAzdq/BXOUC/+JzKXsoE+ld7\nl112afYb3vCGwXP6fUCUMh772Mc2W0nTfX1kzZo1zXZbDPctUUZTmquyj6qFivNE6aLavMt7Vxlg\nSpQe19c+p/2oX5SQ+vHq/FP+UrIxs2ufffZptvtjPfnJT262ffulL32p2Y985COb7X0dp8rzPl+1\njcas+A6r5DvtKvvRfXVsm7b96Rh+7WtfO3h+L4s6V5aWlpptfzhWlMUe9ahHNdtx41xRatthhx2a\n7bis6heuZK4kwgkhhDAJ+eCEEEKYhJklNUM9Q+4qk8PQ1CwQZQEXHhkCujOfi9OUS5TDernEa1R1\nkJTdHvGIRzTbrLarrrqq2WOyjYZ2H+26Wj6ZJ+74V0kV1W6j1e6C1Ra23uvQQw9ttplnu+66a7N7\nv7jFrpmGhusuGDzkkEOafe655w7eRzlQuUNbaaDarncKvyi/Ku1V21o4thyv1TbcziOlTs+33/ux\noVzmfLFPlLkOOuigZitJXX311c12EbDyrbKOz6S/KtlzXnivakttfVLVWPNZfCc5zl3I/Du/8zvN\n9t059Iz6VZ9V9QjNTHN+XHzxxYP3qXZwrXY9HbOFxhgS4YQQQpiEfHBCCCFMwsySmhkbVZaaIZfn\ne9yMDTPQvI7/9glPeEKzDTENTfswsbqnYb64YE4pQnnhoosuarbZaNVCTiXDKSS1MdmDygSG7OIC\nS2U0M2LMiDv44IMHj/ucvZSntFK13awes64cI/a5EpwZdtUiY+tJeZ0xu4j+Otgn1WJCx5l+sQ+V\niX1O/eJzKt94fGjLAe9vPyjHDknXXbdcMjTbSXlP35mdqGSu9KT0qJSnfLoS9IkoGzl+nAdiltra\ntWubXWXsHnXUUc12LPiM/X3t72q3zWonVc+xZpuSp+85pTPfW2a+yUrmSiKcEEIIk5APTgghhEmY\nWVIzs8RQ0PDf8M7w2FDP7BBDNEPZI444otlnnHFGs0888cRmuyCzlyOUTSqJSOnmH//xH5tt+GpI\n6TWV9JQUlDd87ikkNSWMaiGtVGX6/bfVbpouLFu3bl2zH//4xzfbDJre72bD6AtlJKWEU0455R7X\n6LrlCz9PP/30ZivZmWGorx2/VS2veaJf7PNqHFSl+e1PM4w8bv+fdtppzX7jG9/YbBfH9uPVMVxl\nl+mjb33rW81WVrJOmuPC5/76178+eE3lHv2yiCy1yifiO8w54ThUftJv/tsnPelJzT7nnHOaffzx\nxzfb90zvEzMLHb/e358OrKXmYlx9e9ZZZzW7+rnAe9lP85oriXBCCCFMQj44IYQQJmFmSc0MD+UJ\ny5dXMprh/wEHHNBsF1W6mOqGG25o9uMe97hmX3nllc3+yle+0uxeanG3PMM/ZRZlGcNhw1vlB8Nk\n+8DQVzlQOcRwdFHbE7jYzD5U5lM+8LhZKmad2f8+v3603L2hufJKXwtKycd7Kota48msuq9+9avN\ndtdI5SUlGvu52gF1XrsY/iqUqJSHlKuq0vZmZVmDTL/Ybvv8ec97XrNdNKvU1ful39bj7vd0Gwiz\nAW2vsucXvvCFZjuPlHgcO96rquG1CKmzmiu+w5wryk8+uz7xWfy3yo9mqVk/8Mwzz2x2n9HnO8zx\n63vLHT995370ox9t9vr16wfb6LNWi0kXMVcS4YQQQpiEfHBCCCFMwoqy1Kp6QIZ3VQl4ZZELL7yw\n2coo/ttnP/vZzTbb4pJLLml2Xx7dBWuGgv47d/x0YZptqUqBm51ie8V/q+yzKJQ/DPurHTyrhZHK\nlWYOGUZ7TUvSm4GkVOEixB7Hwic/+clmVwvYrA/lGPQ6Sgxm1VW7zk6x46dtVRqpdsvVF7bVcalc\npfykf82O+l//638122zLPnut2jbh5JNPbvb+++/fbCVlx4vSqNuPKE07Nqvag4tahNtTzRX7u5or\n2r57HJ++f7zOU5/61GZ/8YtfbLa+fdWrXtV13fK+cU44V5QGlchsi/Kr2bVKc76ftBcxVxLhhBBC\nmIR8cEIIIUzCzJKaMochl5lYyk+er7RhbSgzeVzgqQRhmGhJfDMv+vu62LPKNlFacHsCpQCfwwws\ndzxUCjB8rsqeL2rhpyXmDYsNo82A8nz94u6C2mY3KSkasrsg1N1V+z7ynsoaXs/j++23X7P1o3Ko\n9biURpWylDiUBhadDdV1y8eQ0pnzxfbpI/2orOJzurBPlHvsL+klU/2s1GLG0gc+8IFmV6XwrYln\nZpzjwj6vqBZKz8tHZgjqE/tYicrzbY+Llx2rjn3lwfPOO6/Z1jJTouzHv++haq6YdeuWEb6HHCtm\ngLojrFJ01d/ZniCEEMK/KPLBCSGEMAkzS2qG2YaLhv/KBaJcYGioRGUGjovX/uZv/qbZr3jFKwbP\n7xchGgoaGr/yla9stiXeX/rSlzb7z/7sz5r9jne8o9lKZ0oQX/7yl5utFGAbtJVP5omLZyu/2L6q\nHfrXcNyMGBewnXrqqc3+oz/6o2Zbh6lfTGq4rvxywgknNHunnXZq9u/+7u8226yrT3/60802vB+z\n42q16+GiFuS6MLjKTPPeQ9s6dN3y+aLE47/VX/bRxz/+8Wa///3vb3afYeg19txzz2a/+MUvbrY1\n05TUXvva1zb7LW95S7Od6/Z/lbHpczt+F+EX+7KaK9Wuox5XTt53332bbbbe0Ucf3ez3vve9aD57\nDwAAC7BJREFUzbYGoHOl34al2h7hZS97WbMf+tCHNtsMuBe96EXN/h//4380u6r3Nua5q2zcWUmE\nE0IIYRJmjnCMGKpqqf5QbTTgV3u33XZrtl/YqrKsGxz519Nhhx3W7P6vIb/G/iXvX5X+sGYbfQ7L\n7/jXtn+d7Lzzzs02p9+/FLzvotZ+GDFUFZF9Nttt4sXDH/7wZvs8ttuqz5a28S/vpzzlKfe4r9GT\nY8H7mMzhX+xWsfUHT/+yNNrVdgxUUeiiIk/bPeZ++tF+8Zn9YV/0o4kzJi48/elPb3bvl2pjsyo5\nxL+4jRAsYePaK3+stsJx9VdzFeHM66/sahNJ56xzxaQLx63JRkayvs8sN+Sc8z06tE6tigR9P/pj\nfzVXnM+WMDLhwJJQPp99P6+EjUQ4IYQQJiEfnBBCCJOwoqSBSioyHPTH7KqiqiG3co2bCr3whS8c\nvNfQupZq3YthoT+qKpdZFdews/oBTanBeylHLOoHabHfqlIprg2qNmzzOsoBHnftywte8ILBew2V\nKFJq9Z5KA/7wvOuuuzbbDcWUQ8dsJKdUsugfpO+O4995Ua0Ncn75DJaCslSMEojPpuzlv7WP+nGi\nFGrfOhfe9KY3NVu/uHmhY+S2225rtuvflHurZIJF+6WSv6oNB30ux5tzyKrQ+rBKILCfh9pgn1Ub\n7v3d3/1ds/2JQp/oB8tN+aw+X/VTwLx8kggnhBDCJOSDE0IIYRJmltRcv2JZDeUSM6Ms4WA2hLaZ\nTub7Kykce+yxzbachtJBHyYqbRm6Kh2YgabMcPHFFzf7wAMPbLbZU/aBbfdeyhhKJoap88QsFUth\nGJor4+ywww7Ntl98No8baiuNmvVk361Zs6bZ/fPbD2ZgKQccc8wxzTbbRmlCScdyIWb+KNOZ1ah8\nUFXAnif2p/JGJfuapebzWP5JeUO/KOO6bkkJ1ON9G5RXzICz7a69Mdvq3e9+d7PNgtJfvg8s46Jf\n9Jd+UZqdV5aaa8r0ie20X50r+s13mBlrnuNc/L3f+71mf+lLX2q275l+jlSSl3OiX7PTdcv71Z8C\njjjiiMHjvq/9t855feK7Qxl3VhLhhBBCmIR8cEIIIUzCzJKamRmGVmZvGKKZlWK2hYuplDOUBSx5\n8vd///fNfuc739nsoYwdpRsXvSnRKVH89V//dbOf+cxnNtsKudqGz2a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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f1bff02e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10... Discriminator Loss: 0.0429... Generator Loss: 6.4644\n",
      "Epoch 1/10... Discriminator Loss: 0.6875... Generator Loss: 13.0754\n",
      "Epoch 1/10... Discriminator Loss: 0.1050... Generator Loss: 4.5472\n",
      "Epoch 1/10... Discriminator Loss: 0.0773... Generator Loss: 3.2685\n",
      "Epoch 1/10... Discriminator Loss: 0.1862... Generator Loss: 2.6961\n",
      "Epoch 1/10... Discriminator Loss: 0.1424... Generator Loss: 3.1470\n",
      "Epoch 1/10... Discriminator Loss: 0.1574... Generator Loss: 2.8757\n",
      "Epoch 1/10... Discriminator Loss: 0.1518... Generator Loss: 2.8625\n",
      "Epoch 1/10... Discriminator Loss: 0.0985... Generator Loss: 6.3181\n",
      "Epoch 1/10... Discriminator Loss: 0.1206... Generator Loss: 3.1591\n"
     ]
    },
    {
     "data": {
      "image/png": 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YIyF1wMriLTbzSrHHZkN5NgevmYvViupGxVoizIxbeeWVTTM7h1l4zFhrFtjn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\nM83ad0WfW2+99Uz/9Kc/Nf3EE0+YZp0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f1bbe1438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10... Discriminator Loss: 0.0846... Generator Loss: 3.6659\n",
      "Epoch 1/10... Discriminator Loss: 0.0971... Generator Loss: 3.4108\n",
      "Epoch 1/10... Discriminator Loss: 0.0355... Generator Loss: 4.7895\n",
      "Epoch 1/10... Discriminator Loss: 0.0466... Generator Loss: 4.0464\n",
      "Epoch 1/10... Discriminator Loss: 0.1194... Generator Loss: 3.5642\n",
      "Epoch 1/10... Discriminator Loss: 0.0460... Generator Loss: 4.8818\n",
      "Epoch 1/10... Discriminator Loss: 0.1162... Generator Loss: 4.0675\n",
      "Epoch 1/10... Discriminator Loss: 0.0965... Generator Loss: 3.2920\n",
      "Epoch 1/10... Discriminator Loss: 0.0922... Generator Loss: 3.6286\n",
      "Epoch 1/10... Discriminator Loss: 0.0634... Generator Loss: 4.2567\n"
     ]
    },
    {
     "data": {
      "image/png": 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47rnnCrbdt5Lh7+Y9byopu+66q+ntttvONFeB06ZNc87V/es4LXzVokmcjcqy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f1bd3a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10... Discriminator Loss: 0.1036... Generator Loss: 3.2982\n",
      "Epoch 1/10... Discriminator Loss: 0.1202... Generator Loss: 3.3805\n",
      "Epoch 1/10... Discriminator Loss: 0.1204... Generator Loss: 3.5760\n",
      "Epoch 1/10... Discriminator Loss: 0.1697... Generator Loss: 6.6033\n",
      "Epoch 1/10... Discriminator Loss: 0.1021... Generator Loss: 5.9242\n",
      "Epoch 1/10... Discriminator Loss: 0.0864... Generator Loss: 4.0038\n",
      "Epoch 1/10... Discriminator Loss: 0.0518... Generator Loss: 4.7603\n",
      "Epoch 1/10... Discriminator Loss: 0.1409... Generator Loss: 3.9582\n",
      "Epoch 1/10... Discriminator Loss: 0.0756... Generator Loss: 4.2929\n",
      "Epoch 1/10... Discriminator Loss: 0.0922... Generator Loss: 3.4716\n"
     ]
    },
    {
     "data": {
      "image/png": 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fSOaNr4dcqYw1i6+jzDz+/1lnnWX69ttvT/W4OBbFYqNRc4HC6Bp3zrl3333X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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f27947a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10... Discriminator Loss: 0.1661... Generator Loss: 2.7608\n",
      "Epoch 1/10... Discriminator Loss: 0.0682... Generator Loss: 3.9645\n",
      "Epoch 1/10... Discriminator Loss: 0.1354... Generator Loss: 3.1347\n",
      "Epoch 1/10... Discriminator Loss: 0.3555... Generator Loss: 1.8577\n",
      "Epoch 1/10... Discriminator Loss: 0.1406... Generator Loss: 4.3552\n",
      "Epoch 1/10... Discriminator Loss: 0.1121... Generator Loss: 3.5015\n",
      "Epoch 2/10... Discriminator Loss: 0.0806... Generator Loss: 4.0883\n",
      "Epoch 2/10... Discriminator Loss: 0.1224... Generator Loss: 3.5916\n",
      "Epoch 2/10... Discriminator Loss: 0.1367... Generator Loss: 4.1136\n",
      "Epoch 2/10... Discriminator Loss: 0.1038... Generator Loss: 4.7899\n"
     ]
    },
    {
     "data": {
      "image/png": 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8OKgrhVkg9957r69jcMBZK//zn/+oxnNC2ydJ2U1BwPNfvny56ttvv131jBkz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\nEGIFdjiEEEKs8P9PLGMMeZ/QngAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f27ddc240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10... Discriminator Loss: 1.2725... Generator Loss: 11.8256\n",
      "Epoch 2/10... Discriminator Loss: 0.1573... Generator Loss: 3.8374\n",
      "Epoch 2/10... Discriminator Loss: 0.1925... Generator Loss: 2.8413\n",
      "Epoch 2/10... Discriminator Loss: 0.2124... Generator Loss: 2.9128\n",
      "Epoch 2/10... Discriminator Loss: 0.1523... Generator Loss: 3.2092\n",
      "Epoch 2/10... Discriminator Loss: 0.3612... Generator Loss: 1.9111\n",
      "Epoch 2/10... Discriminator Loss: 0.2459... Generator Loss: 3.1800\n",
      "Epoch 2/10... Discriminator Loss: 0.4468... Generator Loss: 1.8702\n",
      "Epoch 2/10... Discriminator Loss: 0.1825... Generator Loss: 3.7734\n",
      "Epoch 2/10... Discriminator Loss: 0.1536... Generator Loss: 3.3625\n"
     ]
    },
    {
     "data": {
      "image/png": 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s3XHbbbfZttLSUu/nlEb0e+vYY481rRljaoXp89GmTZuM+1GLWFcc1lVrg2el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\nhAMAAF5gwgEAAC/8f3P04btUPJBzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe4ba8e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10... Discriminator Loss: 0.2916... Generator Loss: 2.3012\n",
      "Epoch 2/10... Discriminator Loss: 0.1657... Generator Loss: 3.4905\n",
      "Epoch 2/10... Discriminator Loss: 0.2652... Generator Loss: 3.6738\n",
      "Epoch 2/10... Discriminator Loss: 0.2147... Generator Loss: 3.7243\n",
      "Epoch 2/10... Discriminator Loss: 0.2815... Generator Loss: 4.5590\n",
      "Epoch 2/10... Discriminator Loss: 0.1448... Generator Loss: 3.6266\n",
      "Epoch 2/10... Discriminator Loss: 0.2944... Generator Loss: 2.3456\n",
      "Epoch 2/10... Discriminator Loss: 0.2029... Generator Loss: 3.1263\n",
      "Epoch 2/10... Discriminator Loss: 0.2961... Generator Loss: 2.8018\n",
      "Epoch 2/10... Discriminator Loss: 0.2666... Generator Loss: 2.7701\n"
     ]
    },
    {
     "data": {
      "image/png": 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SPi+xGhwAACqLkhoAwAsaHACAFzQ4AAAvaHAAAF7Q4AAAvKDBAQB4QYMDAPCC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\npEcAvvB9k1/o4QAAvKDBAQB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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f1bd344a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10... Discriminator Loss: 0.1861... Generator Loss: 3.3100\n",
      "Epoch 2/10... Discriminator Loss: 0.2251... Generator Loss: 2.6816\n",
      "Epoch 2/10... Discriminator Loss: 0.2309... Generator Loss: 3.4973\n",
      "Epoch 2/10... Discriminator Loss: 0.4062... Generator Loss: 1.7640\n",
      "Epoch 2/10... Discriminator Loss: 0.4681... Generator Loss: 1.7148\n",
      "Epoch 2/10... Discriminator Loss: 0.9249... Generator Loss: 5.4265\n",
      "Epoch 2/10... Discriminator Loss: 0.2844... Generator Loss: 2.8931\n",
      "Epoch 2/10... Discriminator Loss: 0.2544... Generator Loss: 2.4198\n",
      "Epoch 2/10... Discriminator Loss: 0.8330... Generator Loss: 1.0963\n",
      "Epoch 2/10... Discriminator Loss: 0.2962... Generator Loss: 3.0631\n"
     ]
    },
    {
     "data": {
      "image/png": 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Q6+yf//yn6f79+5vWSCtdas6bN8/0wIEDTWuNr2JEo2r23ntv03vttZdpjWTT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\nI5PC2qWo7aYbeSljxoyptl2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe600b160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10... Discriminator Loss: 0.2990... Generator Loss: 2.6691\n",
      "Epoch 2/10... Discriminator Loss: 0.2235... Generator Loss: 3.2770\n",
      "Epoch 2/10... Discriminator Loss: 0.5465... Generator Loss: 1.6927\n",
      "Epoch 2/10... Discriminator Loss: 0.3808... Generator Loss: 2.4717\n",
      "Epoch 2/10... Discriminator Loss: 0.4184... Generator Loss: 2.5335\n",
      "Epoch 2/10... Discriminator Loss: 0.3191... Generator Loss: 2.6551\n",
      "Epoch 2/10... Discriminator Loss: 0.3891... Generator Loss: 2.9803\n",
      "Epoch 2/10... Discriminator Loss: 0.5474... Generator Loss: 3.8734\n",
      "Epoch 2/10... Discriminator Loss: 0.4334... Generator Loss: 1.9217\n",
      "Epoch 2/10... Discriminator Loss: 0.3043... Generator Loss: 2.3542\n"
     ]
    },
    {
     "data": {
      "image/png": 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9ZIQDAPCCGw4AwItISmqlKaPp0F07naZMmWK5X79+lt955x3LQSeb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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f275d8ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10... Discriminator Loss: 0.7850... Generator Loss: 3.6884\n",
      "Epoch 2/10... Discriminator Loss: 0.7929... Generator Loss: 1.0322\n",
      "Epoch 2/10... Discriminator Loss: 0.6039... Generator Loss: 1.2298\n",
      "Epoch 3/10... Discriminator Loss: 0.3970... Generator Loss: 1.8018\n",
      "Epoch 3/10... Discriminator Loss: 0.3600... Generator Loss: 2.5111\n",
      "Epoch 3/10... Discriminator Loss: 0.7331... Generator Loss: 1.1185\n",
      "Epoch 3/10... Discriminator Loss: 0.4139... Generator Loss: 2.3563\n",
      "Epoch 3/10... Discriminator Loss: 0.5995... Generator Loss: 1.2616\n",
      "Epoch 3/10... Discriminator Loss: 0.5460... Generator Loss: 1.5321\n",
      "Epoch 3/10... Discriminator Loss: 0.4844... Generator Loss: 1.7606\n"
     ]
    },
    {
     "data": {
      "image/png": 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wAABepFxJLV6p2pUFSpPeF7rT7qRJkyyzi2rJpMN3T6odIz0cAIAXNDgAAC/S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\nOMOyjmoK1ue6+eab4z7ObKP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Hr+EHH3xgOWq3XvxKr5Wew7lz51o+9thjLeu51VG3WkrL\nRPRwAABe0OAAALzIiad73L59+4Jg98bSlCmTzQoKClxOTs7nBQUF7Yt+drRUuS5RgtFpuiaUTkzU\nHUR1q4LSkojr0r59+4L8/PzIEWI+6TE0adLE8sMPP+ycc65+/fr22LBhwyy/9NJLllOk1JN294qe\ney1n6iTpdBbvvUIPBwDgBQ0OAMCLtBmlpiM/orqm6Vxey2RBOWbt2rX2mE7e1SXXkXh6X+j2Bz16\n9HDORU8M5X4qOT2HmVJGKwl6OAAAL2hwAABepE1JrXXr1pYXL15sWcsxiSgB6A59OpmLsk/J6Uin\nnTt3Wk6FkVzZjNIZfKGHAwDwggYHAOBF2pTU5syZYznR5S0t6UQtC54pk01TRbaM2OFzA+xBDwcA\n4AUNDgDAi7jWUsvJyVnvnFuavMPJSo0KCgrqlOQFuC5JUaLrwjVJCu6V1BTzdYmrwQEAoLgoqQEA\nvKDBAQB4QYMDAPCCBgcA4AUNDgDACxocAIAXNDgAAC9ocAAAXtDgAAC8+H9FRKXW541TywAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f279068d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10... Discriminator Loss: 0.5655... Generator Loss: 1.3819\n",
      "Epoch 3/10... Discriminator Loss: 0.3787... Generator Loss: 2.2469\n",
      "Epoch 3/10... Discriminator Loss: 0.4404... Generator Loss: 1.9474\n",
      "Epoch 3/10... Discriminator Loss: 0.4603... Generator Loss: 2.9231\n",
      "Epoch 3/10... Discriminator Loss: 0.4208... Generator Loss: 1.6572\n",
      "Epoch 3/10... Discriminator Loss: 0.4506... Generator Loss: 2.1730\n",
      "Epoch 3/10... Discriminator Loss: 0.4267... Generator Loss: 1.8487\n",
      "Epoch 3/10... Discriminator Loss: 0.6301... Generator Loss: 3.0307\n",
      "Epoch 3/10... Discriminator Loss: 1.3430... Generator Loss: 4.4685\n",
      "Epoch 3/10... Discriminator Loss: 0.5245... Generator Loss: 1.7271\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZfvXVVy3rZ16cMcIBAHjBDQcA4EXsSmrayTRlyhTL//rXvyyffvrp\nlnWNp0GDBjnn9qxH9L/Ph9TT0pl2Fc6dO9fya6+95pwr2vVEKSacltTKgu4mqmW0hQsXWh43blzo\n9yA99Fp55ZVXyvBISo8RDgDAC244AAAvYl1S02W7L7vssmL/LMN7/3RCoJ47XUMqKK9paSBuE9Li\noqzfw9oRpeU1XQpfJxwCyWCEAwDwghsOAMCL2JXUopR1qQHhdu/ebfmtt94K/Z6g1EYZLf70Ohs6\ndKhlnZzItYiSYoQDAPCCGw4AwIuMKakhnrS8Qqml9LTsWBbrqmkn4T/+8Q/LnFukAiMcAIAX3HAA\nAF7kJTNUz8vL2+ScW5W+w8lJDQsLC2uX5gk4L2lRqvPCOUkLrpV4Svi8JHXDAQCgpCipAQC84IYD\nAPCCGw4AwAtuOAAAL7jhAAC84IYDAPCCGw4AwAtuOAAAL7jhAAC84IYDAPCCGw4AwAtuOAAAL7jh\nAAC84IYDAPCCGw4AwI/CwsKEv/Lz8wudc3yl6Ktt27aFzrlNyZyDbDwveXl59lXWx5Kq85Kfnx88\nD1+p+8r5ayVuX8leK0mNcAoKCpL5dhRj7ty5zqVg98GCggJXrlw5+8oEeXl59lWhQgX70r+Hfun3\np1sqzktBQUHwPEidlFwrSJ1kr5UK6TuUzKcfbnHfGfWXX34p60NIir6eP//8s+VM+3sASFxm/HMY\nAJDxuOEAALyIRUktTqUr/R2I5p9++qksDieWEvk9SnHnUZ+DMhoyXZw+w+KMEQ4AwAtuOAAAL2JR\nUivrIagOh/fZZx/LFStWtLxz506vxxQ3+hqlohxW1uccSKU4vZ/1+txvv/0sf/fdd2VxOEUwwgEA\neMENBwDgRSxKamWhfPnyofnKK6+0/NVXX1l+6aWXLMdp+FwW6CoDyl6dOnUsL1682HKNGjUs67V6\n7rnnWp40aVKajy4cIxwAgBfccAAAXuRsSU3pWl6jR4+2rJM9c72Mlut//1yn3Zuav/32W8uUWtOv\ne/fulsePH1/s9+uvC/71r39ZPvzwwy0vW7YsRUdXPEY4AAAvuOEAALzIqZJahQp7/rqXXHKJZR1q\n6mTPOEyUyiY6IS0/P9/y1q1bLVOWiY+qVata7tWrV+jj9913n+Xvv//ez4FlsWCippbzBwwYYPmv\nf/1rUs+nvy7QTjZ93CdGOAAAL7jhAAC8yPqSmm4x8NFHH1lu0qSJ5Vq1all+5ZVXLG/bti3NR5cb\nglLa559/bo81bNjQ8p133mn5jjvu8Hdg+BWdTDht2jTLdevWtfzWW29Z1kmGGzdutBy13p6WtX/4\n4YcUHHHm08+oBg0aOOec27x5sz3Wr18/y9p1pp2j2i14zTXXWNYyZ9OmTS0fccQRltesWWM53aU2\nRjgAAC+44QAAvMiqklowjNdyjZZxtANN3XXXXZZvueUWy++9957lCy64wHKub1WQCC2dzJgxwznn\nXOPGje0xLSPcfPPNlkeOHGlZywpl1VWTzYLrRdcM1JKaGjp0qGW9XqI6OXVyqJZ4hgwZYlnX9nr7\n7bct51qn4n/+8x/LxxxzjHPOuXfffdce0y0G9LXRkuTvfvc7y/PmzQv9s1qau+yyyyxv2LDB8pw5\ncyynY7I3IxwAgBfccAAAXmR8SU2H9Dp8LI4OTbXzQye1nXrqqZa1A6egoMByrpd9tBtJS5kffvih\n5Zo1a/7qe/W10vKbdgk+9NBDlmfPnm155cqVpTzqzKMlyOrVq1vevXu3Ze1UiiqHHHzwwZb/+9//\nOueKnhf9czo5+plnnknqeKtUqWL5xhtvtLz//vtb7tq1q+Wg7JorKleubLlDhw6Wg88i7SLbtWuX\n5ddff93y888/b3nVqlWhz63nYf78+ZZHjRpl+YsvvrAc9V5IFUY4AAAvuOEAALzImJKalhR0nSEd\nAgZ0spOuAfXvf//bclDmcc65RYsWWdbhqD63ln1OO+00y88++6zlXCmp6ety1FFHWdZy2L777ms5\n6ILRbiVdHr1169aWdXLawIEDLffu3dtyrpTUtNR72223WdbyiXYkLViwwLJeL+3bt7eskzaD86il\nE/1zpfH1119bbtOmjeVq1apZ1u6oXNv+4s9//rNl/WwJXge9Vt58803L+hmjn4OtWrUKze+8847l\nm266yfKOHTt+9TN9YIQDAPCCGw4AwIuMKamNGTPGclQnxVVXXeWcc+7JJ58s9vl08ubgwYMtDxs2\nLPT7tVNES3M//vhjsT8r21SqVMnyddddZ1kn1moZ57PPPnPOFT1XEydOtKwlAN0qIlhXyrmiJYBs\npl1cOlEvmBDonHOvvvqqZZ3YrK+vlmm0w0zLdIFErpfS0E5Ozbns3nvvtRy27pxO/Pzmm2+KfT7d\n4qN+/fqWtXNx+/btJTvYFGKEAwDwghsOAMCLWJfU1q5da1mXR9cunUaNGlkuabfF9OnTQx/X59PO\nKB2+5qKOHTta1uH+ySefbDkoo0XR11bLZVouOumkkyyvXr26ZAebAbSkcsMNN1hu166dZS176aRa\nncAc1VWpE0VV0OWkHVNIn2OPPdZyWGeac3tKYIms16glbJ2kqyU17RaMA0Y4AAAvuOEAALyIXUlN\n1/vRdZ90wlPbtm0tp2LSkq5PFEU7hnKdro2l5yVYmysR2i2lZTmdvKjLtutk3myjky11DS3doVZ3\n31RaRtNJy3od6fqAer3MnDnzV48htXSbBt2CIarTNuji0+tDs24foZ1s+h7SLSDiNhmdEQ4AwItY\njHCmTJliWZc5UdpDvmXLllL/zEMPPdRy1C9V9V/Vuu93LtJfcupyNvoa6b/OZ82aZTn45WbUsin6\nr7BJkyb96s85V/RfeXH7V1tp6TJAzZs3tzx+/HjLUXvZ659t0aKFZV3RXOmfDZZMyebXtizo+/yT\nTz6xrO/nsEYB55x75JFHnHNFV6q//vrrLR999NGWdfSk87EmTJhQ4mNPN0Y4AAAvuOEAALyIRUlN\nV1+OokPJVNDlafQXeIsXL7bcvXt3y19++WVKf36mOfLIIy0fcMABlvUX1boskG6oFZTatMw2duxY\ny1qu1FJDrVq1LGvpSPdyzwa6VND69estawOFlpT1NdIN2BYuXGh53bp1lrX5Rldofv/9951zzh12\n2GH22Jlnnml5xIgRlnWjQ+ydns9EVrnXcx40DQwaNMge01KpbjKp5U+9thJZCqesMMIBAHjBDQcA\n4EWZldT69u2b1Pfr8hs333yz5eLmEGgHzooVKyzXq1fPsg5BtXsNe+jSGbqciq7+q5trnXvuuZaD\nlbZ1BWkto+myNVqi0+WMdE6Onq9smEOiSyUFXUrOFd1rPurvqY9HdQxqiUd/VvBnL774YntMSzm6\nYZc+ny45lYurpRdHy2VLly61rEvO6OeSXjdBl+79999vjwWr4DtX9FcLunHkAw88YDnO1wQjHACA\nF9xwAABelFlJTYeMidBJU9qxFDak17KYLvGhw1jtdNKuK4TTDhstqY0cOdLy448/blnPUdAxpeUy\nXVm6f//+lnWjO10hWbur4lwyKAn9+2h3n77OydKutqiNt4IOKn39tfymXVB333235Z49e5b4uHKB\nfrboavZ6TWjZTbsxg/KmljC1y1BpaVMnZscZIxwAgBfccAAAXpTZOEy7ZWrXrh36PcuXL7d86623\nWtYhq66EG6zDdfjhh9tjOvFKSxc6gSrbSjTp8PDDD1vWEs0dd9xhubjXUUs0WgLQ0sCAAQMsb9u2\nLeHnzha6rla6BdeOln2UlvS0ey1XzkVJ6efTiSeeaFk3joxaVy0ou+n/b9CggWW9bnSDwlGjRln+\n9NNPS3zs6cYIBwDgBTccAIAXZVZS00l9H3/8seUrr7zSsnaY6RCzT58+lrt06WI56E6rUqWKPaaT\nPZs2bWqZskBytByg5bVkXkftUtPJbLo9Rdz2YPct3RMptSQTrJum3ZtK1/jSLkEkTl83XZtOtxzQ\nElywvl2rVq3sMT1nmpcsWWL5jTfeSNERpxcjHACAF9xwAABelFlJTTtgdOl7pR1mzZo1szx06FDL\nuutdQDt9dO2h0kykwx66JH4iu0UG5dBjjjnGHmvSpInlDz74INWHmLGSLfXqBELt9tT3eo8ePSxf\nc801lrX7KezPzZ0713K2bQmRDH2NS1OK13XV9Dn1c65Tp07OuaLnQa8x3XaiQ4cOljPls40RDgDA\nC244AAAvYrEAT9QwVYeSb775pmVdkyiMLn2f611P6aCTZnXJ9S1btljW8trvfvc755xzl156qT2m\n20NoSXXmzJmpPdgsp0vbv/DCC5a1lBy1A2VArz/NJ5xwgmVdz+u///2v5Uwp5ZRGsmU0/dw677zz\nLOv6kfo9UZNAA/orgoKCAsuZ+NozwgEAeMENBwDgRSxKalG0M+a+++6zfO+991rWbo+gfNayZUsP\nR5e7dI2z6tWrW9aJnborZ3COtLSj5YAdO3ak5Thzgb7Ob7/9tuXjjz/eclgZLYqWdPR8atlHz6Nu\nJ5HL9t9/f8sXXHCBZZ0kra9bVOdbUIp++umn7TGdJJ2JZTTFCAcA4AU3HACAF7EuqSkdmuowfvPm\nzZbHjx/v9ZhylZYAdK26KC+99JJzrujaeAsWLLDMOl0lp92AgwcPtpyfn2+5d+/elovrUtMS3dSp\nUy3rFhLpXu8tE2n5Xzv6tASm+bvvvrOsE5/POecc55zfbSp8YoQDAPCCGw4AwIuMKanpkPXxxx+3\nHKeujVStuZRtgnO0bNkye2z48OGWW7RoYVnLOEiOvuf+8pe/WO7bt69l7TYLrqmoiZ/6fo7TdRZH\n+vk0bNgwy7olgZY/dT3CXPqsYIQDAPCCGw4AwIuMKanpBENdy0s7nMpiaEoZrWSef/55y3Q9pddP\nP/0UmovD+7lktLyWy9s6hGGEAwDwghsOAMCLjCmptWnTxvK8efMsl/Wwv6x/fqb64osvLBe33QSA\n7MAIBwDgBTccAIAXGVNS06XXmYSW+UraOQWUFp2lZYcRDgDAC244AAAv8pIZUubl5W1yzq1K3+Hk\npIaFhYW1S/MEnJe0KNV54ZykBddKPCV8XpK64QAAUFKU1AAAXnDDAQB4wQ0HAOAFNxwAgBfccAAA\nXnDDAQB4wQ0HAOAFNxwAgBfccAAAXvw/l8EhUVMCCnQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe49026d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10... Discriminator Loss: 0.4989... Generator Loss: 1.9952\n",
      "Epoch 3/10... Discriminator Loss: 0.5517... Generator Loss: 2.5968\n",
      "Epoch 3/10... Discriminator Loss: 0.4929... Generator Loss: 1.4615\n",
      "Epoch 3/10... Discriminator Loss: 0.5530... Generator Loss: 1.7977\n",
      "Epoch 3/10... Discriminator Loss: 0.4145... Generator Loss: 2.4207\n",
      "Epoch 3/10... Discriminator Loss: 0.5317... Generator Loss: 1.3677\n",
      "Epoch 3/10... Discriminator Loss: 0.5350... Generator Loss: 1.5133\n",
      "Epoch 3/10... Discriminator Loss: 0.4250... Generator Loss: 1.9179\n",
      "Epoch 3/10... Discriminator Loss: 0.6873... Generator Loss: 1.1260\n",
      "Epoch 3/10... Discriminator Loss: 0.8165... Generator Loss: 0.8498\n"
     ]
    },
    {
     "data": {
      "image/png": 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gwQEA+FFQUBDzV25uboFzjq8EfbVv377AObc1nnvAfUmP+5Kbmxs8T0p9lSlT\nxr7Kli17xK+KFSvaV7ly5eyrQoUK9qXHc3Jy7CtJ5897JcW+4n2vlHNxyMvLc9u3b4/nW3AEixcv\ndjk5OSXeDCpV7ovuS6852HMplfde0vNNxH3Jy8sLnqfE51ZSeg6VKlWyXLFiRedc9H3Jzc21nJ+f\nH/oce/futfzDDz9YPnDggOUE3veMea9kinjfK5TUAABexNXDAWL1yy+/lPYpxCWVe18lpb/b/v37\nLQe9EP3v2nv56aefQnNeXp7lKlWqWP7oo49CfyYQoIcDAPCCBgcA4AUlNVf4j6pVq1a1vG/fPss/\n//yz13NKR5RRUp+WOsPul77OdRCAltr0j+47d+60fPDgwYSdJzITPRwAgBc0OAAALyipOeemTp1q\nuXXr1pbvv//+0MdQOkK60tdu2OtYy8iad+3aZXnz5s1JOrvsFDZnLVPRwwEAeEGDAwDwImtLaj16\n9LDcrVs3y+XKHbokd9xxh+U333zT8p49eyyn2wTHZCpbtqzl8uXLW/7xxx9L43SAlDV+/HjLHTt2\ntPzggw8655x7/fXX7ZiOBEz3khs9HACAFzQ4AAAvMr6kVrlyZctr1qyxXL9+/dDHa5dVu727d+8O\nfUw2jTApSt26dS3fc889locMGeKcc27Hjh12LNuvFbLP+eefb/l3v/udZf0MGTdunHOucBlaS/tP\nPfWU5XR8D9HDAQB4QYMDAPAiI0tqFSpUsKzrQekINKVrQ7Vv397y2rVF7yuUjt3aRNJywIcffmi5\nefPmlvv06eOcc27ZsmV2rGnTppZ1pN+cOXMsd+/ePbEnm0X0vgQbrTnn3CmnnGK5evXqzjnnjj76\naDv2zTffWH733Xcts05aybVr185y1OjWYMuI5cuX27GHHnrIso7+1PKabh+RyujhAAC8oMEBAHiR\nkSW1uXPnWtYympa/dPJmvXr1LDORs2hlyhz6d8of/vAHy7oTpD4mmBB6wgkn2DEt+SidhPvss89a\n/v3vf2+Ze3SIXscWLVpYrlGjhuVp06ZZbtSo0WHfq2XnTZs2Wb7yyistL1myxDJbdcROtzv59ttv\nLd95552WJ0+ebHnLli3OucKls0cffdTy0KFDLT/wwAOWq1WrZjmV7w89HACAFzQ4AAAvMqqk1rJl\nS+dc4ZE4Wkb77LPPLJ966qmWKdEUTddJW7p0qeWoMppe0yAH5QLnCpcANGuJSEepzZ8/37JOyM32\ne6f35ZacgPHRAAAYj0lEQVRbbrHcqVMny02aNLGs1zcovcyYMcOOaTl6w4YNlrN9NGZRjjrqKMta\n2lQzZ860nJ+fbznsNayjAvv162dZ33v33nuvZS2b6jqRqXbf6OEAALygwQEAeJH2JTUtKbz//vvO\nucKlHV0D7YwzzrCcyiM5UoWOdArWeHKu8ERBvdY6+eyRRx6xHOycGkxqc65waeemm26y3Lt3b8tB\nidQ55wYNGmT51VdftaxLt2cLvXbDhg2zrKPK9N5FrfcXlHLGjh1rx77++mvLWvbR79P3nJaDUq18\nk2x6XSdNmmRZJ3ieddZZlosqo8VCJ3tqie7tt9+2/PTTT1u+8cYbi/VzkoUeDgDACxocAIAXaVlS\n067svHnzLDds2NA559yuXbvsmE50Y+fJ+Ozfv9+yrn2mpZMnn3zSsi6jXtTaW/ocWgLQEkTr1q0t\n632sU6eO5WwsqV1wwQWWBwwYYFknOUdNrNXrHkxE/PTTT0P/u7ruuuss33rrrZYnTJhgWcs9e/fu\njf4FMpBO1NTXvpY2Ez2ictWqVZa1XN2qVSvLqbZ9Cj0cAIAXNDgAAC/SsqSmawjpdgJBl/Gqq66y\nY/v27fN3YhlGS2o6IVBLWromVHGXsNeJujrZU0dDaYlGl9AviVQrNxyJlmZ0lF5UGS1sNJpzhUcb\n9u/f/7Cf07lzZ8svvfRS6M9RDz/8sGUtqerur6l+bYtLfy/dTuDss8+2rNch0fR+63tFJ7Xn5uZa\n3rZtW9LOJVb0cAAAXtDgAAC8SJuSWoMGDSxrKUC7lUFJZ9asWf5OLINpyeCuu+6yPGrUKMuJ2AlS\ntyGImlTYoUMHyzoipyRSvdSjr3kt2VSqVCn08TqZ+fPPP7f83HPPWR4zZozl4PpquUzLYnovYqHn\n1bVrV8tvvfVW6HPqROGoEVypfo8CjRs3tqy/o74/El3C1efTMvPq1astp9pag/RwAABe0OAAALxI\nm5Ka7hYZ1dUPJkLp+l5RS+YjPlr+0O571ATDouhIGr236uOPP7a8YsWKYv2cdKPL3H/44Yehx5WW\nZnS9Od1FUnfx1LJb8N7QNdj0vsRyb/U9paMa//KXv1jWbSmaNWtmeeLEiZb79u0beo7pQne+rV+/\nvuVYJtUWl177Pn36WNYdXGOZGO1ztCY9HACAF2nTw9E/vukfjbVFrlChgnPOuWuvvdaO6b8M9Y9p\n//d//2d5x44dlukFFV/UvIDg3ul/v+SSS0K/T3tPp512WjJOM6XpvLLmzZsX+Xh9/V966aWWg2Vr\nnHPuyy+/tKzz0jp27Oicc+76668Pfb6o94LOidJlpEaOHGlZV/0+9thjLesABX1Pp8vggChr1qyx\nfNlll1nW65NM69atsxzLtdT3nN6TRA3IiUIPBwDgBQ0OAMCLtCmpRXUZ9Q+MVatWdc45d/vtt9ux\n9evXW65evbpl/ePl1KlTQx+f7t18H6KuUdgffitWrGhZVxzW0k02ltGUDnKJ5Y/2+viTTz7Zsq7i\nHCW4d1FzRfQeaolUBwe88MILlo8//njL+v6qXLmyZd0QUZdFSvdStr4Pvvvuu1L9+bGoXbu25Zo1\na1rWPzsk4/OPHg4AwAsaHACAF2lTUrv88sstaxlB54cEoy20pKZzObREoKvvBqU4JI52x4P7pd11\nLa9t2LDBciqsaFuadHXheOc46ePj+V59P6moFaJr1aplWefPbNy40bKODtWND4cNG2ZZy2tIPi2L\nrly50rKWVM855xzLS5cutZyo8ho9HACAFzQ4AAAv0qakppMzgwme/+qaa65xzjk3Y8YMO6ZlNC3j\n6CiNLl26WNYlVBKxEjIOlWx04zbtonfq1Mn7OaWqtWvXWtbRYPqajypvRJXGihLv0ib6eC1H6wRP\nfczmzZstP/nkk8U6x3SSShv7lS9f3rKuOK5/UtCJvPoZmQz0cAAAXtDgAAC8SJuSWo8ePSzrJLGt\nW7daXrZsmXOucJdWN4XSiWnPPPOM5T/+8Y+WKaMlXjAJUMsLOhpN10/LdgsXLrR85plnWta1ybR0\n1qZNG8vt2rWzrKXkKlWqWNaRSsHzaEnls88+s6yreOsaWzqBWkegafkmPz/fcrdu3Sxn0vtLP2d0\n3TtdS00n4GppMdErYlerVs3yiBEjLOufCxo1amRZ33+PPfaYZV13Lxno4QAAvKDBAQB4kTYlNR0N\no5M9dbJZ0MXVrqNuVXDbbbdZ1kltOiERiaHlhueee+6wY1o6wiFaLl6yZIll3eArlnKMvl/0ta5l\nrwYNGjjnCq9TGFWa6dy5s2Uty+l7Ue/vmDFjLAel7kyja5DNnj3bsq4jN3ToUMt6rbTUP3/+fMuP\nP/64c8656dOn2zEtReu91MmbTZo0CX28Tq4dO3as5QEDBlhO9pYEih4OAMALGhwAgBdpU1L74osv\nLOvIHC0dBEul6yieqK6mLo2u660hMXSEVTCxU8tFWt5EOH296miwWHz//fdFPkbL0YGdO3dabtq0\nqWWdEKiTUydPnmz5nXfesTxv3jzLmTQyTelIPF2/sV+/fpZPP/10yzpaUEtjZ511luXgvaKTfqN2\n59SsrxW9r1reS4UtIOjhAAC8oMEBAHiRNiU1XeNMR8lotz/osmoXXpe+/6//+i/LjJJKvLZt21qe\nNGmS5aDrv2bNGjvGyMCilcY6XLpmW4sWLSzrCCsdQTVy5EjLOiKqtNcQ80E/Z15//fXQrLp37255\nypQplsMm4+ouqVF0tOL9998fmlMNPRwAgBc0OAAAL1K6pKbdyrvvvtuyjvYI29lQJzINHjzY8qJF\nixJ9illPr79OJmvYsOFhj9UJbtlQcikOLa8ker2tWOjoqOHDh4c+RkemZVsZrSRee+01yzrqTyeB\nBqPKxo8fb8f0zwI6efO9996znC4jAenhAAC8oMEBAHiRciW1WrVqWdaRZLpDXVgZzblDXfoffvjB\njr300kuH/Xckjq7NpcvQa2kouO66DDoOSaUdIpWORtMJoTrhESWnk3qDXTl14nQmoYcDAPCCBgcA\n4EXKldR01Mstt9xiedSoUZZ1sqeOSBs0aJBzzrnRo0fbsVQqUWQiHUmoI2V0omBQMgjKBShMX6Op\n9HrdsmWL5XjXcgPC0MMBAHhBgwMA8CLlSmpaIps5c6blli1blsbpoAh79uyxPGTIEMv79u2zPG3a\nNOcco5vSgY6Y00mgQCLQwwEAeEGDAwDwIuVKakgvOjJNRwfqrqvBToOpOsERh+j2BLqj5N69ey3H\nu3Mk9x0BejgAAC9ocAAAXlBSQ8JouaQ0ltZHyemEXc2UwpAI9HAAAF7Q4AAAvMiJp6uck5Oz1Tm3\nNnmnk5WaFxQU1C3JE3BfkqJE94V7khS8V1JTzPclrgYHAIDioqQGAPCCBgcA4AUNDgDACxocAIAX\nNDgAAC9ocAAAXtDgAAC8oMEBAHhBgwMA8IIGBwDgBQ0OAMALGhwAgBc0OAAAL2hwAABe0OAAAPwo\nKCiI+Ss3N7fAOZe2Xzk5OfZV2ufinCto3759gXNuazz3INPvS9RXut2X3Nzc4Hn4StxX1r9XUu0r\n3vdKXD2cvLy8eB6eEnJycuy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe5e54e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10... Discriminator Loss: 0.7241... Generator Loss: 3.1345\n",
      "Epoch 3/10... Discriminator Loss: 0.4885... Generator Loss: 1.5999\n",
      "Epoch 3/10... Discriminator Loss: 0.5351... Generator Loss: 1.5265\n",
      "Epoch 3/10... Discriminator Loss: 0.5380... Generator Loss: 1.3031\n",
      "Epoch 3/10... Discriminator Loss: 0.6457... Generator Loss: 1.1500\n",
      "Epoch 3/10... Discriminator Loss: 0.7065... Generator Loss: 2.5016\n",
      "Epoch 3/10... Discriminator Loss: 0.5710... Generator Loss: 1.5068\n",
      "Epoch 3/10... Discriminator Loss: 0.5338... Generator Loss: 1.5060\n",
      "Epoch 3/10... Discriminator Loss: 1.0616... Generator Loss: 3.5073\n",
      "Epoch 3/10... Discriminator Loss: 0.7164... Generator Loss: 2.4671\n"
     ]
    },
    {
     "data": {
      "image/png": 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3ZJ/r///ivZJiX7G+V4q6GOTk5LgtW7bE8l9wGPPnz3dZWVkF3gwqmfflqKMO\ndpJ1b6V03mcpHvclJycneJ54nVZM9L7oOWgOHqOPPXDggOUiRYpY/vnnny3Hem/j+FpI6/dKYRTr\ne4WSGgDAi5h6OMDvFZZeTWGTn/vyyy+/OOciez362F9//TXP5wBiQQ8HAOAFDQ4AwAtKai76H1Kd\nO1hyANKNvo41x/LHf8poiDd6OAAAL2hwAABeZGxJTctovXr1srxq1cEh5WPHjrVMeS06yi6pSUeY\n5fXa5R7CF3o4AAAvaHAAAF5kbElt8ODBljt37my5WLFilosWPXh5xo8fb1lH+uzfvz9Rpxh3YRP8\nkL7Cls7h/qYevVeXX3655Z49e1pesWKFc865hx9+2I5pmT/d0cMBAHhBgwMA8CKjSmoLFiywfOqp\np+b5+C5duliePn265e3bt8f3xAqJsAm0QXmHddfij+uYPurVq2f5jTfesKyrcjdr1sw559zVV19t\nx04//XTLS5YsSeQpJhw9HACAFzQ4AAAvCn1Jbc6cOZZPOeWUqI/RUWdadjv//PMtM/Ezbx07drSs\nowArVqzonAsvqW3YsMHyoEGDLD/11FNRH4/odFSlTvwsVaqUZX0dV6pUyTkXOdJy8+bNiTzFjKNl\n5h07dlgeMmSI5UaNGllevXq1c85FbBKnE9OfeOIJy4sXL7as9zuV0cMBAHhBgwMA8KJQldSC7uv3\n339vx2rUqBH1sbp3+6WXXmp56tSpltOlm5pfiShLderUyfLzzz9vWUfe5OX444+3/OSTT1p+/PHH\nLTdo0MDyN998E/N5FlY6UVlf6zoiSicR6ujM4sWLO+ciy2zdu3e3/OKLL1rW9wsOVaZMGctly5a1\n/NNPP1nW98Tdd99tWUv60QSlT+ece/fddy1rKa58+fKWU/lzix4OAMALGhwAgBdpX1LTkTnBhMzS\npUvbMR0lol3Xvn37Wn7//fctF+bRUPFaS01HkmkJJtpkz1jPS2m5SEfkVK9e3XKmT8LV8kn79u0t\n633RUpveo4CW1HSS4axZsyzrSEK95qlcvkk0vZZalu/fv79lLWe+8sorlvMqo6mtW7da1pGbY8aM\nsfzVV19ZrlOnTr6f2zd6OAAAL2hwAABepH1JrV+/fpZLlizpnIss0ezbt8+ydkG121uYy2ja7S9I\n+UPLlLfccotlvdZ33XWX5eHDh1sO7oGOtglGSDnn3Ntvv21ZR1FpuVS/T9WqVS1nekntmGOOsTxw\n4EDLeu1/L8JgAAAYjklEQVS0ZKYTO7dt2+aciyyRTp482XL9+vUt6z3/8ssvLf/zn/+0XJjfR9Ho\n+2nu3LmWdURfsN2Ac7GV0cK8+eabln/88UfLVapUsVytWjXLWgpNBfRwAABe0OAAALxIy5Karg2l\ny3gH5SMd1VG3bl3LQQkhk8RrFFGTJk2iPqeWKZ9++unDPkfYOl0tWrSwrOUanRCqZaG1a9fm44wz\ng679p6P6wug9CnaSfP311+2YTk7Ucux1111nWcuhtWvXttyjRw/LmVZe27hxo2V9fca7pKXX9Z57\n7rGsa7N98cUXlrWMnQro4QAAvKDBAQB4kZYltWeeecayjtIJyi4nnniiHcvEMloiPProo5a1dPPR\nRx8V+Ll1RJWWAHRkmpba9uzZU+Dvmc7uuOMOy5UrV476GC17fvDBB5afe+65Qx6jJbISJUpYXrRo\nkWVdq0vdeeedlrU02rx5c8uZUF7TUqSOtNRJt/FeA3Dp0qWWdQRchQoVLOtrJa+Stw/0cAAAXtDg\nAAC8SJuS2h/+8AfLuiR+tImNOiEK8aHlLb3mrVu3tjxs2LAjej4d3VSuXDnLOjLtiiuusJwJJZrf\n+/TTTy2fdtpplsMmOffu3dvy0KFDLWuprVatWs65yBKMbkmg9yKMfn8tJS1cuNDyGWecYVknRRam\n+/jII49Y1knSZ555puXZs2dbDttBOLieWqILmzD62WefWdbRcDpy8KGHHrJMSQ0AkDFocAAAXqRN\nSa1ly5aWw7riwXEdpbZs2bLEnliGuPnmmy3Pnz/fcrt27SzrLpM6IicYeaYTP3XHQ13CXUs0WiZY\nuXLlkZ562rr11lst67YBSkszOpJs//79UR+v1/fDDz90zkVu9xC2VUQYfS/qaLeTTz7Z8u7duy2/\n+uqrlnUNvYkTJ1qOx5pjvulIS11XTcuZYWU0FawHuXfv3jwfq9dJS3f6/XWkoZ5jsq4xPRwAgBdp\n08OZMmWKZR0UoH/wDP6YrX9M27Rpk+W2bdta1g2L8vObR6ZbsmSJZf2D9I033mh5xowZlnV5oWC+\ngP5Wddlll1nW37x0D3hd2iZTaA9DN9sKo0uqhPVqVPAbtHPOHX300fk+L+3J6PtF76kOWtDelv4B\n/JprrrGsveOGDRta3rJlS9Tvm2p0iS1dHkhXTc/PPVH56dlEo++3Ro0aWdZBGqnQc6SHAwDwggYH\nAOBF2pTUtMv45JNPWn7wwQctB3+01D9e6h9EdWVdXf32nXfesRxrFzgT6Xh+LZe0b9/ecnZ2tuVg\n9Vy95rqEipZNTjrppKjHM4WWvPLzB3zdjE5LPFr2atasmeXp06db1nuX1/cMuxdapgkr0YXN4dJy\nj5bJ0+W+63nqH+eT/RmigzSU3gddZb9x48aWX375Zcuff/655XitOk8PBwDgBQ0OAMCLtCmpKR2F\npqsI//GPf3TORXb/tNu7Y8cOyzoP5Ouvv7asZTdEp9f0//7v/yzr8iu6Svftt9/unItc8kPpY4NN\nwZA/WqLSsrOO/NMcTUFKWPp/dYShlve0dKZzq+rXrx/1MalMV0q//PLLLQdzmpxL3aW1tKT20ksv\nWdbSqo46PeWUUyx/++23cTkHejgAAC9ocAAAXqRNSa1mzZqWBwwYYDlY8da5g0unrFu3zo5pN1I3\na6tbt65l3RtcJ6Yhb1oK0bKCXvfFixc75yLLEVqK+a//+q8EnmF60RFOU6dOtayTlrVEptdZR7gd\nKS1H63Pr/dKRabpckZbRtLyn76+xY8dGfc50oddef0ZdEfvdd9/1ek75pZviaSlW6bmvXr067udA\nDwcA4AUNDgDAi5QuqWkXffny5Za1W7tr1y7LwWiz9957z45169bNcpkyZaJmnTyH+Lvpppucc5El\nGp2YqKvbZjq9LhdffLFl3UirZ8+elrVMGTYaTctkOiIwKKuEbbSmo5d27txpWddvGzdunOUmTZpY\n3r59u+Xx48dbTscymtKSZzD60jnnzj77bMu6yvdrr71mOZFrNup7SydV62fo8OHDLeuIQi0Ndu/e\n3XIiRg7SwwEAeEGDAwDwIqVLajk5OZa1dKBdU+2iB937v/zlL3Ys2PzLuciJh/r/tNuL+Ljlllss\n64Z4AV2nKd3LLImi16V///6Wn3vuOcta6tLSWdgkTC29dOnSxTkXea+0vKbPoferSpUqUZ9bR2qt\nWLEi6s+R7vSzR6/JhRdeaHnEiBGWhwwZYlk3Jfz73/9uWcuSsZTdtISqa6Pp559uGaEl0qDM7Vzk\npniJ3qqFHg4AwAsaHACAFyldUtMRNevXr7esozC++OILy3/+85+dc5G7B2opTkddLFy40PIrr7wS\npzPObDrab+DAgZaD0VBa8mnRooW/EysEtDSi74X8CFuufujQoc455yZMmGDHdCL19ddfb1lHg86b\nN8/yBRdcEPUcdYfYeC1tn2oeeeQRy23atLGsa5DpaFjdiXPMmDGWdSJtMBq3Y8eOdkzvn/5ZYPLk\nyZb1vffDDz9Y1l1Vv/vuO8vJuif0cAAAXtDgAAC8SOmSmo5u0V0mw5Y179Spk3MucvSGdvN1Qpbu\nbFeYRtH4piVLXUtNRy8FtHSg9wXJEUxi1PfQWWedZVknb2qZWid+PvbYY5bDSkaF9f2lr2Fdpyza\nSEDnIncq1t1R9fOqQYMGzrnIHUT1PgS75zrn3AcffGBZt2zRkXGpdu3p4QAAvKDBAQB4kXIlNZ2c\npEuyX3HFFZaDnT2di+yOBuUd7UbqjqA6Gi3Vupo+6LXS8lZBnHTSSZZ1gqeWFQJ79+6Ny/csbHSp\n+LD15hJJSzb63tJ7qxM5dU2uTZs2WZ4xY4bldNnBMxH0s0UngWrW+6zX/JxzznHOOde3b187phNG\ndWTc7NmzLesE4FRGDwcA4AUNDgDAi5QoqelkpmbNmlnWUR2VK1e2HK1co3RSk44eKawT0PIrESWa\nYFSNc5Flumg7Ci5dujTu37+wScZrVCcW6q64WlIbOXKk5Q0bNljW11Qml9FipWU3nSQb5MGDB9ux\nsJ1X0xE9HACAFzQ4AAAvUqKkpnTC00svvWT5rrvusqwj2bSLGSynrhNDmWB4ULy649rF1yXp9fm1\nvBbcL71vWnLL9FJnsn9+vW96b9esWWNZt0TwNXoOv0n3MpqihwMA8IIGBwDgRUqU1Pbs2RM19+zZ\n07JOhNLRMFqmYWKhH1oOmz59etTH6HLtwRpr/fr1s2PJLiPhIC3ZDBgwwLKWRRmBhnighwMA8IIG\nBwDgRUqU1PIjbNdCJNecOXMsr1q1yrIuWx+U4LRcitShI9N0N08tURemkVJIHno4AAAvaHAAAF6k\nTUkNqUMn/unS9ppVULKhLBN/8ZhAq/dFtxsA4o0eDgDACxocAIAXWbGUObKysjY551bl+UDEolZu\nbm6VgjwB9yUhCnRfuCcJwXslNeX7vsTU4AAAcKQoqQEAvKDBAQB4QYMDAPCCBgcA4AUNDgDACxoc\nAIAXNDgAAC9ocAAAXtDgAAC8oMEBAHhBgwMA8IIGBwDgBQ0OAMALGhwAgBc0OAAAP3Jzc/P9lZ2d\nneuc4ytOX40bN851zm2K5R5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe4d21a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10... Discriminator Loss: 1.2638... Generator Loss: 3.9047\n",
      "Epoch 3/10... Discriminator Loss: 0.5048... Generator Loss: 1.7830\n",
      "Epoch 3/10... Discriminator Loss: 0.6246... Generator Loss: 1.1858\n",
      "Epoch 3/10... Discriminator Loss: 0.5421... Generator Loss: 1.4394\n",
      "Epoch 3/10... Discriminator Loss: 0.7020... Generator Loss: 2.1582\n",
      "Epoch 3/10... Discriminator Loss: 0.6262... Generator Loss: 1.2437\n",
      "Epoch 3/10... Discriminator Loss: 0.6043... Generator Loss: 1.7337\n",
      "Epoch 3/10... Discriminator Loss: 0.6217... Generator Loss: 1.4684\n",
      "Epoch 3/10... Discriminator Loss: 0.5412... Generator Loss: 1.8089\n",
      "Epoch 3/10... Discriminator Loss: 1.0925... Generator Loss: 0.6026\n"
     ]
    },
    {
     "data": {
      "image/png": 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P7ULrekN33HGHz9MoMS2jpVtJozBaFkz2CDQkRzp/59L53FFy9HAAAF7Q4AAAvPBaUssU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\nXtDgAAC8+H96IFVo0fy5UAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f27c5de10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10... Discriminator Loss: 0.8065... Generator Loss: 0.8918\n",
      "Epoch 4/10... Discriminator Loss: 0.5837... Generator Loss: 1.3472\n",
      "Epoch 4/10... Discriminator Loss: 0.6541... Generator Loss: 1.0979\n",
      "Epoch 4/10... Discriminator Loss: 0.6008... Generator Loss: 1.3983\n",
      "Epoch 4/10... Discriminator Loss: 0.9006... Generator Loss: 0.7637\n",
      "Epoch 4/10... Discriminator Loss: 0.6306... Generator Loss: 1.1829\n",
      "Epoch 4/10... Discriminator Loss: 0.8939... Generator Loss: 0.7792\n",
      "Epoch 4/10... Discriminator Loss: 0.5978... Generator Loss: 1.5980\n",
      "Epoch 4/10... Discriminator Loss: 0.9628... Generator Loss: 0.7313\n",
      "Epoch 4/10... Discriminator Loss: 0.5762... Generator Loss: 1.6241\n"
     ]
    },
    {
     "data": {
      "image/png": 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JIu/XH3/8cZ/H6winatWqlitWrGh5+/btlvX664hJn5v3CMIwwgEAeMENBwDg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe49c5be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10... Discriminator Loss: 0.5273... Generator Loss: 1.5422\n",
      "Epoch 4/10... Discriminator Loss: 0.5779... Generator Loss: 1.2870\n",
      "Epoch 4/10... Discriminator Loss: 0.8848... Generator Loss: 2.9737\n",
      "Epoch 4/10... Discriminator Loss: 1.2456... Generator Loss: 3.4898\n",
      "Epoch 4/10... Discriminator Loss: 0.6209... Generator Loss: 1.7816\n",
      "Epoch 4/10... Discriminator Loss: 0.8486... Generator Loss: 2.6666\n",
      "Epoch 4/10... Discriminator Loss: 0.6393... Generator Loss: 1.3075\n",
      "Epoch 4/10... Discriminator Loss: 0.5777... Generator Loss: 1.5744\n",
      "Epoch 4/10... Discriminator Loss: 0.6456... Generator Loss: 1.2249\n",
      "Epoch 4/10... Discriminator Loss: 0.5848... Generator Loss: 2.0877\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe718afd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10... Discriminator Loss: 1.0138... Generator Loss: 0.6548\n",
      "Epoch 4/10... Discriminator Loss: 0.6043... Generator Loss: 1.5268\n",
      "Epoch 4/10... Discriminator Loss: 0.5204... Generator Loss: 1.6027\n",
      "Epoch 4/10... Discriminator Loss: 0.5289... Generator Loss: 1.5802\n",
      "Epoch 4/10... Discriminator Loss: 0.7104... Generator Loss: 1.2081\n",
      "Epoch 4/10... Discriminator Loss: 0.5636... Generator Loss: 1.9406\n",
      "Epoch 4/10... Discriminator Loss: 0.6137... Generator Loss: 1.5743\n",
      "Epoch 4/10... Discriminator Loss: 0.6931... Generator Loss: 2.1789\n",
      "Epoch 4/10... Discriminator Loss: 0.5550... Generator Loss: 1.7830\n",
      "Epoch 4/10... Discriminator Loss: 0.9514... Generator Loss: 2.9928\n"
     ]
    },
    {
     "data": {
      "image/png": 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IvcZvv/225QULFljWMmfYz5wZM2ZYbty4seUvv/zScl5enuVVq1YV/YQ9oocD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\nhBNOsPzqq69a1lJUt27dLF9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe728d0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10... Discriminator Loss: 0.6605... Generator Loss: 1.1885\n",
      "Epoch 4/10... Discriminator Loss: 1.2900... Generator Loss: 0.4765\n",
      "Epoch 4/10... Discriminator Loss: 1.0536... Generator Loss: 0.6120\n",
      "Epoch 4/10... Discriminator Loss: 0.6263... Generator Loss: 1.2844\n",
      "Epoch 4/10... Discriminator Loss: 0.6063... Generator Loss: 1.2155\n",
      "Epoch 4/10... Discriminator Loss: 0.9164... Generator Loss: 0.7663\n",
      "Epoch 4/10... Discriminator Loss: 1.0400... Generator Loss: 0.6368\n",
      "Epoch 4/10... Discriminator Loss: 0.6849... Generator Loss: 1.0749\n",
      "Epoch 4/10... Discriminator Loss: 0.8268... Generator Loss: 0.8408\n",
      "Epoch 4/10... Discriminator Loss: 1.1997... Generator Loss: 0.5153\n"
     ]
    },
    {
     "data": {
      "image/png": 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ypfxPDwcAEAQNDgAgiJwqqUVd/W3btvmxuJ0Kr7jiCs+PPfZYgLPLHdqtjyuj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\nTB4PAbQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe7092e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10... Discriminator Loss: 0.9048... Generator Loss: 0.7587\n",
      "Epoch 4/10... Discriminator Loss: 0.6507... Generator Loss: 1.2616\n",
      "Epoch 4/10... Discriminator Loss: 0.6189... Generator Loss: 2.1042\n",
      "Epoch 4/10... Discriminator Loss: 0.7232... Generator Loss: 2.4336\n",
      "Epoch 4/10... Discriminator Loss: 0.6665... Generator Loss: 1.0795\n",
      "Epoch 4/10... Discriminator Loss: 0.7264... Generator Loss: 0.9492\n",
      "Epoch 4/10... Discriminator Loss: 0.4767... Generator Loss: 1.6516\n",
      "Epoch 5/10... Discriminator Loss: 0.9347... Generator Loss: 0.6797\n",
      "Epoch 5/10... Discriminator Loss: 0.8508... Generator Loss: 0.8207\n",
      "Epoch 5/10... Discriminator Loss: 0.6288... Generator Loss: 1.5065\n"
     ]
    },
    {
     "data": {
      "image/png": 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ltUaNGpmZ2WeffeaPxetGu/XWWz0/9NBDAa6u5Lj//vs9xyuj6Xu9fft2M8tf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\nzNGtuGOV0nQh4axZszzrIkT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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe75dcb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10... Discriminator Loss: 0.6446... Generator Loss: 2.3127\n",
      "Epoch 5/10... Discriminator Loss: 0.4793... Generator Loss: 1.5561\n",
      "Epoch 5/10... Discriminator Loss: 1.2080... Generator Loss: 0.4939\n",
      "Epoch 5/10... Discriminator Loss: 0.6300... Generator Loss: 1.1165\n",
      "Epoch 5/10... Discriminator Loss: 0.8972... Generator Loss: 0.7223\n",
      "Epoch 5/10... Discriminator Loss: 0.6656... Generator Loss: 1.1780\n",
      "Epoch 5/10... Discriminator Loss: 0.8812... Generator Loss: 0.7400\n",
      "Epoch 5/10... Discriminator Loss: 0.5606... Generator Loss: 1.3304\n",
      "Epoch 5/10... Discriminator Loss: 0.8378... Generator Loss: 0.8468\n",
      "Epoch 5/10... Discriminator Loss: 0.7526... Generator Loss: 0.8643\n"
     ]
    },
    {
     "data": {
      "image/png": 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byzoydubMmc455y6//HI7pqNlBw0aZHny5MkJOmN/6OEAALygwQEAeJGVJbVq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\n48YEnXHma9SokWUd+afCRkM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f1bc63b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10... Discriminator Loss: 0.5172... Generator Loss: 1.9415\n",
      "Epoch 5/10... Discriminator Loss: 0.6531... Generator Loss: 2.5467\n",
      "Epoch 5/10... Discriminator Loss: 0.5037... Generator Loss: 1.6301\n",
      "Epoch 5/10... Discriminator Loss: 0.8051... Generator Loss: 1.1787\n",
      "Epoch 5/10... Discriminator Loss: 1.2828... Generator Loss: 0.4424\n",
      "Epoch 5/10... Discriminator Loss: 0.5689... Generator Loss: 1.8350\n",
      "Epoch 5/10... Discriminator Loss: 0.5423... Generator Loss: 1.2657\n",
      "Epoch 5/10... Discriminator Loss: 1.0536... Generator Loss: 0.5897\n",
      "Epoch 5/10... Discriminator Loss: 0.5437... Generator Loss: 1.9424\n",
      "Epoch 5/10... Discriminator Loss: 0.6986... Generator Loss: 1.0028\n"
     ]
    },
    {
     "data": {
      "image/png": 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95bD7ee2111q+9957Ld9yyy2Wd+zYUei1pBP9XtVuzf/7v/+z/Oabb1ou7ARh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\nfLlzruDPJO06O+KIIyyHvZb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe795d208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10... Discriminator Loss: 0.7143... Generator Loss: 2.4167\n",
      "Epoch 5/10... Discriminator Loss: 0.8729... Generator Loss: 0.7976\n",
      "Epoch 5/10... Discriminator Loss: 1.0979... Generator Loss: 0.5705\n",
      "Epoch 5/10... Discriminator Loss: 0.7098... Generator Loss: 1.0083\n",
      "Epoch 5/10... Discriminator Loss: 0.9240... Generator Loss: 3.5960\n",
      "Epoch 5/10... Discriminator Loss: 0.4917... Generator Loss: 2.1517\n",
      "Epoch 5/10... Discriminator Loss: 0.4974... Generator Loss: 1.4366\n",
      "Epoch 5/10... Discriminator Loss: 0.7023... Generator Loss: 2.5392\n",
      "Epoch 5/10... Discriminator Loss: 0.5319... Generator Loss: 1.9668\n",
      "Epoch 5/10... Discriminator Loss: 0.9785... Generator Loss: 0.6940\n"
     ]
    },
    {
     "data": {
      "image/png": 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lFaVlr9GjR5uZ2ZdffunHBgwY4Llhw4aetexWp04dzxs2bPCcn5+f0rlXZAUF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\nNSebKJrL9DWJys96Lbdu3ep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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe4be3b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10... Discriminator Loss: 0.8762... Generator Loss: 0.7904\n",
      "Epoch 5/10... Discriminator Loss: 0.7829... Generator Loss: 3.0377\n",
      "Epoch 5/10... Discriminator Loss: 0.7273... Generator Loss: 1.9277\n",
      "Epoch 5/10... Discriminator Loss: 1.0205... Generator Loss: 2.7719\n",
      "Epoch 5/10... Discriminator Loss: 0.7210... Generator Loss: 0.9711\n",
      "Epoch 5/10... Discriminator Loss: 0.8961... Generator Loss: 0.7670\n",
      "Epoch 5/10... Discriminator Loss: 0.5504... Generator Loss: 1.7780\n",
      "Epoch 5/10... Discriminator Loss: 0.8554... Generator Loss: 0.8154\n",
      "Epoch 5/10... Discriminator Loss: 0.7072... Generator Loss: 2.8040\n",
      "Epoch 5/10... Discriminator Loss: 0.7572... Generator Loss: 2.6231\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe8e014a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10... Discriminator Loss: 0.6894... Generator Loss: 1.8400\n",
      "Epoch 5/10... Discriminator Loss: 0.5973... Generator Loss: 1.5595\n",
      "Epoch 5/10... Discriminator Loss: 0.6615... Generator Loss: 1.0787\n",
      "Epoch 5/10... Discriminator Loss: 0.4993... Generator Loss: 1.9616\n",
      "Epoch 6/10... Discriminator Loss: 0.6086... Generator Loss: 1.2910\n",
      "Epoch 6/10... Discriminator Loss: 2.3219... Generator Loss: 0.2493\n",
      "Epoch 6/10... Discriminator Loss: 0.8081... Generator Loss: 0.9186\n",
      "Epoch 6/10... Discriminator Loss: 0.7901... Generator Loss: 1.3972\n",
      "Epoch 6/10... Discriminator Loss: 0.5784... Generator Loss: 1.4941\n",
      "Epoch 6/10... Discriminator Loss: 0.9371... Generator Loss: 0.6735\n"
     ]
    },
    {
     "data": {
      "image/png": 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hnt94440f/bw777zT85o1azyfd955nu+6666YOZXRwwEABEGDAwAIokSW1Nq3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\nAARBgwMACOL/AREQyl4w6evOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe4b0a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10... Discriminator Loss: 0.6540... Generator Loss: 1.2864\n",
      "Epoch 6/10... Discriminator Loss: 0.7775... Generator Loss: 0.9177\n",
      "Epoch 6/10... Discriminator Loss: 0.9887... Generator Loss: 0.6718\n",
      "Epoch 6/10... Discriminator Loss: 1.0095... Generator Loss: 2.8766\n",
      "Epoch 6/10... Discriminator Loss: 1.0559... Generator Loss: 0.6074\n",
      "Epoch 6/10... Discriminator Loss: 1.1467... Generator Loss: 0.5471\n",
      "Epoch 6/10... Discriminator Loss: 0.7827... Generator Loss: 0.8747\n",
      "Epoch 6/10... Discriminator Loss: 0.9230... Generator Loss: 0.7299\n",
      "Epoch 6/10... Discriminator Loss: 0.5320... Generator Loss: 2.1573\n",
      "Epoch 6/10... Discriminator Loss: 0.7833... Generator Loss: 0.9569\n"
     ]
    },
    {
     "data": {
      "image/png": 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33377WU53CaqwsNDl5OTMLCwsbFma1/FxXbRk0K9fP8uDBw+2HDVptqS0RKNl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\nz3/+Y1l3uk01Ld98++23ll9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe72d04a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10... Discriminator Loss: 0.5635... Generator Loss: 1.7690\n",
      "Epoch 6/10... Discriminator Loss: 0.7458... Generator Loss: 0.9365\n",
      "Epoch 6/10... Discriminator Loss: 0.6111... Generator Loss: 1.2618\n",
      "Epoch 6/10... Discriminator Loss: 0.4578... Generator Loss: 1.5468\n",
      "Epoch 6/10... Discriminator Loss: 0.6773... Generator Loss: 2.4449\n",
      "Epoch 6/10... Discriminator Loss: 0.8485... Generator Loss: 0.7855\n",
      "Epoch 6/10... Discriminator Loss: 0.6339... Generator Loss: 1.8144\n",
      "Epoch 6/10... Discriminator Loss: 0.6318... Generator Loss: 1.9082\n",
      "Epoch 6/10... Discriminator Loss: 0.8264... Generator Loss: 2.6791\n",
      "Epoch 6/10... Discriminator Loss: 0.9717... Generator Loss: 0.6464\n"
     ]
    },
    {
     "data": {
      "image/png": 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8bdo0yxs3brR85MgR55xzd999tx379ttvLV944YWWly1blu+5JBt6OAAAL2hw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\nVVdZ1rWcdu/ebblBgwaW9Xq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe8c12860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10... Discriminator Loss: 0.7457... Generator Loss: 1.3480\n",
      "Epoch 6/10... Discriminator Loss: 1.3152... Generator Loss: 3.8073\n",
      "Epoch 6/10... Discriminator Loss: 0.8085... Generator Loss: 0.8690\n",
      "Epoch 6/10... Discriminator Loss: 1.9265... Generator Loss: 0.2239\n",
      "Epoch 6/10... Discriminator Loss: 0.7266... Generator Loss: 1.2502\n",
      "Epoch 6/10... Discriminator Loss: 0.9801... Generator Loss: 0.6936\n",
      "Epoch 6/10... Discriminator Loss: 0.7742... Generator Loss: 0.9106\n",
      "Epoch 6/10... Discriminator Loss: 0.7015... Generator Loss: 1.3939\n",
      "Epoch 6/10... Discriminator Loss: 0.8638... Generator Loss: 0.7954\n",
      "Epoch 6/10... Discriminator Loss: 0.7031... Generator Loss: 1.0182\n"
     ]
    },
    {
     "data": {
      "image/png": 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SmzFjhuVDDz007Gu+/PJLy8ccc4xlRlKFp6Oh3n77bcunnXaaZZ1AWxR9bdeu\nXS3v27fPspbjdKuITFemTBnLgwcPtlyrVi3Ler/q169v+ZRTTnHOOTdv3jw7Vr16dcu6lmDv3r0t\nawnou+++s9ytWzfLmTACTkvIzz33nGUdmTZ+/PiwWcv7eg9nzpzpnAstlwVlNuecu+uuuyz/4Q9/\nsKz3NZnRwwEAeEGDAwDwIi1LapUqVbKsZQG1Y8cOy5TRitaqVSvLCxYssKzlML12Otpv2rRpzrnQ\nkVP6fhMmTLBcrVo1y1oCXbNmjeUjjzwy9n9Amho5cqRlHdUXaSTZli1bLFetWtU551y9evXsmK4x\n+Pe//92yjtjUe6674vbs2dPy8OHDY/hXpKYXX3zR8sUXX2x5zpw5lt98803LkUrBOqk8yNdcc40d\n00m8OiG0bt26lrXM+e2330b3DygF9HAAAF7Q4AAAvEjLktrSpUsta/dfSwtaRqCMVrQHHnjAso7O\n0Wv61FNPWe7Tp4/lcNdXJ3jqvdByQNmyZS1HmpD7/fffR3X+6URHJF199dWW9b4onVgblDedO1Du\n2b59ux376quvLC9fvtxy27ZtLWu5TstEOkExE+Tk5FjW66A/+/EYUak/+02bNrX86aefWn7llVcs\nt2/fPq7fP57o4QAAvKDBAQB4kVYltaCkcNRRR4X976tXr7a8detWL+eULipXrmxZS2Tvvvuu5aLK\naJHoiMHHHnvMsk5408l0kSbwpjNdn2vFihWWoymjrVy50vKgQYMsayktoJNtdWKhfs9IJbV169ZF\n/gekCf236+eMPgf6ORNvOgF62bJllk844QTLWmpr0aKF5V27diXsvKJFDwcA4AUNDgDAi7QqqQXr\nQEWajKhrTSE2OjJMr+nYsWPDHo+FTtTV3Q21dKbln2QbeZMoWr7RUUi6NprS66+j0fr27WtZ1/Aq\nil5nPRct4+m9++CDDyw3a9bMsq63lk5mzZplWbcbiDe99jpysHXr1pZ14qdO2F28eLHlf/3rX5Z1\nTbZwpdVEoYcDAPCCBgcA4EValdSCtboirSPFBM/i0zXOlE7ajIXeo+7du1s++uiji3z97t27i/U9\nU43u+nj22WdbjrRbre7ced1111nevHlzsb6/btsRzXYTuiVCfn6+ZZ2suHbtWsup+DxqOVFLWvPn\nz7dcklFqwb3VsqmWNnUSqNqwYYPlK6+80nKvXr0s6wjQb775xvLo0aMtJ3oHV3o4AAAvaHAAAF6k\nfElNu/pU2sEjAAAUDklEQVQDBgxwzoWWHLTbnpeXZ1mXFk/Frr1vusaWTjK79dZbLT/66KOWtewV\n3A+9Lw0bNrQcaSl7vS/a7dfJielGf56nTJliOVIZTcstX3zxhWWdTBsLnbypa9nFqly5cpa1xPTh\nhx9a1h1iE13KiZc6depYbtKkSdgcK73nwX0uyUhMvZbPP/+8Zd3mINiawjnnqlSpYlnLson4XKSH\nAwDwIuV7OHfccYdl3Y89HP3NXOd46G8T9HbCO+mkkyzv3bvXcqSVm/U37OA3OP3NS5eqifSbtM4Z\n0ZWoU+W34eLQ5VL051l/LvXfr8ub9OvXz7Jeu0i9o6CHMXv2bDsWaY5PJPrs6P+r31Nzy5YtLXfp\n0sXya6+9FtP3LS2NGze2rP/ekvRwdBmieNNKgw48yc7Otvzkk09a1gEHutlbvNDDAQB4QYMDAPAi\nJUtq2kXX+QbBcS05aJdfN/2ijBYbvaannnqq5REjRljWlWm1ZBmUzPS+RVp+SP9o3aZNG8uxLMmS\navS6nHvuuZajWcJGB1+88MILlvWPwjo/JtLq0rHQ7x+pjBaJvuaWW26xrM9mMj+PupTPnj17LOsf\n2/VnO5Hlslhpmbtjx46WJ0+ebDnRc9zo4QAAvKDBAQB4kZIltWAJG+dCR0kFtEs+ceJEy0888UTY\n1yA2ixYtstyuXTvLjRo1styhQwfL3bp1c86FjnTTjaR01JvOFVi/fn2czji5aZnr9ttvtxxpORkt\nY+loI82JFE3pLBId+XT55ZdbTpXnUVfE1uugz0Gy/lvOOOMMyzoCUjePY2kbAEBaoMEBAHiRkiW1\ne++913K47r3uv37jjTdaTuclUZLBqlWrLOumWwsXLnTOOffmm2/aMZ34qcud6MZhmUJLZ3pdUlmk\nJaWWLFlSGqcTN7rytpaCc3JyLP/f//2f5U8++cTPiUVQvnx5y7r6uC5nU1BQ4O186OEAALygwQEA\neJEyJTWdSBhpNE4wwkJHse3atSuxJwajI1x0XbWLLrrIORc6olBf26NHD8uRNphKZzoJWUswuppy\nNBugReOUU06xrCOuwv13Hb2kI+k0a0l769atlmvXrm05nUrZOpHz0ksvtTxjxgzLc+fOtdygQQPL\nen0SST8rg2fPudDnbOTIkZa1NJho9HAAAF7Q4AAAvEiZkpp2ASMJyjG6ZDtKh46O6dmzp3MutPyi\n92jTpk3+TiwJ6XX505/+ZFnX6rr44ostL1682HIwAtC50HJPcdfEev/99y23atXKspbX9N5qObBT\np06W06mMFsmsWbMs679X17HT0Zo6unbw4MGWiztRVH9u/vjHP1ru06dP2NfoJoZjx4617HO7D3o4\nAAAvaHAAAF4kdUlNu4N9+/Yt8vVPP/20c8657du3J+ycEB1dN61169a/+u/xKCmkCy1pfP7552Ff\no9tA+KLPn659p6PUtHT38ccf+zmxJFSzZk3LOpFSR4zpdgzHH3982OPB9hxaTtUSpo7+051adW20\nLVu2WNZdYGfOnGm5tEqe9HAAAF7Q4AAAvEjqklrz5s0ta9dUhZtsWKZMGTuW6B3scICWWh555BHL\nwb3TEU066gnJ6dFHH7WsWyJoCVTvcyaXRnXHz3LlylnWEZjVqlWz3KVLF8u6y2vweaWj23THVp0Y\nrfdk5cqVlnXCcLL9eYEeDgDACxocAIAXSV1Se+GFF2J6fZMmTZxzftcGwgENGza0HNwLlZ+fb1lH\n4SA5TZ061bKWaXT0mu7sqiWeTFwTL6Bl/ho1ali+8sorLY8ZM8ay/gkgGA2o13jjxo2WzzzzTMvB\niLb//Z7JjB4OAMALGhwAgBdJV1KrV6+e5fr16xf5eh35FKxVlCrdy3QzcOBAyzqqMLgfN910k/dz\nQvEtWLDAcqQRaHXq1LGskxJ1Z8xMLp/qdQsmpjvn3KRJkyw3atTI8tVXX+2cc+6jjz6yYy+99JLl\nVC9V0sMBAHhBgwMA8CLpSmo6UUlHqXXu3NnymjVrLF933XWWP/zwwwSfHX6L7iioo2wCc+bM8Xk6\nKCG9X7om1/z58y2vXbvWMpOso6elNp20+de//rU0TscbejgAAC9ocAAAXiRdSW3r1q2WdaIUkpOW\nziKtt7Vz507nXOj6UEgt48ePL+1TQBqghwMA8IIGBwDgRdKV1JBatHSm66dVrlzZ8qeffur1nIB0\nomXrVN8Cgh4OAMALGhwAgBeU1BA3OgkQQPHpWoRly5a1vGPHjtI4nbihhwMA8IIGBwDgRVYsox6y\nsrIKnHPUTeKrXmFh4eEleQPuS0KU6L5wTxKCZyU5RX1fYmpwAAAoLkpqAAAvaHAAAF7Q4AAAvKDB\nAQB4QYMDAPCCBgcA4AUNDgDACxocAIAXNDgAAC9ocAAAXtDgAAC8oMEBAHhBgwMA8IIGBwDgBQ0O\nAMCPwsLCqL9ycnIKnXN8xekrLy+v0DlXEMs94L6kxn3JyckJ3oev+H3xrCTZV6zPSkw9nNzc3Fhe\njiIsWrTIuTjsPsh9ia943Jfc3NzgfRA/PCtJJtZnhZIaAMALGhwAgBc0OAAALw4p7RNIFQcffLDl\nn3/+uRTPBACiU6FCBcvVq1e3nJ+fb7mwsNDb+dDDAQB4QYMDAPCCklqUKKMBSAVvvfWW5Y4dO1re\nvn275R49eliePn265USX1+jhAAC8oMEBAHiRsSW1rKwsyzoC7aeffiqN00k7xx13nOUOHTo455yb\nOnWqHfv66699nxJKQJ8Xn6OaENkhhxz4+H7qqacsd+rUyfJBBx3oU2RnZ1ueNGmS5caNG1vesmVL\nvE8zBD0cAIAXNDgAAC8ytqTWpUsXyxs3brT80UcfWWZkWmy07LJw4ULL5cqVc845N2rUKDvWpk0b\nywsWLPBwdohEJwTedNNNlnv37m1Zy2gTJkywfMstt1j+5ZdfEnWK+P/KlClj+bPPPrNcu3Zty5FK\nnnr80EMPtazP4syZMy3v3r27ZCcbBj0cAIAXNDgAAC/SvqSmozT+8pe/WB42bJjlPXv2WL722mst\nT548OcFnl160y7569WrLxx57rHMutOQ2ePBgy507d/ZwdvhfwSinV155xY61bt3ast4vLS/37NnT\n8sknn2xZn6m333477P9LmTp2lSpVsvzNN9+EPa7Xdf/+/ZZ37txpWUfjzp071/L69est7927Nw5n\nHBk9HACAFzQ4AAAv0rKkpmU0HQEVlHacc27t2gO7olatWtXyvffea3nJkiWWP/3007ifZzoragKZ\nrvE0aNAgy/fcc0/CzgmhevXq5ZwLLaPps6O0HFOjRo2wuW3btpY3bNhgWUdBMeE3Onq9teSpZTT9\nU8CmTZss69YDTZo0sbxt27awr9fPubJly1pmlBoAIGXR4AAAvEirklrDhg2dc849/vjjduz444+3\nrJMR7777bss6Mq1OnTqWL7nkEsuU1Iqm5Zjc3NyoXztkyBDL559/vuVTTz3VMpMK4+PCCy+0PG7c\nOOdc5DKa0hFr0Vi6dKllLV/jtwXX+dZbb7VjWqrU50BH3b7++uuW8/LyLI8ZM8byt99+a/m6666z\nrCPcElFGU/RwAABe0OAAALx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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe5fb3630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10... Discriminator Loss: 0.7452... Generator Loss: 0.9547\n",
      "Epoch 6/10... Discriminator Loss: 0.6600... Generator Loss: 1.5821\n",
      "Epoch 6/10... Discriminator Loss: 0.5388... Generator Loss: 2.3334\n",
      "Epoch 6/10... Discriminator Loss: 0.9139... Generator Loss: 0.6946\n",
      "Epoch 6/10... Discriminator Loss: 0.6594... Generator Loss: 1.2221\n",
      "Epoch 6/10... Discriminator Loss: 0.5590... Generator Loss: 1.6361\n",
      "Epoch 6/10... Discriminator Loss: 0.7554... Generator Loss: 0.8942\n",
      "Epoch 6/10... Discriminator Loss: 0.6153... Generator Loss: 1.1758\n",
      "Epoch 6/10... Discriminator Loss: 0.8067... Generator Loss: 0.8325\n",
      "Epoch 6/10... Discriminator Loss: 0.7218... Generator Loss: 1.4871\n"
     ]
    },
    {
     "data": {
      "image/png": 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0vGPHjuKcdkY766yzLL/33nuW9T3W++nkk0+2fODAAcs6Mu3HH3+0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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fea20bdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10... Discriminator Loss: 0.8027... Generator Loss: 0.8546\n",
      "Epoch 7/10... Discriminator Loss: 0.5747... Generator Loss: 1.2333\n",
      "Epoch 7/10... Discriminator Loss: 0.6274... Generator Loss: 1.7670\n",
      "Epoch 7/10... Discriminator Loss: 0.6301... Generator Loss: 1.9379\n",
      "Epoch 7/10... Discriminator Loss: 0.6403... Generator Loss: 1.3428\n",
      "Epoch 7/10... Discriminator Loss: 0.6239... Generator Loss: 1.7468\n",
      "Epoch 7/10... Discriminator Loss: 0.6822... Generator Loss: 1.3459\n",
      "Epoch 7/10... Discriminator Loss: 0.5577... Generator Loss: 1.9729\n",
      "Epoch 7/10... Discriminator Loss: 0.4642... Generator Loss: 1.7701\n",
      "Epoch 7/10... Discriminator Loss: 0.6584... Generator Loss: 1.1186\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fea2b6eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10... Discriminator Loss: 0.6554... Generator Loss: 1.1957\n",
      "Epoch 7/10... Discriminator Loss: 0.9632... Generator Loss: 0.6842\n",
      "Epoch 7/10... Discriminator Loss: 0.5824... Generator Loss: 2.0263\n",
      "Epoch 7/10... Discriminator Loss: 1.7808... Generator Loss: 0.3165\n",
      "Epoch 7/10... Discriminator Loss: 0.9960... Generator Loss: 1.9254\n",
      "Epoch 7/10... Discriminator Loss: 0.7309... Generator Loss: 1.7238\n",
      "Epoch 7/10... Discriminator Loss: 0.8098... Generator Loss: 1.0970\n",
      "Epoch 7/10... Discriminator Loss: 1.0592... Generator Loss: 3.0475\n",
      "Epoch 7/10... Discriminator Loss: 0.6395... Generator Loss: 1.1992\n",
      "Epoch 7/10... Discriminator Loss: 1.1078... Generator Loss: 0.5665\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe77e91d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10... Discriminator Loss: 0.7012... Generator Loss: 1.1187\n",
      "Epoch 7/10... Discriminator Loss: 0.6735... Generator Loss: 1.1430\n",
      "Epoch 7/10... Discriminator Loss: 0.7225... Generator Loss: 0.9925\n",
      "Epoch 7/10... Discriminator Loss: 0.6439... Generator Loss: 1.2475\n",
      "Epoch 7/10... Discriminator Loss: 0.6710... Generator Loss: 1.1257\n",
      "Epoch 7/10... Discriminator Loss: 0.6289... Generator Loss: 1.0689\n",
      "Epoch 7/10... Discriminator Loss: 1.8899... Generator Loss: 4.6263\n",
      "Epoch 7/10... Discriminator Loss: 1.0198... Generator Loss: 0.6613\n",
      "Epoch 7/10... Discriminator Loss: 0.6813... Generator Loss: 1.4890\n",
      "Epoch 7/10... Discriminator Loss: 1.0732... Generator Loss: 0.5715\n"
     ]
    },
    {
     "data": {
      "image/png": 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Q4AAAgsj6ktr111/vuWbNmp6//fZbz1rGmT9/fpgTyzBaxvn973/v+aSTTor5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\nQdDgAACC+H8dGBneYunD6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe7a686a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10... Discriminator Loss: 0.5952... Generator Loss: 1.6039\n",
      "Epoch 7/10... Discriminator Loss: 0.4918... Generator Loss: 1.7169\n",
      "Epoch 7/10... Discriminator Loss: 1.0139... Generator Loss: 0.6688\n",
      "Epoch 7/10... Discriminator Loss: 0.6837... Generator Loss: 1.1144\n",
      "Epoch 7/10... Discriminator Loss: 0.6226... Generator Loss: 1.3208\n",
      "Epoch 7/10... Discriminator Loss: 1.0118... Generator Loss: 0.6421\n",
      "Epoch 7/10... Discriminator Loss: 0.5343... Generator Loss: 1.3938\n",
      "Epoch 7/10... Discriminator Loss: 0.6047... Generator Loss: 1.9663\n",
      "Epoch 7/10... Discriminator Loss: 0.5383... Generator Loss: 1.4081\n",
      "Epoch 7/10... Discriminator Loss: 0.6282... Generator Loss: 2.5586\n"
     ]
    },
    {
     "data": {
      "image/png": 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T4RVmfbNEdI0zzUonimrWCb+69lo0wkp3Nvzoo488szYXkB3o4QAAgqDBAQAEQUkNSdOR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\nAEHQ4AAAgqDBAQAEQYMDAAji/wFwNdo8zMXRdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe4ba2208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10... Discriminator Loss: 0.6124... Generator Loss: 1.1628\n",
      "Epoch 7/10... Discriminator Loss: 0.6227... Generator Loss: 1.1937\n",
      "Epoch 7/10... Discriminator Loss: 1.1940... Generator Loss: 0.5288\n",
      "Epoch 7/10... Discriminator Loss: 0.8211... Generator Loss: 0.8688\n",
      "Epoch 7/10... Discriminator Loss: 0.5980... Generator Loss: 2.3086\n",
      "Epoch 7/10... Discriminator Loss: 0.5645... Generator Loss: 2.4380\n",
      "Epoch 7/10... Discriminator Loss: 0.7163... Generator Loss: 1.0132\n",
      "Epoch 8/10... Discriminator Loss: 0.6694... Generator Loss: 1.2646\n",
      "Epoch 8/10... Discriminator Loss: 0.4929... Generator Loss: 1.3956\n",
      "Epoch 8/10... Discriminator Loss: 0.6443... Generator Loss: 1.3190\n"
     ]
    },
    {
     "data": {
      "image/png": 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88UfPOiLk9ddfT8HZZb7mzZt7HjlypOdTTz3Vc1SO2bt3rx+LSge/zDVr1vS8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\nPfRQz998800Sz67003Lwgw8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe76b2b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10... Discriminator Loss: 1.2638... Generator Loss: 3.4477\n",
      "Epoch 8/10... Discriminator Loss: 0.7233... Generator Loss: 1.4669\n",
      "Epoch 8/10... Discriminator Loss: 0.6321... Generator Loss: 1.2291\n",
      "Epoch 8/10... Discriminator Loss: 0.5545... Generator Loss: 1.7301\n",
      "Epoch 8/10... Discriminator Loss: 0.8103... Generator Loss: 0.9507\n",
      "Epoch 8/10... Discriminator Loss: 0.7712... Generator Loss: 1.0549\n",
      "Epoch 8/10... Discriminator Loss: 0.9075... Generator Loss: 0.7964\n",
      "Epoch 8/10... Discriminator Loss: 0.6470... Generator Loss: 1.2316\n",
      "Epoch 8/10... Discriminator Loss: 1.5432... Generator Loss: 0.4196\n",
      "Epoch 8/10... Discriminator Loss: 0.7789... Generator Loss: 1.0339\n"
     ]
    },
    {
     "data": {
      "image/png": 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ktmrVKs/6+O+//75YzjPdValSxbOWYc466yzP3bt39/zNN98U+WfqiE0mOx9K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\nExvLlSvnWSfq1a5d2/PEiRM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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fea1ac400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10... Discriminator Loss: 0.8017... Generator Loss: 2.3768\n",
      "Epoch 8/10... Discriminator Loss: 0.7291... Generator Loss: 1.1408\n",
      "Epoch 8/10... Discriminator Loss: 0.5299... Generator Loss: 1.7424\n",
      "Epoch 8/10... Discriminator Loss: 1.0000... Generator Loss: 0.6348\n",
      "Epoch 8/10... Discriminator Loss: 0.6303... Generator Loss: 1.9234\n",
      "Epoch 8/10... Discriminator Loss: 1.1822... Generator Loss: 3.0827\n",
      "Epoch 8/10... Discriminator Loss: 0.7919... Generator Loss: 2.5594\n",
      "Epoch 8/10... Discriminator Loss: 0.6408... Generator Loss: 2.0102\n",
      "Epoch 8/10... Discriminator Loss: 1.0204... Generator Loss: 2.0987\n",
      "Epoch 8/10... Discriminator Loss: 0.7658... Generator Loss: 1.0881\n"
     ]
    },
    {
     "data": {
      "image/png": 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2nY4MXLJkieV77rnH8oABAyyn+0idROratavlwYMHWx4zZozlu+66y/K6det2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\nAAC8SMuSmo6A0vWEgq6nTlh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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f1bd5c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10... Discriminator Loss: 0.6858... Generator Loss: 1.1806\n",
      "Epoch 8/10... Discriminator Loss: 0.6062... Generator Loss: 1.7125\n",
      "Epoch 8/10... Discriminator Loss: 0.6954... Generator Loss: 2.0914\n",
      "Epoch 8/10... Discriminator Loss: 1.2419... Generator Loss: 0.5351\n",
      "Epoch 8/10... Discriminator Loss: 1.0092... Generator Loss: 0.5932\n",
      "Epoch 8/10... Discriminator Loss: 0.8082... Generator Loss: 0.8727\n",
      "Epoch 8/10... Discriminator Loss: 0.7447... Generator Loss: 0.9680\n",
      "Epoch 8/10... Discriminator Loss: 0.5003... Generator Loss: 1.9407\n",
      "Epoch 8/10... Discriminator Loss: 0.5292... Generator Loss: 1.3514\n",
      "Epoch 8/10... Discriminator Loss: 0.5286... Generator Loss: 1.6717\n"
     ]
    },
    {
     "data": {
      "image/png": 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5v//7vz3ffPPNnqtVq2ZmRSe7aW7QoIHnf/zjH54HDx7suW7dup71euWitm3b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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fea836da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10... Discriminator Loss: 0.8034... Generator Loss: 0.8582\n",
      "Epoch 8/10... Discriminator Loss: 0.8257... Generator Loss: 0.9371\n",
      "Epoch 8/10... Discriminator Loss: 0.8799... Generator Loss: 0.7789\n",
      "Epoch 8/10... Discriminator Loss: 1.0254... Generator Loss: 3.0323\n",
      "Epoch 8/10... Discriminator Loss: 0.7630... Generator Loss: 1.0590\n",
      "Epoch 8/10... Discriminator Loss: 1.7766... Generator Loss: 0.2918\n",
      "Epoch 8/10... Discriminator Loss: 1.0753... Generator Loss: 2.4679\n",
      "Epoch 8/10... Discriminator Loss: 1.1827... Generator Loss: 0.5231\n",
      "Epoch 8/10... Discriminator Loss: 0.5147... Generator Loss: 2.4325\n",
      "Epoch 8/10... Discriminator Loss: 0.7067... Generator Loss: 1.8895\n"
     ]
    },
    {
     "data": {
      "image/png": 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dGPRwAABB0OAAAILI+pKaTt486aSTPGuXtk2bNp61jKaWL9+/x5CW1Hbv3p2U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\nABAEDQ4AIIj/BzPWi/fabZ+yAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe7670518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10... Discriminator Loss: 0.7130... Generator Loss: 1.7794\n",
      "Epoch 8/10... Discriminator Loss: 1.1984... Generator Loss: 0.5252\n",
      "Epoch 8/10... Discriminator Loss: 0.7507... Generator Loss: 1.4114\n",
      "Epoch 8/10... Discriminator Loss: 0.7272... Generator Loss: 0.9633\n",
      "Epoch 9/10... Discriminator Loss: 0.6559... Generator Loss: 1.2977\n",
      "Epoch 9/10... Discriminator Loss: 0.5653... Generator Loss: 1.3838\n",
      "Epoch 9/10... Discriminator Loss: 1.3139... Generator Loss: 0.4729\n",
      "Epoch 9/10... Discriminator Loss: 1.0456... Generator Loss: 0.6426\n",
      "Epoch 9/10... Discriminator Loss: 0.9291... Generator Loss: 0.8048\n",
      "Epoch 9/10... Discriminator Loss: 0.7759... Generator Loss: 0.9792\n"
     ]
    },
    {
     "data": {
      "image/png": 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KahdccIHnUaNGea5evXrM1+/YscPz4MGDPVO6SExUJjjppJP8WK9evTw/9NBD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\niFYebQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fea300ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10... Discriminator Loss: 1.1217... Generator Loss: 0.5792\n",
      "Epoch 9/10... Discriminator Loss: 0.7023... Generator Loss: 0.9895\n",
      "Epoch 9/10... Discriminator Loss: 1.5671... Generator Loss: 0.3593\n",
      "Epoch 9/10... Discriminator Loss: 0.5152... Generator Loss: 1.6362\n",
      "Epoch 9/10... Discriminator Loss: 0.5426... Generator Loss: 2.2578\n",
      "Epoch 9/10... Discriminator Loss: 0.6800... Generator Loss: 1.0352\n",
      "Epoch 9/10... Discriminator Loss: 0.5476... Generator Loss: 1.4268\n",
      "Epoch 9/10... Discriminator Loss: 0.6865... Generator Loss: 0.9413\n",
      "Epoch 9/10... Discriminator Loss: 0.9477... Generator Loss: 0.7145\n",
      "Epoch 9/10... Discriminator Loss: 0.7613... Generator Loss: 1.0527\n"
     ]
    },
    {
     "data": {
      "image/png": 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rOfr80c8VLdVnOno4AIAgaHAAAEHkbElNS2dLly71XKtWrYTfv2PHDs977bWz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\n8f8A4ZRZCqnidvAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe74c65f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10... Discriminator Loss: 0.5537... Generator Loss: 1.7886\n",
      "Epoch 9/10... Discriminator Loss: 0.9849... Generator Loss: 0.7317\n",
      "Epoch 9/10... Discriminator Loss: 0.5797... Generator Loss: 1.2831\n",
      "Epoch 9/10... Discriminator Loss: 1.0101... Generator Loss: 0.6668\n",
      "Epoch 9/10... Discriminator Loss: 0.6390... Generator Loss: 1.2627\n",
      "Epoch 9/10... Discriminator Loss: 0.8129... Generator Loss: 0.7939\n",
      "Epoch 9/10... Discriminator Loss: 0.8698... Generator Loss: 0.7935\n",
      "Epoch 9/10... Discriminator Loss: 0.7946... Generator Loss: 0.8823\n",
      "Epoch 9/10... Discriminator Loss: 0.6860... Generator Loss: 1.2031\n",
      "Epoch 9/10... Discriminator Loss: 0.9552... Generator Loss: 0.7326\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19febfbbe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10... Discriminator Loss: 0.7343... Generator Loss: 0.9391\n",
      "Epoch 9/10... Discriminator Loss: 0.5674... Generator Loss: 1.2842\n",
      "Epoch 9/10... Discriminator Loss: 0.7210... Generator Loss: 0.9929\n",
      "Epoch 9/10... Discriminator Loss: 2.1555... Generator Loss: 0.2357\n",
      "Epoch 9/10... Discriminator Loss: 0.9102... Generator Loss: 0.7437\n",
      "Epoch 9/10... Discriminator Loss: 0.6590... Generator Loss: 1.5784\n",
      "Epoch 9/10... Discriminator Loss: 0.5472... Generator Loss: 1.6501\n",
      "Epoch 9/10... Discriminator Loss: 0.6064... Generator Loss: 1.4776\n",
      "Epoch 9/10... Discriminator Loss: 0.5349... Generator Loss: 1.6984\n",
      "Epoch 9/10... Discriminator Loss: 0.7575... Generator Loss: 0.9678\n"
     ]
    },
    {
     "data": {
      "image/png": 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5vvrqqz3n43vMDAcAEAQDDgAgiIIvqWkZbc6cOZ6jqatZYsnhvffe89yxY8cM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\n6bRfS23/+Mc/PE+fPt1zse+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe8cfce80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10... Discriminator Loss: 0.6957... Generator Loss: 1.2141\n",
      "Epoch 9/10... Discriminator Loss: 0.8475... Generator Loss: 0.8938\n",
      "Epoch 9/10... Discriminator Loss: 0.5531... Generator Loss: 1.5203\n",
      "Epoch 9/10... Discriminator Loss: 0.6588... Generator Loss: 1.8831\n",
      "Epoch 9/10... Discriminator Loss: 0.7604... Generator Loss: 0.9394\n",
      "Epoch 9/10... Discriminator Loss: 0.6212... Generator Loss: 1.4482\n",
      "Epoch 9/10... Discriminator Loss: 0.7241... Generator Loss: 1.0172\n",
      "Epoch 9/10... Discriminator Loss: 0.4717... Generator Loss: 1.8075\n",
      "Epoch 9/10... Discriminator Loss: 0.4817... Generator Loss: 1.7193\n",
      "Epoch 9/10... Discriminator Loss: 0.5853... Generator Loss: 1.2544\n"
     ]
    },
    {
     "data": {
      "image/png": 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ss5aHDBliOSjLZao///nPlvV1GTVqlOUVK1ZYbtiwYYm/59SpUy1fdNFFoY9Z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\nPvvMcp06dSxPmjTJso5QVDq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19febe25630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10... Discriminator Loss: 0.8559... Generator Loss: 2.2504\n",
      "Epoch 10/10... Discriminator Loss: 0.8956... Generator Loss: 0.7569\n",
      "Epoch 10/10... Discriminator Loss: 0.6331... Generator Loss: 1.2887\n",
      "Epoch 10/10... Discriminator Loss: 0.4758... Generator Loss: 1.5306\n",
      "Epoch 10/10... Discriminator Loss: 0.4299... Generator Loss: 2.2774\n",
      "Epoch 10/10... Discriminator Loss: 2.1963... Generator Loss: 0.2520\n",
      "Epoch 10/10... Discriminator Loss: 1.2641... Generator Loss: 0.5560\n",
      "Epoch 10/10... Discriminator Loss: 1.1570... Generator Loss: 0.5492\n",
      "Epoch 10/10... Discriminator Loss: 0.7144... Generator Loss: 1.1171\n",
      "Epoch 10/10... Discriminator Loss: 0.8387... Generator Loss: 0.8075\n"
     ]
    },
    {
     "data": {
      "image/png": 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/8MMPnrUUH5X0f/zxRz/26quveu7Zs6fn999/P7knGwA9HABAEDQ4AIAgsrKk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19feb8ed630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10... Discriminator Loss: 0.5380... Generator Loss: 1.6885\n",
      "Epoch 10/10... Discriminator Loss: 0.5544... Generator Loss: 1.5246\n",
      "Epoch 10/10... Discriminator Loss: 0.6293... Generator Loss: 1.7969\n",
      "Epoch 10/10... Discriminator Loss: 0.5345... Generator Loss: 1.4688\n",
      "Epoch 10/10... Discriminator Loss: 0.5476... Generator Loss: 1.6577\n",
      "Epoch 10/10... Discriminator Loss: 0.4819... Generator Loss: 1.7558\n",
      "Epoch 10/10... Discriminator Loss: 0.6792... Generator Loss: 1.9941\n",
      "Epoch 10/10... Discriminator Loss: 1.6536... Generator Loss: 0.3255\n",
      "Epoch 10/10... Discriminator Loss: 0.6346... Generator Loss: 1.3769\n",
      "Epoch 10/10... Discriminator Loss: 0.9469... Generator Loss: 0.7324\n"
     ]
    },
    {
     "data": {
      "image/png": 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nAQMGeL7ttts8a6ktF+lEwZEjR3quUKGC57POOsuzTiZMxn777fk36MUXX+x5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\nEAQNDgAgCBocAEAQNDgAgCBocAAAQfwfwZEOdLqHPTwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fea049d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10... Discriminator Loss: 0.5372... Generator Loss: 1.5868\n",
      "Epoch 10/10... Discriminator Loss: 0.5504... Generator Loss: 1.3669\n",
      "Epoch 10/10... Discriminator Loss: 0.6224... Generator Loss: 1.2859\n",
      "Epoch 10/10... Discriminator Loss: 1.1487... Generator Loss: 3.2360\n",
      "Epoch 10/10... Discriminator Loss: 0.7357... Generator Loss: 0.9292\n",
      "Epoch 10/10... Discriminator Loss: 0.6659... Generator Loss: 1.0889\n",
      "Epoch 10/10... Discriminator Loss: 0.6157... Generator Loss: 1.4048\n",
      "Epoch 10/10... Discriminator Loss: 0.5842... Generator Loss: 1.4993\n",
      "Epoch 10/10... Discriminator Loss: 0.5880... Generator Loss: 1.8193\n",
      "Epoch 10/10... Discriminator Loss: 0.6201... Generator Loss: 1.1832\n"
     ]
    },
    {
     "data": {
      "image/png": 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azMxOP/10P1axYkXPa9as8XzKKad4Xrt2bUrOMxPVqlXL80knneRZN3nTEZOp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\nqq9p0aKF582bN5f63IuDHg4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      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fe7871d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10... Discriminator Loss: 0.5927... Generator Loss: 1.7505\n",
      "Epoch 10/10... Discriminator Loss: 0.7430... Generator Loss: 1.2837\n",
      "Epoch 10/10... Discriminator Loss: 0.7807... Generator Loss: 1.8078\n",
      "Epoch 10/10... Discriminator Loss: 0.6638... Generator Loss: 1.1796\n",
      "Epoch 10/10... Discriminator Loss: 0.8670... Generator Loss: 0.8959\n",
      "Epoch 10/10... Discriminator Loss: 0.6297... Generator Loss: 1.1366\n",
      "Epoch 10/10... Discriminator Loss: 0.7229... Generator Loss: 1.1867\n",
      "Epoch 10/10... Discriminator Loss: 1.1596... Generator Loss: 0.5584\n",
      "Epoch 10/10... Discriminator Loss: 0.6892... Generator Loss: 1.0592\n",
      "Epoch 10/10... Discriminator Loss: 0.5289... Generator Loss: 1.2669\n"
     ]
    },
    {
     "data": {
      "image/png": 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FjTSrU6eO52jNNDOzYcOGeX7mmWfMrORaa2+88YZnHQ2ny6zr+m38fCHb6fOXr78ioIcD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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19fed507208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10... Discriminator Loss: 0.9777... Generator Loss: 0.7061\n",
      "Epoch 10/10... Discriminator Loss: 1.0451... Generator Loss: 0.6048\n",
      "Epoch 10/10... Discriminator Loss: 0.5019... Generator Loss: 1.7173\n",
      "Epoch 10/10... Discriminator Loss: 0.7810... Generator Loss: 1.9040\n",
      "Epoch 10/10... Discriminator Loss: 0.5604... Generator Loss: 1.9776\n",
      "Epoch 10/10... Discriminator Loss: 0.5992... Generator Loss: 1.1604\n",
      "Epoch 10/10... Discriminator Loss: 0.6600... Generator Loss: 1.0757\n",
      "Epoch 10/10... Discriminator Loss: 0.9362... Generator Loss: 0.7881\n"
     ]
    }
   ],
   "source": [
    "print_every = 10\n",
    "show_every = 100\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "sample_z = np.random.uniform(-1, 1, size=(72, z_size))\n",
    "\n",
    "samples, losses = [], []\n",
    "steps = 0\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    \n",
    "    for e in range(epochs):\n",
    "        for ii in range(mnist.train.num_examples//batch_size):\n",
    "            steps += 1\n",
    "            \n",
    "            # Batch Images\n",
    "            x = mnist.train.next_batch(batch_size)[0]\n",
    "            \n",
    "            # Sample random noise for Generator\n",
    "            batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n",
    "            \n",
    "            # Run optimizers\n",
    "            _ = sess.run(d_opt, feed_dict={input_real: x, input_z: batch_z})\n",
    "            _ = sess.run(g_opt, feed_dict={input_real: x, input_z: batch_z})\n",
    "            \n",
    "            if steps % print_every == 0:\n",
    "                # At the end of each epoch, get the losses and print them out\n",
    "                train_loss_d = d_loss.eval({input_z: batch_z, input_real: x})\n",
    "                train_loss_g = g_loss.eval({input_z: batch_z})\n",
    "\n",
    "                print(\"Epoch {}/{}...\".format(e+1, epochs),\n",
    "                      \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n",
    "                      \"Generator Loss: {:.4f}\".format(train_loss_g))\n",
    "                # Save losses to view after training\n",
    "                losses.append((train_loss_d, train_loss_g))\n",
    "                \n",
    "            \n",
    "            if steps % show_every == 0:\n",
    "                gen_samples = sess.run(\n",
    "                               generator(input_z, 1, reuse=True, training=False),\n",
    "                               feed_dict={input_z: sample_z})\n",
    "                samples.append(gen_samples)\n",
    "                _ = view_samples(-1, samples)\n",
    "                plt.show()\n",
    "\n",
    "    saver.save(sess, './checkpoints/generator.ckpt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x19fe7597198>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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51K/RNB1VRmG2xqzIqTtFNRYSIuhSytkNloNuYo1Axlq9X0uE7j0ULm7ZEXx7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\nNzMrCRd0M7OScEE3MysJF3Qzs5L4E3i6N9kVgpxMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x19f10b38a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "losses = np.array(losses)\n",
    "plt.plot(losses.T[0], label='Generator')\n",
    "plt.plot(losses.T[1], label='Distriminator')\n",
    "plt.title(\"Training Losses\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:tensorflow]",
   "language": "python",
   "name": "conda-env-tensorflow-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}