gan_bhand_working.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "p1a6OHDd5XeL"
   },
   "outputs": [],
   "source": [
    "!mkdir -p drive\n",
    "!google-drive-ocamlfuse drive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1264,
     "status": "ok",
     "timestamp": 1533318681978,
     "user": {
      "displayName": "Md Fahim Sikder",
      "photoUrl": "//lh3.googleusercontent.com/-rdIL4NG3B9s/AAAAAAAAAAI/AAAAAAAAARI/7WtoB5e445g/s50-c-k-no/photo.jpg",
      "userId": "113286591130898257207"
     },
     "user_tz": -360
    },
    "id": "DzFu0kiwInqy",
    "outputId": "7a5bd700-8655-48e7-f9fd-0ecb082112fc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['datalab',\n",
       " '.config',\n",
       " '.ipython',\n",
       " '.cache',\n",
       " '.keras',\n",
       " '.local',\n",
       " 'drive',\n",
       " '.forever',\n",
       " '.gdfuse']"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "wcbCY2GPIUjq"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os \n",
    "import keras\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Input, Dense, Conv2D, BatchNormalization, Dropout, Flatten\n",
    "from keras.layers import Activation, Reshape, Conv2DTranspose, UpSampling2D # new! \n",
    "from keras.optimizers import RMSprop\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import sys\n",
    "import pickle\n",
    "import gzip\n",
    "from time import time\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "GM4NqC016hqL"
   },
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    with gzip.open('drive/colab/bhand/bhand.pkl.gz', 'rb') as f:\n",
    "        if sys.version_info.major > 2:\n",
    "            train_set, valid_set, test_set = pickle.load(f, encoding='latin1')\n",
    "        else:\n",
    "            train_set, valid_set, test_set = pickle.load(f)\n",
    "\n",
    "    train_x, train_y = train_set\n",
    "    \n",
    "    #train_x = np.reshape(train_x, [-1, 32, 32, 1])\n",
    "    \n",
    "\n",
    "    #train_x = (train_x.astype(np.float32) - 127.5) / 127.5\n",
    "    #train_x = np.expand_dims(train_x, axis=3)\n",
    "    \n",
    "\n",
    "    return train_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "Xvzzpr2u63Hy"
   },
   "outputs": [],
   "source": [
    "train_x =load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 991,
     "status": "ok",
     "timestamp": 1533318714626,
     "user": {
      "displayName": "Md Fahim Sikder",
      "photoUrl": "//lh3.googleusercontent.com/-rdIL4NG3B9s/AAAAAAAAAAI/AAAAAAAAARI/7WtoB5e445g/s50-c-k-no/photo.jpg",
      "userId": "113286591130898257207"
     },
     "user_tz": -360
    },
    "id": "ZwaL8NUD67Op",
    "outputId": "ed5c6419-e9b8-45ad-a748-9b75d7c619ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 1024)"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 937,
     "status": "ok",
     "timestamp": 1533318716324,
     "user": {
      "displayName": "Md Fahim Sikder",
      "photoUrl": "//lh3.googleusercontent.com/-rdIL4NG3B9s/AAAAAAAAAAI/AAAAAAAAARI/7WtoB5e445g/s50-c-k-no/photo.jpg",
      "userId": "113286591130898257207"
     },
     "user_tz": -360
    },
    "id": "LijqW4L8JUbr",
    "outputId": "2e2f6559-1d97-48ae-b192-5e9d9cd0a5b2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "pnZlPbMf61Vi"
   },
   "outputs": [],
   "source": [
    "train_x = np.reshape(train_x,(train_x.shape[0],32,32,1)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 989,
     "status": "ok",
     "timestamp": 1533318854557,
     "user": {
      "displayName": "Md Fahim Sikder",
      "photoUrl": "//lh3.googleusercontent.com/-rdIL4NG3B9s/AAAAAAAAAAI/AAAAAAAAARI/7WtoB5e445g/s50-c-k-no/photo.jpg",
      "userId": "113286591130898257207"
     },
     "user_tz": -360
    },
    "id": "MoIRnWwL6-cm",
    "outputId": "5d28172d-6245-4f97-d1d8-49f9cb7988c1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 1)"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_w,img_h = train_x.shape[1:3]\n",
    "train_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 939,
     "status": "ok",
     "timestamp": 1533318880937,
     "user": {
      "displayName": "Md Fahim Sikder",
      "photoUrl": "//lh3.googleusercontent.com/-rdIL4NG3B9s/AAAAAAAAAAI/AAAAAAAAARI/7WtoB5e445g/s50-c-k-no/photo.jpg",
      "userId": "113286591130898257207"
     },
     "user_tz": -360
    },
    "id": "gMTbvi8s8D_6",
    "outputId": "f858ab05-133f-4bb9-ed3b-97aaebb2a501"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f4f8bcd9240>"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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57733vHUrV6YOEjz11FPceeedAGzatMnbzvckV7q5644cOeKtq628vJzWrVsD0KpVK+/7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f8bd01e48>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_x[4242,:,:,0], cmap='Greys')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "wdycwBi-8GoS"
   },
   "outputs": [],
   "source": [
    "def discriminator_builder(depth=64,p=0.4):\n",
    "    \n",
    "    inputs = Input((img_w,img_h,1))\n",
    "    \n",
    "    conv1 = Conv2D(depth*1, 5, strides=2, padding='same', activation='relu')(inputs)\n",
    "    conv1 = Dropout(p)(conv1)\n",
    "    \n",
    "    conv2 = Conv2D(depth*2, 5, strides=2, padding='same', activation='relu')(conv1)\n",
    "    conv2 = Dropout(p)(conv2)\n",
    "    \n",
    "    conv3 = Conv2D(depth*4, 5, strides=2, padding='same', activation='relu')(conv2)\n",
    "    conv3 = Dropout(p)(conv3)\n",
    "    \n",
    "    conv4 = Conv2D(depth*8, 5, strides=1, padding='same', activation='relu')(conv3)\n",
    "    conv4 = Flatten()(Dropout(p)(conv4))\n",
    "    \n",
    "    output = Dense(1, activation='sigmoid')(conv4)\n",
    "    \n",
    "    model = Model(inputs=inputs, outputs=output)\n",
    "    model.summary()\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 510
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 985,
     "status": "ok",
     "timestamp": 1533318940280,
     "user": {
      "displayName": "Md Fahim Sikder",
      "photoUrl": "//lh3.googleusercontent.com/-rdIL4NG3B9s/AAAAAAAAAAI/AAAAAAAAARI/7WtoB5e445g/s50-c-k-no/photo.jpg",
      "userId": "113286591130898257207"
     },
     "user_tz": -360
    },
    "id": "oaxS1yZV80vL",
    "outputId": "cdee5e12-1187-407c-949c-df15552eec4b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 32, 32, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 16, 16, 64)        1664      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 8, 8, 128)         204928    \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 8, 8, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 4, 4, 256)         819456    \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 4, 4, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 4, 4, 512)         3277312   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 8193      \n",
      "=================================================================\n",
      "Total params: 4,311,553\n",
      "Trainable params: 4,311,553\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "discriminator = discriminator_builder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "2Gj3B0Q_8_T3"
   },
   "outputs": [],
   "source": [
    "discriminator.compile(loss='binary_crossentropy', \n",
    "                      optimizer=RMSprop(lr=0.0008, decay=6e-8, clipvalue=1.0), \n",
    "                      metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "vLzGi2BZ9Dia"
   },
   "outputs": [],
   "source": [
    "def generator_builder(z_dim=100,depth=64,p=0.4):\n",
    "    \n",
    "    inputs = Input((z_dim,))\n",
    "    \n",
    "    dense1 = Dense(8*8*64)(inputs)\n",
    "    dense1 = BatchNormalization(momentum=0.9)(dense1) \n",
    "    dense1 = Activation(activation='relu')(dense1)\n",
    "    dense1 = Reshape((8,8,64))(dense1)\n",
    "    dense1 = Dropout(p)(dense1)\n",
    "    \n",
    "    conv1 = UpSampling2D()(dense1)\n",
    "    conv1 = Conv2DTranspose(int(depth/2), kernel_size=5, padding='same', activation=None,)(conv1)\n",
    "    conv1 = BatchNormalization(momentum=0.9)(conv1)\n",
    "    conv1 = Activation(activation='relu')(conv1)\n",
    "    \n",
    "    conv2 = UpSampling2D()(conv1)\n",
    "    conv2 = Conv2DTranspose(int(depth/4), kernel_size=5, padding='same', activation=None,)(conv2)\n",
    "    conv2 = BatchNormalization(momentum=0.9)(conv2)\n",
    "    conv2 = Activation(activation='relu')(conv2)\n",
    "    \n",
    "    conv3 = Conv2DTranspose(int(depth/8), kernel_size=5, padding='same', activation=None,)(conv2)\n",
    "    conv3 = BatchNormalization(momentum=0.9)(conv3)\n",
    "    conv3 = Activation(activation='relu')(conv3)\n",
    "\n",
    "    output = Conv2D(1, kernel_size=5, padding='same', activation='sigmoid')(conv3)\n",
    "\n",
    "    model = Model(inputs=inputs, outputs=output)\n",
    "    model.summary()\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 748
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1375,
     "status": "ok",
     "timestamp": 1533319618410,
     "user": {
      "displayName": "Md Fahim Sikder",
      "photoUrl": "//lh3.googleusercontent.com/-rdIL4NG3B9s/AAAAAAAAAAI/AAAAAAAAARI/7WtoB5e445g/s50-c-k-no/photo.jpg",
      "userId": "113286591130898257207"
     },
     "user_tz": -360
    },
    "id": "qU4m0KbJ-Wr2",
    "outputId": "55a34647-14dc-4f86-efd3-deef1a1625c6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 4096)              413696    \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 4096)              16384     \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "reshape_2 (Reshape)          (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2 (None, 16, 16, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_4 (Conv2DTr (None, 16, 16, 32)        51232     \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 16, 16, 32)        128       \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2 (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_5 (Conv2DTr (None, 32, 32, 16)        12816     \n",
      "_________________________________________________________________\n",
      "batch_normalization_7 (Batch (None, 32, 32, 16)        64        \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 32, 32, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_transpose_6 (Conv2DTr (None, 32, 32, 8)         3208      \n",
      "_________________________________________________________________\n",
      "batch_normalization_8 (Batch (None, 32, 32, 8)         32        \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 32, 32, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 32, 32, 1)         201       \n",
      "=================================================================\n",
      "Total params: 497,761\n",
      "Trainable params: 489,457\n",
      "Non-trainable params: 8,304\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "generator = generator_builder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "8fvf1Xg1-ssV"
   },
   "outputs": [],
   "source": [
    "def adversarial_builder(z_dim=100):\n",
    "    model = Sequential()\n",
    "    model.add(generator)\n",
    "    model.add(discriminator)\n",
    "    model.compile(loss='binary_crossentropy', \n",
    "                  optimizer=RMSprop(lr=0.0004, decay=3e-8, clipvalue=1.0), \n",
    "                  metrics=['accuracy'])\n",
    "    model.summary()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1328,
     "status": "ok",
     "timestamp": 1533319678571,
     "user": {
      "displayName": "Md Fahim Sikder",
      "photoUrl": "//lh3.googleusercontent.com/-rdIL4NG3B9s/AAAAAAAAAAI/AAAAAAAAARI/7WtoB5e445g/s50-c-k-no/photo.jpg",
      "userId": "113286591130898257207"
     },
     "user_tz": -360
    },
    "id": "tpJbM4oVUmb9",
    "outputId": "d06f9eda-004b-4ba4-cef6-72904efa49a3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "model_3 (Model)              (None, 32, 32, 1)         497761    \n",
      "_________________________________________________________________\n",
      "model_1 (Model)              (None, 1)                 4311553   \n",
      "=================================================================\n",
      "Total params: 4,809,314\n",
      "Trainable params: 4,801,010\n",
      "Non-trainable params: 8,304\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "adversarial_model = adversarial_builder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "_5gxx9k5-YSc"
   },
   "outputs": [],
   "source": [
    "def make_trainable(net, val):\n",
    "    net.trainable = val\n",
    "    for l in net.layers:\n",
    "        l.trainable = val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "P58ud3hk-d0y"
   },
   "outputs": [],
   "source": [
    "def train(epochs=2000,batch=128):\n",
    "    \n",
    "    d_metrics = []\n",
    "    a_metrics = []\n",
    "    \n",
    "    running_d_loss = 0\n",
    "    running_d_acc = 0\n",
    "    running_a_loss = 0\n",
    "    running_a_acc = 0\n",
    "    \n",
    "    start_time = time.time()\n",
    "    \n",
    "    for i in range(epochs):\n",
    "        \n",
    "        if i%100 == 0:\n",
    "            print(i)\n",
    "        \n",
    "        real_imgs = np.reshape(train_x[np.random.choice(train_x.shape[0],batch,replace=False)],(batch,32,32,1))\n",
    "        fake_imgs = generator.predict(np.random.uniform(-1.0, 1.0, size=[batch, 100]))\n",
    "\n",
    "        x = np.concatenate((real_imgs,fake_imgs))\n",
    "        y = np.ones([2*batch,1])\n",
    "        y[batch:,:] = 0\n",
    "        \n",
    "        make_trainable(discriminator, True)\n",
    "        \n",
    "        d_metrics.append(discriminator.train_on_batch(x,y))\n",
    "        running_d_loss += d_metrics[-1][0]\n",
    "        running_d_acc += d_metrics[-1][1]\n",
    "        \n",
    "        make_trainable(discriminator, False)\n",
    "        \n",
    "        noise = np.random.uniform(-1.0, 1.0, size=[batch, 100])\n",
    "        y = np.ones([batch,1])\n",
    "\n",
    "        a_metrics.append(adversarial_model.train_on_batch(noise,y)) \n",
    "        running_a_loss += a_metrics[-1][0]\n",
    "        running_a_acc += a_metrics[-1][1]\n",
    "        \n",
    "        if (i+1)%500 == 0:\n",
    "\n",
    "            print('Epoch #{}'.format(i+1))\n",
    "            log_mesg = \"%d: [D loss: %f, acc: %f]\" % (i, running_d_loss/i, running_d_acc/i)\n",
    "            log_mesg = \"%s  [A loss: %f, acc: %f]\" % (log_mesg, running_a_loss/i, running_a_acc/i)\n",
    "            print(log_mesg)\n",
    "\n",
    "            noise = np.random.uniform(-1.0, 1.0, size=[16, 100])\n",
    "            gen_imgs = generator.predict(noise)\n",
    "            gen_imgs = gen_imgs.reshape(16,32,32)\n",
    "\n",
    "            plt.figure(figsize=(5,5))\n",
    "\n",
    "            for k in range(gen_imgs.shape[0]):\n",
    "                plt.subplot(4, 4, k+1)\n",
    "                plt.imshow(gen_imgs[k], interpolation='nearest', cmap = 'gray_r')\n",
    "                plt.axis('off')\n",
    "                \n",
    "            plt.tight_layout()\n",
    "            plt.savefig('drive/colab/bhand/gan/img/generated_%d.png' % (i+1))\n",
    "            plt.show()\n",
    "            \n",
    "            \n",
    "        end_time = (time.time()-start_time)\n",
    "    \n",
    "    return a_metrics, d_metrics, end_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 1030
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 252529,
     "status": "ok",
     "timestamp": 1533323747895,
     "user": {
      "displayName": "Md Fahim Sikder",
      "photoUrl": "//lh3.googleusercontent.com/-rdIL4NG3B9s/AAAAAAAAAAI/AAAAAAAAARI/7WtoB5e445g/s50-c-k-no/photo.jpg",
      "userId": "113286591130898257207"
     },
     "user_tz": -360
    },
    "id": "Qdr4Xs99_Ok6",
    "outputId": "e9297fe3-d88d-4270-90eb-723b8c7d7b86"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras/engine/training.py:975: UserWarning: Discrepancy between trainable weights and collected trainable weights, did you set `model.trainable` without calling `model.compile` after ?\n",
      "  'Discrepancy between trainable weights and collected trainable'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "200\n",
      "300\n",
      "400\n",
      "Epoch #500\n",
      "499: [D loss: 0.600810, acc: 0.737146]  [A loss: 1.257019, acc: 0.145666]\n"
     ]
    },
    {
     "data": {
      "image/png": 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OzoArNLR06VIgFhoaMmQI4HVnEcADGVVOdvXVVwOxPaMjkmQMlfhSuO2TTz5xR77rSy7X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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f81287390>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "500\n",
      "600\n",
      "700\n",
      "800\n",
      "900\n",
      "Epoch #1000\n",
      "999: [D loss: 0.590381, acc: 0.737886]  [A loss: 1.246732, acc: 0.133986]\n"
     ]
    },
    {
     "data": {
      "image/png": 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FtrnWq1fPzRLsd/Hmm28CJHLOQCSzsmSx9+6PP/7ozoi1c6ebNm0KBO/lYpTfSvXYppgy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f8140ecf8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "250.58062744140625"
      ]
     },
     "execution_count": 54,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_metrics_complete, d_metrics_complete, end_time = train(epochs=1000)\n",
    "with open('drive/colab/bhand/gan/img/res.pkl','wb')as outpt:\n",
    "  pickle.dump((a_metrics_complete, d_metrics_complete), outpt)\n",
    "\n",
    "end_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "04XElFxRLgde"
   },
   "outputs": [],
   "source": [
    "with open('drive/colab/bhand/gan/img/res.pkl', 'rb')as rbb:\n",
    "    a,b = pickle.load(rbb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1657,
     "status": "ok",
     "timestamp": 1533323770916,
     "user": {
      "displayName": "Md Fahim Sikder",
      "photoUrl": "//lh3.googleusercontent.com/-rdIL4NG3B9s/AAAAAAAAAAI/AAAAAAAAARI/7WtoB5e445g/s50-c-k-no/photo.jpg",
      "userId": "113286591130898257207"
     },
     "user_tz": -360
    },
    "id": "gkAno2GU_TRv",
    "outputId": "fbc2f453-9665-4758-a92b-20d574cc43a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Loss')"
      ]
     },
     "execution_count": 56,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aPYendBZvhnsm3SJa3U2L2yaI60Q9eNztaO5qQb/Evt5B2Mj1Fc56pYPlyf1n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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f80f5f5c0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.DataFrame(\n",
    "    {\n",
    "        'Generator': [metric[0] for metric in a],\n",
    "        'Discriminator': [metric[0] for metric in b],\n",
    "    }\n",
    ").plot(title='Training Loss', logy=True)\n",
    "ax.set_xlabel(\"Epochs\")\n",
    "ax.set_ylabel(\"Loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     },
     "base_uri": "https://localhost:8080/",
     "height": 311
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 981,
     "status": "ok",
     "timestamp": 1533323776847,
     "user": {
      "displayName": "Md Fahim Sikder",
      "photoUrl": "//lh3.googleusercontent.com/-rdIL4NG3B9s/AAAAAAAAAAI/AAAAAAAAARI/7WtoB5e445g/s50-c-k-no/photo.jpg",
      "userId": "113286591130898257207"
     },
     "user_tz": -360
    },
    "id": "QGlHtL4g_7sI",
    "outputId": "f16dbc20-3c49-4ec9-a8cd-39f46c55fb89"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Accuracy')"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A8KouNT77fR/qGpTtKy9+sRVT3liNV7/erludsT+/DHfOWoFZ87djsz90RI5o\nJny8XN2RYN6fUnuU4Pq651CpxJFBjM/nwwNvrcGTH/GCpFjmqMAh9Ds0svhd6bpLth5Bdn45isO4\n4u7PLw+smN6u8H7lHlwV/pXy9SKhs/avY9ieHRq2Y/uBEsyavx1vmYwJpRfLBACldA2AzYSQNeA9\ngO4ihNxECLmCUloI4GUASwkhqwBspZQqi+omwOXvBF/6YqtEAAAyg5tC35cv+hi/+GMfflyTi/lL\ntQ2x8tFfuA/fjK1NbhwWI/b+EfS5Yr1ptn8GIFxXbFQLM7BVxKwe3iyLN+cHpvmCnlreSez06571\nuM0qPX+vz4cFK3IUj3/3x9149WvVya3qdcNVpbxKX+yl+99Yreu4cMz7cz+en7sl8FswvovbSoJT\nXU37x6ZgnCexswHHBXM1ANJvTBxexOfzhQ6mOIQkIFq/uzDcrQQQhKiY7PxyPDd3s2QhY7hv0qnQ\npnOPVWLmvK0hXkli28J7P+3GawoecIIhWrBLWIWlNgBK6cOyTdtF+94B8I6V1zdLXmEV+nRtG+Ip\nJLBpbxFG9W+vuM8j6lmE0YpaOQBvfHv0vfX453n9cM7Irjh4tAIvf7kVt186EMP6ZiqeY9TDRA2P\n1xvwnBAQPkyPwjCK4zjkFFRIOgGtoGVqtYzEWGgEIRS2WMUl2ELknYaN4wXhw2+vxdUT+uD8U7sF\n9skDq5ldM+L1+lBYWoNOGcnhD4ZZh9JQzLi5KiEP1BfnEJ5lsKbVdW5kHynHi59vwS2XDFAtyy06\nh+M4SVDGD37eg2dvPQ0AJC6+Pl9oR8txnOkMdF6vD8u2qi/gE9xHAf6+GlyekBmIQJyTr5e4rX22\niFfhTZkttePU6Fgxfcz/XVr9pTSmEbjF8NzczXj6X6eq7t+XV6YqAMTB1wQjsZZ7nBCB8fM/9uGc\nkV0x93eKugYPFm04rC4AotQq5v2ZHXClFAjMABTcQW02BDx+5McbQa9BOlJ+WJ2Ls4Z3kTx/Qfic\n1D4FB48GdaocuEBHP29JNvp1a4fKGhcG98rAx79K1UNmBcDPa3OxYOVB3HXFIDgddmS2TUDnTA1h\n0MRuJhy0hZCgexcPAl74PDg4ePcH9bUD3ywLjrw5TrqWICnBgZyCCtTWu5GcGOyilm8vQK9ObSTl\n5BVVGbKHCWzYU4jendWN5wCvmhKY9+d+rNFwhfX5gOXbjuCT37TtNgBC3JMBfrGd4CxQcLwa6/yz\nGLl9I9owAaBCgYZRzOmX9krdgDCVF08ZtdTk8n3CaU6nDS63F18vzcbZI7pIRo3bs7X1yWq4PV58\ntSQbo0gWSLc0rFVo0MLHrORp0ykQAAAgAElEQVQ7beO4EIFhBofdhoWrDqJrVjJGEmVBGi0qa1yS\nDltQP02a0FdiU7DZODhFMxNhBfKEEV0CulsBs/JXiNT55oKgXvfVe04PuEDKEQYHTUWc0y7RV8tx\nKMwA9HLomFj4SnXxCXF2PPup33Ps6mGB7Z8tovj7uB6SctTUbuF4e+Eu/PvvA3UfrzWLB4Bf1x/W\nHQtpT25o8qm7X12JB64ehgE90vGd6J6sFgAxEQvIDFqjPLffrUzphQsduFTHqV6WeHT6xR/7AoZS\nt9uLxZvzsHhzvsTwDEh1qUaYv4Qf8QvJb5Sq9dTHG7F+d6FingTDqgQO2HEg1MBlt3FYuOog3lzw\nV0DXaRVer0/yLoTVronx0rFPVa1LMReBUowfs+PylKRQL6zJs1dh1lfa9oGmIlznE5gBRJh1yCN7\nR2K998vzpG3/hIL7tlmM5K+oDJPsyEggPLVQ3cK9ihdcOpgAiA5nDO1s6Hgtie/2eJFfVBUyMgSC\nKiBxWAS5p0x+URX+2JSHepdHIhwWi0bXLrcXJX4vBMEjZ3fuCX9cFm3UXCzlq23VBNM7P+zSnRBk\nYI809Z0+4I3vQr0YxM/j2+XmRnB6qXd5JB17Wmq86rGFKoukQo4zmSylOawcjXPYJG6aWsRrGHTF\nqHnw6OWrJdkhAdzUiOYz1FvvtilxEQUMNIrYSULJuBxNmApIBa3UhG6PF3tVcggLKiCJp4ysn/2f\nP3DXl4v3I1kl2JfL4w14PKQkOvHmdzuxeV8xenYKv6ZBb1hfLRfOvYf1JRTR0uf7oDzLkIbn9lma\n/1YenkLLZ95qXBHESooGnTKSMP220Xjnh126PGV6d2mDEo2c0pFmLxOzaqc+F18ziW7U2LJPn4qt\nXXK8bo+raCAWcswGEDWiN3LIPVaJlTukDfb8U07C7xvzAgJA3Li0VEBqMXA4cAGVS2lVfcBVU2y4\nVEO9Y5c+g2gYk5X8nyVXVBixia/718ET+OvghpBjooU8fpM8zEc0SIx36FIByPMlRJszhnbSXCuh\ntgZCjTZhVmx/uXg/qmpcjRpx1miYbC2yFYyxSiTERS8CgR7EjiThFhBGSsyogKKJ0og11a/fFdSh\nn/8RWWgjj9cXsCfoDWYloCZw5J+peCTeU+ZdocWFp3VDx/QkXccqGQhLND7irln6XCT1Ija4AlCd\ncUWC3g6iotraUWRGm2Aspv7d2mHCiC6S/XaVNRBqhFvkV13n1h1ssLmgZwYtR3D6UKJdij51ml6K\nymol34zX50NRaY3qYr5IYQIgSggfi9JoyMxI2+P1ms4LW6tiJxB7XgBSQ7XdgG/+0N4ZmH7babqO\nVRocLtbwJEqKt3ZSmqwSDiMS9AqAcN4yHXQKVTVqRbOde64cgkkT+kj2O2zGsoVFmqazOaInD4Ac\nLT38tef2QxudNhU9PPz2WhwRubXW1Lnw2Pvr8bFGOO5IiBkBYGSW6uiUA3u6ubADPq8vJOdAUrwD\nv60/rOh2qYZ4BmAUYQGLvGHKF31Vi4Jd9e+mYcyVkRDnsCyJu9XJ4ZMtSOCRGiWhkhTvQO/O+mdi\ncrqLYuXYOC5kxbVgrxnSS18cptaYI9jMPWmtXLfZuBCPsmjQLiUObVPiUFnjgtvj0+X8YYaYEQBG\ncJ60D3F99LvmjSRZgYZVUFIdYlxye3yYvzQb7/20W7e3Q02d23SGJWFZvZa6I7+4KjBbGTeoo6o3\nz81/6x+yzQo1ioDVo86UxOjXPVpC64rxPfHANcNDMnip4bBzGCjqzMXCw2YL7eyE2DSnD+mEK84I\nH4VSrn6Oj7Pj3FFdddVNToc09YxijYmZ9nVUY6GZjeNM51LWoqyqATaOC8wanVGMhCyGCQCDKH3r\nd10xONCwlEbt4jgnd7+6Qtd1IgkgJzQareiW80W62xv/1l/ROHrxmO7o3z1UMKS1ib4hVSBag06n\nw6Zo14j0QxrQPS3EzTZaJtBBvTIQ57Tr1it3zkwO6P07pCVKPEaUhJIgADiOw6Vje4QtXynVplmv\nlAtO6xb+oAjRXFXtR29f/bfRwfpqqe5sNk53mUYRlxtnkTdQzAiAaHykHKfuliW8LCWDrZHgVNHA\nFxAA6p2d2H3TbuMUG9jYQR0VOyMrPRO8JlaVKmGzcZg6aVj4Aw3isNtCR3wmdXWXjO2uuF3vylob\nxwVW6sY57UgQqSKURrpy9ddIohyKWKsMM6Pdm/7W33LbDsB74gmoOSnonQGc3CMYxlkroYzNZt2s\nVSzErVoQFjMCwCwJcXYM9Y/4bJy6vo/zNwKllIqNvf7Ho0MAFPoXtiQn8Pp8pUZss3FwOux49PqR\nimXoNQQbodTvbz2KZGFQr3TV484dyasiRvZT7sRsHBc2Umm6iZmMwx76rLR8tC4ardzJA3wnddnp\nPUO2i4Xg6YM7qZ5vs3GBePYOu00ixJW6JHknLI5Vr1i+bAYwqn97VR26lmDo3bmNLjuJXtUXwLcP\nOWK31SduOiXw90PXDg/8rbezdoiOa9DwwgunArp0bA9F9apW2w6ULSrXqvUATADIcHQOdWvr7HdN\n5Dh1I6LwYfy6Xl8aQysRdPvtUuNVjYpHS2rQq3MbzLr7dADKxjG7f5s4raIYvVEtjSC4iKa3SVDt\nbC4e0x3XnNsXb0w+Q1UnbbdxIefLR4Uv/HsMZtw51lD9EuIcITYQLa8arQ/XYbcpqg/EE4CLx3TH\nA9cMDz0I0mQ8dhsnGTEqqYASDI7C5R3bbZecrNrZaQ02EuMdurLAOR029O0a2taUmkHHjNARvjhw\nnDh1q1i9qdcIbLfbcOqA9pgwootqIiWBIo0V5CP6ZSl6nrVvF94mIq4rswFEis5RuLOrVAD4EBzB\ncxwXkuShl7+DjZbHRIe0RJxkYrWqOMSBYHJw2m149IZRIQG0BFISnYEOSmlklKGQ43fKxKGG62YE\nYQaVlOBQfaZXntlb8V2Isck6RAD4v7N7S3477DbdWcAEEuLsuOBUmT5bo22VV6mHFLHblGdeYlfi\ntilxqkZ3jhMnZNeotB89emRxZyd+fm2S+LaiNoLW8oRJjHcodqJy4R3nsCFOIfyE0qBLSU2mVjfx\naF63ALBxuOOyQbjufIK7/zFY9Tgf1BdzAqFJYQJ1EqlgR6jNYtkMwDrGD+mkOz6QMMLjuNBp9LhB\nfNbLaKnFn//3GFNuZT06Bl0AhY9DGK31URnBixuV/MMYM7CD4ihSKS2fEmJ9rBkcdpsug7CaBw7f\nuYY/3+h7S4i3o1NGMq47v19gm9bYYn++dLVpqiggnE1hlgJIO7eEOIdiaG7APwMQBIBK5yfu9EPW\neihU/Mmbg6oTcZmXjuNVVWr2Hy1BGh9nVxQA157bT/J7VP/2ITOMQb3SMWZgR8jxKTwSVfWUqLNV\nqv5Zw0L7AXFRnTKSJUZhST3C6HfVvmXhPjlAVcCI68AEQJTp0akN9E4LJDMA2QsVvlW1xmcm+YkZ\nQ5u4kQuNUviA1XS94s5B/mGY0fWK6RLhit5I4xllaKiQzFxHQBiNijtprU5AruMfOyjYmcnVNgLC\niLWfXx0ijsw6fkjQJsBxQXuBUM7syeMlaq2Zd48L/C1/HvJa9zupHdJFq4nFz0Z4zuozgODIvWtW\nMtqKnAdsHBe2A7tkbA9MGNkVf5N5C/XomIpJE/rgnJHS2YJywiLlssXfoFKbOHNYl5BtIWWrBQGX\nVePpW06V2BzU2pfwvvRGCrbKCyhmYgGFrKr16V9oJaglHDYuZIrq9WmPwOw2G9weY2GUjSZMGdgz\nHRPP6o1Ne4v4OgXUAsFGdu6orli8SboCV6xXlDdUTvV+9HWYiQZUK/27tQsJPme3caojXzG9OrXB\nSJKF4rLaQNrLYX0ycf0FJPTjUnjf8mPSUuMDkWAH9UrHP87ohe+W52BU//bYRItw9nC+sxAbarVC\ndcjdaMUftU1FBXTzRQMw78/9uOFCAoC3wQzvm4nxQztjWJ9MFByvxoGCCthsHIRHJJSTnOCUqEzE\nfxsVduJHI5yqVkLPTm3AcRzGDuqI8UM6obbeo9vlGQAG9UyHjeMCidUDdQD/jOQG4jbJoTMK9UGY\n2OMt9NtSei7yDl+tn5Y3qU4ZScgvCq4bCDcA1BqjiO+ntj46Wd1CrmFJqS0AH3SO/31BL4A4pz3U\n2BXhDOD520cH/hYaotEPdeqkYchsl4hbLh6AqZOGBe5LXM6p/TuEnBen4TeuVgf59ngV45/adiWU\n9L4Ou00SPVNt4ZLNxuGuKwZjWJ9g9rR7rxqiGPZZ6X2LO+DX7j0dA/3uf2mp8bh/4jD06NgG908a\nhjOGdsb9E4cFpvRiPb1W0hQbx+HpW4LZ5STJaVQ8SDpnJuP+ScOQ2ZY3FDrsNtxz5ZDAPQpX5jgO\nHp9U3adFiLCRjYDkJYh/C+eqdVhxThse/ucInDG0c8A+86+LBuB/N43SrNPTt5yKq87qrWj8FV9P\n/u7OGyVVMXbKSFIdTYu/wd5dQp0i1AY7SvWQI5/92ThOKjjDlK01CBXLKqVcEtEgdgRA6ARAt2FY\nGOE57aFGKq9POgWXEy4Beof0JFzoNyoKH3GqyZc9bnAnibqHU5jCixHfi3y/mkCTH/fC7aMlemMB\nuaAc3CtD1e9duN8+ok7AbucCoZwH98oI60bokc169CI+3kicGPEMoF4jzLPNBnTN4o36HdKTJB+1\n2UVEARUfx8Hr0X/f8mP6hQv/ITpeyA2gJKwB5RHq6UM6oUfHYIf74h1jME3mUtw1KwUXje4u+X4U\nAxOKekq7bCY+dlBHPHLdyJBO+uX/jMXjN46SlN0lS+pgcfc/BkvewWD/yurMdlIHiKG9eeE7sEca\nZt4VVKvJNVGcTKiLn7l4u2Ac1oqkKq73WSPMrcAOR+wIABk+n09XsDUffEEB4LQhXhYZUHh/asZE\nrZGZ4CMuFyLiRSgCnTKS8NbUMwOdiR6kOtzQ/XEaRmC9NoC2KfGKhuF4px3kpHaB37dcMiAwupbj\ndNjx5pQz8PC1IwLbHDYbGvwda5zDFvZNBVVx6sdEcz2Gx6t/BgAAc+4/A8/eeqrkufbq3EbX6FOO\n4OXFceK2E/48ufDukpmMNyaPRz/RexIjDqUtGHnFbUa8IlprvYNAVrvEgNecFtOuD7YD4ZsQd7Qz\n7j1DcvzogR2QkugMmcFktE1Az05tYLdx6NO1LS4d20NimwBC42Xd939D8OaUM0I8j3p3aYs3Jo/H\n1KuHS2aXSoHixM9ZsphLdKyWa2mvzm0w8ew+slzWzAgcVXwIfkjhEEaiTrstJEuSsJhIrXPRUgFd\ndVZvyblC56W2QCneadc11ReSnmSK3DiVOnQtwxynsktv3BuH3YaH/hn8kG0cp/qMOPDeEuIPRzwD\ncDptYb0tfDJduMpReqquC/EMoEGWc+BGv+5eXJ+EOAfsNpvk+TkddlPuw17RDMATxgsICMZuaqcQ\n7iMpQez2yf8rtLHK2mD4amFGJ27/N/+tP8YP6YQZd47VzLQmRp9hPnRBm/j9CwM3oZ4d0pL825Xh\nOA7TrhuJK87oFdLx8msppL/VPHfEz2radSMxYUQXDFAIlSJ/F0LVxX1BZ/8amgyFb/2xG0bhwtO6\nSewuVq02jiEjcOiGap0R9gQvjDiHDRWiPKKXnd4To/rzSc3VEoLokdwBN1N/c1daYBXwNtLREKZM\nGobcoioMFq1AVDpPq26RrmuQuxzyAkB/B2y32YK2F4cN3f1urmorf82qgAB+bYOwilToXMIVI7jW\nnj6kE+CTZrQSqz3kz13QdY8b3NF0fcWOB3KDvxJP3HwK9ueVhx19C68nNcmJsqoGSR5cQQCIVS9t\nU+Jx80UDDNffCEo2AOGep98+GsdKapDlX1Slp3nZOA4PXTs8kBfbZguGTjFCn65tJSpL+TWUEH9v\nfbq2xd3/GKyZhyNod7Gm8wdiSADI8QEo15OgQ9Q2EuIdGNmvPX5acwgAn2dYeNlKi0Emnt0Hq/8K\nH1ZaPo1PSXTiyjN74dvlOeiSmYwjx6vhtOs3ELdNjsO5p3ZHcXEw/r/iSl+N2YnRjmlA9zTUuzzI\nKahQrCfHaYy/FS7ldHABI3Ccw47endti+m2nBT72kPr6vy01HTWg3kEM1hkeWczAnul45tbTAlEu\nLzitGxZtOIxVO45KRsPy53hyD+l5ZtaPeEWLv8KtAwCAzLaJAYOyEsLIVPj3hgv74/VvduCcEV0R\n77Djzy35gVwFRoz7asy4c6zme5Lgf37id3dSh1RUV9ahfbtEyYpavQMMsaeRjeMCbersEeHdQfWg\n9o3KB1ziBWAz7hyL/85ZI9kfdBWNSrWU62Rd0c0bn88XGPVxnPbo4foLCL5YvB//PK+fpAMSf3Rx\nMtvA6IEdcOFp3bB2V/gcAOJ1BgIXj+mBv43ujrLKenz0615MOruP5JrdO6QiNdmJS8b0CFu+vK4C\nmsJEtmvShD44UaG+qlUIV/CvF5b4y5Y+D5tNWwUkcN9VQ7B8WwFO7pEuUQEB2qEn/nZadxSX1+PS\nMeF10dGA46SuiV0yk/Gviwbgpr/1D0lDKUd8npkZgE80G/T4gsLALNdfQDD3932BxW3D+mTiw4cn\nAAC6d0zFNef1DdRTcLs9bUCoV5lexGsNdCNqPEkJTlRXhmaVM2Pjsdk4xMfZ8f5DZ0dtNb/qimSN\nAZeSCs0irY/0GtZfonkgbxw+Hx+l8O/jegRcytSed6eMZEydNCxk9Cl+QeMGKQftEhpDSqITE8/u\no3iMfOFWsHwO6W0SMHXSMHT16/WFTtvptOH+icNUDXhq9RBjxLB0wandcM25fXUfL2/sNo7TZXQf\n2icT9141BA67DW6/h0ucjjgobZLj8NTtYwKqIsm10wpha1eks+aRYeOUV/eqHm/GCCxyGhjWlx9F\n9uqsrI7QQ4e0JEydNCygSw+po2ztwl1XDA6oPq1ikN+bratfWOrR0pjJTSzcWzST38jLElRvJ2sE\n31NS83A262cAMSMA5AoIH3xIS43H5eN7BabRRoNlyWN1iEd2p/g/EMHolNUuEReqxET3BmYA4a8p\nrAQVrwjVVVeFsq1IZBEsW9q0OA7okqnswaS6ytJPpKsg4/tuRXy/LbpMwMJ7O3ek+VAWRj5YczOA\n4Kj/9isGY8rEoYEFaq2F/1w+CPdPGophfTPDH+zH7Awg2sjLPHdUV0yZOBRXT1AeAIpRDs/CbADR\nR9RYBF14VtsEFKsfFoL8450ycWhAjzfcPzJr4/dxr6xRtzeIvTrCMXpgRwzunWE4taGyDUDDC8hQ\n6aGEGIFtnLqnSJiL6dYXh0FPWI4hvTMxe/L4iFJHGjHa6Y39LyboBsoh3mk3ZcNo7iTGOzCoZ/C+\n9Oj3T2qfgk4ZSTjLgDC0Ygwk/9bsNpuud/TOf8+UCDFbI9gAYmgGIEXcnC4a3R2nD+mEOzWi/ikR\n8qIVOlRhcZHgUaGkRvHJjMDhMNM5KS93tw759bSEm9oewVCa1c6EzlgBeSYvNSLNG2zkg9UaGKgh\nqNJaY85eNfSISafDhum3jQ5ZJayFFTMAszNrp8MuGewIr9fKtxwzMwB5AxKPKFKT4vAvFXc2vUu1\nASDR7yEhXlwiGIcFg6bSRxswAlv4qs0sODKD02GDy+3V9REkxtv5FaQqhz547QjsOngiaiNcKzOZ\niTHSMVfUGE/9GQwAZ/jUFosRF2IjREOIDuuTiW3Zx4NlRqmZBYQT8wKKPtFoT/KpfpzTjhfuGCMJ\nWyCsFBSup+R6aXQGEC20rmfW9/iVu8ehpt5t6Hw1wZeWGs/72bcwjDy6U/u3x09rcnHz3/rrPkfs\nBRQrRClTaAjReIb/uXwQ/j1jma4yZ909Tve3EVABtVQbACHkFQCjwQ/A76OUbhTtOwnAlwDiAGyh\nlN5hZV3kRNKebriQIKegQtGLRp7pR57JZ8zJHbF133GJQTjwQTf6kE79embjjyclOCUrJrVo7FSZ\njYUR4de1fYphF8Rw8adaJc14BuB02HDeqJMCNi6tMpVWY6vWrRFmAJbNiQkhZwLoSykdA+AWAK/L\nDpkJYCal9FQAHkKIsotMtAjVAek7TeG4s4Z1UVUZyZF3pPFxdkyZOFSyhDzwQVs4ootXcKXUavtW\nxR8XE4xqafmlmjVGOyHxQrBYwarBQrRmUdec2zcwqIuWYG7p6wDOAfA9AFBK9wBII4S0AQBCiA3A\neAA/+PffRSlt1GS6etuTGS8NMXpG0sKimglRWomoRHycHdOuHynJbKTVvhxREgAv3DFGkqCbETnh\nkhC1Rgb6k6jLE8ZEilXP8PEbR+GlO8ZEVIYgSKwUBFaqgDoC2Cz6XezfVgEgC0AlgFcIISMArKSU\nPqJVWFpaEhwRJEaOj5eqJZKS4pGVpS+9od7jlEgXLa5RK+f8rFSMHd5VV+JsI8ivl5WVipP7ZOHX\ndbysbds2UXLM5Wf2xvfLDwAAMtKSIrpvtTqIEdp1UlJcVK4V7nrRuoYRrLkmLwGSk+MtvEbzIisr\nFV8+0z6QYD1a99y+faqpFKzhiEb92vlXTAtRca14z2HvnBDSn1K6NwrX4mR/dwHwGoBcAD8TQi6m\nlP6sdnJpaY3pC2dlpaKuXuptUV1dL4mVo4Xe45Rw+j/WtslxYcuprVYPtWCUrKzUsNerqKiTHPP3\nMd0DAqCutiGi+w5H+7RElPkTptdG6VpK93zrJQPwedFvACJ7j2ax4prCrLTeH8ywKe6rqaitrtfV\ntvVyoqQqautMok2832FEeN9m71lLcOiZ539LCFlFCLmZEKK8VlyZAvAjfoHOAITIaMcBHKKUHqCU\negD8CWCggbIjxiq3Mjn9TmqHWy4egMdu0M6M1JgIESk7Zqi/TrnxOtpIcttaaOUaqxKioyUjzgfA\niIzm7EnV1oDB2CxhBQCldCCAOwD0BLCMEPIuIUSPUvd3AFcBgF/NU0AprfSX6QaQQwgRVkWNBEBN\n1N80p1gcy0SA4ziMG9wJGW2js5gpGkydNAzTbzsNHdNDBcClY3sAgO4YQ2bhNMODMrS41r+Y8LST\nzQdkY/A0ZztKY/QZupRflNK/APxFCPkdwPMAfiCE7AdwC6V0v8o5awghmwkhawB4AdxFCLkJQDml\ndAGAyQA+9huEdwL4MfLbUUcY8XdIS8T020frevHD+mRi/NDWN4KMc9pVI2tecUYvXDa+p+UfBgdR\n/998v8FmyVnDu+CMYZ2bdefV3Ln+AoLcoxXNegYgJHayEj02gO4AbgJwDYDdAKYDWATgFABzAZym\ndi6l9GHZpu2ifdkATjdcY5P07NQGG/YUYUD3NN0fzr1XDbG4Vs2TxupYhvTKwOZ9xTjJQJrLlsIt\nFw8wlGPYKKzzj4yzh3cBmnkAvdSkONxy8QC0T1PP5RApemYAywB8AGACpbRAtH0DIWSDJbWygHNH\ndUWnjGT072ataoOhE47PEzw+rzMG9VIPk9tSGTe49c0cGY2P1e1IjxF4KIB9QudPCLmDEJICAJTS\ne6ysXDSx22wY0juj2Vr8Yw0OHBLiHBjSO4ONZhmMJkKPAPgIUm+eJACfWVMdBoPBYDQWegRAOqU0\nEMaBUjoLANOjMBgMRgtHjwCIJ4QEAt8QQkaCD+DGYJiGaX0YjKZHjxF4CoCFhJC2AOzgQzpcb2mt\nGK2WtslxKK9uiDjpCoPBiBw9C8HWU0r7ATgZQD9K6QCwGQDDJA9fNwJ/H9cDoweyRUwMRlOjZx1A\nGwDXAcj0/44HcDP40A4MhiE6pCXh8vG9mroaDAYD+mwAXwEYAr7TTwVwCYD/WFkpBiNaeH3epq4C\ng9Fs0SMAEvzZug5RSh8AcDaAidZWi8GIDvPogqauAoPRbNHrBZQMwEYIyaCUngDQ2+J6NQvK6sub\nugqMCFldsL6pq8BgNFv0CIBPAdwG4H0AewghuwAcs7RWzYQv937b1FVgMBgMy9DjBvoOpdQHAISQ\nPwG0B7DN0lo1E6pdtU1dBQaDwbAMPQJgCXi9PyilRwAcsbRGzYhoJXdmNB0cOPhY4gEGQxE9AmAb\nIeRpAGsANAgbKaVLLKtVM8HKTFUMBoPR1OgRAMP8/44XbfOBnxm0aliUSgaD0ZoJKwAopWc3RkWa\nIxynx0bOaM4w9Q+DoY6elcAroZC9lVJ6hiU1akaw8T+DwWjN6FEBPSb6Ow7ABABV1lSnedEabQC7\nSvbi++xfcO/w25Ea1/pSMTIYDP3oUQEtl236gxDyi0X1YVjMnO0fAgDWHt2I87u3fu0e8wJiMNTR\nowKSR+46CQCxpjoMBoPBaCz0qID+FP3tA1AB4ElLatPMYOsAGAxGa0aPCqgnIcRGKfUCACHESSl1\nWV+1pqeiobKpq8BgMBiWEdbPkRByJYCFok0rCSFXWVel5sORqqPw+Zj+mMFgtE70OLpPBZ8QRuB8\n/7aYwIgBscpVja/o9yitK7OwRs2LRblL8NfxPU1dDQaDYQI9AoCjlAbiIlNKKwC06CwbDZ6G8Af5\nMTIDWJj9C1YcWYO5e742U60Wh8vjwg85v+GtHR81dVUYDIYJ9BiBNxFCvgKwDLzAuBDAZisrZSVF\nNcfx1LqXcPZJp+Oqvn8Pe7wXPth1ll3lqgEAVLpiYplEi3CvbAl1ZDCaCj0zgHsB/Ag+KTwBMBfA\nZCsrZSXZZTkAgKV5q3Qdb2QGwDobBoPRktAzA0gC0EApvQcACCF3+Le1yGGu0dW9rFNvOTCDPYNh\nDL0ZwTqKficB+Mya6jQCBn37fQaSirfG0BFaNKfu9vdDSzFp/p2oclU3dVUYjBaDHgGQTil9XfhB\nKZ0FoJ11VbIWo120l6mAVGlOI+6FB34FAGSXHWzimjAYLQe9SeEHCD8IIaPAB4ULCyHkFULIWkLI\nGkLIKSrHPE8IWaartlGgMVRALWEmEJ06Nh8BwGAwjKNHAEwBsJAQUkgIKQav/rkv3EmEkDMB9KWU\njgFwC4DXFY45GUCzDhBJA/wAACAASURBVCttbJTbOjtEj9eDWZvfwrqjmyTbW+fdMhixQ1gBQCld\nTyntB2AU+AVgBQB+0FH2OQC+95exB0AaIaSN7JiZAB41VOMIMRrfx9wMoGkpqS1FQWVh1Mo7VJmH\nA+UH8dme+bI9zVAENCO1FIPR3NETDXQ0gJsBTAIvMG4H8K2OsjtCul6g2L+twl/uTQCWA8jVU9G0\ntCQ4HHo98kPJykoFALStSQzZpkV6ehLaJYY/DgCccfzjdDjsusq2iru+ehAAMH/SW6rHJCfH665j\nCZICf4vPqWqwKW5vStq0TVStS3OpY7RprfelBbvn6KAqAAghDwK4CUAyeE+gUQC+ppTOM3mtwMCY\nEJIOXqicC6CLnpNLS2tMXpZ/cMXFfGC3yoq6wHZhmxbFJZVwxetLDdlQ7wYAuD1eXWVbjVYdqqvr\nddexrCz47MXnVLuUtwt4fV7M3fM1hrcfjMGZJ+u6VqRUlNeiOF75vprDO4k24rYdK7B7Nn6uGlo9\n23QADQBuopQ+TinNhrE5fwGk7qOdARz1/z0BQBaAlQAWABhBCHnFQNnmMewGGnrL5fUVcHliIiAq\nAHU1WDj12NHqQqw/thlv7/jYgloZpzl5LUVCnbselQ0tchkOo5mhpQI6CcCNAN4mhNgBfAyd3j9+\nfgfwFIB3CCEjABRQSisBgFL6DYBvAIAQ0gPAx5TSKYZrb4JI3UAbPC5MW/0sMhLS8fTYh2VHt44O\nRo5qxxnmdr0G1lA0Bj74WoSHVjgeXPkkPD4P3pzwUlNXhdHCUZ0BUEqPUUpfpJQSAP8C0AdAd0LI\nj4SQi8IVTCldA2AzIWQNeA+guwghNxFCrohW5c3AcfrUOQLyUW6dh1chldSdUL+G8Wq1SGJt3UNz\nwePzNHUVGK0EPaEgQCldAWAFIeQeANcC+B+AsHmBKaXyIfJ2hWNyAZylpx5NgbFYQK0VcyqgpqD5\n1YjBaL7oEgACfhXOO/7/YgKfLPK1PnnQuuYAqjaAFtbb+ny+1vZqGIyIMKYPaQUcMBgqgIWC0CLW\n7rd1UVpXhj8Pr4DHy1RKsYqhGUBrYFn+akPHt9ZQEEZQtwEzAdCSeWPb+zhWU4RERwLGdj61qavD\naAJibgZgFCM2gN0llD+nGXaMOeWHcNeSB02dK76foprjwe0tTAfUHN9LU3KspggAUNEQWz71jc3P\nOb/jvqWPoM5d39RVCYEJgDDIOw2DywiiSkVDJbYV7TR17u+HlkalDhuPbTFwtPGHdbD8EPIqj4Rs\nr3bVYHPh9ohcS1n3z2gKfsldDLfPgyNVR8Mf3MgwARAGIzYAAatUQDM3vYn3/vqs0UMem10IZqbL\nnbH5Tbyw8bWQ7W/v+Bgf7vocW00KQAaDEQoTAGHQ8gL6fM83jVqX4/61B6V1ZY16XUk/LpoCRaoC\n+vXgn/h415e6js0pzwUAHK8tMX/BRlBZHSjLxUsbZ6Osvtzya83Y9CbqXHXhD2RIWFuwEa9vfZcZ\nvxFjAsDMC9fq5NYc3aC8w2I1kdbIW22aaXZW4vV5caBcNOMQPY9Iu9OfDi7CxsKths/LqyyQxCHS\nS2OogN7a8REOVebhj0PLLL/WwYpDWJyzCgVVxyIsSb1tHKsuRHl9RYTlNy/m7v0atDQ7YANpLJpS\nfaxGTAmAnw7+bvicUBVQ02uS1YRSnbsez21QDqlktu2tPLIOv+b+qVYTk6Wap9pVgxc2voqn173c\n6Ndujny67VtM3zDLsrAbz6yfiWmrn7WkbEbTE1MCYO+J/YbPkY+29XiSWO0GqlaHrUU7Ii77t9wl\nWJK3MvB7f1mOej2aQBZWu/mRv7ncv41RYf4aRt2NGYymIKbWAThtxm+3Obo6qhmm5+79OuKyf8z5\nDQAw4aTx/AaN+5fbRxqDcMJVS0A3vzcZPdgq55ZA83tBMTUDcJgRAPIZQDMQCI3Z8WrdbVM8iUg+\noebw7qyCrXFoCTS/dxRTAsAmiwS6vfivsOfIdav6VEDWotSRNXgatI8zbYHSuN+wHWr0n0Rju8C2\nFIx0LdWuGnyzP5jVtfmNSxmNRUwJAHln/u7OT8OeI+9sVcMiNOLo0uV1h2xblheqc450VOjyukNK\n8Kn8rRevzxuR+11R7fHwB6li/TtqskmGgQv/cOBXLM1bpaPIphuxRtpOmifNT9TGjADw+rygpdmG\nzwvtRPUsirL2RX+z/wdsKtwm2VbjrtU8x2iNyurLMXnZNM1ZkpmFYE+sfRGTlz9qsDbRoflNwKOH\nEWGv14DelGqlp9e9jHuXPWLpNVpbzC4zxIwAUBo16yFkBqDzOKtZkP0zluat0nT/06pTOGGYW5Gn\nuF38yZi55xN1paFqNZVy9pTsw66SvZrlbTi2BYcr8g3Xwyqaytfb2JvQV8mmnAEUm1jwV+2qwZ+H\nV+iOuSMIuBpXrf88axfVaT31svpyLJFFZt1RvAv7TAxajRAzXkBmv0uvzhmA5FqN0AmU1Zfjm/0/\nIDUuBaM6DDN8/p4T+0xd14ouQS1V4xvb3wcA1dSH9Z4GfLJ7nqErWU1T9Zmt2cCtly/3foutxTtR\nXl+Bf/S9RPd58/d9j42FW1FSV4qJ/S6zsIbqvL39I+RVFcBpd2J8lzEAgHd2fgJAvf1Hg5iZAZjF\nJxutqrlghgoKdQqqjuGuJQ+aDuwmRis5+N7SbNy15EEcKMuFFWopLRVBXmWBJKZPUU2xejkmOy+v\nwdSIWwojXyfRfIm+ADDSppsDhf42ppWuVYww6Cj0rwiOKMyIDioaqnD3koewIn9NyL58/wr+skZe\ndR0zAsBsU9ZrAzAy9Ft5ZC0A4Ev6nclahXK4MlQNsvAAn7XzN9WVvJGh1XEvFS0mA4C1RzcZKntP\nSfgZyiGDqp8v6Ldhjymvr8DO47sDv0vryvDd/p8s7xwixYi+Xj4U4MCh1l2HLUU74PF6QE9ko7im\npOWlfGvm/HV8N3zw4at93zd1VQLEjArI/ChT7gaqUn4TjZaE6yrp9AP3bJFKysgdaz1/pWcnqH+0\nmL3tPQM10MdzG15Blasa006dgi4pnfDYmucAAH/mrbB0Kh4phpq3TEfpgw+f7f4K24/vwpV9L8W3\n+38EALxy5vQo1rD5E4sm4ZiZAZidA8iNxxtk8fCFji1c6cdrSzBj0xvIqywwVQ8zWC+UolO+VbU0\n40YoeMiYT5IS+d2syF+Ld3d8YnDQYn4GAAD7/CE/jlQGgwmqtZ/9pTmYselNTfVjY/DxrnmSoHts\nMZxxYkYAmG0ccgEgV6cI5Uo/1uAn5vK6cbD8EBZk/4KDFYfxyW594Y+jgVAnDhyKI/KfVynfwDPV\nDNEQRVWDuKzDCollmjOldWUorinBV/sWYPvxXYG4R3rQq6/3+Xw4WH5Ysk3NHVLtvby29R0crDgk\niXdU665TTOQTLRo8DTgk80zbWLgF3/vVnGaoNhVPylqU3oSViWRiRwCY7GPcXpe+8lU+wPl0AWZs\nfhPbinmDb2P6Hgt14hDNRiQKB23goWofqyREWyrm3+9ja57Dk+teDPy2Gfk8dT66Hcd3o7ReKZ+E\nkrpQe82L+FAhkY84ZWg0eXvHx3hp0+xAXggljH5br259B0DzXx/y3IZXLMv7EDMCwLQKyKMtAITR\n1AFRiAJxM9wqW0jVmNPU5jQl1hOkrTnV14yg9vl8qPM0TYIWvc9O0a+cCw6QxPcdvszgsceqCwHw\n6zysQLBxHa0qDNn3/l9zUeWqblbtRxn1NhWu7lblE44ZAWDWpS3cArJZW+YA4BOBCEhXWoYGk9Pj\n6tXgcaGqIfwUVaujElxWjagHjCButGX15aZj0reOkT9wtDq0cxIorSszfJ/KI/XIUFwx7gv8T/JO\nw1WX07ngpcZVG7UOTKif2L6ztWgHfs9dquv82jAr5qtc1YpxtZoaq76QmBEAZp+gmRXEhTXFqgbI\nYzVF2HF8V9gyHl/zHB5a9VTY47RGDoLr4u4SGraccGUpHi86/NHV0zXTO2rPAKxRATX2qtxalZWk\nfx3fg8fWPIcfcxYZKu+5Da/oXgmqN3e1Wgeo/H60y1R6vEoDkgdWPoGpKx7XUTuFGqjc17v+RVIC\nbp9b16ztvyue0NjL4aGVT+HBleG/OzNE0h6tmt3EjAAwbwTWZwOQ4wmMhtXfulZMFqV9yY4kU3XR\nSyQzAADYXLRd62DNkpTKa258sffbgKpDCTX1jxDOYnXB+pB9eZUFWHjgV9XZEy09AADYXLgdawo2\natRO37OrcSkIAEl8j+Cf4WaOWh1ulasa3+7/EeX1xr2pwnn2eLwe/CULEcKBi1r7MfvNh0Pf59W4\no5bYWQcQgQrI5/PB4/MYzCfgk/2rTGldGdIS2sHtdSuW7/P5AlPt1LgUQ54hRjHe8KPlBWS0tPC4\nTcZ+EvAorDJeXbAeW4p2YMYZyiNEPSo7OS9sfBUA0KddL83jPtz1OQBgbOdTFPfrbd9qsxRFO4z4\nT1E7DKLeWX23/yesP7bZcEyfWnetxLNHnujGB2DlodBc3NK6Nb5Hv/D9qn3HPBG0cIvGRrEzAzCr\nAvK48PaOj3DfsmmG1EF6L+f2evDZ7vm4b9k0xUTn80SrhZOdybqvb5RqVw2mrvifoXOMPFPtQwUJ\nEJ1W/mPOIty3bBq/mtUkc7Z/KFkRLKCmQskuO4hP93yluE/PXUWqd9Y7e1OLGqt0vlgYvP/XZyH7\ntVQawhqBCoMzAI9XOhOSq81q3bWYsyE0jDunNo3RQ4TtLr+yAPctm4Zn1s3AfcumqcbZiuQqTAUU\nMeoPsLC6CKuOrFP8CDy+4HSzRqGDVkOvQfTzvV9j3TE+TMKx6qKQ/atEagNblJXaS0QRELUMmGoI\nMVT0Yd4LaE1B6IhPC2Gtxt5SfTmgC6qO4Y9DyyS5kAHe9VAP+0tz8OFfnxuqoxzVNxtlu0i4sOFq\nbFMMC67RHhV2rT+62fB1/zi8TPK7UCWmlF6DtBVs99v0jvm/h7UqqjpD62Zk7z2v3JoFpEwFBODp\n9TMAAFUKHbxUFaC/kekdkWklXQ8pM8qjgG+zf0JRbQmuJleYOv/zvd+o7pPrh+WPQ/x8wq2m1rqO\nFjad72v6hlmmyhd4devbituV1SbWoLdtqM80Qu0w4QzLeu5MXN6ne77C4MwBSHKat2WprY2Qbrcg\n+50BVN+5oRmz9OAFe37D3YNvj6BWylgqAAghrwAYDf7W76OUbhTtOxvA8wA8ACiAWymlliW7XZS7\nRHH7/tJgBywkRBfjFnnzGPmWo91ZVzZUKadD9Pki8p7RitKphHAlo/7e8ufxxNoXQvZF3x00/AtT\nG1GGY+WRtZhHF4Ck9VE9RinM9eoj6zF//0I8PeYRtI1PDWwPl/cgHJE+OuVnb9wILHwjwj55kMLC\nmmLM2vIWbjr5aozsMAwzN89BsjMJdwy5yX9F7WvabMoCIBqC1urop5HMANwWZUezTAVECDkTQF9K\n6RgAtwB4XXbIuwCuopSOA5AK4EKr6gIAK/wROOUodfpiPL6g3t+In7twrJEPU60NN3gaMHfPfMV9\nJXWl2HtCn6pDCaF68o85nGDYcGyrwetIH0SJggCJjtAMlqGnT1DyzNHDPLoAgHZiHflHXOWqxhf0\nW7i9biw6JB2QrDmq7uGj5/1G8uwki78kNmDtMvOrCrC9eBf+PLxCoUxl3tr+Ebw+Lz7dzdtLcspz\nFW0tatg567TWZgcg0QzVIDw3+bO3KoKAlTaAcwB8DwCU0j0A0gghbUT7R1JKheFBMYAMC+uiijxR\nvByxUcqIAIjmDODb7J9C3N4EluWv1hU50ygLD/yqud/wx6IrGmjkz0xcQnNO+bc8PzSHsxI17lqd\nUU8je3bBNxBs4+He8eai7Xh35yf4Lvsn0VbtZx7wYlORzkpqp5VH1mmWCehX92lhVgDI06aqxlaK\nYAZgVUu2UgXUEYDY6lPs31YBAJTSCgAghHQCcD4AzZUiaWlJcDjsUa9kfJxTc7/Yo8uWpDwNW3M8\ndHaRlp6E9MRU2Gwcr+TSQbt2ScjKTA3ZnlelnJ4xGjidNmRlpaIE+vSySUlxyMpKRVKR8nPLyuLr\nn5Ag3Z+Q4Azsk5Oenoy0xFRUN0T+fj/a9UXg78TEONX6CSQdCT0mHGr3IScjMxlOuxOJh7SflSZO\n6aDjmFc54FpaejKyUvTVS05ycjwEERAfH2zw6RlSr7NP932JqeO09dDt2iUhKysVcfFhviuOk9y/\n8Le9NvRjKWw4FvhbXD8xv4lmVPHxDknZxdUluOunx5CVlB5yXlZWKuwOfhDocNok27X4aMt8JDrj\ncfXg0Axitb4avL79bdww/Cr0zegZ2C6OJCyU/96mL9AuITguTkqOR1ZWKhrcMluN7HlFi8Y0AocI\nMUJIewA/AriTUqrps1daat7/XevBHSnX9n6prQ++iG+2K4+KP9+xIGTb8eOV8CTY4fXql/plZTUo\n9lWGzDTcHstMI3A1eFBcXInycn3Pt6amAcXFlaiqUvYnLy7m3f7q6qRrCmrrGgL75BQUlcCdZDPk\nZaWH45XlqvUTqKk17n554Ig+j4yCwlIkORNRU6d8jez88NEz5c/xmWWvKR5XUlIJe22CrnrJKS4v\nCyxcrK8PqjxLSqThntfnb1V9hwJlZTUo5irRUB/GZdonfRfC36V1oeU31AeFQnVt+JAS9fXuQHll\n9eX4wO+hVVwTmiks72gxDpXx76G+Ifis1e6zqqEaVa4q/LqfDz1xTscJIcf8VcSvvH9h+Ry8MF7Z\ntVoo/48DUs+zmup6FBdXol5mrOc06hQOrf7PShVQAfgRv0BnAAFlmV8d9CuAxyilv1tYD03CxVsR\newG5DaQgNKcC4mXk4sPLZVubnyojkrARct7c9gGA6BvhthaFpoCMRgz7R1Y9o+s4tXUBAtNWP/v/\n7Z17fBTV2cd/s7vZ3G+EJCQhIfeHQEggCUQIlwREFFRQULwiiKIIoihiq9h6QSq1altpfe2rrb28\nvtX37ce+Vn2rtfVSoV7AekH0WPUVFUFBJKII5Pb+MTOb2dm5nd2dDdk9Xz9+2MzMzpyzc855znme\n5zxPxGVRcfrLGbWlp3b2x9Ex8s5yBQ4VkFbvz1um6zffahlB9M5X7wm0ZSfhNL67+Rbc8tIdzh5u\n022tVMohKiCXvMncFABPAVgAAETUBOBTxphWhN0B4C7GmLUVdoDRCgCeBCNOY7MYoU+H6Obwzy2o\nHCbA4XnOPjWHawwiQViF34g2b+7bEXaAPBV9AiIzmImh+GhPF3711oMhOQCcYPQ67PY79PT24Htb\nbsP2L962vE7fpu/c9nNc9dw6fNMd+n60drotu/n2hNihNeA66Qs871OChGc+fsH0vH6WryW0LO6M\nAq6pgBhjW4hoGxFtAdALYAURLQbQCeBJAIsA1BDRRcpXHmSM/cKt8oRLpyZyp1F4ADPCmT0FXrFO\n2n+j27yT7c9C59FoJ4/ma2C2A6nB7ex+k1jEAop15NGdX30SkxXcQ+/+EVOHTwr83dPbg0Pd32L7\nvrex9bPXsPWz1xyltAx+B6G/lWW8JwBv7HvLUVJ2/W/yvjJL18YBUrFz1IgWZm3jaE8Xunu7uPcv\nSJDw3/961PBcV08XjvRYqbMGvxEYjLHv6A5pW0+ym8+OFtrUgFwCABHM/HQNUe9zX5wxDJ37oysA\nHDcwScKBI514wYFnhpY+5T+7a+KNrt6jjuvlk7xcakYrfvLPe/F+54eYW3VS2PcIZxXr3F9dMpxN\nG4WNjlW76DXps9dvXo9D3d9y54S2Uttcv/lWXN18mdGXAMRuopJAoSAiRx+nxIpIVEB2XDDqrKjd\nK5zOZRVj56mdzwRtrgs8p68PXx62trfESVqAIHg28BSmF0Tlma/s+WdgRq2dwHzx7X7b9/1qkN0k\njFWsw5mEJBnbaIwE4Jt7ne8T0PIQC3XOsMJs0A03fMaBI6FOCCrfdB/CYYsVgN4e5pYNIGFCQUQD\nvhUAf+dRPTF6bPSMmf4M7nubwT3o9vVZRkVV9w8cV9QScu7+t+zi5cRABRTjVQZPVNLi9GERbyrq\n6e3BA5q809oQCffqYujb4e4vJeGXGpddlR6D32ufA5WSngNHOk03f5oR67Zx1CLboL7duKVGFAKA\nA66NYGFMZ+969R78tP0HeL/TIOTDMcKfd/4tyOfaKS/t2WYruOJRBdTd1+NYyqb6wnPjVHn18zdw\n//bfBR3768f9u3R5QzOH04adfsVsOLOb/Djhtb1vBnJw82DXv99ymFjJKWYxpDZ/+hIefOcPQcfc\nCiklVEAc8KwAnvn4BaVB8XUia8OQe0R78N1nMNhYuWD2RRjTyCmxNgLzeI4leaw3T9mhdx/W08c5\nuBq9w2hhplbh6WPRxq5t/Pz1+2NSjsc+MPKKH3xuoHEHT2fesvtlvMIZLweIzgyID+c+0DwYBq6z\nYIdJDPVocyyrgCL2drGpGm/b0ua5jlohbODpY9HmWFiBSojtJEWogDjgnZ1YGYHM0G7KiQXvd36I\nTa/dh1kjQnc0xpKDRw+iNy06RlArIvXL56W7r9vx+j1SQ9/Og9YhQ7R1j2YKRS2R3rG7L7JMbpGg\nnQS9f+BD/OmDPyM9gtDV4WL0XqKdCyRwX1fuGqfwzqDCMdzok5LEAjmDkXnXbR/e5noZ5GQ47s98\nYi0AjvQcdawYj0ZAM6ek+VJdue8/LKKaOmG/jaeYm2hdt+994wH868AHJolw3C6HUXsRAiCq1A2p\n5f7OQC5P3cZqNnhGbWjAq2jzl4+ejckCPNYqNiO/djOkGG14kp917IUXASLP5RwJWtWLm7m37XDT\nhVxPwgqAcDrAwS6+ODJ7Dn1u6et7LBHLRmeGU93nVU2XhSXAgdivAA73GAfNMyKWg/KxGF9qoOH1\nkgKir6/vg3HeabfeVsIKACfL7UlFEyJ6xkt7+HOg2lGcPsz+ojDo6jX3SY4VTkIIAEBVTrlp4m07\nYi0Avu12LgDM0h26ghj/o8Kn3+yxv4iDj776xPD4YAwGd0xTkJZved7vScKs8o4YlcY5bqljfvf2\nf7lyXx6cJT6JjHAFQEVWWVjfO9x9xLFqqzDduk1GE7ECiA4bXr4rqvczD6InBEDUOItOx4RhTbbX\nuWUoi4RhUQoXoMcuuNt1E1ZjddNyeKXoJ+UJB7+XP5ELoE3V2Yc7tv3MMJ2hEZc2LsE0TbA1p7x3\n4APHaSfH5Y/hvn+4iOF/cCFUQFGkuaDRkc91SoQ7M90gyx/9rEBOKMkoQnVOBcYVxG6QcgNVAOz6\nejc+6Nzp+HsZSek4o4Z/9cUTfjqmNoAYGpwFkXPaKHdSpidsK3AiAGIVhlbAT7iqnN7Axjf+70uS\nhLUtl4f1XDcYX9iEipzSmDyrPEwVmCByCtKGgoZWuXLvhB3hnOpAl41ZhOmlU1wuDT+Xj73Y1ftP\nL52Ckbk1WFp/XtDxcL0eIo1zoydcl9xIjcCeY0QFBsibg8LdzMVrA0iyCAAocJcDR6Kd+6OfBBUA\nfY531jXm12N03kiXy8PPyCE1rt4/Nzkbl4+7GE0FDUHHwx1Aoy1Ewx34eiPcy5GfGppYPJq0Dmvm\nuj4cgTxt+CRuddM4XTsQxI6jFpnDIiUhBYAkSZA4qm4V/vhYprmgMezvejzGM91w8/Za/YYNQ0c7\nvk+kM9FI8w6n+FLCNkA74fy6M/Hjabc6ulaSpLBqc2btPG6j4vCM4jCeNHAMlMoqnFDtU0v4nQui\nRUIKgFRfKtcMaLAufyOxYfhMVB3hqoCsBECWPwOnVc9xdJ+1LavCer4K707gBTWn4uL684OOudke\nJElCktdZVFBPRPF8+ESAG/bpVBe97Aaqz64edyn3/qEU38AlR0w4AVCSUQSAL7iSmyuA06tPdnxt\ntj8r6O/aHNkwtH7SdYbXR+JV4jWpc7ipLs0ECiB7pBxfNs32Hk0FDSjOiGwjnJpwxOnA2VE6GWP1\nnk8Dv2laQeLO6DO+cJz6Tf5nRZlkF1dSWhuHP8Iw2zzkpw3F3Gq+NJwD6VqdcAJAncHaGcG0y3y3\nBMDNE7/DFWhNn0N05diLsH7SdchNyTG8PpLNPmYDthoyIic5m+t+Vo3cqJRGdhej+qgC3SmqPjWS\nSJjRiqI5tzL8fL1AsApInQxYcWrliTh35AL1y3zPckEAeF30skv29fffFWMvMr3OLKRITU5lWM/1\nSB7uHd2xFFB6Ek4AqNh14pUaLxuf5EwAjM2vx5rmlY43kOWlDuFS0+hVA16P13TwByJTAXlNbACq\nANWvRuwwsynIhA4uLYVjHd23KL2QqxxqbKZYh4QwIjclx1TQnkWnoSJrhOX3JY0XULo/3fZ5xRnD\nAm2Idzh3QwXk5sz3zNp5gc9WA6zZ5C4tgjDQvCtvpyo/N0hYAWBHaWa/0Us7m7CacV48ZhEqssuw\natwyx8/haSy8s7BIZm2FJqEy1AGHNz45rxuo3aqrNrcagPFKwYojygqgpzd8AeB0/p+RZD0oeyQJ\nJ4wwDjcypWQi1rSssFwlSJA0K1o+6ofWcV0fblu6oXWN6TnrSUFkDEnJDXw2m8wA5vXKSeab4Gjh\nnXgJFdAxTrqvfzbgZCduaWZJ2M+6eeJ3Tc/xNqxwk0isaV5pKujUmTPvLKfeYqBW79VW7Nx4dsmY\nC3B184qQkB5G70dbF3UF8MKnLzp+llPSk9KwoW1d4G+7neSS5AkIVLOBaEbZVPPva4zAvAP0vKrZ\nmFIy0fH14U8mzMWlnQpoUd1CrG5ajh9NvcnRk5Y3LDE8btVv9LWaVDQB17as4lZxBt8z+K7hqpNU\nnDpIhEPCCQC1w/DYziRJwimVJ2JS0XicUXOq4TVzKmYGfyfMDpOXmmt/kQEXj1kU+HxZ41JU5JZi\nYvF4y++cVD7D0FW0Itvche6M2rkoyyzBWXQ6V/msZvTqL5WfOjRwzChaq1bopPiSUZkdqiIx6uza\nwH9HuuUVwNbP1W3QvwAAEqRJREFUXrMt8zkj55ucMW48k4omIFszc8y1GUQ8kqd/Bm8iUL0eL6pz\nKgzPSZIUKAqvQPZ5fHwB7qKoAlInSOfXLbS8rja3CtU5FY69heqG1KI2pwrn1Z0ZdNxK0Oh/t5Mr\nT0BZ1vCIVs/6e5qtpvuxHozs2lEkDE7/xiigVes44cRy65SJ03TG3FjnFx2bXx/4PDqP0D6yBTt2\nfmh6fUlGEU6unAUA2Pa31wPHrWacgKxzv3b8FZEVVofa2QrS+gWAJEkoTCvAZ4c+t/1+VXYF3u+U\ncxAbJj7XSPvDPcbJyPVkJKWjrbjV8JzZ5EHf8YdnFuNfBz4wfYYHUmBfgtWAM7Os3TDHsoTI2pl2\nT8Q1LStx+9ZNlteneJO581v0QV5BaxOsXNuyypHA4t117fV4cUXTJaHHLe5TnFEUlPVLFeCReNCF\nTF4iNKC4qSJKwBWATEZSOpY3LME1LSuDzi+tP4873stljRdGlDvU6RLRrrNfMe4SXD/hKs31xnpu\nn8cXFEriynGXBj73cujGVzctd3RditdG/6/0j+ANYRJWN12KpfXnIT81z/LrlzRcEPhs5ue/aqxs\nl9m+7x38esfvbcvsdABYVLcwsOrQD+J5Kda7hoNWAI6eFlpGp15tRmiFZXlWGS6uPz9oVZCZ1L+p\nSYKEda1Xh/GMPlzXujpohWr22+oN4tHyErKyAUwvnYzZ5ceHHI/mCsBO3WAnwq3KHykJIQCOdmu2\nUmteRv3QuqAdg36vH00FDRiRxRdgy8gQee7IBchNNvfQ0VKWOVx+voW3ggQJqTYDaW1uVZCfvFnD\naipoCNqxWJNbGbBz8CS+N1NN6FneaKybVVE7m7bjSJB3VTYVNNh2Rq3wXUinhZzvA0BDqpGbnIM+\n9OHlPa/altnalU/+ZTtKJ6O1qDlkEFejhtpFTtVG5LQSOJXZ5QDksCRB3w/aCMY/YOk9ocYWjMF8\njYqzSvN+JUjITcnBiEz+4HM5ydlBK1QtWiN4TW6wK2u0gjFa3ccn+dBcGKoGjWTOrm+vRhO3C0ef\ng4W1clttGDoq5Lx20uNmUMqEEADLHv2O5Xk1zns0l1qTiidgfZvxBi09R3plAWUUYmBTx0Zs6tiI\nuztu458JaITdpo6NGJVHAGAomDweuSlEO2fuleMucSwotGgHRFWHr/XsMKOpoCHEc2aI4iqbbePZ\nsaljY2A26MS9VO3oAUOsUub20jZs6thoa0j0QOo3qlsMOWlJqdjUsRHLNLNolcDw72DE0j9D9VLS\nTga09p88ze+t1s1oMKvKLsekImt7kxknlc8wPWflxMATPsGqX3skj2E61EjCrmvbbnVORcju+ZMr\nZqG5cCymDp+ITR0bkZuSgwtHnxN0jbbtRKJdsCMhBMChLmu9rxpZ0umSc03zCiyyMWCpaIOgLdT4\nJmvp6pHTMRoJAEmSAv/zou2skiRhUd1CzKuajVkG9gy1k0TbP96Jhtpo8NMeO7/uTMyrmo0TLQaL\n4GcGP1U10GebdOqyzBJcOe4SSJKEmSPaMa9qNhaPPtvx/Y3KrL4vfXC3jZO/H3SNXniYoZ6/VhMK\nQzYCG6uALhx9rqFqQ0tD/micVj0Ha5pXGp7vKJ1scDS07n2aoxIkzKuarTln3QL0eQm00Wetchbw\ndAc7I7BRGZsKG4O8b+xcek3vr7HzqGhVswHBqhMSPX29uKH1apxRMzesVZdT4l4A6H9Yo+aoznqd\n7vityB6B1iK5Y2v1pEZUKct3AJhqklFK3Z4/S1kOD+Pc3GSGvq6Z/gzMHNFuuAVf7SQ8KiA9xrr+\n/lKYNmRNZz6uqAVAsCtthj/dtNxG6OutumNmmawA1rasCqgf/F4/Zo5otwzqdZIysI7VZfAyGsQr\ndJ5KGZoNW54gN1BnXbEsa3ggzo0ECaeOPAEAMH7YuKBZY3NhI2pyrW1LHiUEx1CTCKfaDUr9q51Q\n+vr6Av0sJzkbM0e0o1Gx52hXbYVpodns9AbTpoKGgHeXVTyfJsV7TZ1gGa1A1DZk7QYqBcqotUGp\nv82oIfKqmTdKqzqjn1JyXMgYVJ8XugdDLySmlkzEsPRCtJe2uZooKO69gLqU2C9WqJuUeN2tftK+\nwVY/naQbtM6snYeH3/1j0LG6vFrcNW19YAWwsnEp1m3ZwFUWI3gCt+UkZ+OLw1+GHbf/B5NvQLov\nDaueDd7H4NXsol7TsgKrn1uHbt070e6cPm/kGVhYOy+iiJtmwbXMdi/zdrCZI9oxbfikkDIaDeJW\nQivZ6w/YdXg2HmX7s7Dv8H6k+VIxq2Ya6jPr4ff6saJxKW59+U7T7yVxhhwwbNsGbSrDn460JPkd\nqmq2i8csQldvN/waIbKu9aoQFaPR4Ly6aTl6enssB+7qnIpAnzmlcpZh3da2XG6ZN4JyqyFJElJ9\nKbhr2q2GAueyxgvR1duN53dtkeualI7Tqufgt28/jCSPDz+cciOufv57ISvn5sKxGDN0FPxeP7Z/\n8Q4A2SlgXetVhm1b21e1Y4HbuCoAiOguAMdBnjhcwRh7RXPueAAbAPQAeIIxdosbZTikcT+TCW3A\nJ5XPQE9fD2aU2gck0+JkxVA3pAbTS6cEQhuYCQztC89NycHs8uODjHBus3j02fjLzucwp+IEru+t\naFyKnV99EtCZXtqwGHu++RzVORV4fe9bQb76HsmDa1tW4emPnoPP48Our3ejq7cLHRo1mSRJYTX+\nc2g+huTIZWgrmoCPD+7Ci7u3Bl3TmD8aj/3fk9z3NsJYXRd6XXNBIz766hMc6v42YMS9uvkyvLF3\nB8oyh6MwLR/fdB9Cx3Dn+RJWjF2KZz/ZgnZFRePk95pRNtV2RaBHW51+G0Awqb5UnE2nI8mTFNSH\n5PcYPCh7JE/IoG4kfD2SBx5v8HWL6hbiYNfXeOS9xwPH1Hqb1V+9j3YSeO34VXjm4xeQ7E0Ocu3W\nl1VbPr83CVNLJmL/4QOYVjIRealD8MnBTzGxeDz8Xj+ubVmFjVt/igt0amG1XNrNk2Zl1aqhYjX4\nAy4KACKaBqCGMTaRiOoA/BKAduvhTwHMArALwHNE9AfG2I5ol6NTl03HSN+XlpQWFDskmngkD+bX\nnBL4uzxbVoPYxeqfU8k3EBuRlSwPiE4Cpg1JycVC4v8NRuVRwLgMAGOGjsIYxatBr/4A5Hg0i0Y5\ns5/w0FbSivz8TOzdexBJ3iScQ/Px4u6tQca8SCOJmjGpaAK27H4ZVdmhAtvr8WJBbfDmwcrs8oBn\nT4ovhbvtFaTl48za0PzE6uxbfd/qxrqq7HKuqLM5ydk4cKQzaPWqDtP6/rOwdl7gueH2ofnVJ+MP\n7z2GxnzzvBCtRc3o7u3GI+89jtxU3kCEsjDJSEpHWeZwXDDqLO4y+r3+oN9c+06HZxbj7o7bTL+r\nzu6NNjeqqOoqvaeX27i5ApgB4I8AwBh7m4hyiSiLMfYVEVUC2M8Y+xgAiOgJ5fqoC4BdX+8J+pt3\nGRxtyjKH4+aJ30Vuinu7+1QyktKxftJ1yAgjScVgx+vxYkPbuhCV1o+m3oQvD3eip68HXb3dtnsM\nnHD2yNNxYvl05LmcLcyO9KS0oPedm5KD9ZOu405S8v3j1uJQ96EglYhqkPV7gmen0fBR7yidgob8\n0bb7JnweHza0rUPZsHx0ful8Q5pH8hi2hVihagqsZvYlGUW4eeJ3I4pBFA5uCoBhALZp/t6rHPtK\n+Xev5tznACzj2ebmpsHn429s45II7KsGLBxzCh7a/hjObZiH/KzwXbyiQT5i8/z8/MyYPetYIT+/\nv77Gdc9EGUKNkZFSCPcFuhlWdQ7//cuD8dVty7Bt15sYXVYBSZJwxeQl+NWrD+P1PTuQnZKFGSNb\n4fNGPowUwNnAp9YnP59PTTKQ/WDJhAXo2dZtO/bYlVH7nqNFLI3AVpY2Wyvcl1/qdfnOSEUW1k5Z\njr17D2IJnQscAfbuPRjWvQYTqjokkRB1jj6VydWorKzGvn1fAwCSkIZloxYDyt6lL/c7C60RTQbf\ne/ZGPPZEUmcrweGmG+inkGf6KsUAdpucK1GOCQQCgSBGuCkAngKwAACIqAnAp4yxgwDAGPsQQBYR\nlRORD8DJyvUCgUAgiBGuqYAYY1uIaBsRbQHQC2AFES0G0MkYewTAcgD/qVz+EGPsXbfKIhAIBIJQ\nXLUBMMb0QXhe15x7HsFuoQKBQCCIIXEfCkIgEAgExggBIBAIBAmKEAACgUCQoAgBIBAIBAmKxBMx\nUiAQCATxg1gBCAQCQYIiBIBAIBAkKEIACAQCQYIiBIBAIBAkKEIACAQCQYIiBIBAIBAkKEIACAQC\nQYISy4QwA4JVYvrBDhH9EMAUyO/xBwBeAfBbAF7IuRfOZ4wdIaJzAVwJOSrrLxhj9w9QkaMCEaUC\n2A7gFgB/RZzXWanLWgDdAL4H4A3EcZ2JKAPAbwDkAkgGcBOAPQDugdyP32CMLVeuvQbAGcrxmxhj\nTwxIoSOAiOoB/A+Auxhjm4ioFA7fLxElAXgAwAgAPQCWMMY+cPrsuF4BaBPTA1gKORF9XEBEHQDq\nlbqdCODHAG4G8DPG2BQA7wG4kIjSIQ8axwNoB7CaiAY2eW3krAOwX/kc13UmojwA3wcwGXLejLmI\n8zoDWAyAMcY6IOcU+Qnk9n0FY6wNQDYRnUREFQDOQv9vcycRRZ6kOIYo7+1uyBMZFZ73ew6AA4yx\nyQBuhTwRdExcCwDoEtMDyCWi2GZddo/nIc98AOAAgHTIDeNR5difIDeWVgCvMMY6GWPfAtgMoC22\nRY0eRDQSckLCx5VD7YjvOh8P4GnG2EHG2G7G2DLEf533AchTPudCFvYVmtW7WucOAP/LGDvKGNsL\nYCcCySoHDUcAzEZwRsR2OH+/MwA8olz7NDjfebwLAH3yeTUx/aCHMdbDGPtG+XMpgCcApDPGjijH\nPgdQhNDfQD0+WLkDwFWav+O9zuUA0ojoUSL6OxHNQJzXmTH2ewBlRPQe5InOGgBfai6JmzozxrqV\nAV0Lz/sNHGeM9QLoIyK/0+fHuwDQY5t8frBBRHMhC4CVulNmdR20vwERLQLwD8bY/5lcEnd1hlz2\nPACnQ1aN/ArB9Ym7OhPReQA+YoxVA5gO4He6S+Kuzhbw1pXrN4h3AWCVmH7QQ0SzAFwP4CTGWCeA\nrxUDKQCUQK6//jdQjw9G5gCYS0QvArgIwA2I/zp/BmCLMlN8H8BBAAfjvM5tAJ4EAMbY6wBSAQzV\nnI/HOmvhadOB44pBWGKMHXX6oHgXAKaJ6Qc7RJQN4HYAJzPGVIPo0wDmK5/nA/gzgJcAjCeiHMW7\nog3A32Nd3mjAGFvIGBvPGDsOwH2QvYDius6Q2/B0IvIoBuEMxH+d34Os8wYRjYAs9N4mosnK+dMh\n1/lvAOYQkZ+IiiEPijsGoLzRhuf9PoV+W+ApAJ7heVDch4MmotsATIWSmF6ZUQx6iGgZgBsBvKs5\nfAHkgTEFskFsCWOsi4gWALgGsqvc3Yyx/4hxcaMOEd0I4EPIM8XfII7rTESXQFbzAcB6yO6+cVtn\nZYD7JYBCyC7ON0B2A70X8qT1JcbYVcq1lwM4F3Kd1zHG/mp402MUImqGbNcqB9AFYBfk+jwAB+9X\n8Xq6D0ANZIPyYsbYx06fH/cCQCAQCATGxLsKSCAQCAQmCAEgEAgECYoQAAKBQJCgCAEgEAgECYoQ\nAAKBQJCgxH00UIHACiIqB8AA/EN36nHG2O1RuH87gPVKsC6B4JhCCACBANjLGGsf6EIIBLFGCACB\nwAQi6oa827gD8g7cxYyx7UTUCnnzThfkTTkrGWM7iKgGwL9DVq0eBrBEuZWXiO4BMA7yZp05yvEH\nIUe7TALwJ8bYrbGpmUAgI2wAAoE5XgDbldXBPZDjtAPyLtzVSrz6OwH8TDn+bwBuZ4xNhbyTVd2i\nXwfgRiWERReAWQBmAkhSYr5Pghz/RfRHQUwRKwCBAMgnomd1x9Yq/z6p/LsZwDVElAOgUBOb/lkA\nv1c+typ/qyGNVRvAO4yxz5RrPgGQAznO+81E9DDkUN73KeF8BYKYIQSAQGBiAyAioH+VLEFW9+hj\np0iaY30wXlV367/DGPuciBoBTISc5WsrETUZxIYXCFxDLDkFAmumK/9OhpyLthPAbsUOAMjZml5U\nPm+BnJ4TRLSQiDaY3ZSITgAwhzG2mTG2FsDXAArcqIBAYIZYAQgExiogNenMOCJaDtlYu0g5tghy\n/tkeyIm4lyvHVwL4BRGtgKzrvxBAlckzGYBfE9Fa5R5PMcZ2RqMyAoFTRDRQgcAEIuqDbKjVq3AE\ngrhAqIAEAoEgQRErAIFAIEhQxApAIBAIEhQhAAQCgSBBEQJAIBAIEhQhAAQCgSBBEQJAIBAIEpT/\nB7ZPtatvhcggAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4f803d80f0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pd.DataFrame(\n",
    "    {\n",
    "        'Generator': [metric[1] for metric in a],\n",
    "        'Discriminator': [metric[1] for metric in b],\n",
    "    }\n",
    ").plot(title='Training Accuracy')\n",
    "ax.set_xlabel(\"Epochs\")\n",
    "ax.set_ylabel(\"Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "id": "3RthI--lAW8O"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
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