Gans_Implemetation_part-1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "11a9a153",
   "metadata": {},
   "source": [
    "# GANs Traning"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6bcff9b2",
   "metadata": {},
   "source": [
    "@Author : Saurabh\n",
    "@Date   : 12/Jan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "877b61bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns\n",
    "import os\n",
    "import sys \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0fd20636",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.layers import Dense,Flatten, Input ,Dropout,Reshape\n",
    "from tensorflow.keras.layers import BatchNormalization, Activation , ZeroPadding2D\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from keras.layers.advanced_activations import LeakyReLU ,ReLU\n",
    "from keras.layers.convolutional import UpSampling2D, Conv2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8eb0dd8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4048302e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Gans():\n",
    "    def __init__(self):\n",
    "        self.img_rows = 28\n",
    "        self.img_cols =28\n",
    "        self.channels =1\n",
    "        self.img_shape =(self.img_rows , self.img_cols, self.channels )\n",
    "        self.latent_dims =100\n",
    "\n",
    "        optimizer = Adam(0.0002,0.5)\n",
    "        \n",
    "        #Build and Complile discriminator\n",
    "        self.discriminator = self.build_discriminator()\n",
    "        self.discriminator.compile(loss=\"binary_crossentropy\" , metrics =['accuracy'], optimizer = optimizer)\n",
    "\n",
    "\n",
    "        #Build the Generators\n",
    "        self.generator = self.build_generator()\n",
    "\n",
    "        #input to generators\n",
    "        z =Input(shape=(self.latent_dims,))\n",
    "        img = self.generator(z)\n",
    "\n",
    "        #now will train discriminator only \n",
    "        self.discriminator.trainable = False\n",
    "    \n",
    "        #discriminator take generated image as input and detemine the validity\n",
    "        validity =self.discriminator(img)\n",
    "    \n",
    "        # stacking both model\n",
    "        self.combined = Model(z,validity)\n",
    "        \n",
    "        #compile both model\n",
    "        # Trains the generator to fool the discriminator\n",
    "        self.combined.compile(loss=\"binary_crossentropy\",optimizer = optimizer)\n",
    "    \n",
    "    \n",
    "    def build_generator(self):\n",
    "        model = Sequential()\n",
    "        #layers_1\n",
    "        model.add(Dense(256, input_dim = self.latent_dims))\n",
    "        model.add(LeakyReLU(alpha = 0.2))\n",
    "        model.add(BatchNormalization(momentum=0.8))\n",
    "        #layers_2\n",
    "        model.add(Dense(512))\n",
    "        model.add(LeakyReLU(alpha = 0.2 ))\n",
    "        model.add(BatchNormalization(momentum =0.8))\n",
    "        #layers_3\n",
    "        model.add(Dense(1024))\n",
    "        model.add(LeakyReLU(alpha =0.2))\n",
    "        model.add(BatchNormalization(momentum = 0.8))\n",
    "        \n",
    "        model.add(Dense(np.prod(self.img_shape), activation='tanh'))\n",
    "        model.add(Reshape(self.img_shape))\n",
    "        \n",
    "        model.summary()\n",
    "        noise = Input(shape=(self.latent_dims))\n",
    "        \n",
    "        img =model(noise)\n",
    "        \n",
    "        return Model(noise,img)\n",
    "        \n",
    "        \n",
    "    def build_discriminator(self):\n",
    "        \n",
    "        model = Sequential()\n",
    "        #input_layers\n",
    "        model.add(Flatten(input_shape = self.img_shape))\n",
    "        #layer_1\n",
    "        model.add(Dense(512))\n",
    "        model.add(LeakyReLU(alpha = 0.2))\n",
    "        #layer_2\n",
    "        model.add(Dense(256))\n",
    "        model.add(LeakyReLU(alpha = 0.2))\n",
    "        #output_layer\n",
    "        model.add(Dense(1, activation='sigmoid'))\n",
    "        #model summary\n",
    "        model.summary()\n",
    "        \n",
    "        img = Input(shape=self.img_shape)\n",
    "        validity = model(img)\n",
    "\n",
    "        return Model(img, validity)\n",
    "    \n",
    "    def train(self , epochs , batch_size = 128 , sample_interval=50):\n",
    "        \n",
    "        #load the dataset \n",
    "        (X_train, _),(_,_) = mnist.load_data()\n",
    "        \n",
    "        #reshape -1 to 1\n",
    "        X_train = X_train/127.5 -1\n",
    "        X_train = np.expand_dims(X_train, axis=3)\n",
    "        \n",
    "        #Adversarial Truth\n",
    "        valid =np.ones((batch_size ,1))\n",
    "        fake  =np.zeros((batch_size,1))\n",
    "        \n",
    "        \n",
    "        for epoch in range(epochs):\n",
    "            \n",
    "            #----- Train discriminator -------\n",
    "            #---------------------------------\n",
    "            #---------------------------------\n",
    "            \n",
    "            #Select a random batch images\n",
    "            idx = np.random.randint(0, X_train.shape[0], batch_size)\n",
    "            imgs = X_train[idx]\n",
    "            \n",
    "            noise = np.random.normal(0,1,(batch_size, self.latent_dims))\n",
    "            \n",
    "            #generating new batch images\n",
    "            gens_img  = self.generator(noise)\n",
    "            \n",
    "            #Train discriminator \n",
    "            d_loss_real =self.discriminator.train_on_batch(imgs , valid)\n",
    "            d_loss_fake = self.discriminator.train_on_batch(gens_img , fake)\n",
    "            d_loss = 0.5 * np.add(d_loss_real , d_loss_fake)\n",
    "            \n",
    "            #----- Train Generators-----------\n",
    "            #---------------------------------\n",
    "            #---------------------------------\n",
    "            \n",
    "            noise = np.random.normal(0, 1, (batch_size,self.latent_dims))\n",
    "            \n",
    "            #Train the generators\n",
    "            g_loss =self.combined.train_on_batch(noise, valid)\n",
    "            \n",
    "            #Plot the progress \n",
    "            print (\"%d [D loss: %f, acc.: %.2f%%] [G loss: %f]\" % (epoch, d_loss[0], 100*d_loss[1], g_loss))\n",
    "            \n",
    "            # If at save interval => save generated image samples\n",
    "            if epoch % sample_interval == 0:\n",
    "                self.sample_images(epoch)\n",
    "                \n",
    "    def sample_images(self, epoch):\n",
    "        r, c = 5, 5\n",
    "        noise = np.random.normal(0, 1, (r * c, self.latent_dims))\n",
    "        gen_imgs = self.generator.predict(noise)\n",
    "\n",
    "        # Rescale images 0 - 1\n",
    "        gen_imgs = 0.5 * gen_imgs + 0.5\n",
    "\n",
    "        fig, axs = plt.subplots(r, c)\n",
    "        cnt = 0\n",
    "        for i in range(r):\n",
    "            for j in range(c):\n",
    "                axs[i,j].imshow(gen_imgs[cnt, :,:,0], cmap='gray')\n",
    "                axs[i,j].axis('off')\n",
    "                cnt += 1\n",
    "        fig.savefig(\"images/%d.png\" % epoch)\n",
    "        plt.close()\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f9cfefca",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " flatten_3 (Flatten)         (None, 784)               0         \n",
      "                                                                 \n",
      " dense_21 (Dense)            (None, 512)               401920    \n",
      "                                                                 \n",
      " leaky_re_lu_15 (LeakyReLU)  (None, 512)               0         \n",
      "                                                                 \n",
      " dense_22 (Dense)            (None, 256)               131328    \n",
      "                                                                 \n",
      " leaky_re_lu_16 (LeakyReLU)  (None, 256)               0         \n",
      "                                                                 \n",
      " dense_23 (Dense)            (None, 1)                 257       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 533,505\n",
      "Trainable params: 533,505\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"sequential_7\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_24 (Dense)            (None, 256)               25856     \n",
      "                                                                 \n",
      " leaky_re_lu_17 (LeakyReLU)  (None, 256)               0         \n",
      "                                                                 \n",
      " batch_normalization_9 (Batc  (None, 256)              1024      \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dense_25 (Dense)            (None, 512)               131584    \n",
      "                                                                 \n",
      " leaky_re_lu_18 (LeakyReLU)  (None, 512)               0         \n",
      "                                                                 \n",
      " batch_normalization_10 (Bat  (None, 512)              2048      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dense_26 (Dense)            (None, 1024)              525312    \n",
      "                                                                 \n",
      " leaky_re_lu_19 (LeakyReLU)  (None, 1024)              0         \n",
      "                                                                 \n",
      " batch_normalization_11 (Bat  (None, 1024)             4096      \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dense_27 (Dense)            (None, 784)               803600    \n",
      "                                                                 \n",
      " reshape_3 (Reshape)         (None, 28, 28, 1)         0         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,493,520\n",
      "Trainable params: 1,489,936\n",
      "Non-trainable params: 3,584\n",
      "_________________________________________________________________\n",
      "0 [D loss: 0.858880, acc.: 50.00%] [G loss: 0.754421]\n",
      "1 [D loss: 0.377000, acc.: 95.31%] [G loss: 0.684508]\n",
      "2 [D loss: 0.326273, acc.: 93.75%] [G loss: 0.709346]\n",
      "3 [D loss: 0.327743, acc.: 82.81%] [G loss: 0.808183]\n",
      "4 [D loss: 0.312288, acc.: 90.62%] [G loss: 0.918779]\n",
      "5 [D loss: 0.304793, acc.: 82.81%] [G loss: 1.009342]\n",
      "6 [D loss: 0.264718, acc.: 96.88%] [G loss: 1.128062]\n",
      "7 [D loss: 0.210035, acc.: 100.00%] [G loss: 1.234196]\n",
      "8 [D loss: 0.197955, acc.: 100.00%] [G loss: 1.435360]\n",
      "9 [D loss: 0.163236, acc.: 100.00%] [G loss: 1.513451]\n",
      "10 [D loss: 0.136161, acc.: 100.00%] [G loss: 1.780190]\n",
      "11 [D loss: 0.112704, acc.: 100.00%] [G loss: 1.797627]\n",
      "12 [D loss: 0.102247, acc.: 100.00%] [G loss: 2.012918]\n",
      "13 [D loss: 0.095671, acc.: 100.00%] [G loss: 2.163823]\n",
      "14 [D loss: 0.070734, acc.: 100.00%] [G loss: 2.228574]\n",
      "15 [D loss: 0.081835, acc.: 100.00%] [G loss: 2.260772]\n",
      "16 [D loss: 0.073923, acc.: 100.00%] [G loss: 2.472668]\n",
      "17 [D loss: 0.064035, acc.: 100.00%] [G loss: 2.565036]\n",
      "18 [D loss: 0.075005, acc.: 100.00%] [G loss: 2.550111]\n",
      "19 [D loss: 0.050294, acc.: 100.00%] [G loss: 2.716000]\n",
      "20 [D loss: 0.045295, acc.: 100.00%] [G loss: 2.814992]\n",
      "21 [D loss: 0.040743, acc.: 100.00%] [G loss: 2.874129]\n",
      "22 [D loss: 0.042689, acc.: 100.00%] [G loss: 2.856989]\n",
      "23 [D loss: 0.034599, acc.: 100.00%] [G loss: 2.967332]\n",
      "24 [D loss: 0.032292, acc.: 100.00%] [G loss: 3.124150]\n",
      "25 [D loss: 0.045542, acc.: 100.00%] [G loss: 3.095347]\n",
      "26 [D loss: 0.029149, acc.: 100.00%] [G loss: 3.095077]\n",
      "27 [D loss: 0.040565, acc.: 100.00%] [G loss: 3.219604]\n",
      "28 [D loss: 0.027754, acc.: 100.00%] [G loss: 3.181133]\n",
      "29 [D loss: 0.028328, acc.: 100.00%] [G loss: 3.323883]\n",
      "30 [D loss: 0.028185, acc.: 100.00%] [G loss: 3.157618]\n",
      "31 [D loss: 0.027949, acc.: 100.00%] [G loss: 3.293275]\n",
      "32 [D loss: 0.028173, acc.: 100.00%] [G loss: 3.474885]\n",
      "33 [D loss: 0.020700, acc.: 100.00%] [G loss: 3.472817]\n",
      "34 [D loss: 0.023739, acc.: 100.00%] [G loss: 3.457831]\n",
      "35 [D loss: 0.022980, acc.: 100.00%] [G loss: 3.508808]\n",
      "36 [D loss: 0.021379, acc.: 100.00%] [G loss: 3.466183]\n",
      "37 [D loss: 0.023497, acc.: 100.00%] [G loss: 3.519869]\n",
      "38 [D loss: 0.021271, acc.: 100.00%] [G loss: 3.589807]\n",
      "39 [D loss: 0.023832, acc.: 100.00%] [G loss: 3.582317]\n",
      "40 [D loss: 0.020189, acc.: 100.00%] [G loss: 3.596399]\n",
      "41 [D loss: 0.015713, acc.: 100.00%] [G loss: 3.792868]\n",
      "42 [D loss: 0.015344, acc.: 100.00%] [G loss: 3.685440]\n",
      "43 [D loss: 0.017850, acc.: 100.00%] [G loss: 3.819695]\n",
      "44 [D loss: 0.017051, acc.: 100.00%] [G loss: 3.623159]\n",
      "45 [D loss: 0.018476, acc.: 100.00%] [G loss: 3.827973]\n",
      "46 [D loss: 0.016795, acc.: 100.00%] [G loss: 3.717091]\n",
      "47 [D loss: 0.022931, acc.: 100.00%] [G loss: 3.870776]\n",
      "48 [D loss: 0.013472, acc.: 100.00%] [G loss: 3.873608]\n",
      "49 [D loss: 0.014911, acc.: 100.00%] [G loss: 3.803834]\n",
      "50 [D loss: 0.020027, acc.: 100.00%] [G loss: 4.041398]\n",
      "51 [D loss: 0.011098, acc.: 100.00%] [G loss: 3.988492]\n",
      "52 [D loss: 0.014121, acc.: 100.00%] [G loss: 3.940039]\n",
      "53 [D loss: 0.014530, acc.: 100.00%] [G loss: 3.916680]\n",
      "54 [D loss: 0.011496, acc.: 100.00%] [G loss: 4.026262]\n",
      "55 [D loss: 0.012697, acc.: 100.00%] [G loss: 3.892318]\n",
      "56 [D loss: 0.012569, acc.: 100.00%] [G loss: 4.022394]\n",
      "57 [D loss: 0.013640, acc.: 100.00%] [G loss: 3.846963]\n",
      "58 [D loss: 0.013725, acc.: 100.00%] [G loss: 3.924385]\n",
      "59 [D loss: 0.012179, acc.: 100.00%] [G loss: 3.974286]\n",
      "60 [D loss: 0.012007, acc.: 100.00%] [G loss: 3.974969]\n",
      "61 [D loss: 0.012224, acc.: 100.00%] [G loss: 4.035923]\n",
      "62 [D loss: 0.015728, acc.: 100.00%] [G loss: 4.052219]\n",
      "63 [D loss: 0.017092, acc.: 100.00%] [G loss: 3.964142]\n",
      "64 [D loss: 0.010177, acc.: 100.00%] [G loss: 3.913410]\n",
      "65 [D loss: 0.015195, acc.: 100.00%] [G loss: 4.145524]\n",
      "66 [D loss: 0.017961, acc.: 100.00%] [G loss: 4.144998]\n",
      "67 [D loss: 0.016263, acc.: 100.00%] [G loss: 4.087794]\n",
      "68 [D loss: 0.013768, acc.: 100.00%] [G loss: 4.177165]\n",
      "69 [D loss: 0.011855, acc.: 100.00%] [G loss: 4.021360]\n",
      "70 [D loss: 0.017481, acc.: 100.00%] [G loss: 4.219220]\n",
      "71 [D loss: 0.016321, acc.: 100.00%] [G loss: 4.203997]\n",
      "72 [D loss: 0.016842, acc.: 100.00%] [G loss: 4.437220]\n",
      "73 [D loss: 0.013085, acc.: 100.00%] [G loss: 4.294360]\n",
      "74 [D loss: 0.015512, acc.: 100.00%] [G loss: 4.411570]\n",
      "75 [D loss: 0.010231, acc.: 100.00%] [G loss: 4.389553]\n",
      "76 [D loss: 0.011997, acc.: 100.00%] [G loss: 4.342811]\n",
      "77 [D loss: 0.007476, acc.: 100.00%] [G loss: 4.454720]\n",
      "78 [D loss: 0.014092, acc.: 100.00%] [G loss: 4.400704]\n",
      "79 [D loss: 0.010998, acc.: 100.00%] [G loss: 4.426988]\n",
      "80 [D loss: 0.009614, acc.: 100.00%] [G loss: 4.324401]\n",
      "81 [D loss: 0.009260, acc.: 100.00%] [G loss: 4.476840]\n",
      "82 [D loss: 0.008821, acc.: 100.00%] [G loss: 4.469532]\n",
      "83 [D loss: 0.010971, acc.: 100.00%] [G loss: 4.513975]\n",
      "84 [D loss: 0.014277, acc.: 100.00%] [G loss: 4.368563]\n",
      "85 [D loss: 0.010146, acc.: 100.00%] [G loss: 4.348717]\n",
      "86 [D loss: 0.010941, acc.: 100.00%] [G loss: 4.337553]\n",
      "87 [D loss: 0.015926, acc.: 100.00%] [G loss: 4.423703]\n",
      "88 [D loss: 0.012105, acc.: 100.00%] [G loss: 4.568993]\n",
      "89 [D loss: 0.011982, acc.: 100.00%] [G loss: 4.648750]\n",
      "90 [D loss: 0.010925, acc.: 100.00%] [G loss: 4.611065]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "91 [D loss: 0.008679, acc.: 100.00%] [G loss: 4.568930]\n",
      "92 [D loss: 0.009429, acc.: 100.00%] [G loss: 4.618530]\n",
      "93 [D loss: 0.011569, acc.: 100.00%] [G loss: 4.554133]\n",
      "94 [D loss: 0.011160, acc.: 100.00%] [G loss: 4.650434]\n",
      "95 [D loss: 0.013304, acc.: 100.00%] [G loss: 4.559005]\n",
      "96 [D loss: 0.010352, acc.: 100.00%] [G loss: 4.622860]\n",
      "97 [D loss: 0.010105, acc.: 100.00%] [G loss: 4.535788]\n",
      "98 [D loss: 0.010040, acc.: 100.00%] [G loss: 4.512580]\n",
      "99 [D loss: 0.014787, acc.: 100.00%] [G loss: 4.636339]\n",
      "100 [D loss: 0.012515, acc.: 100.00%] [G loss: 4.449138]\n",
      "101 [D loss: 0.014412, acc.: 100.00%] [G loss: 4.534746]\n",
      "102 [D loss: 0.020488, acc.: 100.00%] [G loss: 4.916183]\n",
      "103 [D loss: 0.013104, acc.: 100.00%] [G loss: 4.731406]\n",
      "104 [D loss: 0.012916, acc.: 100.00%] [G loss: 4.525829]\n",
      "105 [D loss: 0.015259, acc.: 100.00%] [G loss: 4.718676]\n",
      "106 [D loss: 0.014349, acc.: 100.00%] [G loss: 4.835748]\n",
      "107 [D loss: 0.016243, acc.: 100.00%] [G loss: 4.885096]\n",
      "108 [D loss: 0.018312, acc.: 100.00%] [G loss: 4.583370]\n",
      "109 [D loss: 0.013491, acc.: 100.00%] [G loss: 4.774204]\n",
      "110 [D loss: 0.014167, acc.: 100.00%] [G loss: 4.935817]\n",
      "111 [D loss: 0.023963, acc.: 100.00%] [G loss: 4.744400]\n",
      "112 [D loss: 0.030183, acc.: 100.00%] [G loss: 5.198479]\n",
      "113 [D loss: 0.036759, acc.: 100.00%] [G loss: 4.398068]\n",
      "114 [D loss: 0.042546, acc.: 98.44%] [G loss: 4.870314]\n",
      "115 [D loss: 0.012968, acc.: 100.00%] [G loss: 4.826164]\n",
      "116 [D loss: 0.009615, acc.: 100.00%] [G loss: 4.667189]\n",
      "117 [D loss: 0.024762, acc.: 100.00%] [G loss: 5.038202]\n",
      "118 [D loss: 0.023261, acc.: 100.00%] [G loss: 4.723954]\n",
      "119 [D loss: 0.049654, acc.: 100.00%] [G loss: 5.269126]\n",
      "120 [D loss: 0.141491, acc.: 95.31%] [G loss: 4.332440]\n",
      "121 [D loss: 0.036225, acc.: 98.44%] [G loss: 4.949265]\n",
      "122 [D loss: 0.009191, acc.: 100.00%] [G loss: 4.786450]\n",
      "123 [D loss: 0.037452, acc.: 100.00%] [G loss: 4.808621]\n",
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    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "986 [D loss: 0.574825, acc.: 78.12%] [G loss: 0.897048]\n",
      "987 [D loss: 0.630144, acc.: 68.75%] [G loss: 0.789977]\n",
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      "1134 [D loss: 0.569614, acc.: 84.38%] [G loss: 0.880948]\n",
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      "1283 [D loss: 0.575701, acc.: 70.31%] [G loss: 0.986321]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1432 [D loss: 0.533473, acc.: 75.00%] [G loss: 0.919762]\n",
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    },
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     "text": [
      "1579 [D loss: 0.573146, acc.: 75.00%] [G loss: 1.037542]\n",
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     ]
    },
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     "text": [
      "1725 [D loss: 0.593891, acc.: 64.06%] [G loss: 0.970094]\n",
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    },
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     "text": [
      "1872 [D loss: 0.542994, acc.: 75.00%] [G loss: 0.987861]\n",
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      "1934 [D loss: 0.586281, acc.: 70.31%] [G loss: 0.883074]\n",
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      "1976 [D loss: 0.494322, acc.: 79.69%] [G loss: 0.984713]\n",
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      "1983 [D loss: 0.657507, acc.: 54.69%] [G loss: 0.966166]\n",
      "1984 [D loss: 0.555387, acc.: 78.12%] [G loss: 0.907860]\n",
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      "1987 [D loss: 0.567431, acc.: 81.25%] [G loss: 0.987440]\n",
      "1988 [D loss: 0.547852, acc.: 78.12%] [G loss: 0.982052]\n",
      "1989 [D loss: 0.613154, acc.: 65.62%] [G loss: 0.974597]\n",
      "1990 [D loss: 0.526933, acc.: 76.56%] [G loss: 1.006831]\n",
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      "1992 [D loss: 0.590904, acc.: 70.31%] [G loss: 1.034710]\n",
      "1993 [D loss: 0.586894, acc.: 68.75%] [G loss: 0.970126]\n",
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      "1997 [D loss: 0.600341, acc.: 71.88%] [G loss: 0.977835]\n",
      "1998 [D loss: 0.626016, acc.: 65.62%] [G loss: 1.075701]\n",
      "1999 [D loss: 0.611596, acc.: 67.19%] [G loss: 0.918174]\n",
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      "2002 [D loss: 0.546503, acc.: 79.69%] [G loss: 0.933394]\n",
      "2003 [D loss: 0.552668, acc.: 70.31%] [G loss: 0.943541]\n",
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      "2010 [D loss: 0.622219, acc.: 67.19%] [G loss: 1.013174]\n",
      "2011 [D loss: 0.573701, acc.: 73.44%] [G loss: 1.045842]\n",
      "2012 [D loss: 0.633618, acc.: 65.62%] [G loss: 1.130531]\n",
      "2013 [D loss: 0.608549, acc.: 60.94%] [G loss: 0.990446]\n",
      "2014 [D loss: 0.621446, acc.: 64.06%] [G loss: 0.956037]\n",
      "2015 [D loss: 0.517870, acc.: 82.81%] [G loss: 0.931918]\n",
      "2016 [D loss: 0.594356, acc.: 76.56%] [G loss: 0.926397]\n",
      "2017 [D loss: 0.506528, acc.: 85.94%] [G loss: 0.947922]\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2018 [D loss: 0.610533, acc.: 67.19%] [G loss: 0.951761]\n",
      "2019 [D loss: 0.517720, acc.: 81.25%] [G loss: 0.987096]\n",
      "2020 [D loss: 0.630152, acc.: 65.62%] [G loss: 1.037704]\n",
      "2021 [D loss: 0.602682, acc.: 64.06%] [G loss: 1.013633]\n",
      "2022 [D loss: 0.613681, acc.: 60.94%] [G loss: 0.988064]\n",
      "2023 [D loss: 0.656771, acc.: 59.38%] [G loss: 0.925719]\n",
      "2024 [D loss: 0.556884, acc.: 78.12%] [G loss: 0.968029]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2165 [D loss: 0.589978, acc.: 68.75%] [G loss: 1.081998]\n",
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    },
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      "2313 [D loss: 0.580775, acc.: 79.69%] [G loss: 0.958858]\n",
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      "2459 [D loss: 0.551344, acc.: 76.56%] [G loss: 0.990407]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2608 [D loss: 0.637425, acc.: 57.81%] [G loss: 0.942638]\n",
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    },
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      "2752 [D loss: 0.550763, acc.: 73.44%] [G loss: 0.913516]\n",
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     "text": [
      "2896 [D loss: 0.556329, acc.: 68.75%] [G loss: 1.070842]\n",
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    },
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     "text": [
      "3044 [D loss: 0.567653, acc.: 78.12%] [G loss: 0.896275]\n",
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      "3188 [D loss: 0.549941, acc.: 76.56%] [G loss: 0.996760]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3332 [D loss: 0.603456, acc.: 68.75%] [G loss: 0.954687]\n",
      "3333 [D loss: 0.583960, acc.: 67.19%] [G loss: 0.939673]\n",
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      "3476 [D loss: 0.588641, acc.: 62.50%] [G loss: 0.950247]\n",
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      "3625 [D loss: 0.619347, acc.: 62.50%] [G loss: 0.891538]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3774 [D loss: 0.610490, acc.: 64.06%] [G loss: 1.023860]\n",
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    },
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      "3920 [D loss: 0.605232, acc.: 73.44%] [G loss: 0.901772]\n",
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     "text": [
      "4066 [D loss: 0.524819, acc.: 76.56%] [G loss: 1.096995]\n",
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     "text": [
      "4210 [D loss: 0.573243, acc.: 71.88%] [G loss: 1.008184]\n",
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      "4355 [D loss: 0.635554, acc.: 65.62%] [G loss: 0.952243]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "4499 [D loss: 0.627699, acc.: 60.94%] [G loss: 1.019311]\n",
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    },
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     "text": [
      "4644 [D loss: 0.580679, acc.: 62.50%] [G loss: 0.953294]\n",
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      "4791 [D loss: 0.589352, acc.: 67.19%] [G loss: 1.075275]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4938 [D loss: 0.636715, acc.: 67.19%] [G loss: 1.014785]\n",
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    },
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      "5086 [D loss: 0.586822, acc.: 73.44%] [G loss: 0.921615]\n",
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     "text": [
      "5232 [D loss: 0.571686, acc.: 75.00%] [G loss: 0.963276]\n",
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    },
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     "text": [
      "5377 [D loss: 0.706383, acc.: 48.44%] [G loss: 1.082612]\n",
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      "5524 [D loss: 0.638314, acc.: 68.75%] [G loss: 0.971625]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5672 [D loss: 0.664656, acc.: 59.38%] [G loss: 0.982729]\n",
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    },
    {
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     "text": [
      "5818 [D loss: 0.616229, acc.: 71.88%] [G loss: 0.887024]\n",
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     "text": [
      "5966 [D loss: 0.708740, acc.: 45.31%] [G loss: 0.955921]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "6114 [D loss: 0.688573, acc.: 60.94%] [G loss: 0.885202]\n",
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    },
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      "6259 [D loss: 0.674767, acc.: 60.94%] [G loss: 0.830548]\n",
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    },
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     "text": [
      "6406 [D loss: 0.697315, acc.: 59.38%] [G loss: 0.936323]\n",
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      "6555 [D loss: 0.675111, acc.: 59.38%] [G loss: 0.976152]\n",
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      "6699 [D loss: 0.657723, acc.: 59.38%] [G loss: 0.921082]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6844 [D loss: 0.668425, acc.: 60.94%] [G loss: 0.936834]\n",
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    },
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     "text": [
      "6989 [D loss: 0.600728, acc.: 65.62%] [G loss: 0.928664]\n",
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     "text": [
      "7136 [D loss: 0.680847, acc.: 54.69%] [G loss: 0.936158]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7283 [D loss: 0.602326, acc.: 68.75%] [G loss: 1.010818]\n",
      "7284 [D loss: 0.599328, acc.: 70.31%] [G loss: 0.890620]\n",
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    },
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     "text": [
      "7427 [D loss: 0.627037, acc.: 68.75%] [G loss: 0.944977]\n",
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     "text": [
      "7572 [D loss: 0.616016, acc.: 57.81%] [G loss: 0.929722]\n",
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    },
    {
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      "7718 [D loss: 0.646688, acc.: 64.06%] [G loss: 0.905915]\n",
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     "text": [
      "7866 [D loss: 0.687898, acc.: 59.38%] [G loss: 0.919657]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8013 [D loss: 0.615342, acc.: 65.62%] [G loss: 1.032803]\n",
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    },
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     "text": [
      "8161 [D loss: 0.597180, acc.: 71.88%] [G loss: 0.934177]\n",
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     "text": [
      "8307 [D loss: 0.677822, acc.: 51.56%] [G loss: 0.919663]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8453 [D loss: 0.601535, acc.: 70.31%] [G loss: 0.978455]\n",
      "8454 [D loss: 0.642764, acc.: 62.50%] [G loss: 0.986771]\n",
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    },
    {
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     "text": [
      "8598 [D loss: 0.593792, acc.: 67.19%] [G loss: 0.940702]\n",
      "8599 [D loss: 0.642097, acc.: 56.25%] [G loss: 0.933485]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "8742 [D loss: 0.589429, acc.: 67.19%] [G loss: 1.094259]\n",
      "8743 [D loss: 0.691068, acc.: 59.38%] [G loss: 0.958930]\n",
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    },
    {
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     "text": [
      "8888 [D loss: 0.577642, acc.: 70.31%] [G loss: 1.022153]\n",
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      "9035 [D loss: 0.711386, acc.: 60.94%] [G loss: 0.919709]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9179 [D loss: 0.544093, acc.: 79.69%] [G loss: 0.897552]\n",
      "9180 [D loss: 0.647216, acc.: 64.06%] [G loss: 0.962893]\n",
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    },
    {
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     "text": [
      "9325 [D loss: 0.587792, acc.: 73.44%] [G loss: 0.931011]\n",
      "9326 [D loss: 0.657043, acc.: 62.50%] [G loss: 0.845170]\n",
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     ]
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     "text": [
      "9469 [D loss: 0.545147, acc.: 75.00%] [G loss: 0.979936]\n",
      "9470 [D loss: 0.689088, acc.: 54.69%] [G loss: 0.935703]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9616 [D loss: 0.645063, acc.: 65.62%] [G loss: 1.129297]\n",
      "9617 [D loss: 0.706795, acc.: 54.69%] [G loss: 0.976426]\n",
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    },
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      "9765 [D loss: 0.626657, acc.: 62.50%] [G loss: 0.947408]\n",
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     "text": [
      "9913 [D loss: 0.688270, acc.: 59.38%] [G loss: 0.890988]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10341 [D loss: 0.631761, acc.: 65.62%] [G loss: 0.976179]\n",
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    },
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      "10483 [D loss: 0.626985, acc.: 65.62%] [G loss: 1.093981]\n",
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     "text": [
      "10626 [D loss: 0.668194, acc.: 56.25%] [G loss: 0.917986]\n",
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    },
    {
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     "text": [
      "10770 [D loss: 0.665121, acc.: 57.81%] [G loss: 0.910716]\n",
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     ]
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     "text": [
      "10916 [D loss: 0.659655, acc.: 59.38%] [G loss: 0.849964]\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "11063 [D loss: 0.645358, acc.: 62.50%] [G loss: 0.982532]\n",
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      "11351 [D loss: 0.646314, acc.: 60.94%] [G loss: 0.937137]\n",
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     "text": [
      "11495 [D loss: 0.594214, acc.: 65.62%] [G loss: 0.865954]\n",
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     "text": [
      "11637 [D loss: 0.665427, acc.: 50.00%] [G loss: 0.868167]\n",
      "11638 [D loss: 0.662765, acc.: 71.88%] [G loss: 0.924173]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11779 [D loss: 0.646137, acc.: 60.94%] [G loss: 0.993345]\n",
      "11780 [D loss: 0.655830, acc.: 64.06%] [G loss: 1.015085]\n",
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    },
    {
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      "11922 [D loss: 0.676375, acc.: 54.69%] [G loss: 0.865746]\n",
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     "text": [
      "12066 [D loss: 0.632843, acc.: 75.00%] [G loss: 0.958085]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12208 [D loss: 0.613843, acc.: 70.31%] [G loss: 0.899813]\n",
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    },
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      "12353 [D loss: 0.694368, acc.: 54.69%] [G loss: 1.037163]\n",
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      "12498 [D loss: 0.637728, acc.: 65.62%] [G loss: 0.956970]\n",
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    },
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      "12643 [D loss: 0.595115, acc.: 68.75%] [G loss: 0.921376]\n",
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      "12787 [D loss: 0.602981, acc.: 65.62%] [G loss: 0.964904]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12933 [D loss: 0.687141, acc.: 56.25%] [G loss: 0.922907]\n",
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    },
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     "text": [
      "13075 [D loss: 0.677479, acc.: 62.50%] [G loss: 1.001397]\n",
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     "text": [
      "13220 [D loss: 0.697278, acc.: 48.44%] [G loss: 0.953514]\n",
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    },
    {
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     "text": [
      "13362 [D loss: 0.649586, acc.: 60.94%] [G loss: 0.952811]\n",
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      "13506 [D loss: 0.671228, acc.: 56.25%] [G loss: 0.914739]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13649 [D loss: 0.614617, acc.: 67.19%] [G loss: 0.953678]\n",
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    },
    {
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     "text": [
      "13795 [D loss: 0.660845, acc.: 60.94%] [G loss: 0.996298]\n",
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     ]
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     "text": [
      "13939 [D loss: 0.593498, acc.: 67.19%] [G loss: 1.020569]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14084 [D loss: 0.653169, acc.: 62.50%] [G loss: 0.957129]\n",
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    },
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      "14228 [D loss: 0.692666, acc.: 51.56%] [G loss: 0.970398]\n",
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      "14371 [D loss: 0.697289, acc.: 53.12%] [G loss: 0.948998]\n",
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    },
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      "14515 [D loss: 0.615918, acc.: 64.06%] [G loss: 0.851326]\n",
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      "14660 [D loss: 0.641736, acc.: 64.06%] [G loss: 0.930963]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14802 [D loss: 0.634969, acc.: 67.19%] [G loss: 0.959935]\n",
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      "14919 [D loss: 0.595633, acc.: 70.31%] [G loss: 0.931251]\n",
      "14920 [D loss: 0.614968, acc.: 60.94%] [G loss: 0.895844]\n",
      "14921 [D loss: 0.557668, acc.: 73.44%] [G loss: 0.992939]\n",
      "14922 [D loss: 0.622777, acc.: 65.62%] [G loss: 1.040197]\n",
      "14923 [D loss: 0.670438, acc.: 62.50%] [G loss: 0.924256]\n",
      "14924 [D loss: 0.601579, acc.: 68.75%] [G loss: 1.063398]\n",
      "14925 [D loss: 0.686172, acc.: 53.12%] [G loss: 0.866215]\n",
      "14926 [D loss: 0.658384, acc.: 62.50%] [G loss: 0.994024]\n",
      "14927 [D loss: 0.665966, acc.: 54.69%] [G loss: 0.913711]\n",
      "14928 [D loss: 0.699682, acc.: 48.44%] [G loss: 0.986544]\n",
      "14929 [D loss: 0.661644, acc.: 60.94%] [G loss: 0.995094]\n",
      "14930 [D loss: 0.635970, acc.: 68.75%] [G loss: 1.034968]\n",
      "14931 [D loss: 0.591469, acc.: 64.06%] [G loss: 0.997753]\n",
      "14932 [D loss: 0.645601, acc.: 60.94%] [G loss: 0.971240]\n",
      "14933 [D loss: 0.555697, acc.: 68.75%] [G loss: 0.972518]\n",
      "14934 [D loss: 0.631558, acc.: 62.50%] [G loss: 0.939954]\n",
      "14935 [D loss: 0.629510, acc.: 62.50%] [G loss: 1.009337]\n",
      "14936 [D loss: 0.735821, acc.: 53.12%] [G loss: 1.035998]\n",
      "14937 [D loss: 0.674435, acc.: 56.25%] [G loss: 0.881606]\n",
      "14938 [D loss: 0.583177, acc.: 62.50%] [G loss: 0.980742]\n",
      "14939 [D loss: 0.641248, acc.: 64.06%] [G loss: 1.019558]\n",
      "14940 [D loss: 0.649750, acc.: 60.94%] [G loss: 1.089110]\n",
      "14941 [D loss: 0.603392, acc.: 71.88%] [G loss: 0.900876]\n",
      "14942 [D loss: 0.532973, acc.: 76.56%] [G loss: 0.827528]\n",
      "14943 [D loss: 0.613534, acc.: 71.88%] [G loss: 1.022565]\n",
      "14944 [D loss: 0.611824, acc.: 65.62%] [G loss: 0.972704]\n",
      "14945 [D loss: 0.666075, acc.: 60.94%] [G loss: 0.934565]\n",
      "14946 [D loss: 0.591753, acc.: 70.31%] [G loss: 0.935653]\n",
      "14947 [D loss: 0.683395, acc.: 56.25%] [G loss: 1.011697]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14948 [D loss: 0.674385, acc.: 53.12%] [G loss: 0.986757]\n",
      "14949 [D loss: 0.636186, acc.: 67.19%] [G loss: 0.942900]\n",
      "14950 [D loss: 0.619651, acc.: 70.31%] [G loss: 1.086105]\n",
      "14951 [D loss: 0.607113, acc.: 64.06%] [G loss: 0.981356]\n",
      "14952 [D loss: 0.690125, acc.: 56.25%] [G loss: 0.996492]\n",
      "14953 [D loss: 0.565409, acc.: 78.12%] [G loss: 1.002575]\n",
      "14954 [D loss: 0.679939, acc.: 56.25%] [G loss: 1.048946]\n",
      "14955 [D loss: 0.528103, acc.: 75.00%] [G loss: 1.018645]\n",
      "14956 [D loss: 0.764804, acc.: 51.56%] [G loss: 0.934667]\n",
      "14957 [D loss: 0.682411, acc.: 56.25%] [G loss: 0.974955]\n",
      "14958 [D loss: 0.665057, acc.: 54.69%] [G loss: 0.971033]\n",
      "14959 [D loss: 0.658609, acc.: 54.69%] [G loss: 0.977504]\n",
      "14960 [D loss: 0.634459, acc.: 67.19%] [G loss: 0.995885]\n",
      "14961 [D loss: 0.669384, acc.: 60.94%] [G loss: 1.071512]\n",
      "14962 [D loss: 0.632777, acc.: 62.50%] [G loss: 1.077748]\n",
      "14963 [D loss: 0.682665, acc.: 62.50%] [G loss: 1.044850]\n",
      "14964 [D loss: 0.633352, acc.: 65.62%] [G loss: 1.000526]\n",
      "14965 [D loss: 0.646808, acc.: 65.62%] [G loss: 0.966342]\n",
      "14966 [D loss: 0.602852, acc.: 73.44%] [G loss: 0.940003]\n",
      "14967 [D loss: 0.636910, acc.: 57.81%] [G loss: 0.936790]\n",
      "14968 [D loss: 0.667680, acc.: 57.81%] [G loss: 1.022026]\n",
      "14969 [D loss: 0.611099, acc.: 67.19%] [G loss: 0.935109]\n",
      "14970 [D loss: 0.612306, acc.: 60.94%] [G loss: 0.930664]\n",
      "14971 [D loss: 0.631527, acc.: 64.06%] [G loss: 0.950237]\n",
      "14972 [D loss: 0.576739, acc.: 76.56%] [G loss: 0.943005]\n",
      "14973 [D loss: 0.621000, acc.: 65.62%] [G loss: 0.860002]\n",
      "14974 [D loss: 0.710689, acc.: 56.25%] [G loss: 0.973300]\n",
      "14975 [D loss: 0.616861, acc.: 67.19%] [G loss: 1.116760]\n",
      "14976 [D loss: 0.677103, acc.: 60.94%] [G loss: 0.972371]\n",
      "14977 [D loss: 0.591774, acc.: 75.00%] [G loss: 1.055858]\n",
      "14978 [D loss: 0.597123, acc.: 68.75%] [G loss: 0.977713]\n",
      "14979 [D loss: 0.558328, acc.: 71.88%] [G loss: 0.900490]\n",
      "14980 [D loss: 0.599847, acc.: 73.44%] [G loss: 0.971874]\n",
      "14981 [D loss: 0.649726, acc.: 62.50%] [G loss: 0.920930]\n",
      "14982 [D loss: 0.622658, acc.: 67.19%] [G loss: 1.046862]\n",
      "14983 [D loss: 0.662232, acc.: 56.25%] [G loss: 0.994437]\n",
      "14984 [D loss: 0.579457, acc.: 70.31%] [G loss: 1.006158]\n",
      "14985 [D loss: 0.671800, acc.: 67.19%] [G loss: 0.951812]\n",
      "14986 [D loss: 0.652091, acc.: 65.62%] [G loss: 1.020031]\n",
      "14987 [D loss: 0.663081, acc.: 60.94%] [G loss: 0.907941]\n",
      "14988 [D loss: 0.611035, acc.: 65.62%] [G loss: 0.950105]\n",
      "14989 [D loss: 0.657578, acc.: 59.38%] [G loss: 0.948025]\n",
      "14990 [D loss: 0.628972, acc.: 62.50%] [G loss: 1.002161]\n",
      "14991 [D loss: 0.664067, acc.: 56.25%] [G loss: 0.934511]\n",
      "14992 [D loss: 0.651477, acc.: 65.62%] [G loss: 1.068325]\n",
      "14993 [D loss: 0.648672, acc.: 64.06%] [G loss: 1.055393]\n",
      "14994 [D loss: 0.559805, acc.: 78.12%] [G loss: 1.044378]\n",
      "14995 [D loss: 0.636900, acc.: 65.62%] [G loss: 0.952918]\n",
      "14996 [D loss: 0.657482, acc.: 62.50%] [G loss: 0.936620]\n",
      "14997 [D loss: 0.633401, acc.: 60.94%] [G loss: 0.991140]\n",
      "14998 [D loss: 0.706989, acc.: 54.69%] [G loss: 1.079477]\n",
      "14999 [D loss: 0.599602, acc.: 71.88%] [G loss: 0.980293]\n"
     ]
    }
   ],
   "source": [
    "if __name__ == '__main__':\n",
    "    gan = Gans()\n",
    "    gan.train(epochs=15000, batch_size=32, sample_interval=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d07043a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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