QML_HEP_GSoC_2021_Task_2.ipynb

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  "metadata": {
    "colab": {
      "name": "QML-HEP GSoC 2021 Task 2.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMN1ARgOWv0ApcI7EFfJ6fW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ipsit1234/QML-HEP-Evaluation-Test-GSOC-2021/blob/main/QML_HEP_GSoC_2021_Task_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0T-iBh5NoXDH"
      },
      "source": [
        "# Task II: Quantum Generative Adversarial Network (QGAN) Part\n",
        "You will explore how best to apply a quantum generative adversarial network\n",
        "(QGAN) to solve a High Energy Data analysis issue, more specifically, separating\n",
        "the signal events from the background events. You should use the Google Cirq and\n",
        "Tensorflow Quantum (TFQ) libraries for this task.  \n",
        "A set of input samples (simulated with Delphes) is provided in NumPy NPZ format\n",
        "[Download Input](https://drive.google.com/file/d/1r_MZB_crfpij6r3SxPDeU_3JD6t6AxAj/view). In the input file, there are only 100 samples for training and 100\n",
        "samples for testing so it won’t take much computing resources to accomplish this\n",
        "task. The signal events are labeled with 1 while the background events are labeled\n",
        "with 0.  \n",
        "Be sure to show that you understand how to fine tune your machine learning model\n",
        "to improve the performance. The performance can be evaluated with classification\n",
        "accuracy or Area Under ROC Curve (AUC)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGSPAoeQpmdH"
      },
      "source": [
        "## Downloading the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLlyKHbGppEU",
        "outputId": "b9e9583f-429e-42c4-91a5-3a717057f5ac"
      },
      "source": [
        "!gdown --id 1r_MZB_crfpij6r3SxPDeU_3JD6t6AxAj -O events.npz"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1r_MZB_crfpij6r3SxPDeU_3JD6t6AxAj\n",
            "To: /content/events.npz\n",
            "\r  0% 0.00/9.14k [00:00<?, ?B/s]\r100% 9.14k/9.14k [00:00<00:00, 8.67MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RKzTb64mp_1t"
      },
      "source": [
        "## Setting up the required libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ss7ffcuyTiB-"
      },
      "source": [
        "!pip install -q tensorflow==2.3.1\n",
        "!pip install -q tensorflow-quantum\n",
        "import tensorflow as tf\n",
        "import tensorflow_quantum as tfq\n",
        "\n",
        "import cirq\n",
        "import sympy\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "from cirq.contrib.svg import SVGCircuit"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eW70FBbgqGyV"
      },
      "source": [
        "## Loading the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NcPMDux2T6oe"
      },
      "source": [
        "data = np.load('./events.npz', allow_pickle=True)\n",
        "training_input = data['training_input']\n",
        "test_input = data['test_input']"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xozUTJh7T79S",
        "outputId": "ea574bc9-8f90-413f-b30d-4132cd151715"
      },
      "source": [
        "training_input"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L1yRQZJyT-bj",
        "outputId": "cb0b24cc-b1d2-443c-8766-2dc83c0415a5"
      },
      "source": [
        "def prepare_data(training_input, test_input):\n",
        "  x_train_0 = training_input.item()['0']\n",
        "  x_train_1 = training_input.item()['1']\n",
        "  x_test_0 = test_input.item()['0']\n",
        "  x_test_1 = test_input.item()['1']\n",
        "  x_train = np.zeros((len(x_train_0) + len(x_train_1), x_train_0.shape[1]), dtype=np.float32)\n",
        "  x_test = np.zeros((len(x_test_0) + len(x_test_1), x_test_0.shape[1]), dtype=np.float32)\n",
        "  y_train = np.zeros((len(x_train_0) + len(x_train_1),), dtype=np.int32)\n",
        "  y_test = np.zeros((len(x_test_0) + len(x_test_1),), dtype=np.int32)\n",
        "  x_train[:len(x_train_0), :] = x_train_0\n",
        "  x_train[len(x_train_0):, :] = x_train_1\n",
        "  y_train[:len(x_train_0)] = 0\n",
        "  y_train[len(x_train_0):] = 1\n",
        "\n",
        "  x_test[:len(x_test_0), :] = x_test_0\n",
        "  x_test[len(x_test_0):, :] = x_test_1\n",
        "  y_test[:len(x_test_0)] = 0\n",
        "  y_test[len(x_test_0):] = 1\n",
        "\n",
        "  idx1 = np.random.permutation(len(x_train))\n",
        "  idx2 = np.random.permutation(len(x_test))\n",
        "  x_train, y_train = x_train[idx1], y_train[idx1]\n",
        "  x_test, y_test = x_test[idx2], y_test[idx2]\n",
        "\n",
        "  print('Shape of the training set:', x_train.shape)\n",
        "  print('Shape of the test set:', x_test.shape)\n",
        "\n",
        "  return x_train, y_train, x_test, y_test\n",
        "\n",
        "x_train, y_train, x_test, y_test = prepare_data(training_input, test_input)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of the training set: (100, 5)\n",
            "Shape of the test set: (100, 5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1L9EWgF9HAvh"
      },
      "source": [
        "## Approach\n",
        "We will make use of a Quantum GAN in the following:\n",
        "1. Train a GAN to produce samples that look like they came from quantum circuits.\n",
        "2. Add a classification path to the discriminator and minimize both the minimax loss and  classification loss.\n",
        "3. We will use a random quantum circuit to generate random inputs for the generator. The intution behind this is that the data that was provided are the results (measurements) taken from some quantum experiment. So if we succeed in training a GAN which generates outputs similar to the experimental data, this will help in identifying new or other possible outcomes of the same quantum experiment which have been missed in the dataset provided.\n",
        "4. Simultaneously training the discriminator to classify signal events and background events will help in identifying the signal events generated from the fully trained generator."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5B3o_MGRmYk7"
      },
      "source": [
        "## Data Generation\n",
        "As provided in the dataset, each datapoint is 5-dimensional. Hence we will use 5 qubits and pass them through a random quantum circuit and then use these measurements as inputs to the GAN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOWxrXWHUBEe"
      },
      "source": [
        "def generate_circuit(qubits):\n",
        "  \"\"\"Generate a random circuit on qubits.\"\"\"\n",
        "  random_circuit = cirq.generate_boixo_2018_supremacy_circuits_v2(qubits, cz_depth=2, seed=123242)\n",
        "  return random_circuit\n",
        "\n",
        "def generate_data(circuit, n_samples):\n",
        "  \"\"\"Draw `n_samples` samples from circuit into a tf.Tensor.\"\"\"\n",
        "  return tf.squeeze(tfq.layers.Sample()(circuit, repetitions=n_samples).to_tensor())"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "zIo-t2qLUE2J",
        "outputId": "48879b7d-f8d8-49e8-e1e7-fc4b694f5e3d"
      },
      "source": [
        "# sample data and circuit structure\n",
        "qubits = cirq.GridQubit.rect(1, 5)\n",
        "random_circuit_m = generate_circuit(qubits) + cirq.measure_each(*qubits)\n",
        "SVGCircuit(random_circuit_m)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
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          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fuzoXJwSUGzR",
        "outputId": "81d824ad-bee3-4b2b-a018-df17f7957265"
      },
      "source": [
        "generate_data(random_circuit_m, 10)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 5), dtype=int8, numpy=\n",
              "array([[0, 0, 0, 0, 0],\n",
              "       [0, 1, 0, 0, 0],\n",
              "       [0, 1, 1, 0, 0],\n",
              "       [0, 1, 1, 0, 0],\n",
              "       [0, 1, 1, 0, 0],\n",
              "       [1, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0],\n",
              "       [1, 0, 0, 0, 0],\n",
              "       [1, 0, 1, 0, 0],\n",
              "       [1, 0, 1, 0, 0]], dtype=int8)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DQI6L17LpJNU"
      },
      "source": [
        "We will generate 200 random training data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7v4xx3wUKJV",
        "outputId": "663e26be-e696-493c-ad06-d1e37df28157"
      },
      "source": [
        "N_SAMPLES = 200\n",
        "N_QUBITS = 5\n",
        "QUBITS = cirq.GridQubit.rect(1, N_QUBITS)\n",
        "REFERENCE_CIRCUIT = generate_circuit(QUBITS)\n",
        "random_data = generate_data(REFERENCE_CIRCUIT, N_SAMPLES)\n",
        "random_data"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(200, 5), dtype=int8, numpy=\n",
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              "       [0, 0, 0, 0, 0],\n",
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              "       [0, 0, 0, 0, 1],\n",
              "       [0, 0, 0, 0, 1],\n",
              "       [0, 0, 0, 0, 1],\n",
              "       [0, 0, 0, 1, 0],\n",
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              "       [1, 1, 1, 0, 0],\n",
              "       [1, 1, 1, 0, 0],\n",
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              "       [1, 1, 1, 0, 0],\n",
              "       [1, 1, 1, 0, 0],\n",
              "       [1, 1, 1, 0, 0],\n",
              "       [1, 1, 1, 0, 0],\n",
              "       [1, 1, 1, 0, 0],\n",
              "       [1, 1, 1, 0, 0],\n",
              "       [1, 1, 1, 0, 1],\n",
              "       [1, 1, 1, 0, 1],\n",
              "       [1, 1, 1, 0, 1],\n",
              "       [1, 1, 1, 0, 1],\n",
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              "       [1, 1, 1, 1, 0]], dtype=int8)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ylaXFXklrncJ"
      },
      "source": [
        "## Building a Model\n",
        "This GAN will be used to produce measurements corresponding to signal/background events."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NGqrV2BXUQqa"
      },
      "source": [
        "def make_generator():\n",
        "  \"\"\"Construct generator model.\"\"\"\n",
        "  model = tf.keras.Sequential()\n",
        "  model.add(tf.keras.layers.Dense(256, use_bias=False, input_shape=(N_QUBITS,), activation='elu'))\n",
        "  model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "  model.add(tf.keras.layers.Dropout(0.3))\n",
        "  model.add(tf.keras.layers.Dense(64, activation='relu'))\n",
        "  model.add(tf.keras.layers.Dropout(0.3))\n",
        "  model.add(tf.keras.layers.Dense(N_QUBITS, activation=tf.keras.activations.tanh))\n",
        "  return model\n",
        "\n",
        "def make_discriminator():\n",
        "  \"\"\"Construct discriminator model along with a classifier.\"\"\"\n",
        "  inp = tf.keras.Input(shape=(N_QUBITS, ), dtype=tf.float32)\n",
        "  out = tf.keras.layers.Dense(256, use_bias=False, activation='elu')(inp)\n",
        "  out = tf.keras.layers.Dense(128, activation='relu')(out)\n",
        "  out = tf.keras.layers.Dropout(0.4)(out)\n",
        "  out = tf.keras.layers.Dense(64, activation='relu')(out)\n",
        "  out = tf.keras.layers.Dropout(0.3)(out)\n",
        "  classification = tf.keras.layers.Dense(2, activation='softmax')(out)\n",
        "  discrimination = tf.keras.layers.Dense(1, activation='sigmoid')(out)\n",
        "  model = tf.keras.Model(inputs=[inp], outputs=[discrimination, classification])\n",
        "  return model"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WUzRHLH62VKv"
      },
      "source": [
        "Let us instantiate our models, define the losses and define the `train_step` function which will be executed in each epoch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2cRiizKUS-V"
      },
      "source": [
        "generator = make_generator()\n",
        "discriminator = make_discriminator()"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qy7NUNAzUUXJ"
      },
      "source": [
        "cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
        "\n",
        "def discriminator_loss(real_output, fake_output):\n",
        "  \"\"\"Computes the discriminator loss.\"\"\"\n",
        "  real_loss = cross_entropy(tf.ones_like(real_output), real_output)\n",
        "  fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)\n",
        "  total_loss = real_loss + fake_loss\n",
        "  return total_loss\n",
        "\n",
        "def generator_loss(fake_output):\n",
        "  \"\"\"Compute the generator loss.\"\"\" \n",
        "  return cross_entropy(tf.ones_like(fake_output), fake_output)\n",
        "\n",
        "generator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)\n",
        "discriminator_optimizer = tf.keras.optimizers.Adam(learning_rate=0.0002, beta_1=0.5)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUL4KaCEUXV0"
      },
      "source": [
        "BATCH_SIZE = 16\n",
        "bce = tf.keras.losses.BinaryCrossentropy(from_logits=False)\n",
        "# auc = tf.keras.metrics.AUC()\n",
        "@tf.function\n",
        "def train_step(images, labels, noise):\n",
        "  \"\"\"Run train step on provided image batch.\"\"\"\n",
        "  with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n",
        "    generated_data = generator(noise, training=True)\n",
        "\n",
        "    real_output, real_preds = discriminator(images, training=True)\n",
        "    fake_output, fake_preds = discriminator(generated_data, training=True)\n",
        "\n",
        "    gen_loss = generator_loss(fake_output)\n",
        "    disc_loss = discriminator_loss(real_output, fake_output)\n",
        "    disc_loss = disc_loss + bce(tf.one_hot(tf.squeeze(labels), depth=2), real_preds)\n",
        "  \n",
        "  gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)\n",
        "  gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)\n",
        "\n",
        "  generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))\n",
        "  discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))\n",
        "  # auc.update_state(tf.one_hot(tf.squeeze(labels), depth=2), real_preds)\n",
        "\n",
        "  return gen_loss, disc_loss"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JoygRDKfUY8t"
      },
      "source": [
        "def train(data, labels, noise, epochs):\n",
        "  \"\"\"Launch full training for the given number of epochs.\"\"\"\n",
        "  batched_data = tf.data.Dataset.from_tensor_slices(data).batch(BATCH_SIZE)\n",
        "  batched_labels = tf.data.Dataset.from_tensor_slices(labels).batch(BATCH_SIZE)\n",
        "  batched_noise = tf.data.Dataset.from_tensor_slices(noise).batch(BATCH_SIZE)\n",
        "  AUC = tf.keras.metrics.AUC()\n",
        "  g_losses = []\n",
        "  d_losses = []\n",
        "  # aucs = []\n",
        "  for epoch in range(epochs):\n",
        "    g_epoch_losses = []\n",
        "    d_epoch_losses = []\n",
        "    # aucs_epoch = []\n",
        "    for i, (data_batch, labels_batch, noise_batch) in enumerate(zip(batched_data, batched_labels, batched_noise)):\n",
        "      gl, dl = train_step(data_batch, labels_batch, noise_batch)\n",
        "      g_epoch_losses.append(gl)\n",
        "      d_epoch_losses.append(dl)\n",
        "      # aucs_epoch.append(auc_roc)\n",
        "    \n",
        "    g_losses.append(tf.reduce_mean(g_epoch_losses))\n",
        "    d_losses.append(tf.reduce_mean(d_epoch_losses))\n",
        "    print('Epoch: {}, Generator Loss: {}, Discriminator Loss: {}'.format(epoch, tf.reduce_mean(g_epoch_losses), tf.reduce_mean(d_epoch_losses)))\n",
        "    # aucs.append(tf.reduce_mean(aucs_epoch))\n",
        "  return g_losses, d_losses"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pj9YZiiyUagT",
        "outputId": "26a80375-c630-4740-fc40-3f566798feec"
      },
      "source": [
        "gen_losses, disc_losses = train(x_train, y_train, random_data, 2000)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch: 0, Generator Loss: 0.47288545966148376, Discriminator Loss: 2.1426262855529785\n",
            "Epoch: 1, Generator Loss: 0.47159355878829956, Discriminator Loss: 2.1388280391693115\n",
            "Epoch: 2, Generator Loss: 0.470787912607193, Discriminator Loss: 2.0825936794281006\n",
            "Epoch: 3, Generator Loss: 0.4702128767967224, Discriminator Loss: 2.075483798980713\n",
            "Epoch: 4, Generator Loss: 0.47020336985588074, Discriminator Loss: 2.0505168437957764\n",
            "Epoch: 5, Generator Loss: 0.4700762927532196, Discriminator Loss: 2.022904872894287\n",
            "Epoch: 6, Generator Loss: 0.46946173906326294, Discriminator Loss: 2.0530457496643066\n",
            "Epoch: 7, Generator Loss: 0.4677629768848419, Discriminator Loss: 2.0332977771759033\n",
            "Epoch: 8, Generator Loss: 0.4693600833415985, Discriminator Loss: 2.048710346221924\n",
            "Epoch: 9, Generator Loss: 0.4706823527812958, Discriminator Loss: 2.019632339477539\n",
            "Epoch: 10, Generator Loss: 0.4711288809776306, Discriminator Loss: 2.0153427124023438\n",
            "Epoch: 11, Generator Loss: 0.47589582204818726, Discriminator Loss: 2.0159380435943604\n",
            "Epoch: 12, Generator Loss: 0.4789740741252899, Discriminator Loss: 2.0038421154022217\n",
            "Epoch: 13, Generator Loss: 0.4811742901802063, Discriminator Loss: 1.968962550163269\n",
            "Epoch: 14, Generator Loss: 0.48366862535476685, Discriminator Loss: 1.984475016593933\n",
            "Epoch: 15, Generator Loss: 0.4791991710662842, Discriminator Loss: 2.003448009490967\n",
            "Epoch: 16, Generator Loss: 0.4744584262371063, Discriminator Loss: 1.9951093196868896\n",
            "Epoch: 17, Generator Loss: 0.4726622998714447, Discriminator Loss: 1.9844080209732056\n",
            "Epoch: 18, Generator Loss: 0.4746064245700836, Discriminator Loss: 1.9852495193481445\n",
            "Epoch: 19, Generator Loss: 0.4767344892024994, Discriminator Loss: 1.9912365674972534\n",
            "Epoch: 20, Generator Loss: 0.47933536767959595, Discriminator Loss: 1.9573673009872437\n",
            "Epoch: 21, Generator Loss: 0.4778843820095062, Discriminator Loss: 1.997721791267395\n",
            "Epoch: 22, Generator Loss: 0.473391056060791, Discriminator Loss: 1.95980966091156\n",
            "Epoch: 23, Generator Loss: 0.4726634919643402, Discriminator Loss: 1.9842766523361206\n",
            "Epoch: 24, Generator Loss: 0.4760975241661072, Discriminator Loss: 1.9720042943954468\n",
            "Epoch: 25, Generator Loss: 0.48795565962791443, Discriminator Loss: 1.9472404718399048\n",
            "Epoch: 26, Generator Loss: 0.4994671046733856, Discriminator Loss: 1.9453903436660767\n",
            "Epoch: 27, Generator Loss: 0.5054343342781067, Discriminator Loss: 1.9152615070343018\n",
            "Epoch: 28, Generator Loss: 0.508525550365448, Discriminator Loss: 1.9260485172271729\n",
            "Epoch: 29, Generator Loss: 0.5018197894096375, Discriminator Loss: 1.9515644311904907\n",
            "Epoch: 30, Generator Loss: 0.48237642645835876, Discriminator Loss: 1.9889060258865356\n",
            "Epoch: 31, Generator Loss: 0.47576358914375305, Discriminator Loss: 1.9954891204833984\n",
            "Epoch: 32, Generator Loss: 0.47649702429771423, Discriminator Loss: 1.9981650114059448\n",
            "Epoch: 33, Generator Loss: 0.47945308685302734, Discriminator Loss: 2.0044641494750977\n",
            "Epoch: 34, Generator Loss: 0.48640182614326477, Discriminator Loss: 1.9839919805526733\n",
            "Epoch: 35, Generator Loss: 0.5043705105781555, Discriminator Loss: 1.964692234992981\n",
            "Epoch: 36, Generator Loss: 0.5061931610107422, Discriminator Loss: 1.9607325792312622\n",
            "Epoch: 37, Generator Loss: 0.5090411305427551, Discriminator Loss: 1.9640862941741943\n",
            "Epoch: 38, Generator Loss: 0.49888092279434204, Discriminator Loss: 2.0025503635406494\n",
            "Epoch: 39, Generator Loss: 0.49890926480293274, Discriminator Loss: 1.9784700870513916\n",
            "Epoch: 40, Generator Loss: 0.4987829327583313, Discriminator Loss: 1.9651585817337036\n",
            "Epoch: 41, Generator Loss: 0.5023303627967834, Discriminator Loss: 1.9435651302337646\n",
            "Epoch: 42, Generator Loss: 0.5030931830406189, Discriminator Loss: 1.970147967338562\n",
            "Epoch: 43, Generator Loss: 0.5103368163108826, Discriminator Loss: 1.948770523071289\n",
            "Epoch: 44, Generator Loss: 0.5141062140464783, Discriminator Loss: 1.9382458925247192\n",
            "Epoch: 45, Generator Loss: 0.5192903280258179, Discriminator Loss: 1.923143982887268\n",
            "Epoch: 46, Generator Loss: 0.5226637125015259, Discriminator Loss: 1.9440218210220337\n",
            "Epoch: 47, Generator Loss: 0.5246140956878662, Discriminator Loss: 1.9339735507965088\n",
            "Epoch: 48, Generator Loss: 0.5278750658035278, Discriminator Loss: 1.9330395460128784\n",
            "Epoch: 49, Generator Loss: 0.5215190052986145, Discriminator Loss: 1.9442846775054932\n",
            "Epoch: 50, Generator Loss: 0.5162323117256165, Discriminator Loss: 1.9721218347549438\n",
            "Epoch: 51, Generator Loss: 0.5048933625221252, Discriminator Loss: 1.9678698778152466\n",
            "Epoch: 52, Generator Loss: 0.509625256061554, Discriminator Loss: 1.9625767469406128\n",
            "Epoch: 53, Generator Loss: 0.5198760628700256, Discriminator Loss: 1.9369432926177979\n",
            "Epoch: 54, Generator Loss: 0.5260513424873352, Discriminator Loss: 1.9441606998443604\n",
            "Epoch: 55, Generator Loss: 0.5376901626586914, Discriminator Loss: 1.936803936958313\n",
            "Epoch: 56, Generator Loss: 0.5560902953147888, Discriminator Loss: 1.902709722518921\n",
            "Epoch: 57, Generator Loss: 0.5605687499046326, Discriminator Loss: 1.8947279453277588\n",
            "Epoch: 58, Generator Loss: 0.5672340393066406, Discriminator Loss: 1.898500680923462\n",
            "Epoch: 59, Generator Loss: 0.5689089894294739, Discriminator Loss: 1.871029019355774\n",
            "Epoch: 60, Generator Loss: 0.5697230696678162, Discriminator Loss: 1.8731046915054321\n",
            "Epoch: 61, Generator Loss: 0.5583624839782715, Discriminator Loss: 1.8996940851211548\n",
            "Epoch: 62, Generator Loss: 0.5345408320426941, Discriminator Loss: 1.9253835678100586\n",
            "Epoch: 63, Generator Loss: 0.5130673050880432, Discriminator Loss: 1.9621416330337524\n",
            "Epoch: 64, Generator Loss: 0.5060693621635437, Discriminator Loss: 1.965281367301941\n",
            "Epoch: 65, Generator Loss: 0.523578941822052, Discriminator Loss: 1.9250215291976929\n",
            "Epoch: 66, Generator Loss: 0.5341764688491821, Discriminator Loss: 1.9169458150863647\n",
            "Epoch: 67, Generator Loss: 0.5437857508659363, Discriminator Loss: 1.8830159902572632\n",
            "Epoch: 68, Generator Loss: 0.5410133004188538, Discriminator Loss: 1.9133960008621216\n",
            "Epoch: 69, Generator Loss: 0.535211443901062, Discriminator Loss: 1.8671048879623413\n",
            "Epoch: 70, Generator Loss: 0.5227625966072083, Discriminator Loss: 1.9147918224334717\n",
            "Epoch: 71, Generator Loss: 0.5165602564811707, Discriminator Loss: 1.9021399021148682\n",
            "Epoch: 72, Generator Loss: 0.5219352841377258, Discriminator Loss: 1.8909399509429932\n",
            "Epoch: 73, Generator Loss: 0.5299494862556458, Discriminator Loss: 1.913786768913269\n",
            "Epoch: 74, Generator Loss: 0.5370683073997498, Discriminator Loss: 1.8993290662765503\n",
            "Epoch: 75, Generator Loss: 0.5543458461761475, Discriminator Loss: 1.886603593826294\n",
            "Epoch: 76, Generator Loss: 0.5559374690055847, Discriminator Loss: 1.8833478689193726\n",
            "Epoch: 77, Generator Loss: 0.5516044497489929, Discriminator Loss: 1.88387930393219\n",
            "Epoch: 78, Generator Loss: 0.5634049773216248, Discriminator Loss: 1.8520065546035767\n",
            "Epoch: 79, Generator Loss: 0.5627610087394714, Discriminator Loss: 1.8465543985366821\n",
            "Epoch: 80, Generator Loss: 0.5510416030883789, Discriminator Loss: 1.8662376403808594\n",
            "Epoch: 81, Generator Loss: 0.5503302216529846, Discriminator Loss: 1.915558099746704\n",
            "Epoch: 82, Generator Loss: 0.5445582866668701, Discriminator Loss: 1.9156755208969116\n",
            "Epoch: 83, Generator Loss: 0.5506776571273804, Discriminator Loss: 1.876555323600769\n",
            "Epoch: 84, Generator Loss: 0.5673772692680359, Discriminator Loss: 1.8943994045257568\n",
            "Epoch: 85, Generator Loss: 0.5826940536499023, Discriminator Loss: 1.8510185480117798\n",
            "Epoch: 86, Generator Loss: 0.5903192758560181, Discriminator Loss: 1.828652262687683\n",
            "Epoch: 87, Generator Loss: 0.5916497111320496, Discriminator Loss: 1.8171485662460327\n",
            "Epoch: 88, Generator Loss: 0.5896720886230469, Discriminator Loss: 1.7961512804031372\n",
            "Epoch: 89, Generator Loss: 0.5867503881454468, Discriminator Loss: 1.8081086874008179\n",
            "Epoch: 90, Generator Loss: 0.561775803565979, Discriminator Loss: 1.8444185256958008\n",
            "Epoch: 91, Generator Loss: 0.552556037902832, Discriminator Loss: 1.8795503377914429\n",
            "Epoch: 92, Generator Loss: 0.544338583946228, Discriminator Loss: 1.8654581308364868\n",
            "Epoch: 93, Generator Loss: 0.5510985255241394, Discriminator Loss: 1.845579981803894\n",
            "Epoch: 94, Generator Loss: 0.5495144724845886, Discriminator Loss: 1.839232087135315\n",
            "Epoch: 95, Generator Loss: 0.5556743741035461, Discriminator Loss: 1.8285958766937256\n",
            "Epoch: 96, Generator Loss: 0.5433610081672668, Discriminator Loss: 1.8459646701812744\n",
            "Epoch: 97, Generator Loss: 0.5369371771812439, Discriminator Loss: 1.8679707050323486\n",
            "Epoch: 98, Generator Loss: 0.5460633635520935, Discriminator Loss: 1.8344632387161255\n",
            "Epoch: 99, Generator Loss: 0.5498455762863159, Discriminator Loss: 1.873538851737976\n",
            "Epoch: 100, Generator Loss: 0.5569822788238525, Discriminator Loss: 1.8296430110931396\n",
            "Epoch: 101, Generator Loss: 0.5649753212928772, Discriminator Loss: 1.8473527431488037\n",
            "Epoch: 102, Generator Loss: 0.5755732655525208, Discriminator Loss: 1.8351280689239502\n",
            "Epoch: 103, Generator Loss: 0.5833231210708618, Discriminator Loss: 1.8038426637649536\n",
            "Epoch: 104, Generator Loss: 0.5890332460403442, Discriminator Loss: 1.8082283735275269\n",
            "Epoch: 105, Generator Loss: 0.5832279920578003, Discriminator Loss: 1.8057630062103271\n",
            "Epoch: 106, Generator Loss: 0.5815386176109314, Discriminator Loss: 1.821812391281128\n",
            "Epoch: 107, Generator Loss: 0.5709091424942017, Discriminator Loss: 1.8217051029205322\n",
            "Epoch: 108, Generator Loss: 0.5474334359169006, Discriminator Loss: 1.8874613046646118\n",
            "Epoch: 109, Generator Loss: 0.5683592557907104, Discriminator Loss: 1.8294686079025269\n",
            "Epoch: 110, Generator Loss: 0.5851566195487976, Discriminator Loss: 1.862564206123352\n",
            "Epoch: 111, Generator Loss: 0.5988563299179077, Discriminator Loss: 1.8206329345703125\n",
            "Epoch: 112, Generator Loss: 0.6115578413009644, Discriminator Loss: 1.773983359336853\n",
            "Epoch: 113, Generator Loss: 0.6199137568473816, Discriminator Loss: 1.8008215427398682\n",
            "Epoch: 114, Generator Loss: 0.6248680949211121, Discriminator Loss: 1.8012686967849731\n",
            "Epoch: 115, Generator Loss: 0.6262296438217163, Discriminator Loss: 1.783456563949585\n",
            "Epoch: 116, Generator Loss: 0.6282876133918762, Discriminator Loss: 1.7769858837127686\n",
            "Epoch: 117, Generator Loss: 0.6285579800605774, Discriminator Loss: 1.7650648355484009\n",
            "Epoch: 118, Generator Loss: 0.6294587254524231, Discriminator Loss: 1.7778254747390747\n",
            "Epoch: 119, Generator Loss: 0.6305748820304871, Discriminator Loss: 1.7778427600860596\n",
            "Epoch: 120, Generator Loss: 0.6291638612747192, Discriminator Loss: 1.775632619857788\n",
            "Epoch: 121, Generator Loss: 0.6280948519706726, Discriminator Loss: 1.7537888288497925\n",
            "Epoch: 122, Generator Loss: 0.6300249099731445, Discriminator Loss: 1.7844229936599731\n",
            "Epoch: 123, Generator Loss: 0.6283208727836609, Discriminator Loss: 1.754599928855896\n",
            "Epoch: 124, Generator Loss: 0.6298123002052307, Discriminator Loss: 1.7848894596099854\n",
            "Epoch: 125, Generator Loss: 0.6309837698936462, Discriminator Loss: 1.7474391460418701\n",
            "Epoch: 126, Generator Loss: 0.6320422887802124, Discriminator Loss: 1.7583450078964233\n",
            "Epoch: 127, Generator Loss: 0.6400904059410095, Discriminator Loss: 1.7694214582443237\n",
            "Epoch: 128, Generator Loss: 0.6324629187583923, Discriminator Loss: 1.7774016857147217\n",
            "Epoch: 129, Generator Loss: 0.6124916076660156, Discriminator Loss: 1.8186702728271484\n",
            "Epoch: 130, Generator Loss: 0.5734853148460388, Discriminator Loss: 1.837594747543335\n",
            "Epoch: 131, Generator Loss: 0.5636682510375977, Discriminator Loss: 1.887058973312378\n",
            "Epoch: 132, Generator Loss: 0.5782691240310669, Discriminator Loss: 1.8639087677001953\n",
            "Epoch: 133, Generator Loss: 0.5855530500411987, Discriminator Loss: 1.8551642894744873\n",
            "Epoch: 134, Generator Loss: 0.5991559624671936, Discriminator Loss: 1.8197938203811646\n",
            "Epoch: 135, Generator Loss: 0.6184874773025513, Discriminator Loss: 1.8181065320968628\n",
            "Epoch: 136, Generator Loss: 0.6258505582809448, Discriminator Loss: 1.8089030981063843\n",
            "Epoch: 137, Generator Loss: 0.6345445513725281, Discriminator Loss: 1.8162752389907837\n",
            "Epoch: 138, Generator Loss: 0.6385933756828308, Discriminator Loss: 1.8260458707809448\n",
            "Epoch: 139, Generator Loss: 0.6473274230957031, Discriminator Loss: 1.7981773614883423\n",
            "Epoch: 140, Generator Loss: 0.6472809910774231, Discriminator Loss: 1.7697031497955322\n",
            "Epoch: 141, Generator Loss: 0.650701642036438, Discriminator Loss: 1.743865728378296\n",
            "Epoch: 142, Generator Loss: 0.6521008610725403, Discriminator Loss: 1.7176873683929443\n",
            "Epoch: 143, Generator Loss: 0.6444106698036194, Discriminator Loss: 1.7693579196929932\n",
            "Epoch: 144, Generator Loss: 0.641905665397644, Discriminator Loss: 1.7705204486846924\n",
            "Epoch: 145, Generator Loss: 0.6374165415763855, Discriminator Loss: 1.8058663606643677\n",
            "Epoch: 146, Generator Loss: 0.6235266923904419, Discriminator Loss: 1.7381870746612549\n",
            "Epoch: 147, Generator Loss: 0.5892828702926636, Discriminator Loss: 1.852325439453125\n",
            "Epoch: 148, Generator Loss: 0.5753353834152222, Discriminator Loss: 1.8449815511703491\n",
            "Epoch: 149, Generator Loss: 0.6026539206504822, Discriminator Loss: 1.7581708431243896\n",
            "Epoch: 150, Generator Loss: 0.6236670613288879, Discriminator Loss: 1.760986089706421\n",
            "Epoch: 151, Generator Loss: 0.6250039339065552, Discriminator Loss: 1.768956184387207\n",
            "Epoch: 152, Generator Loss: 0.6256861090660095, Discriminator Loss: 1.7794551849365234\n",
            "Epoch: 153, Generator Loss: 0.6346012949943542, Discriminator Loss: 1.760243535041809\n",
            "Epoch: 154, Generator Loss: 0.628298819065094, Discriminator Loss: 1.749287724494934\n",
            "Epoch: 155, Generator Loss: 0.6368821859359741, Discriminator Loss: 1.7152551412582397\n",
            "Epoch: 156, Generator Loss: 0.6314118504524231, Discriminator Loss: 1.7351112365722656\n",
            "Epoch: 157, Generator Loss: 0.6265581250190735, Discriminator Loss: 1.736602544784546\n",
            "Epoch: 158, Generator Loss: 0.6100559234619141, Discriminator Loss: 1.7446779012680054\n",
            "Epoch: 159, Generator Loss: 0.5922154188156128, Discriminator Loss: 1.7559831142425537\n",
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            "Epoch: 1969, Generator Loss: 0.6526427268981934, Discriminator Loss: 1.5957614183425903\n",
            "Epoch: 1970, Generator Loss: 0.6581749320030212, Discriminator Loss: 1.6084855794906616\n",
            "Epoch: 1971, Generator Loss: 0.6546540856361389, Discriminator Loss: 1.5959855318069458\n",
            "Epoch: 1972, Generator Loss: 0.6539710164070129, Discriminator Loss: 1.5776625871658325\n",
            "Epoch: 1973, Generator Loss: 0.6556568145751953, Discriminator Loss: 1.6415863037109375\n",
            "Epoch: 1974, Generator Loss: 0.6558828949928284, Discriminator Loss: 1.6174284219741821\n",
            "Epoch: 1975, Generator Loss: 0.6526843905448914, Discriminator Loss: 1.6208572387695312\n",
            "Epoch: 1976, Generator Loss: 0.6447165608406067, Discriminator Loss: 1.6345770359039307\n",
            "Epoch: 1977, Generator Loss: 0.6538957953453064, Discriminator Loss: 1.6503320932388306\n",
            "Epoch: 1978, Generator Loss: 0.6530653834342957, Discriminator Loss: 1.580855369567871\n",
            "Epoch: 1979, Generator Loss: 0.6594002842903137, Discriminator Loss: 1.6303014755249023\n",
            "Epoch: 1980, Generator Loss: 0.6576257944107056, Discriminator Loss: 1.6405789852142334\n",
            "Epoch: 1981, Generator Loss: 0.6554898023605347, Discriminator Loss: 1.6009682416915894\n",
            "Epoch: 1982, Generator Loss: 0.6556205153465271, Discriminator Loss: 1.655796766281128\n",
            "Epoch: 1983, Generator Loss: 0.6558471918106079, Discriminator Loss: 1.6093695163726807\n",
            "Epoch: 1984, Generator Loss: 0.6546164155006409, Discriminator Loss: 1.613748550415039\n",
            "Epoch: 1985, Generator Loss: 0.6542171835899353, Discriminator Loss: 1.6093980073928833\n",
            "Epoch: 1986, Generator Loss: 0.6668943762779236, Discriminator Loss: 1.608987808227539\n",
            "Epoch: 1987, Generator Loss: 0.6544974446296692, Discriminator Loss: 1.6112470626831055\n",
            "Epoch: 1988, Generator Loss: 0.6623495817184448, Discriminator Loss: 1.606998085975647\n",
            "Epoch: 1989, Generator Loss: 0.6614161133766174, Discriminator Loss: 1.5976201295852661\n",
            "Epoch: 1990, Generator Loss: 0.6475855708122253, Discriminator Loss: 1.629447102546692\n",
            "Epoch: 1991, Generator Loss: 0.6543802618980408, Discriminator Loss: 1.5875195264816284\n",
            "Epoch: 1992, Generator Loss: 0.6524730324745178, Discriminator Loss: 1.6025962829589844\n",
            "Epoch: 1993, Generator Loss: 0.6493362784385681, Discriminator Loss: 1.5991889238357544\n",
            "Epoch: 1994, Generator Loss: 0.645079493522644, Discriminator Loss: 1.599306344985962\n",
            "Epoch: 1995, Generator Loss: 0.6447820663452148, Discriminator Loss: 1.6743837594985962\n",
            "Epoch: 1996, Generator Loss: 0.6398566365242004, Discriminator Loss: 1.6614418029785156\n",
            "Epoch: 1997, Generator Loss: 0.6429937481880188, Discriminator Loss: 1.6341878175735474\n",
            "Epoch: 1998, Generator Loss: 0.6486613154411316, Discriminator Loss: 1.6576141119003296\n",
            "Epoch: 1999, Generator Loss: 0.6412960886955261, Discriminator Loss: 1.5965783596038818\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ZjJsERcGUh7B",
        "outputId": "d898abe1-24f4-43e8-81d9-73839b41fff4"
      },
      "source": [
        "plt.title('Generator Loss')\n",
        "plt.plot(gen_losses, 'r-')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NN929evbZXJh/7yvBu+86AZ81K74Lk7Q89pibrr++cLyBA53DPsqx7xNXQyr44xUlbF/7Wuuwp58ubE9GOZJMhUNEhojITBFpFpExMXGOFZEZIjJdRO4KhK8SkWneNDEQ3l9EXvLSvNcblrZ+WLEChg6F//7XrY8Z48JKKeZJQoYdnQHpPnQff+yKGd56y63H1blfutTlYIJCEPwzBye2kCsm8Vs3L13qrqWIexG33NJ9kK+8Mrdv+GX63vdczZ/774evfrWwgBx2WOuwoBAEbV5vvdzykiW59jLB4rkghYTjxhvjberUKX6bTzHhCFb7TYLf+tsX5jT41XKDtbD22Se/XUSQa64pXB01zJZbuo4Nt922ddcj5fCd7+Se3ULEVeH1OeIIVwIQJniPooTj3/8ufuwwhQSsHKIcH5WYcMPFvg1sDXQGXgF2CMUZALwM9PDWNw5s+ywm3fuA473lPwM/KmZLm3KONzU5p9WgQW59m23c+qxZ2RyvFMfh4MHJHWzDhuXHveIK1YYGt+2991Tffz8X96abXJwf/KB02/xp882j0/jRj1TXWy8/7Nxz89effrrwNTrkENW33y5+PX2efDLaxpNPzsUJ1gIqZVJVPfjg/LAPPnDhF1wQv1+3bm6+eHH8NX/kkfTPi4jq1Kmln0v4eIWOr6p6yy3J0vYrHBS6v7WeDjpI9X//ax1+wQU5m7ffPnrf8eNVf/KTXMWHYsd6773o5zghVLtWFbA3MCmwPhYYG4rzW+DUmP1bCQdunPGPgY5Rx4ib2pRwPPecu+yDB7v1bbd16zNnZnO8tA/11KmqL76YHxau7RHk0EOj0wke23/I/WqqoHr77eW/gJdeWtp+Tz2Vsz+udo1/f4KEa9T4dO0af6zPP3dxuncv/Tybmlwawaq0oLr11snv8XnnRdfYGjmyvOclOG24YfGqxDvvnH+83/1O9fHHCx9fNXmV2yg6dSr/WavktPXW7oeqkP3F0pgxQ/X001U33bRwvNdei74mCYkTjiyLqvoAwXzsXC8syEBgoIg8LyKTRSTgKaWriDR54cO8sI2AT1TVz89HpQmAiIz09m9akKRZf7XwfRm+s813lKrm4rS05HouLZVFi0rrRG233fKLWMAVY5x7br6NPnE+jmC228+WX3ZZLqwSDZX84qm0BGsZxfW4Guz2evVqOPVUOP306LiF+jy64w5X5l5O319bbunmYV9Gmj67Lr00uvZTUid1+N6vs07rOF26wO67x6dx4omt/T7nnAMHH5xbj+uYL64CQJBBg6LD0zqpfS65JLohZ5hdd02X7l57FfZLffBB8TQaG+HPf87FPess+Na3Wsf7xz/S2ZaUKDWpxAQcDdwcWD8R+GMozsPAA0AnoD9OaDb0tvXx5lsDc4BtgF5Ac2D/LYDXi9nSpnIcf/ub+xP4znfcup8lDf4ZDB0a//eUlGJ/foX+2MINyvxpyZLWx4lL5w9/yC2PHl04biWnDTdMFm/mzOJ18leudI0FH344/lq9+WbhNCZMUH355fLOyb/u11wTbYO/fPXVhdN5/vnWYSeckH8/77gj/lyDz9Rpp7WOc8YZuWK7nXbKz1X+858u15CEqGOHw3feuXW8XXaJTm/lSndePXuq3ndfsms+fnzxZ9yfrr8+3f387DPVRYvir/OAAemfkUK2rlqV7LpH3oq2WVT1Z+CUwPqTwB4Rad3qCVH9F1X5Zd3HHOPW/Zt73HG5OOGHIS1ffJH+wQPVLbdsbUNw+uij3PYf/Shd2h9+WJpNWU7HHVd4+09+4uZ+i+Kol7XYMR59VHXKlPLsXL7cHWvVqtbbnnoqtzxrVnFbwmEjRuQ/Ow880DpOsBh14sTcfsE48+e7D7SqazHvL5fyLC9b1vo6h6912A8HTqyK8fjjya55kPC2sP/BP+ck6Y4a5eIvXVr6M1XI3qhtX36Z7vrnnXr1i6qmAAO8WlCdgeOBiaE4DwL7A4hIL1zR1WwR6SEiXQLh+wIzvBN52hMRgJOBhzI8h8qz/vpu3rt3fnhwRLRyKdZfUZBtt80tF+tkMFhluFi1xDCbbJIufhyVGpMCXIO7Qtxxh5sHR8sLUqimkt/QTrX8qtZ+ramo+3PggbnlYt2u//73rcP82mk+Xbu2jhNsLHfYYa6H23DNs003zR1/jz3K6wLeL8YthF+1/MILc/GTdB7p29Wjh7tHO+xQfJ8zzshfD/ZJdfjhbt69e/F0IHcPK9VFfhzBIsEMesvNTDjU+SFGAZOAN4D7VHW6iIwTkaFetEnAQhGZgROEc1V1IfAVoElEXvHCL1NV/26NBs4WkWacz+MvWZ1DJoR75vQJv8DlkKb31X32yS0XK0duC4M79ehRvWNtvnnh7YU6I9xoIzdfuTK/SuT3vpf8+P442UEfSqGeT4t9jKJaS4c/0gcf7Hw5hQY36tgx+667/XYiUXTpAj/9qRPlCy7I/SipFk/XF+EePdy5RnVX/t3v5q+HqypH+bSSCkfPnm6exGcDzh9SCsGW+YWe0xLJtB2Hqj6qqgNVdRtVvcQLO19VJ3rLqqpnq+oOqrqTqt7jhb/gre/izf8SSHO2qu6pqtuq6jGq2kb7nojBf+HCLXmHDWsdt5TxCCD+Bdp//9ZhwZcg+Ecb9XdaTeGIGzwo6QtXCdZdNzrcd8IWan3sf6DCwpGmdf0NN7Ru1/Htb8fH94UjruuKqJ8Tvwt1n4YGl5ss1Atvp07J2+8MHQqjRiWLG+TQQ1uH+U7uddeNf26L4V8j/90KPk9dujinf7jCR6Eu0/13rZBwXH55bvmss9y8Uyc3Fs7WW0enB65tUZrxUMDlBocPz3/O6inHYcTgq39YOMIN3KC0VsQQLRxbbRX9tx73Ap59dus/7lK7lyjE8OHRjbR+8pPo+OWO2paGuCK/Aw5w81KEI+o+xyGSTij9axMnHH5jUL+WFrgu0dOyYAE0NSWL+9BD8Ic/pD9GFP4HPFx0uP327nnxu0kvhH9fon7KvvwyujajX7wc5JZb8tcLlRicey6cf74r4vJzAiKuL6uLL45Pp1OnZI07gzQ3uyLWYO7ThKMdEJfjiKoyXKpwRFUhvPzy4n9m4Y/UyJHR6QbHdCiXiy+Gv0SUNlYzZ/HUU+ni+4KaVDiC93HkSPeBe+aZZKO/pcH/YYj7iPn+oa98JRdWqHjrnXeii8Y+/LC0MTZKIViUGlXdFNxzfe21yQaKCuc4khD13gwf7oqwfF9foQ+8iOuaJXjdfY4+On8M9uBHvmPH9MIRRb0VVRkRxOU4/D5/gt1XlCIc//lPdPl0166tPygdOuT/wY8enb/9/PPz1/12E1ddld6uODp3jhaJLITj17+ODt955+RprLtu7s+9kHM9KBzBj1THjq7LiW98A3r1Sn7cJPh/xmHB9/GLYOKK4ML061e4aCxrPvssvy+mYj6nJJQiHFF06gR/+lPOpihxueii/PZAUXTunN8dTpBKCYflOOqYF15wRQR+f0VR/oJ77nF/Mj6l3PDgMLBBunaFq6/OLwIK9jN0881w0kn5+1SjWCjuxQge+7DD3HjKfk0Rv5+vQrzwQv7123ff+AaDaV7O7343N4rbhRfmwvv0ceXyEya44V39NE89Nd1HatNNk8cNs8467rmKE0ifjTcu/RjgnPZBO7MadW7ddfOL3ZIKXiGiiqq23db1zJyEn/0s+bFGjizduQ1OjCohHOXe7yii6ui2t6lNtOPYf39Xp7pLFzffdVcX3r9/fN1svy+iNHznO9FpvfFGLk6w7ve11+qa9gBRhNMp1FbgxBPT1z/3+1AKN6IK21nMLn/aay83/+KL/Ovrjy4YtU+43UCh6X//c910hMN/+tN8+xYsaG3Tu++2Po+HHsqNDAiq//2v6gEHuD6m4ihWlz8cJ9gQ7+CDVX/848LXthinn67au3cujeDohVkSbJ9UKv7zu+mm6fbzjxvsTyoujj9FDaSVdN+zznLhUY02i917n1/+UvWqq5LbEGmWDeTUNgj7OKIcbz5Ji6paWuCNN1xuJq6LhO23jw4fNcqNw+A7fMP89a/56wMGxNuRpv2IT6HBa665Bl56KV16kye7V8lvj/D22+7vMq5rkQkTig+gE6Rr11xXLnvumQs/5JD8eME0/XOI6qZj6FD3V+qPS9K5s/O5FKqI8O9/p2tHE/xTX726/GKahoZckeu4cZX5K05CJaqs+85pv3ZTWqLGVY+jnBz7CSe4+T77uOf5q19161HVh+O4+OLSz7MIJhzVxn9ply1zTuZCI4TFFVUdc0zuQQJXlrrDDi5LmraPK5HC4zAcfXT8tjB+24UgH3wQ7WD95jdh7Njoj+kll7j5mWfmf5yL8YMftA4TKfwC77JLvj/FHzAojoYGd54775z7uP/5z67L7SBRH9NClRNuv911nZ5kdLt994Vjjy0cJ3jOQdF88sl0Iy1GERSOrBuyBalE0Wnv3s5HFR5kKyl7712+DUkIX1f/ngWfoYnh9tTVw4SjWoQf+nffdU7m8IBFQeJyHPffD9On54TFL9Mu908yDV265A9fGnf89daLdrAedVR+J4y+gBx/vBOUUig2yh207gjPz5m88ILLTUU5/oOt+n2R6do1J9JRuca0wtGrV67BXxL8hmTg7Ah3Vrh6tcsF/fznrSsalPsX2tCQ89FNmlReWrWge/fSRahaQhm+Z1HtTg47zP0IlJLTLxMTjmpRyoP64YeFty9ZEp8jOPLI+P1GjIgvukrKbru1bpykEe1HknQfAa5F9SWXOCd9mmt11FG55SRFe+GGbX7xx957u0F6ooq09t67dY/BnTu76qoQ/TGJqhWWpqFaGtZZBzbcsHX4pEmuGnbYlqhqoWkIpjdlSnlp1RuFhCPqHpRKnHCEn6EDD3RdvFQZE462TLCGVRSLF0ePotexowuPa59wyy3OJ1IOUR/BoHBccEF8vHBccDafd176mjP3359bTjKOdLhoLKpvpih8gfZzEkEfRtTHJEr8shKOYlS6anPwPKJ+Ftozhe7h4sWupuKmm+ae/1IJP1NxwlEj2oYVRvQDMW9e/vrbb+d/kOIcqH55qO/wjquiWw5R9ga7TbnwwsIflbAzuRIkaegUbkQW5XANdhDnt7K+8UY3frcvPEFRTlp80ZaEY7vt8sfBKDW9QpU71kb69oX58/Oraich3Mlp+J5F+ThqSBU9W0ZBovwDQSdmt26ty/DjyvSDH+ys/gj9B3vsWNeYba+93Eek2OBKhx5auAO7pJxwQu6jdeWVrqJBEuEQcX+E/gA4UcIRzJX4ObpOneL7mUr6Mlf6pX/77WS5iag45eQ4g+kV62HYSMbee+c/l+F2XttuC3PmJC/6zRgTjmrwv/+l79YCch/91aujRWLw4PLsKgc/5xM1yuApp7QOmzkTbrstv3uFcgiO4jdyJNx3X3zHiGGCghwlHMGwJLmJpF06VFo4wh3kxRH80P/ud25eTg2lYHpp+t4yChO8ruHuU+67z1XD3myz6toUgwlHNYhrQ1AM/4866y6sizFjhvvjDrbhCHYFEWTFiug/3IEDi7doLpV114UXX0weP9hHVJStQb9H3B/98OFw551uOS7nt9de+e1QqtlBY5DgOey0U2XTq+RwAGs7/nW9+urW17VHD1eLqo3QNgrMjGj8P/dSOzusFF/5issqjx9fPG6nTm2mHLYo/iA8YYIvbdzHPtgfVJxwhMfeqNV1KbUL8jhMOLLBv67F7lG4K/waUCdveB1T6hgWQadj2hxHJTqDi+KUUwo3Fqw34v6+gzmOOOEIljUn8TVB+xGOYBuVtUU4bropf1yNLPCLRYvdo+bmmg+qlumTLCJDRGSmiDSLyJiYOMeKyAwRmS4id3lhg0TkRS/sVRE5LhD/VhF5R0SmedOgqHTbDKUOddrQ4Mri33svXTcDAwa4LkSyIosO02pFXCvxJF1oBIUjaVuaWhVVVVo4gpUH0nTXUs+cemrprc2TkjTH0blz8mrkGZGZcIhIA3Ad8G1gB+AEEdkhFGcAMBbYV1V3BPwmrcuAk7ywIcDVIhJsXXOuqg7ypmlZnUNF8Aed8btNT4ovHFtu6WotFWOffVwNp7feKq+H1WL89KfZpV1t4ipjA9sAAB7ASURBVHJPwQ9tkhxHePx4ny22yO+BuC1QCeEItrWpZpcj7Z1CY8u3MbK863sCzao6G0BE7gEOBwIjvXMacJ2qLgZQ1Y+8+Vt+BFV9X0Q+AnoDBfrnaKN8+qmbB0ff69ChePcgvnAk5dhjq/NRjxp+tj0T5xxPWi2ymgNSxZFECNMQ1b+YUT5Ji6raAFkKRx8g+Ls1Fwh3Tj8QQESeBxqAC1X1H8EIIrIn0Bl4OxB8iYicDzwJjNGIccdFZCQwEmDL4FCZ1cYXjqDPomvX4sOwJhGXcPxqUK2eULPklVeSF+fFjfmctKigrQlHJZ6TWhZP3X13/lC87QkTjlTHHwDsD/QFnhWRnVT1EwAR2Qy4HThZVf2v6FjgA5yY3AiMBloNeaeqN3rbaWxsrF2/CP5DnlY4lixxDX6SUq3y87bwISyXnXdONurfN74Rf13DfVfF0RauV6WFo5YftuOPr92xs8aKqgCYBwTrjfX1woLMBV5S1ZXAOyLyFk5IpojI+sAjwC9Udc34i6o631tcLiK3ACmG5KoBfo7DHwcAWtdEEWldA2fZMvjnP5Mfp5qO1yOPbN8vMLh+rwp99JP+dZtwGEmpoxxHlhZOAQaISH8R6QwcD4Q7kH8Ql9tARHrhiq5me/EfAG5T1fuDO3i5EEREgGHA6xmeQ/lE5TjCwtFGuhFIzN/+5sYEac/EjYUeprGx8Pa2IBxBTDjaLnUkHJnlOFS1RURGAZNw/ovxqjpdRMbhhiOc6G07RERmAKtwtaUWisj3ga8DG4nICC/JEV4NqjtFpDcgwDTg9KzOoSIkEY5K+A3qTXzaAx9/XNxR3BaEo9LO8Tr4sNUlfqlDW3hmipCpj0NVHwUeDYWdH1hW4GxvCsa5A7gjJs0DK29phixf7l60oFiEhaPcF3G33eDEE8tLw0hP1IiHYdrCR8CKquqDOhIOewKypqXFPQjBly1cPh5+UAr9FT79NDzxRH4RyUUXtY/aTu2RtvARMOGoD6JG+Wuj2BOQNatWubLL4MsW/siHH5RCL+b++8NBB+VX1a1Vi2SjOP693WST2tlgwlEfWI7DWENLS2vhCLe2TSMcPiYc9YEI3H47TJ5cPG41qMRH3563bKijHEet23G0f6KEI5zjCL/MSV7uYKtye5HbNt//fm2Pb87x+sByHMYaonwcxYqqRGBcqzaN+ViOw0iKFVXVB3WU47AnIGuifBxJiqqKObuDOY64bjEMA0w46gXLcRhr8Iuqgi9vkhxHlHD88Y+5Zf/vZMqU5EOIGmsnJhz1geU4jDX4RVWFhCPKxxElHD/+cW75hz908223rYydRvvFhKM+MOEw1hCV46hEUdW557qRATfcsHA8wwhizvG2iy8cdTDGiT0BWeP7OAoJR5IcRzC3AS69OnjAjDaA5TjqA/NxGGsoJccR5eOwmlNGqZhw1AcmHMYakvg4SimqMoykmHDUB+bjMNZQSq2qDh3q4uEx6gQTjvrAchzGGkrxcYi0Dqvl8LdG+8Gc420Xy3EYayg1xxF8OX/5Szj7bAyjJCzHUR/4OY46uL6ZWigiQ0Rkpog0i8iYmDjHisgMEZkuIncFwk8WkVnedHIgfHcRec1L81pvJMC2S5SPI0l13GD8I46oi78Qo41iwlEf+DmONv5Jgww7ORSRBuA64GDc2OJTRGSiqs4IxBkAjAX2VdXFIrKxF94TuABoBBSY6u27GLgeOA14CTdI1BDgsazOo2yiiqrCIlAsx2GiYZSDCUd9YDkOAPYEmlV1tqquAO4BDg/FOQ24zhMEVPUjL/xbwBOqusjb9gQwxBtvfH1VneyNHngbbtzxtktUUVWxbtTDPg4TDqMcTDjqgzrKcWT5BPQB3gusz/XCggwEBorI8yIyWUSGFNm3j7dcKM22RRLhKFZUZcJhlIN1q14f1FGOo9ZNjzsCA4D9gb7AsyKyUyUSFpGRwEiALWtZIynKx1FMOCzHYWSF5TjaLpbjAGAesEVgva8XFmQuMFFVV6rqO8BbOCGJ23eet1woTQBU9UZVbVTVxt69e5d1ImWxenV64Qj7OOxFNcrBiqrqgzrKcWRp4RRggIj0F5HOwPHAxFCcB3G5DUSkF67oajYwCThERHqISA/gEGCSqs4HlorIYK821UnAQxmeQ/msWlW86CmqryrLcRiVwoSjPqijHEdmRVWq2iIio3Ai0ACMV9XpIjIOaFLVieQEYgawCjhXVRcCiMjFOPEBGKeqi7zlM4BbgW642lRtt0YVlJ7jaGmJ324YaTDhqA/qKMeRqY9DVR/FVZkNhp0fWFbgbG8K7zseGB8R3gR8teLGZsXq1cVzHOE/DBH47LP4+IaRBnOO1wd33w2XXgqbb15rS4pSa+d4+ydKOKKq3wYZNsyKqoxssBxH22XffeGRR2ptRSJMOLLG93EEKSQEH30EPXvCAw/kwuxFNSqFCYdRAewJqDQvvggnnZRzdPk+jiCFhKN3b7fdL+8sFt8w0mDCYVQAewIqzTe/CbffDgsXunW/qCpIEiEw4TCywHwcRgWwoqpKs3Klmy9d6nrB/fzz4sIR9TKbcBhtFROOtR57AirNeuu5+SefQI8e8PHH6YqqfPyirqTxDaNa+MJRB+0NjGww4ag0QeHwKaWoKigc9odntCVMONZ67ItUabp3d/NCwpHkhbOiKqOtYsKx1mPCUWn8QZpWrMiFlSIcVlRltFX859eEY63FhKPS+DmFVatyYaV8+IM5DiuqMtoS/vM8fHht7TBqhtWqyopyfRRB4TCMtkRDg6tuvv76tbbEqBH2K1tp/A9+ucJx3HGVsccwsqBnz1yxrLHWYcJRaXzh8NtzQOuiqnBuIqqsuFu3ytplGIZRIUw4suLLL3PL5qMwDKMdYXnNrFi+PLdcSq0qw6gkG2+cX2HDMMrAhKPS+MVQQeGw6rRGrWlutufQqBiZlqGIyBARmSkizSIyJmL7CBFZICLTvOlUL/yAQNg0EflSRIZ5224VkXcC2wZleQ4lUyjHEcZyIEbWdO8O66xTayuMdkJmOQ4RaQCuAw4G5gJTRGSiqs4IRb1XVUcFA1T1aWCQl05PoBl4PBDlXFW9Pyvby8LPcZiPwzCMdkqWX7Q9gWZVna2qK4B7gMNLSOdo4DFVXVZR67IiqqjKhMMwjHZEoi+aiKwrIh285YEiMlREOhXZrQ/wXmB9rhcW5igReVVE7heRLSK2Hw/cHQq7xNvnKhHpEmPzSBFpEpGmBQsWFDG1giTxcRxyCGy1FYwYUTWzDMMwKkXSX+Fnga4i0gdXZHQicGsFjv93oJ+q7gw8AUwIbhSRzYCdgEmB4LHA9sAeQE9gdFTCqnqjqjaqamPv3r0rYGpKgkVV4XYbG20Ec+bALrtU1STDMIxKkFQ4xCsqOhL4k6oeA+xYZJ95QDAH0dcLW4OqLlRV/9f8ZmD3UBrHAg+o6srAPvPVsRy4BVck1naIynHEVYO0zuIMw6hDEguHiOwNDAce8cKK1e2bAgwQkf4i0hlX5DQxlOhmgdWhwBuhNE4gVEzl7yMiAgwDXk94DtUhSjhaWqLjmu/DMIw6JGmtqrNwRUQPqOp0EdkaeLrQDqraIiKjcMVMDcB4b99xQJOqTgTOFJGhQAuwCBjh7y8i/XA5ln+Fkr5TRHoDAkwDTk94DtUliXBYTsMwjDokkXCo6r/wPuCek/xjVT0zwX6PAo+Gws4PLI/FCVLUvnOIcKar6oFJbK4ap58Of/lLrm+qqOq4cViOwzCMOiRpraq7RGR9EVkXVzQ0Q0TOzda0OuGGG/JzFFFFVXFYjsMwjDok6S/vDqq6FOdTeAzoj6tZZYTxHeFB4YgTCHOOG4ZRhyQVjk5eu41hwESvlpONNBSFLxzBoqo4YbCiKsMw6pCkX64bgDnAusCzIrIVsDQro+qaqBxHHJbTMAyjDknqHL8WuDYQ9K6IHJCNSXVOmqKqTp3y54ZhGHVAUuf4BiJypd+Fh4j8Hpf7MHxWr4bf/Q4++sitJymq+t734Kyz4LLLsrfPMAyjQiRtxzEeV5vqWG/9RFyr7SOzMKouWbUKzg1UNIsqqpo2Lb/fqs6d4aqr4qvuNjfnj11uGIbRBkgqHNuo6lGB9YtEZFoWBtUt4UZ+UUVVcX1TxeVIttmmfLsMwzAqTFLn+Bci8jV/RUT2Bb7IxqQ6JdwfVRIfh4/VrjIMo45ImuM4HbhNRDbw1hcDJ2djUh0R7PU2nOMI94hbCKtdZRhGHZG0VtUrwC4isr63vlREzgJezdK4Nk/Q/xDXHxVYjsMwjHZFqi+Wqi71WpADnJ2BPfVFUCwKObGLCYflOAzDqCPK+dW1r11UH1WlYMJhGEYdUY5wWJcjQeGYMiU+ngmDYRjtiII+DhH5lGiBEKBbJhbVE0HhmDkzPp4Jh2EY7YiCwqGq3atlSF0SFI6VK+PjGYZhtCMyrc4jIkNEZKaINIvImIjtI0RkgYhM86ZTA9tWBcInBsL7i8hLXpr3esPS1oagcHRMWrPZMAyjvslMOESkAbgO+DawA3CCiOwQEfVeVR3kTTcHwr8IhA8NhF8OXKWq2+Lak/wwq3MoSlA4GgoMwW5FVYZhtCOyzHHsCTSr6mxVXQHcAxxeToIiIsCBwP1e0ATcGCG1ISgc4ZbjQUw4DMNoR2QpHH2A9wLrc4kYQxw4SkReFZH7RWSLQHhXryfeySLii8NGwCeq6n+x49JEREb6vfkuWLCgzFOJIejXKNQA0DAMox1R6ybLfwf6qerOwBO4HITPVqraCHwPuFpEUvX4p6o3qmqjqjb27t27chYHCYpFOS3HDcMw6ogshWMeEMxB9PXC1qCqC1XV7w3wZmD3wLZ53nw28AywK7AQ2FBEfE90qzSrStJaVd2tcpphGO2HLIVjCjDAqwXVGTgemBiMICKbBVaHAm944T1EpIu33AvYF5ihqgo8DRzt7XMy8FCG51CYJDmOK66A00+vjj2GYRhVILM6pKraIiKjgElAAzBeVaeLyDigSVUnAmeKyFCgBVgEjPB2/wpwg4isxonbZao6w9s2GrhHRH4NvAz8JatzKEoS5/jPflYdWwzDMKpEpo0PVPVR4NFQ2PmB5bHA2Ij9XgB2iklzNq7GVu1J2nLcMAyjHVFr53h9ExSOBx908z/8Af7619rYYxiGUQWsuXM5RPk1jjgCevasvi2GYRhVwoSjHKKEo0OH0rofuf122HHH8m0yDMPIGBOOcqikcHz/++XbYxiGUQXMx1EOccJhDf4Mw2jHmHCUQ1Sjv0KdHRqGYbQDTDjKIS7HYRiG0Y4xH0c5RAmHn+NYf30YNaq69hiGYVQBE45yiBKOzt64UkuWVNcWwzCMKmHlKuUQJRydOlXfDsMwjCpiwlEO5uMwDGMtxL5y5WCDNxmGsRZiwlEOJhyGYayFmHCUgwmHYRhrISYc5WDCYRjGWogJR6ksWeI6JjQMw1jLyFQ4RGSIiMwUkWYRGROxfYSILBCRad50qhc+SEReFJHpIvKqiBwX2OdWEXknsM+gLM8hlu9/H954oyaHNgzDqCWZNQAUkQbgOuBgYC4wRUQmBoaA9blXVcNNrJcBJ6nqLBHZHJgqIpNU9RNv+7mqen9WtifizTdrenjDMIxakWWOY0+gWVVnq+oK4B7g8CQ7qupbqjrLW34f+AjonZmlpbBsWa0tMAzDqAlZCkcf4L3A+lwvLMxRXnHU/SKyRXijiOwJdAbeDgRf4u1zlYh0iTq4iIwUkSYRaVqwYEEZpxHDF19UPk3DMIw6oNbO8b8D/VR1Z+AJYEJwo4hsBtwOnKKqq73gscD2wB5AT2B0VMKqeqOqNqpqY+/eGWRWLMdhGMZaSpbCMQ8I5iD6emFrUNWFqrrcW70Z2N3fJiLrA48Av1DVyYF95qtjOXALrkis+ixfXjyOYRhGOyRL4ZgCDBCR/iLSGTgemBiM4OUofIYCb3jhnYEHgNvCTnB/HxERYBjwemZnYBiGYbQis1pVqtoiIqOASUADMF5Vp4vIOKBJVScCZ4rIUKAFWASM8HY/Fvg6sJGI+GEjVHUacKeI9AYEmAacntU5GIZhGK0RVa21DZnT2NioTU1NlU00blzxteB6GoaxdiAiU1W1MRxea+e4YRiGUWeYcBiGYRipMOGoJLvuWmsLDMMwMseEo5I8/3ytLTAMw8gcE45SiHOAd+tWXTsMwzBqgAlHKaxYUWsLDMMwaoYJRylYq3HDMNZiTDhKwYTDMIy1GBOOUvjyy1pbYBiGUTNMONKwfDnMmAFDh9baEsMwjJphwpGG006DHXeEadNqbYlhGEbNMOFIw6OP1toCwzCMmmPCkQZzihuGYZhwpCKu/cb118Orr1bXFsMwjBqR2Xgc7ZJwV+qDBsG4cXDYYbWxxzAMowaYcKShQyiDdvPNsPvu0XENwzDaKZkWVYnIEBGZKSLNIjImYvsIEVkgItO86dTAtpNFZJY3nRwI311EXvPSvNYbQrY6hIVjq62qdmjDMIy2QmbCISINwHXAt4EdgBNEZIeIqPeq6iBvutnbtydwAbAXsCdwgYj08OJfD5wGDPCmIVmdQ1E22qhmhzYMw6gVWeY49gSaVXW2qq4A7gEOT7jvt4AnVHWRqi4GngCGiMhmwPqqOlndmLe3AcOyMD6ScK+4VczsGIZhtBWyFI4+wHuB9bleWJijRORVEblfRLYosm8fb7lYmojISBFpEpGmBQsWlHoO+dh44oZhGDWvjvt3oJ+q7ozLVUyoVMKqeqOqNqpqY+/evSuVaG55WPUyOoZhGG2JLIVjHrBFYL2vF7YGVV2oqn6rupuB3YvsO89bjk0zU4LCccstVTusYRhGWyJL4ZgCDBCR/iLSGTgemBiM4PksfIYCb3jLk4BDRKSH5xQ/BJikqvOBpSIy2KtNdRLwUIbnkE9QOBoaqnZYwzCMtkRm7ThUtUVERuFEoAEYr6rTRWQc0KSqE4EzRWQo0AIsAkZ4+y4SkYtx4gMwTlUXectnALcC3YDHvKk6BIUjXDXXMAxjLUF0LXD4NjY2alNTU/kJdeoELS1uedkyG2PcMIx2jYhMVdXGcLj9NqfBchyGYRgmHKkw4TAMwzDhSIU5xw3DMEw4UhEUDms1bhjGWooJR6mYcBiGsZZiwmEYhmGkwoTDMAzDSIUJh2EYhpEKEw7DMAwjFTZ0bBJefhleeaXWVhiGYbQJTDiSsNtutbbAMAyjzWBFVYZhGEYqTDgMwzCMVJhwGIZhGKkw4TAMwzBSYcJhGIZhpCJT4RCRISIyU0SaRWRMgXhHiYiKSKO3PlxEpgWm1SIyyNv2jJemv23jLM/BMAzDyCez6rgi0gBcBxwMzAWmiMhEVZ0Ritcd+Cnwkh+mqncCd3rbdwIeVNVpgd2Gq2oFhvRLwFowQqJhGEYassxx7Ak0q+psVV0B3AMcHhHvYuBy4MuYdE7w9q0NK1a0Dps8ufp2GIZhtBGyFI4+wHuB9ble2BpEZDdgC1V9pEA6xwF3h8Ju8YqpfiUS3b+5iIwUkSYRaVqwYEEJ5nt8/nn++iWXwF57lZ6eYRhGnVMz57iIdACuBM4pEGcvYJmqvh4IHq6qOwH7edOJUfuq6o2q2qiqjb179y7NyBNOgK99LT+spaW0tAzDMNoJWQrHPGCLwHpfL8ynO/BV4BkRmQMMBib6DnKP4wnlNlR1njf/FLgLVySWDV27whtv5Na33x5OPTWzwxmGYdQDWQrHFGCAiPQXkc44EZjob1TVJaraS1X7qWo/YDIw1Hd6ezmSYwn4N0Sko4j08pY7AYcCwdxIZRk4MLf84INORDbfPLPDGYZh1AOZ1apS1RYRGQVMAhqA8ao6XUTGAU2qOrFwCnwdeE9VZwfCugCTPNFoAP4J3JSB+Y7Bg3PLgwZldhjDMIx6QnQtqG7a2NioTU0l1N5VhQ5epmzFCujUqbKGGYZhtGFEZKqqNobDreV4IYIVtkw0DMMwABuPozhPPQXvvFNrKwzDMNoMJhzFOOAANxmGYRiAFVUZhmEYKTHhMAzDMFJhwmEYhmGkwoTDMAzDSIUJh2EYhpEKEw7DMAwjFSYchmEYRipMOAzDMIxUrBV9VYnIAuDdEnfvBXxcQXMqhdmVDrMrHWZXOtqqXVCebVupaqsBjdYK4SgHEWmK6uSr1phd6TC70mF2paOt2gXZ2GZFVYZhGEYqTDgMwzCMVJhwFOfGWhsQg9mVDrMrHWZXOtqqXZCBbebjMAzDMFJhOQ7DMAwjFSYchmEYRipMOAogIkNEZKaINIvImCoedwsReVpEZojIdBH5qRd+oYjME5Fp3vSdwD5jPTtnisi3MrZvjoi85tnQ5IX1FJEnRGSWN+/hhYuIXOvZ9qqI7JaRTdsFrss0EVkqImfV4pqJyHgR+UhEXg+Epb4+InKyF3+WiJyckV1XiMib3rEfEJENvfB+IvJF4Lr9ObDP7t79b/Zsl6jjlWlX6vtW6fc1xq57AzbNEZFpXng1r1fc96F6z5iq2hQxAQ3A28DWQGfgFWCHKh17M2A3b7k78BawA3Ah8LOI+Dt49nUB+nt2N2Ro3xygVyjst8AYb3kMcLm3/B3gMUCAwcBLVbp3HwBb1eKaAV8HdgNeL/X6AD2B2d68h7fcIwO7DgE6esuXB+zqF4wXSuc/nq3i2f7tDOxKdd+yeF+j7Apt/z1wfg2uV9z3oWrPmOU44tkTaFbV2aq6ArgHOLwaB1bV+ar6X2/5U+ANoE+BXQ4H7lHV5ar6DtCMs7+aHA5M8JYnAMMC4bepYzKwoYhslrEt3wTeVtVCvQVkds1U9VlgUcTx0lyfbwFPqOoiVV0MPAEMqbRdqvq4qrZ4q5OBvoXS8GxbX1Unq/v63BY4l4rZVYC4+1bx97WQXV6u4Vjg7kJpZHS94r4PVXvGTDji6QO8F1ifS+GPdyaISD9gV+AlL2iUl90c72dFqb6tCjwuIlNFZKQXtomqzveWPwA2qZFtAMeT/0K3hWuW9vrU4rr9APdn6tNfRF4WkX+JyH5eWB/PlmrYlea+Vft67Qd8qKqzAmFVv16h70PVnjETjjaMiKwH/A04S1WXAtcD2wCDgPm4rHIt+Jqq7gZ8G/ixiHw9uNH7s6pJPW8R6QwMBf7qBbWVa7aGWl6fOETkF0ALcKcXNB/YUlV3Bc4G7hKR9atoUpu7byFOIP/npOrXK+L7sIasnzETjnjmAVsE1vt6YVVBRDrhHoo7VfX/AFT1Q1VdpaqrgZvIFa1U1VZVnefNPwIe8Oz40C+C8uYf1cI2nJj9V1U/9GxsE9eM9NenavaJyAjgUGC498HBKwpa6C1PxfkPBno2BIuzMrGrhPtWzevVETgSuDdgb1WvV9T3gSo+YyYc8UwBBohIf+8v9nhgYjUO7JWf/gV4Q1WvDIQHfQNHAH5tj4nA8SLSRUT6AwNwDrksbFtXRLr7yzjn6uueDX6tjJOBhwK2neTV7BgMLAlkp7Mg70+wLVyzwPHSXJ9JwCEi0sMrpjnEC6soIjIE+DkwVFWXBcJ7i0iDt7w17vrM9mxbKiKDvef0pMC5VNKutPetmu/rQcCbqrqmCKqa1yvu+0A1n7FyvPvtfcLVRngL9/fwiyoe92u4bOarwDRv+g5wO/CaFz4R2Cywzy88O2dSZq2NIrZtjaux8gow3b8uwEbAk8As4J9ATy9cgOs8214DGjO0bV1gIbBBIKzq1wwnXPOBlbhy4x+Wcn1wPodmbzolI7uaceXc/nP2Zy/uUd79nQb8FzgskE4j7kP+NvBHvB4oKmxX6vtW6fc1yi4v/Fbg9FDcal6vuO9D1Z4x63LEMAzDSIUVVRmGYRipMOEwDMMwUmHCYRiGYaTChMMwDMNIhQmHYRiGkQoTDsMoERFZJfk98lasB2Vxva2+XjymYVSfjrU2wDDqmC9UdVCtjTCMamM5DsOoMOLGafituDEY/iMi23rh/UTkKa/jvidFZEsvfBNxY2G84k37eEk1iMhN4sZceFxEunnxzxQ3FsOrInJPjU7TWIsx4TCM0ukWKqo6LrBtiaruhGspfLUX9gdggqrujOtM8Fov/FrgX6q6C278h+le+ADgOlXdEfgE1zoZ3FgLu3rpnJ7VyRlGHNZy3DBKREQ+U9X1IsLnAAeq6myvM7oPVHUjEfkY13XGSi98vqr2EpEFQF9VXR5Iox9urIQB3vpooJOq/lpE/gF8BjwIPKiqn2V8qoaRh+U4DCMbNGY5DcsDy6vI+SS/i+t7aDdgitdbq2FUDRMOw8iG4wLzF73lF3C9tgIMB57zlp8EfgQgIg0iskFcoiLSAdhCVZ8GRgMbAK1yPYaRJfanYhil001EpgXW/6GqfpXcHiLyKi7XcIIX9hPgFhE5F1gAnOKF/xS4UUR+iMtZ/AjXK2sUDcAdnrgIcK2qflKxMzKMBJiPwzAqjOfjaFTVj2tti2FkgRVVGYZhGKmwHIdhGIaRCstxGIZhGKkw4TAMwzBSYcJhGIZhpMKEwzAMw0iFCYdhGIaRiv8Pi5TB2jVpMS4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "EDgTQSF7UjXN",
        "outputId": "17206d8a-c8b3-4012-96ff-59539192169d"
      },
      "source": [
        "plt.title('Discriminator Loss')\n",
        "plt.plot(disc_losses, 'b-')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yp6xoYHic9FR"
      },
      "source": [
        "## Using the Discriminator for Classification\n",
        "We will now evaluate the performance of discriminator on the original training data as a classifier. We will check both the classification accuracy and Area Under ROC Curve as the metrics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uEH1mO3eUm1C",
        "outputId": "86b31904-00ba-43b2-8f63-970447bc3e90"
      },
      "source": [
        "_, train_predictions = discriminator(tf.convert_to_tensor(x_train))\n",
        "train_predictions.shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([100, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sunWHDnYUoGP",
        "outputId": "311efa9a-137e-4939-a5de-2333ed42e2f1"
      },
      "source": [
        "binary_accuracy = tf.keras.metrics.BinaryAccuracy()\n",
        "binary_accuracy.update_state(tf.one_hot(tf.squeeze(y_train), depth=2), train_predictions)\n",
        "print('Training Accuracy: %.4f %s' % (binary_accuracy.result().numpy()*100, '%'))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Accuracy: 88.0000 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "501qLgvRUpSl",
        "outputId": "881f06a8-a67f-4b7b-ebae-0e66cd9e7d4e"
      },
      "source": [
        "fpr, tpr, _ = roc_curve(y_train, tf.argmax(train_predictions,1).numpy())\n",
        "roc_auc = auc(fpr, tpr)\n",
        "plt.figure()\n",
        "lw = 2\n",
        "plt.plot(fpr, tpr, color='darkorange',\n",
        "lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Lzy8TupUqz7",
        "outputId": "b93f8bc8-c920-45e7-f1c7-2c9979bea3da"
      },
      "source": [
        "_, test_predictions = discriminator(tf.convert_to_tensor(x_test))\n",
        "test_predictions.shape"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([100, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bQpcUnI3Ur-d",
        "outputId": "c6e6444e-d234-44aa-97b8-501a6d53ca40"
      },
      "source": [
        "binary_accuracy = tf.keras.metrics.BinaryAccuracy()\n",
        "binary_accuracy.update_state(tf.one_hot(tf.squeeze(y_test), depth=2), test_predictions)\n",
        "print('Test Accuracy: %.4f %s' % (binary_accuracy.result().numpy()*100, '%'))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Accuracy: 69.0000 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "bJVaUUMAUtLE",
        "outputId": "b95f9d10-6f78-42ec-ea45-3e30ed8d1e9d"
      },
      "source": [
        "fpr, tpr, _ = roc_curve(y_test, tf.argmax(test_predictions,1).numpy())\n",
        "roc_auc = auc(fpr, tpr)\n",
        "plt.figure()\n",
        "lw = 2\n",
        "plt.plot(fpr, tpr, color='darkorange',\n",
        "lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic Curve')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MZjIRnfYmZJG"
      },
      "source": [
        "We will now look at the predictions on the generated synthetic data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wx3BZCj0Uv3M",
        "outputId": "b271c250-eeba-4ed1-cf6e-1a56142c74e1"
      },
      "source": [
        "generator_outputs = generator(random_data)\n",
        "generator_outputs.shape"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([200, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eTHmzXj_UxKe",
        "outputId": "37c72767-3967-4d5b-f57a-359d31e7e306"
      },
      "source": [
        "_, predictions_synthetic = discriminator(generator_outputs)\n",
        "predictions_synthetic.shape"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TensorShape([200, 2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-Q30zS7UySO",
        "outputId": "de99e2fd-5f87-4ff0-908c-6f1a64e99f77"
      },
      "source": [
        "predicted_labels_synthetic = tf.argmax(predictions_synthetic, 1)\n",
        "predicted_labels_synthetic[:20]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(20,), dtype=int64, numpy=array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0])>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2gWuTtR5WLD"
      },
      "source": [
        "## Improving the Performance\n",
        "It can be seen from the loss vs iterations plots that the generator has more or less converged, but the discriminator hasn't. The AUC scores suggest that the model is actually learning. This can be improved in the fillowing ways:\n",
        "1. Use slightly higher learning rates while training the discriminator.  \n",
        "2. As the generator converged, we can take the synthetic data generated by it and add to our original training set. We can again start training the GAN so that the discriminator becomes more robust. \n",
        "3. Training for a larger number of epochs.\n",
        "4. Using adaptive learning rates and learning rate scheduling."
      ]
    }
  ]
}