GANWithBetterParamswithLotofModifs.ipynb

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    "colab": {
      "name": "GANWithBetterParamswithLotofModifs.ipynb",
      "provenance": [],
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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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      "file_extension": ".py",
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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "_iaQa45ebnmi",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import collections\n",
        "import gzip\n",
        "import os\n",
        "import time\n",
        "\n",
        "import numpy\n",
        "from six.moves import urllib\n",
        "from six.moves import xrange  # pylint: disable=redefined-builtin\n",
        "\n",
        "from tensorflow.python.framework import dtypes\n",
        "from tensorflow.python.framework import random_seed\n",
        "from tensorflow.python.platform import gfile\n",
        "from tensorflow.python.util.deprecation import deprecated\n",
        "\n",
        "_Datasets = collections.namedtuple('_Datasets', ['train', 'validation', 'test'])\n",
        "\n",
        "# CVDF mirror of http://yann.lecun.com/exdb/mnist/\n",
        "DEFAULT_SOURCE_URL = 'https://storage.googleapis.com/cvdf-datasets/mnist/'\n",
        "\n",
        "\n",
        "def _read32(bytestream):\n",
        "  dt = numpy.dtype(numpy.uint32).newbyteorder('>')\n",
        "  return numpy.frombuffer(bytestream.read(4), dtype=dt)[0]\n",
        "\n",
        "\n",
        "@deprecated(None, 'Please use tf.data to implement this functionality.')\n",
        "def _extract_images(f):\n",
        "  \"\"\"Extract the images into a 4D uint8 numpy array [index, y, x, depth].\n",
        "  Args:\n",
        "    f: A file object that can be passed into a gzip reader.\n",
        "  Returns:\n",
        "    data: A 4D uint8 numpy array [index, y, x, depth].\n",
        "  Raises:\n",
        "    ValueError: If the bytestream does not start with 2051.\n",
        "  \"\"\"\n",
        "  print('Extracting', f.name)\n",
        "  with gzip.GzipFile(fileobj=f) as bytestream:\n",
        "    magic = _read32(bytestream)\n",
        "    if magic != 2051:\n",
        "      raise ValueError('Invalid magic number %d in MNIST image file: %s' %\n",
        "                       (magic, f.name))\n",
        "    num_images = _read32(bytestream)\n",
        "    rows = _read32(bytestream)\n",
        "    cols = _read32(bytestream)\n",
        "    buf = bytestream.read(rows * cols * num_images)\n",
        "    data = numpy.frombuffer(buf, dtype=numpy.uint8)\n",
        "    data = data.reshape(num_images, rows, cols, 1)\n",
        "    return data\n",
        "\n",
        "\n",
        "@deprecated(None, 'Please use tf.one_hot on tensors.')\n",
        "def _dense_to_one_hot(labels_dense, num_classes):\n",
        "  \"\"\"Convert class labels from scalars to one-hot vectors.\"\"\"\n",
        "  num_labels = labels_dense.shape[0]\n",
        "  index_offset = numpy.arange(num_labels) * num_classes\n",
        "  labels_one_hot = numpy.zeros((num_labels, num_classes))\n",
        "  labels_one_hot.flat[index_offset + labels_dense.ravel()] = 1\n",
        "  return labels_one_hot\n",
        "\n",
        "\n",
        "@deprecated(None, 'Please use tf.data to implement this functionality.')\n",
        "def _extract_labels(f, one_hot=False, num_classes=10):\n",
        "  \"\"\"Extract the labels into a 1D uint8 numpy array [index].\n",
        "  Args:\n",
        "    f: A file object that can be passed into a gzip reader.\n",
        "    one_hot: Does one hot encoding for the result.\n",
        "    num_classes: Number of classes for the one hot encoding.\n",
        "  Returns:\n",
        "    labels: a 1D uint8 numpy array.\n",
        "  Raises:\n",
        "    ValueError: If the bystream doesn't start with 2049.\n",
        "  \"\"\"\n",
        "  print('Extracting', f.name)\n",
        "  with gzip.GzipFile(fileobj=f) as bytestream:\n",
        "    magic = _read32(bytestream)\n",
        "    if magic != 2049:\n",
        "      raise ValueError('Invalid magic number %d in MNIST label file: %s' %\n",
        "                       (magic, f.name))\n",
        "    num_items = _read32(bytestream)\n",
        "    buf = bytestream.read(num_items)\n",
        "    labels = numpy.frombuffer(buf, dtype=numpy.uint8)\n",
        "    if one_hot:\n",
        "      return _dense_to_one_hot(labels, num_classes)\n",
        "    return labels\n",
        "\n",
        "\n",
        "class _DataSet(object):\n",
        "  \"\"\"Container class for a _DataSet (deprecated).\n",
        "  THIS CLASS IS DEPRECATED.\n",
        "  \"\"\"\n",
        "\n",
        "  @deprecated(None, 'Please use alternatives such as official/mnist/_DataSet.py'\n",
        "              ' from tensorflow/models.')\n",
        "  def __init__(self,\n",
        "               images,\n",
        "               labels,\n",
        "               fake_data=False,\n",
        "               one_hot=False,\n",
        "               dtype=dtypes.float32,\n",
        "               reshape=True,\n",
        "               seed=None):\n",
        "    \"\"\"Construct a _DataSet.\n",
        "    one_hot arg is used only if fake_data is true.  `dtype` can be either\n",
        "    `uint8` to leave the input as `[0, 255]`, or `float32` to rescale into\n",
        "    `[0, 1]`.  Seed arg provides for convenient deterministic testing.\n",
        "    Args:\n",
        "      images: The images\n",
        "      labels: The labels\n",
        "      fake_data: Ignore inages and labels, use fake data.\n",
        "      one_hot: Bool, return the labels as one hot vectors (if True) or ints (if\n",
        "        False).\n",
        "      dtype: Output image dtype. One of [uint8, float32]. `uint8` output has\n",
        "        range [0,255]. float32 output has range [0,1].\n",
        "      reshape: Bool. If True returned images are returned flattened to vectors.\n",
        "      seed: The random seed to use.\n",
        "    \"\"\"\n",
        "    seed1, seed2 = random_seed.get_seed(seed)\n",
        "    # If op level seed is not set, use whatever graph level seed is returned\n",
        "    numpy.random.seed(seed1 if seed is None else seed2)\n",
        "    dtype = dtypes.as_dtype(dtype).base_dtype\n",
        "    if dtype not in (dtypes.uint8, dtypes.float32):\n",
        "      raise TypeError('Invalid image dtype %r, expected uint8 or float32' %\n",
        "                      dtype)\n",
        "    if fake_data:\n",
        "      self._num_examples = 10000\n",
        "      self.one_hot = one_hot\n",
        "    else:\n",
        "      assert images.shape[0] == labels.shape[0], (\n",
        "          'images.shape: %s labels.shape: %s' % (images.shape, labels.shape))\n",
        "      self._num_examples = images.shape[0]\n",
        "\n",
        "      # Convert shape from [num examples, rows, columns, depth]\n",
        "      # to [num examples, rows*columns] (assuming depth == 1)\n",
        "      if reshape:\n",
        "        assert images.shape[3] == 1\n",
        "        images = images.reshape(images.shape[0],\n",
        "                                images.shape[1] * images.shape[2])\n",
        "      if dtype == dtypes.float32:\n",
        "        # Convert from [0, 255] -> [0.0, 1.0].\n",
        "        images = images.astype(numpy.float32)\n",
        "        images = numpy.multiply(images, 1.0 / 255.0)\n",
        "    self._images = images\n",
        "    self._labels = labels\n",
        "    self._epochs_completed = 0\n",
        "    self._index_in_epoch = 0\n",
        "\n",
        "  @property\n",
        "  def images(self):\n",
        "    return self._images\n",
        "\n",
        "  @property\n",
        "  def labels(self):\n",
        "    return self._labels\n",
        "\n",
        "  @property\n",
        "  def num_examples(self):\n",
        "    return self._num_examples\n",
        "\n",
        "  @property\n",
        "  def epochs_completed(self):\n",
        "    return self._epochs_completed\n",
        "\n",
        "  def next_batch(self, batch_size, fake_data=False, shuffle=True):\n",
        "    \"\"\"Return the next `batch_size` examples from this data set.\"\"\"\n",
        "    if fake_data:\n",
        "      fake_image = [1] * 784\n",
        "      if self.one_hot:\n",
        "        fake_label = [1] + [0] * 9\n",
        "      else:\n",
        "        fake_label = 0\n",
        "      return [fake_image for _ in xrange(batch_size)\n",
        "             ], [fake_label for _ in xrange(batch_size)]\n",
        "    start = self._index_in_epoch\n",
        "    # Shuffle for the first epoch\n",
        "    if self._epochs_completed == 0 and start == 0 and shuffle:\n",
        "      perm0 = numpy.arange(self._num_examples)\n",
        "      numpy.random.shuffle(perm0)\n",
        "      self._images = self.images[perm0]\n",
        "      self._labels = self.labels[perm0]\n",
        "    # Go to the next epoch\n",
        "    if start + batch_size > self._num_examples:\n",
        "      # Finished epoch\n",
        "      self._epochs_completed += 1\n",
        "      # Get the rest examples in this epoch\n",
        "      rest_num_examples = self._num_examples - start\n",
        "      images_rest_part = self._images[start:self._num_examples]\n",
        "      labels_rest_part = self._labels[start:self._num_examples]\n",
        "      # Shuffle the data\n",
        "      if shuffle:\n",
        "        perm = numpy.arange(self._num_examples)\n",
        "        numpy.random.shuffle(perm)\n",
        "        self._images = self.images[perm]\n",
        "        self._labels = self.labels[perm]\n",
        "      # Start next epoch\n",
        "      start = 0\n",
        "      self._index_in_epoch = batch_size - rest_num_examples\n",
        "      end = self._index_in_epoch\n",
        "      images_new_part = self._images[start:end]\n",
        "      labels_new_part = self._labels[start:end]\n",
        "      return numpy.concatenate((images_rest_part, images_new_part),\n",
        "                               axis=0), numpy.concatenate(\n",
        "                                   (labels_rest_part, labels_new_part), axis=0)\n",
        "    else:\n",
        "      self._index_in_epoch += batch_size\n",
        "      end = self._index_in_epoch\n",
        "      return self._images[start:end], self._labels[start:end]\n",
        "\n",
        "\n",
        "@deprecated(None, 'Please write your own downloading logic.')\n",
        "def _maybe_download(filename, work_directory, source_url):\n",
        "  \"\"\"Download the data from source url, unless it's already here.\n",
        "  Args:\n",
        "      filename: string, name of the file in the directory.\n",
        "      work_directory: string, path to working directory.\n",
        "      source_url: url to download from if file doesn't exist.\n",
        "  Returns:\n",
        "      Path to resulting file.\n",
        "  \"\"\"\n",
        "  if not gfile.Exists(work_directory):\n",
        "    gfile.MakeDirs(work_directory)\n",
        "  filepath = os.path.join(work_directory, filename)\n",
        "  if not gfile.Exists(filepath):\n",
        "    urllib.request.urlretrieve(source_url, filepath)\n",
        "    with gfile.GFile(filepath) as f:\n",
        "      size = f.size()\n",
        "    print('Successfully downloaded', filename, size, 'bytes.')\n",
        "  return filepath\n",
        "\n",
        "\n",
        "@deprecated(None, 'Please use alternatives such as:'\n",
        "            ' tensorflow_datasets.load(\\'mnist\\')')\n",
        "def read_data_sets(train_dir,\n",
        "                   fake_data=False,\n",
        "                   one_hot=False,\n",
        "                   dtype=dtypes.float32,\n",
        "                   reshape=True,\n",
        "                   validation_size=5000,\n",
        "                   seed=None,\n",
        "                   source_url=DEFAULT_SOURCE_URL):\n",
        "  if fake_data:\n",
        "\n",
        "    def fake():\n",
        "      return _DataSet([], [],\n",
        "                      fake_data=True,\n",
        "                      one_hot=one_hot,\n",
        "                      dtype=dtype,\n",
        "                      seed=seed)\n",
        "\n",
        "    train = fake()\n",
        "    validation = fake()\n",
        "    test = fake()\n",
        "    return _Datasets(train=train, validation=validation, test=test)\n",
        "\n",
        "  if not source_url:  # empty string check\n",
        "    source_url = DEFAULT_SOURCE_URL\n",
        "\n",
        "  train_images_file = 'train-images-idx3-ubyte.gz'\n",
        "  train_labels_file = 'train-labels-idx1-ubyte.gz'\n",
        "  test_images_file = 't10k-images-idx3-ubyte.gz'\n",
        "  test_labels_file = 't10k-labels-idx1-ubyte.gz'\n",
        "\n",
        "  local_file = _maybe_download(train_images_file, train_dir,\n",
        "                               source_url + train_images_file)\n",
        "  with gfile.Open(local_file, 'rb') as f:\n",
        "    train_images = _extract_images(f)\n",
        "\n",
        "  local_file = _maybe_download(train_labels_file, train_dir,\n",
        "                               source_url + train_labels_file)\n",
        "  with gfile.Open(local_file, 'rb') as f:\n",
        "    train_labels = _extract_labels(f, one_hot=one_hot)\n",
        "\n",
        "  local_file = _maybe_download(test_images_file, train_dir,\n",
        "                               source_url + test_images_file)\n",
        "  with gfile.Open(local_file, 'rb') as f:\n",
        "    test_images = _extract_images(f)\n",
        "\n",
        "  local_file = _maybe_download(test_labels_file, train_dir,\n",
        "                               source_url + test_labels_file)\n",
        "  with gfile.Open(local_file, 'rb') as f:\n",
        "    test_labels = _extract_labels(f, one_hot=one_hot)\n",
        "\n",
        "  if not 0 <= validation_size <= len(train_images):\n",
        "    raise ValueError(\n",
        "        'Validation size should be between 0 and {}. Received: {}.'.format(\n",
        "            len(train_images), validation_size))\n",
        "\n",
        "  validation_images = train_images[:validation_size]\n",
        "  validation_labels = train_labels[:validation_size]\n",
        "  train_images = train_images[validation_size:]\n",
        "  train_labels = train_labels[validation_size:]\n",
        "\n",
        "  options = dict(dtype=dtype, reshape=reshape, seed=seed)\n",
        "\n",
        "  train = _DataSet(train_images, train_labels, **options)\n",
        "  validation = _DataSet(validation_images, validation_labels, **options)\n",
        "  test = _DataSet(test_images, test_labels, **options)\n",
        "\n",
        "  return _Datasets(train=train, validation=validation, test=test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3BshiewAbfPU",
        "scrolled": true,
        "colab": {}
      },
      "source": [
        "import numpy as np,sys,time\n",
        "from sklearn.utils import shuffle\n",
        "import matplotlib\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets\n",
        "import os\n",
        "import numpy as np,sys\n",
        "from sklearn.utils import shuffle\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.gridspec as gridspec"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZffTWt8ZbfPo",
        "colab": {}
      },
      "source": [
        "batch_size = 128\n",
        "def ReLu(x):\n",
        "    mask = (x>0) * 1.0\n",
        "    return (mask *x).reshape(x.shape[0],x.shape[1])\n",
        "def d_ReLu(x):\n",
        "    mask = (x>0) * 1.0\n",
        "    return mask.reshape(x.shape[0],x.shape[1])\n",
        "\n",
        "def Lrelu(x):\n",
        "#    alpha = 0.0001\n",
        "#    return np.where(x > 0, x, x * alpha) \n",
        "    y1 = ((x > 0) * x)                                                 \n",
        "    y2 = ((x <= 0) * x * 0.01)                                         \n",
        "    return (y1 + y2)\n",
        "def d_Lrelu(x,alpha = 0.01):\n",
        "  dx = np.ones_like(x)\n",
        "  dx[x < 0] = alpha\n",
        "  return dx\n",
        "def arctan(x):\n",
        "    return np.arctan(x)\n",
        "def d_arctan(x):\n",
        "    return (1 / (1 + x ** 2))\n",
        "\n",
        "def log(x):\n",
        "    return (1 / ( 1+ np.exp(-1*x)))\n",
        "def d_log(x):\n",
        "    return log(x) * (1 - log(x))\n",
        "\n",
        "def tanh(x):\n",
        "    return np.tanh(x).reshape(x.shape[0],x.shape[1])\n",
        "def d_tanh(x):\n",
        "    return (1 - np.tanh(x) ** 2)\n",
        "\n",
        "def plot(samples, title):\n",
        "    print(len(samples))\n",
        "    plt.figure(figsize=(4, 4))\n",
        "    gs = gridspec.GridSpec(10, 10)\n",
        "    gs.update(wspace=0.05, hspace=0.05)\n",
        "\n",
        "    for i, sample in enumerate(samples):\n",
        "        ax = plt.subplot(gs[i])\n",
        "        plt.axis('off')\n",
        "        ax.set_xticklabels([])\n",
        "        ax.set_yticklabels([])\n",
        "        ax.set_aspect('equal')\n",
        "        plt.title(title)\n",
        "        sample_resized = sample.reshape(batch_size, 28, 28)\n",
        "        # plt.imshow(sample.reshape(5, 28, 28), cmap='Greys_r')\n",
        "        plt.imshow(sample_resized[0], cmap='Greys_r')\n",
        "    plt.show()\n",
        "def plots(samples, title):\n",
        "    print(len(samples))\n",
        "    plt.figure()\n",
        "    gs = gridspec.GridSpec(4,4)\n",
        "    gs.update(wspace=0.05, hspace=0.05)\n",
        "    for i, sample in enumerate(samples):\n",
        "        ax = plt.subplot(gs[i])\n",
        "        plt.axis('off')\n",
        "        ax.set_xticklabels([])\n",
        "        ax.set_yticklabels([])\n",
        "        ax.set_aspect('equal')\n",
        "        sample_resized = sample.reshape(28, 28)\n",
        "        # plt.imshow(sample.reshape(5, 28, 28), cmap='Greys_r')\n",
        "        plt.imshow(sample_resized, cmap='Greys_r')\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "aPAKJeiAbfP4",
        "colab": {}
      },
      "source": [
        "# random_numer = 1222 #int(input(\"Please Input a Random Number to Seed\"))\n",
        "# np.random.seed(random_numer)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ulnl3-qYbfQH",
        "outputId": "5393373b-b4d6-4954-c3b3-5729f74e36c8",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        }
      },
      "source": [
        "# 1. Load Data and declare hyper\n",
        "print('--------- Load Data ----------')\n",
        "mnist = read_data_sets('MNIST_data', one_hot=False)\n",
        "temp = mnist.test\n",
        "images, labels = temp.images, temp.labels\n",
        "images, labels = shuffle(np.asarray(images),np.asarray(labels))\n",
        "num_epoch = 50\n",
        "learing_rate = 0.00002\n",
        "learing_rate2 = 0.00002\n",
        "G_input = 100\n",
        "hidden_input,hidden_input2,hidden_input3 = 128,256,346\n",
        "hidden_input4,hidden_input5,hidden_input6 = 480,560,686\n",
        "hidden_input7,hidden_input8,hidden_input9 = 800,1020,1400\n",
        "hidden_input10 = 1800"
      ],
      "execution_count": 624,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------- Load Data ----------\n",
            "WARNING:tensorflow:From <ipython-input-624-7ac0ac0f4408>:2: read_data_sets (from __main__) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as: tensorflow_datasets.load('mnist')\n",
            "WARNING:tensorflow:From <ipython-input-621-03a9021563ce>:267: _maybe_download (from __main__) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please write your own downloading logic.\n",
            "WARNING:tensorflow:From <ipython-input-621-03a9021563ce>:269: _extract_images (from __main__) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
            "WARNING:tensorflow:From <ipython-input-621-03a9021563ce>:274: _extract_labels (from __main__) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use tf.data to implement this functionality.\n",
            "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
            "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
            "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
            "WARNING:tensorflow:From <ipython-input-621-03a9021563ce>:298: _DataSet.__init__ (from __main__) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use alternatives such as official/mnist/_DataSet.py from tensorflow/models.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "nh1txYT4bfQT",
        "outputId": "9f386708-a69b-4c10-8f66-d32af4ac4698",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print('--------- Declare Hyper Parameters ----------')\n",
        "D_W1 = np.random.normal(size=(784,hidden_input),scale=(1. / np.sqrt(784 / 2.)))   *0.002\n",
        "# D_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))       *0.002\n",
        "D_b1 = np.zeros(hidden_input).reshape(1,hidden_input)\n",
        "\n",
        "D_W2 = np.random.normal(size=(hidden_input,hidden_input2),scale=(1. / np.sqrt(hidden_input / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "D_b2 = np.zeros(hidden_input2).reshape(1,hidden_input2)\n",
        "\n",
        "D_W3 = np.random.normal(size=(hidden_input2,hidden_input3),scale=(1. / np.sqrt(hidden_input2 / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "D_b3 = np.zeros(hidden_input3).reshape(1,hidden_input3)\n",
        "\n",
        "D_W4 = np.random.normal(size=(hidden_input3,1),scale=(1. / np.sqrt(hidden_input / 2.)))     *0.002\n",
        "# D_b2 = np.random.normal(size=(1),scale=(1. / np.sqrt(1 / 2.)))           *0.002\n",
        "D_b4 = np.zeros(1).reshape(1,1)\n",
        "\n",
        "\n",
        "G_W1 = np.random.normal(size=(G_input,hidden_input),scale=(1. / np.sqrt(G_input / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "G_b1 = np.zeros(hidden_input).reshape(1,hidden_input)\n",
        "\n",
        "G_W2 = np.random.normal(size=(hidden_input,hidden_input2),scale=(1. / np.sqrt(hidden_input / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "G_b2 = np.zeros(hidden_input2).reshape(1,hidden_input2)\n",
        "\n",
        "G_W3 = np.random.normal(size=(hidden_input2,hidden_input3),scale=(1. / np.sqrt(hidden_input2 / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "G_b3 = np.zeros(hidden_input3).reshape(1,hidden_input3)\n",
        "\n",
        "G_W4 = np.random.normal(size=(hidden_input3,hidden_input4),scale=(1. / np.sqrt(hidden_input3 / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "G_b4 = np.zeros(hidden_input4).reshape(1,hidden_input4)\n",
        "\n",
        "G_W5 = np.random.normal(size=(hidden_input4,hidden_input5),scale=(1. / np.sqrt(hidden_input4 / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "G_b5 = np.zeros(hidden_input5).reshape(1,hidden_input5)\n",
        "\n",
        "G_W6 = np.random.normal(size=(hidden_input5,hidden_input6),scale=(1. / np.sqrt(hidden_input5 / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "G_b6 = np.zeros(hidden_input6).reshape(1,hidden_input6)\n",
        "\n",
        "G_W7 = np.random.normal(size=(hidden_input6,hidden_input7),scale=(1. / np.sqrt(hidden_input5 / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "G_b7 = np.zeros(hidden_input7).reshape(1,hidden_input7)\n",
        "\n",
        "G_W8 = np.random.normal(size=(hidden_input7,hidden_input8),scale=(1. / np.sqrt(hidden_input5 / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "G_b8 = np.zeros(hidden_input8).reshape(1,hidden_input8)\n",
        "\n",
        "G_W9 = np.random.normal(size=(hidden_input8,hidden_input9),scale=(1. / np.sqrt(hidden_input5 / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "G_b9 = np.zeros(hidden_input9).reshape(1,hidden_input9)\n",
        "\n",
        "G_W10 = np.random.normal(size=(hidden_input9,hidden_input10),scale=(1. / np.sqrt(hidden_input5 / 2.)))   *0.002\n",
        "# G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002\n",
        "G_b10 = np.zeros(hidden_input10).reshape(1,hidden_input10)\n",
        "\n",
        "G_W11 = np.random.normal(size=(hidden_input10,784),scale=(1. / np.sqrt(hidden_input6 / 2.)))  *0.002\n",
        "# G_b2 = np.random.normal(size=(784),scale=(1. / np.sqrt(784 / 2.)))      *0.002\n",
        "G_b11 = np.zeros(784).reshape(1,784)\n",
        "# 2. Declare Weights\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "grad_D_w1 = np.zeros(784*hidden_input).reshape(784,hidden_input)\n",
        "grad_D_b1 = np.zeros(hidden_input).reshape(1,hidden_input)\n",
        "\n",
        "grad_D_w2 = np.zeros(hidden_input*hidden_input2).reshape(hidden_input, hidden_input2)\n",
        "grad_D_b2 = np.zeros(hidden_input2).reshape(1,hidden_input2)\n",
        "\n",
        "grad_D_w3 = np.zeros(hidden_input2*hidden_input3).reshape(hidden_input2, hidden_input3)\n",
        "grad_D_b3 = np.zeros(hidden_input3).reshape(1,hidden_input3)\n",
        "\n",
        "grad_D_w4 = np.zeros(hidden_input3).reshape(hidden_input3,1)\n",
        "grad_D_b4 = np.zeros(1).reshape(1,1)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "grad_g_w1 = np.zeros(G_input*hidden_input).reshape(G_input, hidden_input)\n",
        "grad_g_b1 = np.zeros(hidden_input).reshape(1,hidden_input)\n",
        "\n",
        "grad_g_w2 = np.zeros(hidden_input*hidden_input2).reshape(hidden_input, hidden_input2)\n",
        "grad_g_b2 = np.zeros(hidden_input2).reshape(1,hidden_input2)\n",
        "\n",
        "grad_g_w3 = np.zeros(hidden_input2*hidden_input3).reshape(hidden_input2, hidden_input3)\n",
        "grad_g_b3 = np.zeros(hidden_input3).reshape(1,hidden_input3)\n",
        "\n",
        "grad_g_w4 = np.zeros(hidden_input3*hidden_input4).reshape(hidden_input3, hidden_input4)\n",
        "grad_g_b4 = np.zeros(hidden_input4).reshape(1,hidden_input4)\n",
        "\n",
        "grad_g_w5 = np.zeros(hidden_input4*hidden_input5).reshape(hidden_input4, hidden_input5)\n",
        "grad_g_b5 = np.zeros(hidden_input5).reshape(1,hidden_input5)\n",
        "\n",
        "grad_g_w6 = np.zeros(hidden_input5*hidden_input6).reshape(hidden_input5, hidden_input6)\n",
        "grad_g_b6 = np.zeros(hidden_input6).reshape(1,hidden_input6)\n",
        "\n",
        "grad_g_w7 = np.zeros(hidden_input6*hidden_input7).reshape(hidden_input6, hidden_input7)\n",
        "grad_g_b7 = np.zeros(hidden_input7).reshape(1,hidden_input7)\n",
        "\n",
        "grad_g_w8 = np.zeros(hidden_input7*hidden_input8).reshape(hidden_input7, hidden_input8)\n",
        "grad_g_b8 = np.zeros(hidden_input8).reshape(1,hidden_input8)\n",
        "\n",
        "grad_g_w9 = np.zeros(hidden_input8*hidden_input9).reshape(hidden_input8, hidden_input9)\n",
        "grad_g_b9 = np.zeros(hidden_input9).reshape(1,hidden_input9)\n",
        "\n",
        "grad_g_w10 = np.zeros(hidden_input9*hidden_input10).reshape(hidden_input9, hidden_input10)\n",
        "grad_g_b10 = np.zeros(hidden_input10).reshape(1,hidden_input10)\n",
        "\n",
        "grad_g_w11 = np.zeros(hidden_input10*784).reshape(hidden_input10, 784)\n",
        "grad_g_b11 = np.zeros(784).reshape(1,784)\n",
        "\n"
      ],
      "execution_count": 633,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------- Declare Hyper Parameters ----------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "efhLIYTtbfQn",
        "colab": {}
      },
      "source": [
        "\n",
        "# 3. For Adam Optimzier\n",
        "m1w1,m1b1 = 0,0\n",
        "m2w2,m2b2 = 0,0\n",
        "m3w3,m3b3 = 0,0\n",
        "m4w4,m4b4 = 0,0\n",
        "\n",
        "v1w1,v1b1 = 0,0\n",
        "v2w2,v2b2 = 0,0\n",
        "v3w3,v3b3 = 0,0\n",
        "v4w4,v4b4 = 0,0\n",
        "\n",
        "v5,m5 = 0,0\n",
        "v6,m6 = 0,0\n",
        "v7,m7 = 0,0\n",
        "v8,m8 = 0,0\n",
        "v9,m9 = 0,0\n",
        "v10,m10 = 0,0\n",
        "v11,m11 = 0,0\n",
        "v12,m12 = 0,0\n",
        "v13,m13 = 0,0\n",
        "v14,m14 = 0,0\n",
        "v15,m15 = 0,0\n",
        "v16,m16 = 0,0\n",
        "v17,m17 = 0,0\n",
        "v18,m18 = 0,0\n",
        "v19,m19 = 0,0\n",
        "v20,m20 = 0,0\n",
        "v21,m21 = 0,0\n",
        "v22,m22 = 0,0\n",
        "v23,m23 = 0,0\n",
        "v24,m24 = 0,0\n",
        "v25,m25 = 0,0\n",
        "v26,m26 = 0,0\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "iYhaQBh8bfQy",
        "colab": {}
      },
      "source": [
        "beta_1,beta_2,eps = 0.5,0.5,0.000001\n",
        "D_list_cost = []\n",
        "G_list_cost = []\n",
        "forwardPropGen1 = []\n",
        "forwardPropGen2 = []\n",
        "forwardPropDisc1 = []\n",
        "forwardPropDisc2 = []\n",
        "forwardPropDisc3 = []\n",
        "backwardPropGen = []\n",
        "backwardPropDisc = []\n",
        "gradientComputeDisc1 = []\n",
        "gradientComputeDisc2 = []\n",
        "gradientComputeGen = []\n",
        "def dropout1():\n",
        "    return np.random.normal(size=(hidden_input2,),scale=0.2)\n",
        "def dropout2():\n",
        "    return np.random.normal(size=(hidden_input3,),scale=0.2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-BDK4OlWLGM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def dupli_bias(bias, batch_size):\n",
        "  return np.tile(bias, (batch_size, 1)).reshape(batch_size,bias.shape[1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9TaGvlfRiKC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.autograd import Variable\n",
        "import numpy as np\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "from torch.optim import Optimizer\n",
        "import torchvision.utils as vutils\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tltNk0tcR44t",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "75892ed1-4338-4e8e-e527-42abd4988729"
      },
      "source": [
        "D_b1.shape"
      ],
      "execution_count": 638,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 128)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 638
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4-ojuMORpag",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "    transforms.Normalize([0.5],[0.5])])\n",
        "#Make range of image from 0-1 to -1,1. So that you can use tanh while producing fake image in Generator(tanh rules!!)\n",
        "\n",
        "###########CHANGE BATCH SIZE HERE\n",
        "batch_size = 128\n",
        "trainset = torchvision.datasets.MNIST(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,\n",
        "                                          shuffle=True, num_workers=1,pin_memory=True)\n",
        "\n",
        "testset = torchvision.datasets.MNIST(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,\n",
        "                                         shuffle=False, num_workers=1,pin_memory=True)\n",
        "device = torch.device(\"cuda:0\" if (torch.cuda.is_available()) else \"cpu\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sIXkEq74bfQ7",
        "outputId": "3c2256f1-dcd2-4078-8524-d4da4439e4ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print('--------- Started Training ----------')\n",
        "current_images = []\n",
        "current_fake_images = []\n",
        "batch_cost = []\n",
        "\n",
        "for iter in range(1,1000):\n",
        "  for i,data in enumerate(trainloader):\n",
        "    inp,op = data\n",
        "    inp = inp.to(device)\n",
        "    current_images = np.asarray(inp.reshape(inp.shape[0],28*28))\n",
        "    # current_images = np.array([])\n",
        "    # current_fake_images = np.array([])\n",
        "    # # print(current_fake_images.shape)\n",
        "    # random_int = [ np.random.randint(len(images) - 5) for i in range(batch_size) ]\n",
        "    \n",
        "    # current_image = [np.expand_dims(images[i],axis=0) for i in random_int]\n",
        "    \n",
        "    # current_images = np.vstack(current_image)\n",
        "\n",
        "    # Func: Generate The first Fake Data\n",
        "    # fake_images = []\n",
        "    \n",
        "    # for i in range(5):\n",
        "    Z = np.random.normal(0., 1., size=[batch_size, G_input])\n",
        "    \n",
        "    tic = time.time()\n",
        "    Gl1 = Z.dot(G_W1) + dupli_bias(G_b1, batch_size)\n",
        "    Gl1A = Lrelu(Gl1)\n",
        "    Gl2 = Gl1A.dot(G_W2) + dupli_bias(G_b2, batch_size)\n",
        "    Gl2A = Lrelu(Gl2)\n",
        "    Gl3 = Gl2A.dot(G_W3) + dupli_bias(G_b3, batch_size)\n",
        "    Gl3A = Lrelu(Gl3)\n",
        "    Gl4 = Gl3A.dot(G_W4) + dupli_bias(G_b4, batch_size)\n",
        "    Gl4A = Lrelu(Gl4)\n",
        "    Gl5 = Gl4A.dot(G_W5) + dupli_bias(G_b5, batch_size)\n",
        "    Gl5A = Lrelu(Gl5)\n",
        "    Gl6 = Gl5A.dot(G_W6) + dupli_bias(G_b6, batch_size)\n",
        "    Gl6A = Lrelu(Gl6)\n",
        "    Gl7 = Gl6A.dot(G_W7) + dupli_bias(G_b7, batch_size)\n",
        "    Gl7A = Lrelu(Gl7)\n",
        "    Gl8 = Gl7A.dot(G_W8) + dupli_bias(G_b8, batch_size)\n",
        "    Gl8A = Lrelu(Gl8)\n",
        "    Gl9 = Gl8A.dot(G_W9) + dupli_bias(G_b9, batch_size)\n",
        "    Gl9A = Lrelu(Gl9)\n",
        "    Gl10 = Gl9A.dot(G_W10) + dupli_bias(G_b10, batch_size)\n",
        "    Gl10A = Lrelu(Gl10)\n",
        "    Gl11 = Gl10A.dot(G_W11) + dupli_bias(G_b11, batch_size)\n",
        "    current_fake_data = log(Gl11)\n",
        "    toc = time.time()\n",
        "    \n",
        "    forwardPropGen1.append((toc-tic)*1000)\n",
        "    #print(\"stuff: \", current_fake_data.shape)\n",
        "    # fake_images.append(current_fake_data)\n",
        "    # print(\"shape of fake image: \", current_fake_data.shape)\n",
        "    # current_fake_images.append(current_fake_data)\n",
        "\n",
        "    # current_fake_images = np.vstack((fake_images[0], fake_images[1], fake_images[2], fake_images[3], fake_images[4]))\n",
        "    # print(\"shape of fake image: \", current_fake_images.shape)\n",
        "\n",
        "    # Func: Forward Feed for Real data\n",
        "    # Dl1_r = current_image.dot(D_W1) + D_b1\n",
        "    tic = time.time()\n",
        "    Dl1_r = current_images.dot(D_W1) + dupli_bias(D_b1, batch_size)\n",
        "    Dl1_rA = Lrelu(Dl1_r)\n",
        "    Dl2_r = Dl1_rA.dot(D_W2) + dupli_bias(D_b2, batch_size)  + dropout1()\n",
        "    Dl2_rA = Lrelu(Dl2_r)\n",
        "    Dl3_r = Dl2_rA.dot(D_W3) + dupli_bias(D_b3, batch_size) + dropout2()\n",
        "    Dl3_rA = Lrelu(Dl3_r)\n",
        "    Dl4_r = Dl3_rA.dot(D_W4) + dupli_bias(D_b4, batch_size)\n",
        "    Dl4_rA = log(Dl4_r)\n",
        "    toc=time.time()\n",
        "    forwardPropDisc1.append((toc-tic)*1000)\n",
        "    # Func: Forward Feed for Fake Data\n",
        "    tic = time.time()\n",
        "    Dl1_f = current_fake_data.dot(D_W1) + dupli_bias(D_b1, batch_size)\n",
        "    Dl1_fA = Lrelu(Dl1_f)\n",
        "    Dl2_f = Dl1_fA.dot(D_W2) + dupli_bias(D_b2, batch_size) + dropout1()\n",
        "    Dl2_fA = Lrelu(Dl2_f)\n",
        "    Dl3_f = Dl2_fA.dot(D_W3) + dupli_bias(D_b3, batch_size) + dropout2()\n",
        "    Dl3_fA = Lrelu(Dl3_f)\n",
        "    Dl4_f = Dl3_fA.dot(D_W4) + dupli_bias(D_b4, batch_size)\n",
        "    Dl4_fA = log(Dl4_f)\n",
        "    toc = time.time()\n",
        "    forwardPropDisc2.append((toc-tic)*1000)\n",
        "    # Func: Cost D\n",
        "    \n",
        "\n",
        "    D_cost = np.mean(-( np.log(Dl4_rA) +  np.log(1.0- Dl4_fA)))\n",
        "    D_list_cost.append(D_cost)\n",
        "    # batch_cost.append(D_cost[0[0]])\n",
        "    fBatch = Dl4_fA\n",
        "    rBatch = Dl4_rA\n",
        "    totgradFake = 0\n",
        "    tic = time.time()\n",
        "    for batch_item in range(0,batch_size):\n",
        "\n",
        "      grad_r_w4_part_1 =  (-1/((1-rBatch[batch_item])+ eps)).reshape(1,1)\n",
        "      grad_r_w4_part_2 =  d_log(Dl4_r[batch_item]).reshape(1,1)\n",
        "      grad_r_w4_part_3 =   Dl3_rA[batch_item].reshape(1,hidden_input3)\n",
        "      grad_r_w4 =     (grad_r_w4_part_3.T.dot(grad_r_w4_part_1 * grad_r_w4_part_2)).reshape(hidden_input3,1) \n",
        "      grad_r_b4 = grad_r_w4_part_1 * grad_r_w4_part_2\n",
        "\n",
        "      grad_D_w4 = np.add(grad_D_w4,grad_r_w4)\n",
        "      grad_D_b4 = np.add(grad_D_b4,grad_r_b4)\n",
        "\n",
        "      grad_r_w3_part_1 =  (grad_r_w4_part_1 * grad_r_w4_part_2).dot(D_W4.T)\n",
        "      grad_r_w3_part_2 =  d_Lrelu(Dl3_r[batch_item])\n",
        "      grad_r_w3_part_3 =   Dl2_rA[batch_item].reshape(1,hidden_input2)\n",
        "      grad_r_w3 =       grad_r_w3_part_3.T.dot(grad_r_w3_part_1 * grad_r_w3_part_2) \n",
        "      grad_r_b3 =      grad_r_w3_part_1 * grad_r_w3_part_2\n",
        "      \n",
        "      grad_D_w3 = np.add(grad_D_w3, grad_r_w3)\n",
        "      grad_D_b3 = np.add(grad_D_b3, grad_r_b3)\n",
        "\n",
        "      grad_r_w2_part_1 = (grad_r_w3_part_1 * grad_r_w3_part_2).dot(D_W3.T)\n",
        "      grad_r_w2_part_2 = d_Lrelu(Dl2_r[batch_item])\n",
        "      grad_r_w2_part_3 = Dl1_rA[batch_item].reshape(1, hidden_input)\n",
        "      grad_r_w2 = grad_r_w2_part_3.T.dot(grad_r_w2_part_1 * grad_r_w2_part_2)\n",
        "      grad_r_b2 = (grad_r_w2_part_1 * grad_r_w2_part_2)\n",
        "\n",
        "      grad_D_w2 = np.add(grad_D_w2, grad_r_w2)\n",
        "      grad_D_b2 = np.add(grad_D_b2, grad_r_b2)\n",
        "      \n",
        "      grad_r_w1_part_1 = (grad_r_w2_part_1 * grad_r_w2_part_2).dot(D_W2.T)\n",
        "      grad_r_w1_part_2 = d_Lrelu(Dl1_r[batch_item])\n",
        "      grad_r_w1_part_3 = current_images[batch_item].reshape(1,784)\n",
        "      grad_r_w1 = grad_r_w1_part_3.T.dot(grad_r_w1_part_1 * grad_r_w1_part_2)\n",
        "      grad_r_b1 = grad_r_w1_part_1 * grad_r_w1_part_2\n",
        "\n",
        "      grad_D_w1 = np.add(grad_D_w1, grad_r_w1)\n",
        "      grad_D_b1 = np.add(grad_D_b1, grad_r_b1)\n",
        "\n",
        "    toc = time.time()\n",
        "    \n",
        "    gradientComputeDisc1.append((toc-tic)*1000)\n",
        "\n",
        "    tic = time.time()\n",
        "    for batch_item in range(0,batch_size):\n",
        "\n",
        "      grad_f_w4_part_1 =  (-1/((1-fBatch[batch_item])+ eps)).reshape(1,1)\n",
        "      grad_f_w4_part_2 =  d_log(Dl4_f[batch_item]).reshape(1,1)\n",
        "      grad_f_w4_part_3 =   Dl3_rA[batch_item].reshape(1,hidden_input3)\n",
        "      grad_f_w4 =     (grad_f_w4_part_3.T.dot(grad_f_w4_part_1 * grad_f_w4_part_2)).reshape(hidden_input3,1) \n",
        "      grad_f_b4 = grad_r_w4_part_1 * grad_r_w4_part_2\n",
        "\n",
        "      grad_D_w4 = np.add(grad_D_w4,grad_f_w4)\n",
        "      grad_D_b4 = np.add(grad_D_b4,grad_f_b4)\n",
        "\n",
        "\n",
        "      grad_f_w3_part_1 =  (grad_f_w4_part_1 * grad_f_w4_part_2).dot(D_W4.T)\n",
        "      grad_f_w3_part_2 =  d_Lrelu(Dl3_f[batch_item])\n",
        "      grad_f_w3_part_3 =   Dl2_fA[batch_item].reshape(1,hidden_input2)\n",
        "      grad_f_w3 =       grad_f_w3_part_3.T.dot(grad_f_w3_part_1 * grad_f_w3_part_2) \n",
        "      grad_f_b3 =      grad_f_w3_part_1 * grad_f_w3_part_2\n",
        "      \n",
        "      grad_D_w3 = np.add(grad_D_w3, grad_f_w3)\n",
        "      grad_D_b3 = np.add(grad_D_b3, grad_f_b3)\n",
        "\n",
        "      grad_f_w2_part_1 = (grad_f_w3_part_1 * grad_f_w3_part_2).dot(D_W3.T)\n",
        "      grad_f_w2_part_2 = d_Lrelu(Dl2_f[batch_item])\n",
        "      grad_f_w2_part_3 = Dl1_fA[batch_item].reshape(1, hidden_input)\n",
        "      grad_f_w2 = grad_f_w2_part_3.T.dot(grad_f_w2_part_1 * grad_f_w2_part_2)\n",
        "      grad_f_b2 = (grad_f_w2_part_1 * grad_f_w2_part_2)\n",
        "\n",
        "      grad_D_w2 = np.add(grad_D_w2, grad_r_w2)\n",
        "      grad_D_b2 = np.add(grad_D_b2, grad_r_b2)\n",
        "      \n",
        "      grad_f_w1_part_1 = (grad_f_w2_part_1 * grad_f_w2_part_2).dot(D_W2.T)\n",
        "      grad_f_w1_part_2 = d_Lrelu(Dl1_f[batch_item])\n",
        "      grad_f_w1_part_3 = current_images[batch_item].reshape(1,784)\n",
        "      grad_f_w1 = grad_f_w1_part_3.T.dot(grad_f_w1_part_1 * grad_f_w1_part_2)\n",
        "      grad_f_b1 = grad_f_w1_part_1 * grad_f_w1_part_2\n",
        "\n",
        "      grad_D_w1 = np.add(grad_D_w1, grad_f_w1)\n",
        "      grad_D_b1 = np.add(grad_D_b1, grad_f_b1)\n",
        "\n",
        "    toc = time.time()\n",
        "    \n",
        "    gradientComputeDisc2.append((toc-tic)*1000)\n",
        "    #LATER\n",
        "    \n",
        "    \n",
        "    tic = time.time()\n",
        "    grad_w1 = grad_D_w1\n",
        "    grad_w2 =grad_D_w2\n",
        "    grad_w3 =grad_D_w3\n",
        "    grad_w4 =grad_D_w4\n",
        "\n",
        "    grad_b1 =grad_D_b1\n",
        "    grad_b2 =grad_D_b2\n",
        "    grad_b3 =grad_D_b3\n",
        "    grad_b4 =grad_D_b4\n",
        "\n",
        "\n",
        "    print(\"W4 before\", D_W4.shape)\n",
        "    # ---- Update Gradient ----\n",
        "    m1w1 = beta_1 * m1w1 + (1 - beta_1) * grad_w1\n",
        "    v1w1 = beta_2 * v1w1 + (1 - beta_2) * grad_w1 ** 2\n",
        "    \n",
        "    m2w2 = beta_1 * m2w2 + (1 - beta_1) * grad_w2\n",
        "    v2w2 = beta_2 * v2w2 + (1 - beta_2) * grad_w2 ** 2\n",
        "    \n",
        "    m3w3 = beta_1 * m3w3 + (1 - beta_1) * grad_w3\n",
        "    v3w3 = beta_2 * v3w3 + (1 - beta_2) * grad_w3 ** 2\n",
        "    \n",
        "    m4w4 = beta_1 * m4w4 + (1 - beta_1) * grad_w4\n",
        "    v4w4 = beta_2 * v4w4 + (1 - beta_2) * grad_w4 ** 2\n",
        "    \n",
        "    m1b1 = beta_1 * m1b1 + (1 - beta_1) * grad_b1\n",
        "    v1b1 = beta_2 * v1b1 + (1 - beta_2) * grad_b1 ** 2\n",
        "    \n",
        "    m2b2 = beta_1 * m2b2 + (1 - beta_1) * grad_b2\n",
        "    v2b2 = beta_2 * v2b2 + (1 - beta_2) * grad_b2 ** 2\n",
        "    \n",
        "    m3b3 = beta_1 * m3b3 + (1 - beta_1) * grad_b3\n",
        "    v3b3 = beta_2 * v3b3 + (1 - beta_2) * grad_b3 ** 2\n",
        "    \n",
        "    m4b4 = beta_1 * m4b4 + (1 - beta_1) * grad_b4\n",
        "    v4b4 = beta_2 * v4b4 + (1 - beta_2) * grad_b4 ** 2\n",
        "\n",
        "    print(\"M$V$\",m4w4.shape)\n",
        "    D_W1 = D_W1 - (learing_rate2 / (np.sqrt(v1w1 /(1-beta_2**iter )) + eps)) * (m1w1/(1-beta_1**iter)  + eps)\n",
        "    D_b1 = D_b1 - (learing_rate2 / (np.sqrt(v1b1 /(1-beta_2**iter )) + eps)) * (m1b1/(1-beta_1**iter)  + eps)\n",
        "        \n",
        "    D_W2 = D_W2 - (learing_rate2 / (np.sqrt(v2w2 /(1-beta_2**iter )) + eps)) * (m2w2/(1-beta_1**iter)  + eps)\n",
        "    D_b2 = D_b2 - (learing_rate2 / (np.sqrt(v2b2 /(1-beta_2**iter )) + eps)) * (m2b2/(1-beta_1**iter)  + eps)\n",
        "    \n",
        "  \n",
        "    D_W3 = D_W3 - (learing_rate2 / (np.sqrt(v3w3 /(1-beta_2**iter )) + eps)) * (m3w3/(1-beta_1**iter)  + eps)\n",
        "    D_b3 = D_b3 - (learing_rate2 / (np.sqrt(v3b3 /(1-beta_2**iter )) + eps)) * (m3b3/(1-beta_1**iter)  + eps)\n",
        "\n",
        "    D_W4 = D_W4 - (learing_rate2 / (np.sqrt(v4w4 /(1-beta_2**iter )) + eps)) * (m4w4/(1-beta_1**iter)  + eps)\n",
        "    D_b4 = D_b4 - (learing_rate2 / (np.sqrt(v4b4 /(1-beta_2**iter )) + eps)) * (m4b4/(1-beta_1**iter)  + eps)\n",
        "\n",
        "    print(\"W4 after\", D_W4.shape)\n",
        "\n",
        "    toc = time.time()\n",
        "    backwardPropDisc.append((toc-tic)*1000)\n",
        "    #current_images = np.delete(current_images, [0,1,2,3,4])\n",
        "    # current_fake_images = np.delete(current_fake_images, [0,1,2,3,4])\n",
        "    # batch_cost = []\n",
        "\n",
        "    # Func: Forward Feed for G\n",
        "    # fake_images = []\n",
        "    if(i%3==0):\n",
        "        tic = time.time()\n",
        "\n",
        "        Gl1 = Z.dot(G_W1) + dupli_bias(G_b1, batch_size)\n",
        "        Gl1A = Lrelu(Gl1)\n",
        "        Gl2 = Gl1A.dot(G_W2) + dupli_bias(G_b2, batch_size)\n",
        "        Gl2A = Lrelu(Gl2)\n",
        "        Gl3 = Gl2A.dot(G_W3) + dupli_bias(G_b3, batch_size)\n",
        "        Gl3A = Lrelu(Gl3)\n",
        "        Gl4 = Gl3A.dot(G_W4) + dupli_bias(G_b4, batch_size)\n",
        "        Gl4A = Lrelu(Gl4)\n",
        "        Gl5 = Gl4A.dot(G_W5) + dupli_bias(G_b5, batch_size)\n",
        "        Gl5A = Lrelu(Gl5)\n",
        "        Gl6 = Gl5A.dot(G_W6) + dupli_bias(G_b6, batch_size)\n",
        "        Gl6A = Lrelu(Gl6)\n",
        "        Gl7 = Gl6A.dot(G_W7) + dupli_bias(G_b7, batch_size)\n",
        "        Gl7A = Lrelu(Gl7)\n",
        "        Gl8 = Gl7A.dot(G_W8) + dupli_bias(G_b8, batch_size)\n",
        "        Gl8A = Lrelu(Gl8)\n",
        "        Gl9 = Gl8A.dot(G_W9) + dupli_bias(G_b9, batch_size)\n",
        "        Gl9A = Lrelu(Gl9)\n",
        "        Gl10 = Gl9A.dot(G_W10) + dupli_bias(G_b10, batch_size)\n",
        "        Gl10A = Lrelu(Gl10)\n",
        "        Gl11 = Gl10A.dot(G_W11) + dupli_bias(G_b11, batch_size)\n",
        "        current_fake_data = log(Gl11)\n",
        "\n",
        "        toc = time.time()\n",
        "\n",
        "        forwardPropGen2.append((toc-tic)*1000)\n",
        "\n",
        "        tic = time.time()\n",
        "        Dl1 = current_fake_data.dot(D_W1) + dupli_bias(D_b1, batch_size)\n",
        "        Dl1_A = Lrelu(Dl1)\n",
        "        Dl2 = Dl1_A.dot(D_W2) + dupli_bias(D_b2, batch_size) + dropout1()\n",
        "        Dl2_A = Lrelu(Dl2)\n",
        "        Dl3 = Dl2_A.dot(D_W3) + dupli_bias(D_b3, batch_size) + dropout2()\n",
        "        Dl3_A = Lrelu(Dl3)\n",
        "        Dl4 = Dl3_A.dot(D_W4) + dupli_bias(D_b4, batch_size)\n",
        "        Dl4_A = log(Dl4)\n",
        "        toc = time.time()\n",
        "\n",
        "        forwardPropDisc3.append((toc - tic)*1000)\n",
        "        # Func: Cost G\n",
        "        G_cost = np.mean(-np.log(Dl4_A))\n",
        "        G_list_cost.append(G_cost)\n",
        "        # batch_cost.append(G_cost[0][0])\n",
        "\n",
        "        tic = time.time()\n",
        "\n",
        "        for batch_item in range(0,batch_size):\n",
        "          grad_G_w11_part_1 = ((((((( (-1/(Dl4_A[batch_item] + eps)) * d_log(Dl4[batch_item]).dot(D_W4.T) * (d_Lrelu(Dl3[batch_item])) ).dot(D_W3.T) * (d_Lrelu(Dl2[batch_item]))).dot(D_W2.T)))* (d_Lrelu(Dl1[batch_item])))).dot(D_W1.T)).reshape(1,784)\n",
        "          grad_G_w11_part_2 = d_log(Gl11[batch_item])\n",
        "          grad_G_w11_part_3 = Gl10A[batch_item].reshape(1,hidden_input10)\n",
        "          grad_G_w11 = grad_G_w11_part_3.T.dot(grad_G_w11_part_1 * grad_G_w11_part_1)\n",
        "          grad_G_b11 = grad_G_w11_part_1 * grad_G_w11_part_2\n",
        "\n",
        "          grad_g_w11 = np.add(grad_g_w11,grad_G_w11)\n",
        "          grad_g_b11 = np.add(grad_g_b11,grad_G_b11)\n",
        "\n",
        "          grad_G_w10_part_1 = (grad_G_w11_part_1 * grad_G_w11_part_2).dot(G_W11.T)\n",
        "          grad_G_w10_part_2 = d_Lrelu(Gl10[batch_item])\n",
        "          grad_G_w10_part_3 = Gl9A[batch_item].reshape(1,hidden_input9)\n",
        "          grad_G_w10 = grad_G_w10_part_3.T.dot(grad_G_w10_part_1 * grad_G_w10_part_2)\n",
        "          grad_G_b10 = (grad_G_w10_part_1 * grad_G_w10_part_2)\n",
        "\n",
        "          grad_g_w10 = np.add(grad_g_w10,grad_G_w10)\n",
        "          grad_g_b10 = np.add(grad_g_b10,grad_G_b10)\n",
        "\n",
        "          grad_G_w9_part_1 = (grad_G_w10_part_1 * grad_G_w10_part_2).dot(G_W10.T)\n",
        "          grad_G_w9_part_2 = d_Lrelu(Gl9[batch_item])\n",
        "          grad_G_w9_part_3 = Gl8A[batch_item].reshape(1,hidden_input8)\n",
        "          grad_G_w9 = grad_G_w9_part_3.T.dot(grad_G_w9_part_1 * grad_G_w9_part_2)\n",
        "          grad_G_b9 = (grad_G_w9_part_1 * grad_G_w9_part_2)\n",
        "\n",
        "          grad_g_w9 = np.add(grad_g_w9,grad_G_w9)\n",
        "          grad_g_b9 = np.add(grad_g_b9,grad_G_b9)\n",
        "\n",
        "          grad_G_w8_part_1 = (grad_G_w9_part_1 * grad_G_w9_part_2).dot(G_W9.T)\n",
        "          grad_G_w8_part_2 = d_Lrelu(Gl8[batch_item])\n",
        "          grad_G_w8_part_3 = Gl7A[batch_item].reshape(1,hidden_input7)\n",
        "          grad_G_w8 = grad_G_w8_part_3.T.dot(grad_G_w8_part_1 * grad_G_w8_part_2)\n",
        "          grad_G_b8 = (grad_G_w8_part_1 * grad_G_w8_part_2)\n",
        "\n",
        "          grad_g_w8 = np.add(grad_g_w8,grad_G_w8)\n",
        "          grad_g_b8 = np.add(grad_g_b8,grad_G_b8)\n",
        "\n",
        "          grad_G_w7_part_1 = (grad_G_w8_part_1 * grad_G_w8_part_2).dot(G_W8.T)\n",
        "          grad_G_w7_part_2 = d_Lrelu(Gl7[batch_item])\n",
        "          grad_G_w7_part_3 = Gl6A[batch_item].reshape(1,hidden_input6)\n",
        "          grad_G_w7 = grad_G_w7_part_3.T.dot(grad_G_w7_part_1 * grad_G_w7_part_2)\n",
        "          grad_G_b7 = (grad_G_w7_part_1 * grad_G_w7_part_2)\n",
        "\n",
        "          grad_g_w7 = np.add(grad_g_w7,grad_G_w7)\n",
        "          grad_g_b7 = np.add(grad_g_b7,grad_G_b7)\n",
        "\n",
        "          grad_G_w6_part_1 = (grad_G_w7_part_1 * grad_G_w7_part_2).dot(G_W7.T)\n",
        "          grad_G_w6_part_2 = d_Lrelu(Gl6[batch_item])\n",
        "          grad_G_w6_part_3 = Gl5A[batch_item].reshape(1,hidden_input5)\n",
        "          grad_G_w6 = grad_G_w6_part_3.T.dot(grad_G_w6_part_1 * grad_G_w6_part_2)\n",
        "          grad_G_b6 = (grad_G_w6_part_1 * grad_G_w6_part_2)\n",
        "\n",
        "          grad_g_w6 = np.add(grad_g_w6,grad_G_w6)\n",
        "          grad_g_b6 = np.add(grad_g_b6,grad_G_b6)\n",
        "\n",
        "          grad_G_w5_part_1 = (grad_G_w6_part_1 * grad_G_w6_part_2).dot(G_W6.T)\n",
        "          grad_G_w5_part_2 = d_Lrelu(Gl5[batch_item])\n",
        "          grad_G_w5_part_3 = Gl4A[batch_item].reshape(1,hidden_input4)\n",
        "          grad_G_w5 = grad_G_w5_part_3.T.dot(grad_G_w5_part_1 * grad_G_w5_part_2)\n",
        "          grad_G_b5 = (grad_G_w5_part_1 * grad_G_w5_part_2)\n",
        "\n",
        "          grad_g_w5 = np.add(grad_g_w5,grad_G_w5)\n",
        "          grad_g_b5 = np.add(grad_g_b5,grad_G_b5)\n",
        "\n",
        "          grad_G_w4_part_1 = (grad_G_w5_part_1 * grad_G_w5_part_2).dot(G_W5.T)\n",
        "          grad_G_w4_part_2 = d_Lrelu(Gl4[batch_item])\n",
        "          grad_G_w4_part_3 = Gl3A[batch_item].reshape(1,hidden_input3)\n",
        "          grad_G_w4 = grad_G_w4_part_3.T.dot(grad_G_w4_part_1 * grad_G_w4_part_2)\n",
        "          grad_G_b4 = (grad_G_w4_part_1 * grad_G_w4_part_2)\n",
        "\n",
        "          grad_g_w4 = np.add(grad_g_w4,grad_G_w4)\n",
        "          grad_g_b4 = np.add(grad_g_b4,grad_G_b4)\n",
        "\n",
        "          grad_G_w3_part_1 = (grad_G_w4_part_1 * grad_G_w4_part_2).dot(G_W4.T)\n",
        "          grad_G_w3_part_2 = d_Lrelu(Gl3[batch_item])\n",
        "          grad_G_w3_part_3 = Gl2A[batch_item].reshape(1,hidden_input2)\n",
        "          grad_G_w3 = grad_G_w3_part_3.T.dot(grad_G_w3_part_1 * grad_G_w3_part_2)\n",
        "          grad_G_b3 = (grad_G_w3_part_1 * grad_G_w3_part_2)\n",
        "\n",
        "          grad_g_w3 = np.add(grad_g_w3,grad_G_w3)\n",
        "          grad_g_b3 = np.add(grad_g_b3,grad_G_b3)\n",
        "\n",
        "          grad_G_w2_part_1 = (grad_G_w3_part_1 * grad_G_w3_part_2).dot(G_W3.T)\n",
        "          grad_G_w2_part_2 = d_Lrelu(Gl2[batch_item])\n",
        "          grad_G_w2_part_3 = Gl1A[batch_item].reshape(1,hidden_input)\n",
        "          grad_G_w2 = grad_G_w2_part_3.T.dot(grad_G_w2_part_1 * grad_G_w2_part_2)\n",
        "          grad_G_b2 = (grad_G_w2_part_1 * grad_G_w2_part_2)\n",
        "\n",
        "          grad_g_w2 = np.add(grad_g_w2,grad_G_w2)\n",
        "          grad_g_b2 = np.add(grad_g_b2,grad_G_b2)\n",
        "\n",
        "          grad_G_w1_part_1 = (grad_G_w2_part_1 * grad_G_w2_part_2).dot(G_W2.T)\n",
        "          grad_G_w1_part_2 = d_Lrelu(Gl1[batch_item])\n",
        "          grad_G_w1_part_3 = Z[batch_item].reshape(1,100)\n",
        "          grad_G_w1 = grad_G_w1_part_3.T.dot(grad_G_w1_part_1 * grad_G_w1_part_2)\n",
        "          grad_G_b1 = grad_G_w1_part_1 * grad_G_w1_part_2\n",
        "\n",
        "          grad_g_w1 = np.add(grad_g_w1,grad_G_w1)\n",
        "          grad_g_b1 = np.add(grad_g_b1,grad_G_b1)\n",
        "\n",
        "          \n",
        "\n",
        "\n",
        "        toc = time.time()\n",
        "        gradientComputeGen.append((toc-tic)*1000)\n",
        "        # ---- Update Gradient ----\n",
        "\n",
        "        tic = time.time()\n",
        "        m5 = beta_1 * m5 + (1 - beta_1) * grad_g_w1\n",
        "        v5 = beta_2 * v5 + (1 - beta_2) * grad_g_w1 ** 2\n",
        "\n",
        "        m6 = beta_1 * m6 + (1 - beta_1) * grad_g_b1\n",
        "        v6 = beta_2 * v6 + (1 - beta_2) * grad_g_b1 ** 2\n",
        "\n",
        "        m7 = beta_1 * m7 + (1 - beta_1) * grad_g_w2\n",
        "        v7 = beta_2 * v7 + (1 - beta_2) * grad_g_w2 ** 2\n",
        "\n",
        "        m8 = beta_1 * m8 + (1 - beta_1) * grad_g_b2\n",
        "        v8 = beta_2 * v8 + (1 - beta_2) * grad_g_b2 ** 2\n",
        "\n",
        "        m9 = beta_1 * m9 + (1 - beta_1) * grad_g_w3\n",
        "        v9 = beta_2 * v9 + (1 - beta_2) * grad_g_w3 ** 2\n",
        "\n",
        "        m10 = beta_1 * m10 + (1 - beta_1) * grad_g_b3\n",
        "        v10 = beta_2 * v10 + (1 - beta_2) * grad_g_b3 ** 2\n",
        "\n",
        "        m11 = beta_1 * m11 + (1 - beta_1) * grad_g_w4\n",
        "        v11 = beta_2 * v11 + (1 - beta_2) * grad_g_w4 ** 2\n",
        "\n",
        "        m12 = beta_1 * m12 + (1 - beta_1) * grad_g_b4\n",
        "        v12 = beta_2 * v12 + (1 - beta_2) * grad_g_b4 ** 2\n",
        "\n",
        "        m13 = beta_1 * m13 + (1 - beta_1) * grad_g_w5\n",
        "        v13 = beta_2 * v13 + (1 - beta_2) * grad_g_w5 ** 2\n",
        "\n",
        "        m14 = beta_1 * m14 + (1 - beta_1) * grad_g_b5\n",
        "        v14 = beta_2 * v14 + (1 - beta_2) * grad_g_b5 ** 2\n",
        "\n",
        "        m15 = beta_1 * m15 + (1 - beta_1) * grad_g_w6\n",
        "        v15 = beta_2 * v15 + (1 - beta_2) * grad_g_w6 ** 2\n",
        "\n",
        "        m16 = beta_1 * m16 + (1 - beta_1) * grad_g_b6\n",
        "        v16 = beta_2 * v16 + (1 - beta_2) * grad_g_b6 ** 2\n",
        "\n",
        "        m17 = beta_1 * m17 + (1 - beta_1) * grad_g_w7\n",
        "        v17 = beta_2 * v17 + (1 - beta_2) * grad_g_w7 ** 2\n",
        "\n",
        "        m18 = beta_1 * m18 + (1 - beta_1) * grad_g_b7\n",
        "        v18 = beta_2 * v18 + (1 - beta_2) * grad_g_b7 ** 2\n",
        "\n",
        "        m19 = beta_1 * m19 + (1 - beta_1) * grad_g_w8\n",
        "        v19 = beta_2 * v19 + (1 - beta_2) * grad_g_w8 ** 2\n",
        "        m20 = beta_1 * m20 + (1 - beta_1) * grad_g_b8\n",
        "        v20 = beta_2 * v20 + (1 - beta_2) * grad_g_b8 ** 2\n",
        "\n",
        "        m21 = beta_1 * m21 + (1 - beta_1) * grad_g_w9\n",
        "        v21 = beta_2 * v21 + (1 - beta_2) * grad_g_w9 ** 2\n",
        "\n",
        "        m22 = beta_1 * m22 + (1 - beta_1) * grad_g_b9\n",
        "        v22 = beta_2 * v22 + (1 - beta_2) * grad_g_b9 ** 2\n",
        "\n",
        "        m23 = beta_1 * m23 + (1 - beta_1) * grad_g_w10\n",
        "        v23 = beta_2 * v23 + (1 - beta_2) * grad_g_w10 ** 2\n",
        "\n",
        "        m24 = beta_1 * m24 + (1 - beta_1) * grad_g_b10\n",
        "        v24 = beta_2 * v24 + (1 - beta_2) * grad_g_b10 ** 2\n",
        "\n",
        "        m25 = beta_1 * m25 + (1 - beta_1) * grad_g_w11\n",
        "        v25 = beta_2 * v25 + (1 - beta_2) * grad_g_w11 ** 2\n",
        "\n",
        "        m26 = beta_1 * m26 + (1 - beta_1) * grad_g_b11\n",
        "        v26 = beta_2 * v26 + (1 - beta_2) * grad_g_b11 ** 2\n",
        "\n",
        "\n",
        "        G_W1 = G_W1 - (learing_rate / (np.sqrt(v5 /(1-beta_2**iter )) + eps)) * (m5/(1-beta_1**iter)  + eps)\n",
        "        G_b1 = G_b1 - (learing_rate / (np.sqrt(v6 /(1-beta_2**iter )) + eps)) * (m6/(1-beta_1**iter)  + eps)\n",
        "\n",
        "        G_W2 = G_W2 - (learing_rate / (np.sqrt(v7 /(1-beta_2**iter )) + eps)) * (m7/(1-beta_1**iter)  + eps)\n",
        "        G_b2 = G_b2 - (learing_rate / (np.sqrt(v8 /(1-beta_2**iter )) + eps)) * (m8/(1-beta_1**iter)  + eps)\n",
        "\n",
        "        G_W3 = G_W3 - (learing_rate / (np.sqrt(v9 /(1-beta_2**iter )) + eps)) * (m9/(1-beta_1**iter)  + eps)\n",
        "        G_b3 = G_b3 - (learing_rate / (np.sqrt(v10 /(1-beta_2**iter )) + eps)) * (m10/(1-beta_1**iter)  + eps)\n",
        "\n",
        "        G_W4 = G_W4 - (learing_rate / (np.sqrt(v11 /(1-beta_2**iter )) + eps)) * (m11/(1-beta_1**iter)  + eps)\n",
        "        G_b4 = G_b4 - (learing_rate / (np.sqrt(v12 /(1-beta_2**iter )) + eps)) * (m12/(1-beta_1**iter)  + eps)\n",
        "\n",
        "        G_W5 = G_W5 - (learing_rate / (np.sqrt(v13 /(1-beta_2**iter )) + eps)) * (m13/(1-beta_1**iter)  + eps)\n",
        "        G_b5 = G_b5 - (learing_rate / (np.sqrt(v14 /(1-beta_2**iter )) + eps)) * (m14/(1-beta_1**iter)  + eps)\n",
        "\n",
        "        G_W6 = G_W6 - (learing_rate / (np.sqrt(v15 /(1-beta_2**iter )) + eps)) * (m15/(1-beta_1**iter)  + eps)\n",
        "        G_b6 = G_b6 - (learing_rate / (np.sqrt(v16 /(1-beta_2**iter )) + eps)) * (m16/(1-beta_1**iter)  + eps)\n",
        "\n",
        "        G_W7 = G_W7 - (learing_rate / (np.sqrt(v17 /(1-beta_2**iter ))+ eps)) * (m17/(1-beta_1**iter)  + eps)\n",
        "        G_b7 = G_b7 - (learing_rate / (np.sqrt(v18 /(1-beta_2**iter )) + eps)) * (m18/(1-beta_1**iter)  + eps)\n",
        "\n",
        "        G_W8 = G_W8 - (learing_rate / (np.sqrt(v19 /(1-beta_2**iter )) + eps)) * (m19/(1-beta_1**iter)  + eps)\n",
        "        G_b8 = G_b8 - (learing_rate / (np.sqrt(v20 /(1-beta_2**iter )) + eps)) * (m20/(1-beta_1**iter)  + eps)\n",
        "\n",
        "        G_W9 = G_W9 - (learing_rate / (np.sqrt(v21 /(1-beta_2**iter )) + eps)) * (m21/(1-beta_1**iter)  + eps)\n",
        "        G_b9 = G_b9 - (learing_rate / (np.sqrt(v22 /(1-beta_2**iter )) + eps)) * (m22/(1-beta_1**iter)  + eps)\n",
        "\n",
        "        G_W10 = G_W10 - (learing_rate / (np.sqrt(v23 /(1-beta_2**iter )) + eps)) * (m23/(1-beta_1**iter)  + eps)\n",
        "        G_b10 = G_b10 - (learing_rate / (np.sqrt(v24 /(1-beta_2**iter )) + eps)) * (m24/(1-beta_1**iter)  + eps)\n",
        "\n",
        "        G_W11 = G_W11 - (learing_rate / (np.sqrt(v25 /(1-beta_2**iter )) + eps)) * (m25/(1-beta_1**iter)  + eps)\n",
        "        G_b11 = G_b11 - (learing_rate / (np.sqrt(v26 /(1-beta_2**iter )) + eps)) * (m26/(1-beta_1**iter)  + eps)\n",
        "\n",
        "        # random_data = np.delete(random_data,[0,1,2,3,4,5])\n",
        "        toc = time.time()\n",
        "\n",
        "        backwardPropGen.append((toc-tic)*1000)\n",
        "    print(\"Current Epoch: \",iter, \" Current Iter:\",i, \" Current D cost:\",D_cost, \" Current G cost: \", G_cost)\n",
        "    \n",
        "    if i%5 == 0:\n",
        "        Z = np.random.normal(0., 1., size=[16, G_input]) \n",
        "        Gl1 = Z.dot(G_W1) + dupli_bias(G_b1, 16)\n",
        "        Gl1A = Lrelu(Gl1)\n",
        "        Gl2 = Gl1A.dot(G_W2) + dupli_bias(G_b2, 16)\n",
        "        Gl2A = Lrelu(Gl2)\n",
        "        Gl3 = Gl2A.dot(G_W3) + dupli_bias(G_b3, 16)\n",
        "        Gl3A = Lrelu(Gl3)\n",
        "        Gl4 = Gl3A.dot(G_W4) + dupli_bias(G_b4, 16)\n",
        "        Gl4A = Lrelu(Gl4)\n",
        "        Gl5 = Gl4A.dot(G_W5) + dupli_bias(G_b5, 16)\n",
        "        Gl5A = Lrelu(Gl5)\n",
        "        Gl6 = Gl5A.dot(G_W6) + dupli_bias(G_b6, 16)\n",
        "        Gl6A = Lrelu(Gl6)\n",
        "        Gl7 = Gl6A.dot(G_W7) + dupli_bias(G_b7, 16)\n",
        "        Gl7A = Lrelu(Gl7)\n",
        "        Gl8 = Gl7A.dot(G_W8) + dupli_bias(G_b8, 16)\n",
        "        Gl8A = Lrelu(Gl8)\n",
        "        Gl9 = Gl8A.dot(G_W9) + dupli_bias(G_b9, 16)\n",
        "        Gl9A = Lrelu(Gl9)\n",
        "        Gl10 = Gl9A.dot(G_W10) + dupli_bias(G_b10, 16)\n",
        "        Gl10A = Lrelu(Gl10)\n",
        "        Gl11 = Gl10A.dot(G_W11) + dupli_bias(G_b11, 16)\n",
        "        current_fake_data = log(Gl11)\n",
        "        plots(current_fake_data,\"TITLE\")\n",
        "    # ---- Print to Out put ----\n",
        "# -- end code --"
      ],
      "execution_count": 647,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------- Started Training ----------\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 0  Current D cost: 1.387148534321966  Current G cost:  0.684411187220252\n",
            "16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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E083mRNU8JJUAVqlQv4ymabBYLCQ0nqbpng7BngAcs28YTvXDcKoXQ+LzZP89y9olO6pZ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\ncwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 1  Current D cost: 1.3861726322299333  Current G cost:  0.684411187220252\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 2  Current D cost: 1.3857324307066545  Current G cost:  0.684411187220252\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 3  Current D cost: 1.3865079191207648  Current G cost:  0.6824938118533127\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 4  Current D cost: 1.3835576396234357  Current G cost:  0.6824938118533127\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 5  Current D cost: 1.3873267332552814  Current G cost:  0.6824938118533127\n",
            "16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yprYqhefeDVH1dz6vRq24WnK9hiEpr9/mObY9k9crECZn1QPQC6gn0OvzsvqW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\n68KQWQ9viTH2vnDfmdrLmdq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            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 6  Current D cost: 1.3863980122631627  Current G cost:  0.6828721324739816\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 7  Current D cost: 1.3855459916287574  Current G cost:  0.6828721324739816\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 8  Current D cost: 1.3854126519557897  Current G cost:  0.6828721324739816\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 9  Current D cost: 1.388860843246742  Current G cost:  0.6815780829475285\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 10  Current D cost: 1.3876732070682736  Current G cost:  0.6815780829475285\n",
            "16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 11  Current D cost: 1.3835219684784583  Current G cost:  0.6815780829475285\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 12  Current D cost: 1.3883848809853019  Current G cost:  0.6793147898438712\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 13  Current D cost: 1.3867636685530251  Current G cost:  0.6793147898438712\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 14  Current D cost: 1.3858321904255804  Current G cost:  0.6793147898438712\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 15  Current D cost: 1.3827250323883664  Current G cost:  0.6771720716823246\n",
            "16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OGSD+liIw3U6K9nQ7TGPTNF17Ojy+X5YlJEnS3AiNI5Dr3kMqFOHbDmFkBTCn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\nQMMwosUCoGEY0fJ/5N7RTP/nnmYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 16  Current D cost: 1.3849345033628153  Current G cost:  0.6771720716823246\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 17  Current D cost: 1.388321849667675  Current G cost:  0.6771720716823246\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 18  Current D cost: 1.386022483010625  Current G cost:  0.6771841439327578\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 19  Current D cost: 1.3823967209179424  Current G cost:  0.6771841439327578\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 20  Current D cost: 1.3820431258900556  Current G cost:  0.6771841439327578\n",
            "16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 21  Current D cost: 1.3851773107537646  Current G cost:  0.6754797905048283\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 22  Current D cost: 1.3882618929072237  Current G cost:  0.6754797905048283\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 23  Current D cost: 1.3905897667905567  Current G cost:  0.6754797905048283\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 24  Current D cost: 1.3865487474888125  Current G cost:  0.6706711722729329\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 25  Current D cost: 1.381123168189724  Current G cost:  0.6706711722729329\n",
            "16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 26  Current D cost: 1.3860050163736641  Current G cost:  0.6706711722729329\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 27  Current D cost: 1.3853431580550342  Current G cost:  0.6713196812506615\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 28  Current D cost: 1.3786460544587449  Current G cost:  0.6713196812506615\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 29  Current D cost: 1.3869284051079767  Current G cost:  0.6713196812506615\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 30  Current D cost: 1.3856831353427093  Current G cost:  0.6675331899875795\n",
            "16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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COJUlFZ/eDlBnYv6cxZwa87iAC5sApRT1ej26c+cO3bp1q95+cHBAk8mEHj16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\nDj3+AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 31  Current D cost: 1.3797854987536395  Current G cost:  0.6675331899875795\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 32  Current D cost: 1.3818963260050774  Current G cost:  0.6675331899875795\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 33  Current D cost: 1.3813573183006633  Current G cost:  0.6696395839854722\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 34  Current D cost: 1.3819194989041401  Current G cost:  0.6696395839854722\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 35  Current D cost: 1.3801797642276314  Current G cost:  0.6696395839854722\n",
            "16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 36  Current D cost: 1.3824683027622435  Current G cost:  0.6672362152898792\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 37  Current D cost: 1.3813622923557065  Current G cost:  0.6672362152898792\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 38  Current D cost: 1.3757901821322434  Current G cost:  0.6672362152898792\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 39  Current D cost: 1.383943941174505  Current G cost:  0.6681812646791123\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 40  Current D cost: 1.3766201646607574  Current G cost:  0.6681812646791123\n",
            "16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Tly9faHd317ifS7paNtvv6wRO+YFTXlLyuVIPcFWKoqDFYkGj0Yjevn1L4/GY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\nSJb/ADy9c9D3B2TfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 41  Current D cost: 1.3800698982967323  Current G cost:  0.6681812646791123\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 42  Current D cost: 1.3769064488331342  Current G cost:  0.6625092505730628\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 43  Current D cost: 1.3728021384211089  Current G cost:  0.6625092505730628\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 44  Current D cost: 1.3757101437632384  Current G cost:  0.6625092505730628\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 45  Current D cost: 1.3778225943261027  Current G cost:  0.664375221038716\n",
            "16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8Lw5TeSH3sgCdLgM6laWrPr0awGUkRavCtre3cffuXWRZhjRNARzdF3ju3Dlc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\nEkKihQ0gISRa2AASQqKFDSAhJFr+B9cYn66PgCIzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 46  Current D cost: 1.3818019123340926  Current G cost:  0.664375221038716\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 47  Current D cost: 1.3755222438463295  Current G cost:  0.664375221038716\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 48  Current D cost: 1.371193373298155  Current G cost:  0.6602246923743655\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 49  Current D cost: 1.378347101561052  Current G cost:  0.6602246923743655\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 50  Current D cost: 1.3680513990142336  Current G cost:  0.6602246923743655\n",
            "16\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PHz54JejNaVfaYoNO5aFTWVLyKdIFrmlbGldVhel0it3dXXz//h17e3vY29vD\n8+fP8ebNm4UyVkuK/WFrgk7loVNZVtln42YIvlrAF1HoYCgAXL16FdeuXcPx8TFmsxl2dnawv78f\nlAHznrb49eMqghf36VQWOpUlJZ/BO0K7IuoiInSKui2xP1gAnf4X0KksKfns3AXuOgPkkxDap7fV\nSL64V6WLQafy0KksffO50IaoTSX5Wc14KaW8A7GrBJ3KQ6ey9Mlnqy6wLyHzG1oS40ukfq5p1qkp\nbh1XrRJbd80FnYZDp7Kk5HPhLfHbNltN6uls/VMfTxU6lYdOZemLT5EvRVqELMvmr8LUX4tnxmH2\n5UMGYG1hVuFhpVN56FSWPvkUmQTpOi1+8+ZN3L17d74dzqtXr/D169f5/et7+gY/bfG60rJKA8x0\nKgOdytI7n3WEtg8A1fTJTtYMWY+5Pnmeq8FgoB4/fqz+/v2rJpOJmkwm6tGjR2ptbU3leX4qfNN9\n9fO+dJ1k153fs/jQKZ3G7jQlnwu/CaLUv6+l1P8OIcsyfPv2De/evcN0OsXh4SE+f/6MqqpO1QJm\nXPW15r186dKvXQXoVB46laUvPjsvhG5KiDfSf/r/V65cwfXr17G9vY0fP36YtVCr+7nSaIpQkc1Y\n0uni0KksKfkMngX2Jc5VavsyVVUV/vz5g+3tbezv789rAB29JK9/7xN0Kg+dytJ3n0EtQIlmuZkJ\nV7NWP+87V1/bNm2xtFboVA46lSUln6K7wYQS0s+3NW+bMu5rEvcdOpWHTmWJ0WdjARgSsS2Ma4Cy\nKQ69BjDDt8lcl1rirKBTeehUllR8escAQ0td87hZIvuavW0zF3qtfi6mMRk6lYdOZUnJZ6dX4c7q\nD2WLp8vMU/0zlgfMBp3KQ6ey9NFnpzHApubqoqL0miPkXhLClg2dykOnsvTRZ+fNENpOj5Nm6FQe\nOpWlbz47tQD1WsBWIyxSE/gGMbvIX5UZNzqVh05l6aPPhbfDMiNcRjM/5IGJ9aHyQafy0Kksq+7T\nuxCaEEL6jFgLkBBCVg0WgISQZGEBSAhJFhaAhJBkYQFICEkWFoCEkGT5H0ybkWsG+01yAAAAAElF\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 51  Current D cost: 1.3734110856002995  Current G cost:  0.6577069141522394\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 52  Current D cost: 1.3695474891123016  Current G cost:  0.6577069141522394\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n",
            "Current Epoch:  1  Current Iter: 53  Current D cost: 1.3686719329155959  Current G cost:  0.6577069141522394\n",
            "W4 before (346, 1)\n",
            "M$V$ (346, 1)\n",
            "W4 after (346, 1)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-647-cc91ecd17df5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    302\u001b[0m           \u001b[0mgrad_g_b11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad_g_b11\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgrad_G_b11\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m           \u001b[0mgrad_G_w10_part_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mgrad_G_w11_part_1\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mgrad_G_w11_part_2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mG_W11\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    305\u001b[0m           \u001b[0mgrad_G_w10_part_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md_Lrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGl10\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbatch_item\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m           \u001b[0mgrad_G_w10_part_3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGl9A\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbatch_item\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhidden_input9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FzrLFQvGIga",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "f052820e-6441-4d3e-8d0e-476d3b314c59"
      },
      "source": [
        ""
      ],
      "execution_count": 645,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.00498669, -0.00119476,  0.0013546 , ...,  0.00174775,\n",
              "        -0.0026043 , -0.00346045],\n",
              "       [ 0.00360424, -0.0008431 , -0.00403571, ...,  0.00473237,\n",
              "         0.00352494, -0.00284963],\n",
              "       [-0.00066019, -0.00515017,  0.00677892, ..., -0.00150088,\n",
              "        -0.00613317, -0.00070498],\n",
              "       ...,\n",
              "       [ 0.00534563,  0.00152124, -0.00229269, ...,  0.0053063 ,\n",
              "        -0.00458631, -0.00319274],\n",
              "       [ 0.00410986,  0.00246199,  0.00226438, ...,  0.00060968,\n",
              "        -0.00056571, -0.0005924 ],\n",
              "       [-0.00012656, -0.00482443,  0.00019516, ..., -0.00247348,\n",
              "        -0.00251386, -0.00522462]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 645
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQmZ2XFXX890",
        "colab_type": "code",
        "outputId": "701439dc-4703-4318-ecd1-b1f1316335cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        }
      },
      "source": [
        ""
      ],
      "execution_count": 509,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-509-eef32dfae893>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgrad_G_w11_part_1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDl4_A\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbatch_item\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0md_log\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDl4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbatch_item\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m346\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD_W4\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0md_Lrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDl3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbatch_item\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD_W3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0md_Lrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDl2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbatch_item\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD_W2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0md_Lrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDl1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbatch_item\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD_W1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m784\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mgrad_G_w11_part_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md_log\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mGl11\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbatch_item\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mgrad_G_w11_part_3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGl10A\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbatch_item\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mgrad_G_w11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrad_G_w11_part_3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad_G_w11_part_1\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mgrad_G_w11_part_1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mgrad_G_b11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrad_G_w11_part_1\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mgrad_G_w11_part_2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: cannot reshape array of size 271264 into shape (1,784)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-949rVgZWeCt",
        "colab_type": "code",
        "outputId": "9f3e3d88-f9a6-47af-9c39-ec5cd13c7d01",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        ""
      ],
      "execution_count": 600,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 256)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 600
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zYry6Fe6eL4m",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_59RtsZGIgx",
        "colab_type": "code",
        "outputId": "643b4f71-735f-476c-fcce-f591f2b68d6e",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(5,7))\n",
        "plt.plot(forwardPropGen1, color = 'tab:blue', label = 'Forward Prop Gen 1')\n",
        "plt.plot(forwardPropDisc1,color = 'tab:green', label = 'Forward Prop Disc Real')\n",
        "plt.plot(forwardPropDisc2,color = 'tab:red', label = 'Forward Prop Disc Fake')\n",
        "plt.plot(gradientComputeDisc1,color = 'tab:gray', label = 'Gradient Discriminator Real')\n",
        "plt.plot(gradientComputeDisc2,color = 'tab:olive', label = 'Gradient Discriminator Real')\n",
        "plt.plot(backwardPropDisc,color = 'tab:pink', label = 'Backward Prop Disc')\n",
        "plt.plot(forwardPropGen2,color = 'tab:orange', label = 'Forward Prop Gen 2 (Train Gen)')\n",
        "plt.plot(forwardPropDisc3,color = 'tab:purple', label = 'Forward Prop Disc (Train Gen)')\n",
        "plt.plot(gradientComputeGen,color = 'tab:cyan', label = 'Gradient Compute Gen')\n",
        "plt.plot(backwardPropGen,color = 'tab:brown', label = 'Backward Prop Gen')\n",
        "plt.legend(bbox_to_anchor=(1.1, 1.05))\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Time in ms\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Sub4goywbfRI",
        "outputId": "be533668-df23-4863-b996-4292364b6c32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "l=[]\n",
        "for i in range(15):\n",
        "    Z = np.random.normal(0., 1., size=[100, G_input])\n",
        "    Gl1 = Z.dot(G_W1) + G_b1\n",
        "    Gl1A = Lrelu(Gl1)\n",
        "    Gl2 = Gl1A.dot(G_W2) + G_b2\n",
        "    Gl2A = Lrelu(Gl2)\n",
        "    Gl3 = Gl2A.dot(G_W3) + G_b3\n",
        "    Gl3A = Lrelu(Gl3)\n",
        "    Gl4 = Gl3A.dot(G_W4) + G_b4\n",
        "    Gl4A = Lrelu(Gl4)\n",
        "    Gl5 = Gl4A.dot(G_W5) + G_b5\n",
        "    Gl5A = Lrelu(Gl5)\n",
        "    Gl6 = Gl5A.dot(G_W6) + G_b6\n",
        "    Gl6A = Lrelu(Gl6)\n",
        "    Gl7 = Gl6A.dot(G_W7) + G_b7\n",
        "    Gl7A = Lrelu(Gl7)\n",
        "    Gl8 = Gl7A.dot(G_W8) + G_b8\n",
        "    Gl8A = Lrelu(Gl8)\n",
        "    Gl9 = Gl8A.dot(G_W9) + G_b9\n",
        "    Gl9A = Lrelu(Gl9)\n",
        "    Gl10 = Gl9A.dot(G_W10) + G_b10\n",
        "    Gl10A = Lrelu(Gl10)\n",
        "    Gl11 = Gl10A.dot(G_W11) + G_b11\n",
        "    \n",
        "    current_fake_data = log(Gl11)\n",
        "    l.append(current_fake_data)\n",
        "\n",
        "fig = plot(l,\"TEST\")\n",
        "fig.savefig('Click_Me_{}.png'.format(str(iter).zfill(3)+\"_Ginput_\"+str(G_input)+ \"_hiddenone\"+str(hidden_input) + \"_hiddentwo\"+str(hidden_input2) + \"_LR_\" + str(learing_rate)), bbox_inches='tight')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "15\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IrWFPT08g19vbCxRjVDx+aPWU6LV2Pp4xY0ZQAn2tqYKJe4SMj4w1LJXzSz958mSwclpo+Xrd3t7eoBTGzi8FjrHza4bzMmbOyMgoj1KeOSMj4/xF9swZGZEg38wZGZGglJp9//3316GQ1y1DG7/pzgBf3HnnnQA8//zzANx3331A0RvqRELrU/v6+kINrPOVVBld1+mk/4MHDwKFgmiZnh0rPT09oYvGMjoLAkxddHd3B3Ehdn4pcIydXzOUipmr1WodirI2E+YS+uKLLwJxa2JVE90+P3HMil0mVtQMDQ2F15ibs2XNwngbu2+99VagGKI2sd1tx44dYVi59baOaHGMy4YNG8IXFTu/FDjGzq8Z8jE7IyMSlDpm33XXXQAhiW4uT0tWrVZDXaltY9agevzwsbk8h4W7MqStrY333nsPKKyaRxRzdxayOzTN12r9tKDXX399yOd5zLLK5quvvkqOXwocY+fXDNkzZ2REglKe2XGlWh+raqx+Wb58OZ999hkAd9xxB1DEKlq9jz/+GIClS5cCheV04fWZM2dYu3YtUFhTR5w6+MxxpcYdxhbGQK7PHD/G1LlLdtF4/ZT4pcAxdn7NkD1zRkYkKOWZXWil1Vm0aBFQjBHduXNnkNWdTuhzVc2V7B0jqiXVYt19993s3bv3rOvai/rggw8C8PnnnwOwatUqoLCc48e6QKNu1p5QFUwt9vjl26nwS4Fj7PyaodTN/NFHHwGNMSsAH374IdBoCYPGvKWJe3o6OzuB4rhhc7Yf2FyaExGHh4dDOqFarQKF7K9078gVUwcWyPul+xnWr18fPodfmAPYzPelxC8FjrHza4Z8zM7IiASlPLMDx2zrsmlbSX3evHnBMu3atQsokur2ddpG5rHHzfKOebn88stD1Y6igYPNlP89qniU8bVeS7Gjs7MzbDTwCOX2Pi1nSvxS4Bg7v2bInjkjIxKU8swKA1o761/FggULQqLbmMD4Q+lesUFrZ6xh+dvkyZOD1VIkMO4wpvH6jz76KFCkErTKWrhJkyaF5nEtp3uGfO+U+KXAMXZ+zZA9c0ZGJCjlmT/99FOgkNstizMBXqvVwvlfJU5LqXUzEa7VmZhEHx4eDpbS2MHN9W4rmDhGVZhycHjbVVddFYrYjWXGj2tJjV8KHGPn1wzZM2dkRIJSntmys76+PqDInWlBBgcHQ2mcxer2aE7ccKe66HtoqebMmRNiGQvczfOpBPre5hLd42OcooWdNm1aUCTFiy++CBRxSUr8UuAYO79myJ45IyMSlPLMKnCqek5RcJj4+FUcVsio8vnz7du3A0Xs4OqPJUuWAI04RVXPSQ+OL3Uiw+7du4Fiw70b9bSgFsRv3bo1KJOW+Rnr2JKWEr8UOMbOrxmyZ87IiASlPLNN057xrXt1ntHp06eDAqjVc8D34sWLgaLKRsXOBnDrYA8fPhwslHGOf6scWnUjHPdi+5nzmGq1WohZjKG0wl4/JX4pcIydXzNkz5yREQlKeeaJqzdWr14NFPm69vb2ECu4AmTNmjVAsRVeq2fuTou1ceNGAFauXBmuYzxirlALqnKohX333XeBYtGW1nHbtm1BmXRt5ieffAIUecCU+KXAMXZ+zZA9c0ZGJCjlmR2rYm+oXR3Wlp44cSJUwHR1dYWfQVEhs3DhQqDoWLFix7xbf39/sIz2j2r1tJxaWdU++1GNW2wq37lzZ8jvOfPY5nHVyJT4pcAxdn7NUOpmfuCBB4CimNwvyGPL7NmzQzLdgnJnJBnkuw1vfKM3FMePlpaWcESx7M7X+Ni0w/jB5lA0pnvEqlar4ZijAPL2228DjamIqfFLgWPs/JohH7MzMiJBKc/s0cHGbEvTtEbHjh0LQfyrr74KFMlxpfiVK1cCjc0CUBxDlOHb29vD8UYBwH/zGOIRx8/hBMabb74ZKCT+6dOnB6FBUcF5xh6tUuKXAsfY+TVD9swZGZGglGfesmULUBSk2/g9foqgLV8WiWsRFQ+0aKYFtGjGFEeOHAklcFosW8S0nL5WscHWNKEYMTAwEK6rtbWo3ra3lPilwDF2fs2QPXNGRiQo5ZnvueceoDGFH4qEuZblzJkzIb5Q7VMpVBk0HaCVU3632Ly1tTVYOd/DES0qk1o1x7r42ldeeQUo5g+PjIyE67zxxhtAYTF9Tkr8UuAYO79myJ45IyMSlPLMnuW1Om60U6mbNWtWSIqr2hmrmPdzsr+xhkPUfHzgwIHw/iqBPscWNRvCv/zyy7OuYbLfHF9XV1d4jlbOAnlb0VLilwLH2Pk1Q/bMGRmR4Hs1Wqjkebb38bx584JSaBWLw8htCZu4GsQSOq3T/PnzgwV0vIvxjlbWIeZa1nfeeQcohqmpKO7ZsycMI7fRfN++fUBhQVPilwLH2Pk1Q/bMGRmRoJRn1mJZQdPT0wMUy7DGxsZoa2sDijpWc3kTx6aoEFqjaswxPDwcqmpUGW3WNk5xG70WTIuqYmhcMn737cTr/a4xprHzS4Fj7PyaIXvmjIxIUMoz292xfv16oKic0dJVKpVg5cy7OZ7UuMDYxREtNnObD5w7d26oSdVSOSTNOMPYQiXRrhatoErh8uXLw5gW84G+ly1oKfFLgWPs/Jqh1M1cBk899RTQCOJPnTrFlClTaGlpoaWlhXXr1nHo0CGee+65QKBSqTBp0iQGBgYAeP/993nmmWcYHBxkypQpXHnllaxZs4bXX389LMm2qN4Zxe3t7dx0000/FqWk+KXAMTZ+pW5mLYbxgdbO/Fxvb29Q9Z5++mmgUVXz5JNP8vjjj9PV1RWqbN566y1WrVrFE088ARSq4+joKFu2bOGxxx7jhRdeYNmyZbz22mv09/czY8YM1q1bF/o+N2/ezLFjx7j99tuBRmP6oUOHQsyxe/fuMIxt4oBzG8JT4pcCx9j5NcOP5pn/EPT393PppZeGDfIXXHAB11xzTRA3fuqInR/Ez/F85FfqZjZHpoX64IMPAFi2bBnQyMfZP6oSt3jxYiqVCkNDQwwODoY45fjx44yOjtLd3Q0U8cro6CjVapX9+/fzyCOPcO2117JkyRIuvPBC3nzzTaCo5pk6dSqTJ08OFswl2aqSHR0dob/U7hWrex566KHk+KXAMXZ+zVApk5i+4oor6lAcXSxRU+KfOXNmiBFM3t9www28/PLLXHfddXR2doZk+saNG9m+fXuIJVpaWrjsssu48cYbaW9vZ3BwkD179tDX18e3337LihUrWLhwIVOnTg2J+f7+fkZGRrjllluAopTPuU9z584NAoQjYvysChebNm2qpMIvBY6x82uGc3rMrlarYWj51VdfDRR5wUsuuST0d9ZqNZ599llGRkbCFIafAmLnB/Fz/CnxK3UzW/Y2cfes6YChoaHQDG7A//XXX1Ov1zl48CBjY2NBsq/VaoyNjYU9PlrSEydOBLVQEaO7u5t6vc7AwAD79+8PR6jxe4Og2NGjJW1tbQ3FAw5cM6m/efPm5PilwDF2fs1wXhaNHD16lG+++Sbk8mq1GkePHi3dRXK+InZ+ED/H85FfKc9sUG/e7bbbbgOKpHtHR0doCnfr3qlTp6jX60yfPp22trYgw3d0dLBr164gRnh0uffee0M88tJLL3H69Gm+++47WltbWbRoUZDtoZGIr9VqYSeuFtf4ZMOGDaFZ3KOSBQDGJSnxS4Fj7Pya4Y8SM69evTpUxogVK1awYsUKduzYARQdKvV6nYsvvpilS5eGo8qmTZsAzvqSoJiAeK4ROz+In2MM/Eqp2Q8//HAdCuncGEJr2NPTE8j19vYCxRgVjx9aPSV6rZ2PZ8yYEZRAX2uqYOIeIeMjYw1L5fzST548GaycFlq+Xre3tzcohbHzS4Fj7Pya4byMmTMyMsqjlGfOyMg4f5E9c0ZGJMg3c0ZGJCilZt9///11KOR1y9DGb7ozwBd33nknAM8//zwA9913H1D0hjqR0PrUvr6+UAPrfCVVRtd1Oun/4MGDQKEgWqZnx0pPT0/oorGMzoIAUxfd3d1BXIidXwocY+fXDKVi5mq1WoeirM2EuYS++OKLQNyaWNVEt89PHLNil4kVNUNDQ+E15uZsWbMw3sbuW2+9FSiGqE1sd9uxY0cYVm69rSNaHOOyYcOG8EXFzi8FjrHza4Z8zM7IiASljtl33XUXQEiim8vTklWr1VBXatuYNageP3xsLs9h4a4MaWtr47333gMKq+YRxdydhewOTfO1Wj8t6PXXXx/yeR6zrLL56quvkuOXAsfY+TVD9swZGZGglGd2XKnWx6oaq1+WL1/OZ599BsAdd9wBFLGKVu/jjz8GYOnSpUBhOV14febMGdauXQsU1tQRpw4+c1ypcYexhTGQ6zPHjzF17pJdNF4/JX4pcIydXzNkz5yREQlKeWYXWml1Fi1aBBRjRHfu3BlkdacT+lxVcyV7x4hqSbVYd999N3v37j3ruvaiPvjggwB8/vnnAKxatQooLOf4sS7QqJu1J1QFU4s9fvl2KvxS4Bg7v2YodTN/9NFHQGPMCsCHH34INFrCoDFvaeKens7OTqA4btic7Qc2l+ZExOHh4ZBOqFarQCH7K907csXUgQXyful+hvXr14fP4RfmADbzfSnxS4Fj7PyaIR+zMzIiQSnP7MAx27ps2lZSnzdvXrBMu3btAoqkun2dtpF57HGzvGNeLr/88lC1o2jgYDPlf48qHmV8rddS7Ojs7AwbDTxCub1Py5kSvxQ4xs6vGbJnzsiIBKU8s8KA1s76V7FgwYKQ6DYmMP5Qulds0NoZa1j+Nnny5GC1FAmMO4xpvP6jjz4KFKkErbIWbtKkSaF5XMvpniHfOyV+KXCMnV8zZM+ckREJSnnmTz/9FCjkdsviTIDXarVw/leJ01Jq3UyEa3UmJtGHh4eDpTR2cHO92womjlEVphwc3nbVVVeFInZjmfHjWlLjlwLH2Pk1Q/bMGRmRoJRntuysr68PKHJnWpDBwcFQGmexuj2aEzfcqS76HlqqOXPmhFjGAnfzfCqBvre5RPf4GKdoYadNmxYUSfHiiy8CRVySEr8UOMbOrxmyZ87IiASlPLMKnKqeUxQcJj5+FYcVMqp8/nz79u1AETu4+mPJkiVAI05R1XPSg+NLnciwe/duoNhw70Y9LagF8Vu3bg3KpGV+xjq2pKXELwWOsfNrhuyZMzIiQSnPbNO0Z3zrXp1ndPr06aAAavUc8L148WKgqLJRsbMB3DrYw4cPBwtlnOPfKodW3QjHvdh+5jymWq0WYhZjKK2w10+JXwocY+fXDNkzZ2REglKeeeLqjdWrVwNFvq69vT3ECq4AWbNmDVBshdfqmbvTYm3cuBGAlStXhusYj5gr1IKqHGph3333XaBYtKV13LZtW1AmXZv5ySefAEUeMCV+KXCMnV8zZM+ckREJSnlmx6rYG2pXh7WlJ06cCBUwXV1d4WdQVMgsXLgQKDpWrNgx79bf3x8so/2jWj0tp1ZWtc9+VOMWm8p37twZ8nvOPLZ5XDUyJX4pcIydXzOUupkfeOABoCgm9wvy2DJ79uyQTLeg3BlJBvluwxvf6A3F8aOlpSUcUSy78zU+Nu0wfrA5FI3pHrGq1Wo45iiAvP3220BjKmJq/FLgGDu/ZsjH7IyMSFDKM3t0sDHb0jSt0bFjx0IQ/+qrrwJFclwpfuXKlUBjswAUxxBl+Pb29nC8UQDw3zyGeMTxcziB8eabbwYKiX/69OlBaFBUcJ6xR6uU+KXAMXZ+zZA9c0ZGJCjlmbds2QIUBek2fo+fImjLl0XiWkTFAy2aaQEtmjHFkSNHQgmcFssWMS2nr1VssDVNKEYMDAyE62ptLaq37S0lfilwjJ1fM2TPnJERCUp55nvuuQdoTOGHImGuZTlz5kyIL1T7VApVBk0HaOWU3y02b21tDVbO93BEi8qkVs2xLr72lVdeAYr5wyMjI+E6b7zxBlBYTJ+TEr8UOMbOrxmyZ87IiASlPLNnea2OG+1U6mbNmhWS4qp2xirm/Zzsb6zhEDUfHzhwILy/SqDPsUXNhvAvv/zyrGuY7DfH19XVFZ6jlbNA3la0lPilwDF2fs2QPXNGRiT4Xo0WKnme7X08b968oBRaxeIwclvCJq5OJV7YAAAOnUlEQVQGsYRO6zR//vxgAR3vYryjlXWIuZb1nXfeAYphaiqKe/bsCcPIbTTft28fUFjQlPilwDF2fs2QPXNGRiQo5Zm1WFbQ9PT0AMUyrLGxMdra2oCijtVc3sSxKSqE1qgacwwPD4eqGlVGm7WNU9xGrwXToqoYGpeM33078Xq/a4xp7PxS4Bg7v2bInjkjIxKU8sx2d6xfvx4oKme0dJVKJVg5826OJzUuMHZxRIvN3OYD586dG2pStVQOSTPOMLZQSbSrRSuoUrh8+fIwpsV8oO9lC1pK/FLgGDu/Zih1M5fBU089BTSC+FOnTjFlyhRaWlpoaWlh3bp1HDp0iOeeey4QqFQqTJo0iYGBAQDef/99nnnmGQYHB5kyZQpXXnkla9as4fXXXw9Lsi2qd0Zxe3s7N910049FKSl+qXCMCaVuZi2G8YHWzvxcb29vUPWefvppoFFV8+STT/L444/T1dUVqmzeeustVq1axRNPPAEUquPo6Chbtmzhscce44UXXmDZsmW89tpr9Pf3M2PGDNatWxf6Pjdv3syxY8e4/fbbgUZj+qFDh0LMsXv37jCMbeKAcxvCU+KXAscy/IyZfY4jbn1flWdVZQ3O6OhoeI6ef8OGDQAsW7YMgN/85jdAkZv28zgs4Q/5Hf4+/Gie+Q9Bf38/l156adggf8EFF3DNNdcEceOnjtj5QRoczzeUupm1VFrgDz74ACis0YIFC0L/qErc4sWLqVQqDA0NMTg4GOKU48ePMzo6Snd3N1DEK6Ojo1SrVfbv388jjzzCtddey5IlS7jwwgt58803gaKaZ+rUqUyePDlYMJdkq0p2dHSE/lK7V6zueeihh5LjlwLH78sPCqVZfnpwh+KP56dXd1WrHU4T+dm1tXfv3h+EXzNUyiSmr7jiijoUxxJL1CQ2c+bMEAOZvL/hhht4+eWXue666+js7AzJ9I0bN7J9+/ZwdGlpaeGyyy7jxhtvpL29ncHBQfbs2UNfXx/ffvstK1asYOHChUydOjUk5vv7+xkZGeGWW24BilI+jzJz584NAoQjYvysChebNm2qpMIvBY7flx8UopT8vOnF1VdfDTTSXRaU+FyP297cE8tIval/iN/h78M5PWZXq9UwtHz8FwWN+lT7O2u1Gs8++ywjIyNhCsNPAbHzgzQ4/lRQ6ma27G3i7lnTAUNDQ8ECKWh8/fXX1Ot1Dh48yNjYWJDsa7UaY2NjYY+PlvTEiRNBDVU06O7upl6vMzAwwP79+8MRavzeICgEDD1Fa2trKB5w4JpJ/c2bNyfHLwWO35cfED7zxLSSQ/ma8fv1r38NFEMI5GfDhdf8IX6Hvw/nZdHI0aNH+eabb0Iur1arcfTo0dJdJOcrYucHaXA831DKM3v+N6942223AUXSvaOjIzSFu3Xv1KlT1Ot1pk+fTltbW5DhOzo62LVrV7CKHs3uvffeEG+99NJLnD59mu+++47W1lYWLVoUZHtoJOJrtVrYiav1M/7asGFDaBb3KGgBgHFJSvxS4Ph9+UHheeVncYkxtnHuRRddFEQyyzn13p4MhD+3WeOH+B3+PvxRYubVq1eHyhixYsUKVqxYwY4dO4CiQ6Ver3PxxRezdOnScFTZtGkTwFn/CaCYgHiuETs/SIPjTx2l1OyHH364DoV0riXTGvb09IRfXm9vL1CMUfF4pdVTotea+3jGjBlBCfS1WsGJe4S0gsYalsr5n+rkyZPBymmh5et1e3t7g1IYO78UOMbOrxnOy5g5IyOjPEp55oyMjPMX2TNnZESCfDNnZESCUmr2/fffX4dCXp+YVP/5z38eAnxx5513AvD8888DcN999wFFb6gTCa1P7evrCzWwzldSRXVdp3WwBw8eBAqF1BSCHTk9PT2hi8YyOgsCTF10d3cHcSF2fqlwTBWlYuZqtVqHomzPggB/YV988UX4xVoTq5ro9vmJY1bsorGiZmhoKLzG/J8F7xbGW0N76623AkUd7MR2tx07doRh5dbQOqLFMS4bNmwI/xFi55cKx1SRj9kZGZGg1DH7rrvuAghFAubytNTVajXUldoWZw2qxysfm8uzdtWVIW1tbbz33ntAYbU9gpm7s1DfoWm+Vuuuh7j++utDPs9jpFU2X331VXL8UuGYKrJnzsiIBKU8s+NKta5W1Vj9snz5cj777DMA7rjjDqCIxbTq9nsuXboUKDyDC6/PnDnD2rVrgcJbOOLUwWc2kRtXGTsZ49m5Mn6MqXOl7KLx+inxS4VjqsieOSMjEpTyzC600qouWrQIKMaI7ty5M6QNnL7oc1XNTUk4RlRPoUW+++67w4gVYS/qgw8+CMDnn38OwKpVq4DCM4wf6wKNull7QlVo9Ujjl2+nwi8Vjqmi1M380UcfAcWYlQ8//BBotLxBY97SxD09nZ2dQHGcsjnbX4i5Qic+Dg8Ph3RJtVoFirSGqQlb0UyNWCDvfyo/w/r168Pn8D+EA+bMZ6bELxWOqSIfszMyIkEpz+zAMdu6bEo3ZTBv3rxgeXft2gUURQP2rdpG5rHOzfKOebn88stDVZKiiIPNTG94FPOo5mu9lmJOZ2dnGLTmEdE5xnqGlPilwjFVZM+ckREJSnlmhQ+tufW9YsGCBSGRb8xjfGVqQjFFa24sZXnf5MmTg1VWBDGuMmbz+o8++ihQpEr0OlrwSZMmheZxPYOjYHzvlPilwjFVZM+ckREJSnnmTz/9FCjSCZb9meCv1WohvlFp1BNovU30a1UnFgkMDw8HT2Bs5DA0txVMHKMqTKk4vO2qq64KRfrGauPHtaTGLxWOqSJ75oyMSFDKM1tW19fXBxS5QS3k4OBgKP2zGN8e1Ikb7lRPfQ8t8Zw5c0KsZgG/eUyVTt/bXKl7iozD9CDTpk0Liqt48cUXgSLuSolfKhxTRfbMGRmRoJRnVmFUtXRKhMPEx68asQJIFdOfu1HP2MjVJkuWLAEacZiqpZMsHF/qxIndu3cDxYZ7N+rpISz437p1a1BeLWM0lrPlLiV+qXBMFdkzZ2REglKe2aZwYxjrep3XdPr06aBwatUd8O0OXKuIVCRtcLfO9/Dhw8ECG8f5t8qoVUXCcTa21zlvqlarhZjMGFEv4/VT4pcKx1SRPXNGRiQo5Zknrt5YvXo1UOQj29vbQyzkCpA1a9YAxVZ4rbq5SS3yxo0bAVi5cmW4jvGWuVA9hMqoHuTdd98FikVbWv9t27YF5dW1mZ988glQ5DlT4pcKx1SRPXNGRiQo5ZkdG2Pvq10r1s6eOHEiVPh0dXWFn0FRAbRw4UKg6MixIsm8Yn9/f7D89sdq1fUMehHVTPttjctsmt+5c2fIXzrT2eZ41daU+KXCMVWUupkfeOABoCiW9z+Ax7LZs2eHYgEL5p0BpYjhNrzxjexQHK9aWlrCEcyyQl/jY9Mq4we3Q9F47xGyWq2GY5wCz9tvvw00pj6mxi8VjqkiH7MzMiJBKc/s0cjGc0vvtLbHjh0LIsWrr74KFMl/Uw0rV64EGpsToDhmmWZob28PxzcFDv/NY5ZHOD+HEyZvvvlmoEhhTJ8+PQgpiibOa/bomBK/VDimiuyZMzIiQSnPvGXLFqAouLexffyURFvaLILX4iuOaLFNe2ixjZmOHDkSSvy0yLbA6Rl8rWKKrXdCsWVgYCBcV29i04BtfSnxS4VjqsieOSMjEpTyzPfccw/Q2DIARUGAlvPMmTMhflLNVAlV+TTdoRU3vWAxfWtra7DivocjaFRetdqOrfG1r7zyClDMVx4ZGQnXeeONN4DCI/iclPilwjFVZM+ckREJSnlmYxWtqhv7VCJnzZoVkv6qksZi5jXdXGAs5ZA4Hx84cCC8v0qnz7EFz4b3L7/88qxrWMxgDrOrqys8RytuA4CtdinxS4VjqsieOSMjEnyvRguVSmMXH8+bNy8ooVbpOGzdlreJq08sEdT6zp8/P1h4x9cYz+lFHNKu53jnnXeAYliciumePXvCsHUb6fft2wcUHiIlfqlwTBXZM2dkRIJSnlmLbIVQT08PUCz7Ghsbo62tDSjqdM1VThwLowJqDa4x1fDwcKgaUkW1Gd04bNu2bUBhofUYKqLGXeN3+0683u8a0xo7v1Q4porsmTMyIkEpz2z3yvr164GiMkhLXqlUghU3r+j4VeMeYzNH0Nisbr5z7ty5oeZWS+wQOOMoYyeVUrt2tPIqocuXLw9jaMx3+l622KXELxWOqSJ75oyMSFDKM2sRjX+05uYfe3t7g2ppvOVzHI9qo7mqpYqkQ9FHR0fDc/QaGzZsAGDZsmUA/OY3vwGKvKafx0Z7Y6rdu3eHYXMTB7h7jZT4pcIxVWTPnJERCUp5Zi2xuckPPvgAKKztggULQn+sSqNxlSqlcZjW34HqxmOjo6PBI7jm0+6YN998Eyiqlez42bt3L1AsAVd17ejoCP2zdudYvfTQQw8lxy8Vjqmi1M1sUYDHLn8hfrkzZ84Mu4T8BXtcUtCwWGDHjh1nvbfP6+npCcUIPlfhw+OVI2i8vs93FpVHtalTp4ajoQX+/qdyPM3atWuT4ZcKx1SRj9kZGZGglGe2rG/ibl3THUNDQ+HYdPvttwPFWJqBgQHg/09JONBNT3HixInwXEWRX//610DRwO4R0WJ9r6kFV4hpbW0NxRF6AosWNm/enBy/VDimiuyZMzIiQSnPbHxj/HPbbbcBRVFBR0dHaHp3q6Dxl1bbNIOFCQ6YM0a66KKLgsBiKaCWX68i/LmF/lr3W265BWikQ4yz3LFkgYMCTUr8UuGYKrJnzsiIBJUybWQPP/xwHQrlc6Ii2tPTE2Kh3t5eoFAgLc7XqpuCsNDfxzNmzAhtcr5WKz9xT5JW3ljKUkCV0ZMnTwYrrgeSr9ft7e2tpMIvFY6pInvmjIxIUMozZ2RknL/InjkjIxLkmzkjIxLkmzkjIxLkmzkjIxLkmzkjIxLkmzkjIxLkmzkjIxLkmzkjIxLkmzkjIxLkmzkjIxL8P67SxiPC9pAEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 288x288 with 15 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FnpFQdTpbfRS",
        "outputId": "2f5c385d-f573-49c3-cb7b-8233b8231c6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "source": [
        "# This is currently not working. \n",
        "\n",
        "Z = np.random.normal(0., 1., size=[1, G_input])\n",
        "Gl1 = Z.dot(G_W1) + G_b1\n",
        "print(\"bias\", G_b1.shape)\n",
        "Gl1A = Lrelu(Gl1)\n",
        "Gl2 = Gl1A.dot(G_W2) + G_b2\n",
        "Gl2A = Lrelu(Gl2)\n",
        "Gl3 = Gl2A.dot(G_W3) + G_b3\n",
        "Gl3A = Lrelu(Gl3)\n",
        "Gl4 = Gl3A.dot(G_W4) + G_b4\n",
        "Gl4A = Lrelu(Gl4)\n",
        "Gl5 = Gl4A.dot(G_W5) + G_b5\n",
        "Gl5A = Lrelu(Gl5)\n",
        "Gl6 = Gl5A.dot(G_W6) + G_b6\n",
        "Gl6A = Lrelu(Gl6)\n",
        "Gl7 = Gl6A.dot(G_W7) + G_b7\n",
        "Gl7A = Lrelu(Gl7)\n",
        "Gl8 = Gl7A.dot(G_W8) + G_b8\n",
        "Gl8A = Lrelu(Gl8)\n",
        "Gl9 = Gl8A.dot(G_W9) + G_b9\n",
        "Gl9A = Lrelu(Gl9)\n",
        "Gl10 = Gl9A.dot(G_W10) + G_b10\n",
        "Gl10A = Lrelu(Gl10)\n",
        "Gl11 = Gl10A.dot(G_W11) + G_b11\n",
        "\n",
        "current_fake_data = log(Gl11)\n",
        "print(\"current_fake shape: \", current_fake_data.shape)\n",
        "\n",
        "Y = np.random.normal(0., 1., size=[1, G_input])\n",
        "Gl1 = Y.dot(G_W1) + G_b1\n",
        "Gl1A = Lrelu(Gl1)\n",
        "Gl2 = Gl1A.dot(G_W2) + G_b2\n",
        "Gl2A = Lrelu(Gl2)\n",
        "Gl3 = Gl2A.dot(G_W3) + G_b3\n",
        "Gl3A = Lrelu(Gl3)\n",
        "Gl4 = Gl3A.dot(G_W4) + G_b4\n",
        "Gl4A = Lrelu(Gl4)\n",
        "Gl5 = Gl4A.dot(G_W5) + G_b5\n",
        "Gl5A = Lrelu(Gl5)\n",
        "Gl6 = Gl5A.dot(G_W6) + G_b6\n",
        "Gl6A = Lrelu(Gl6)\n",
        "Gl7 = Gl6A.dot(G_W7) + G_b7\n",
        "Gl7A = Lrelu(Gl7)\n",
        "Gl8 = Gl7A.dot(G_W8) + G_b8\n",
        "Gl8A = Lrelu(Gl8)\n",
        "Gl9 = Gl8A.dot(G_W9) + G_b9\n",
        "Gl9A = Lrelu(Gl9)\n",
        "Gl10 = Gl9A.dot(G_W10) + G_b10\n",
        "Gl10A = Lrelu(Gl10)\n",
        "Gl11 = Gl10A.dot(G_W11) + G_b11\n",
        "\n",
        "s_data = log(Gl11)\n",
        "print(\"s_data shape: \", s_data.shape)\n",
        "print((s_data - current_fake_data).shape)\n",
        "fig = plot(s_data - current_fake_data,\"TEST\")\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "bias (128,)\n",
            "current_fake shape:  (1, 784)\n",
            "s_data shape:  (1, 784)\n",
            "(1, 784)\n",
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "cannot reshape array of size 784 into shape (100,28,28)",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[1;32m<ipython-input-18-62dc71b00172>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     54\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"s_data shape: \"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms_data\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mcurrent_fake_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms_data\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mcurrent_fake_data\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"TEST\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[1;32m<ipython-input-3-2002454c9538>\u001b[0m in \u001b[0;36mplot\u001b[1;34m(samples, title)\u001b[0m\n\u001b[0;32m     44\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_aspect\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'equal'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     45\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m         \u001b[0msample_resized\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msample\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m28\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m28\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     47\u001b[0m         \u001b[1;31m# plt.imshow(sample.reshape(5, 28, 28), cmap='Greys_r')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m         \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_resized\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Greys_r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
            "\u001b[1;31mValueError\u001b[0m: cannot reshape array of size 784 into shape (100,28,28)"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "RfjgFlc9bfRf",
        "colab": {}
      },
      "source": [
        "print((grad_r_w4_part_1 * grad_r_w4_part_2).shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sXhuAW8zbfRs",
        "outputId": "0353964f-6ad3-41d3-fcf1-b19f6a09cae1",
        "colab": {}
      },
      "source": [
        "print(len(l[0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZA9tB9_2bfR2",
        "outputId": "10447fea-2504-415f-8d79-4523df3e1634",
        "colab": {}
      },
      "source": [
        "plot([current_images],\"REAL\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "56R3Qb35bfSC",
        "colab": {}
      },
      "source": [
        "print(grad_r_w4_part_3.T.dot(grad_r_w4_part_1 * grad_r_w4_part_2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4twoZKgvbfSk",
        "colab": {}
      },
      "source": [
        "    \n",
        "current_image = [np.expand_dims(images[i],axis=0) for i in range(50)]\n",
        "    \n",
        "current_images = np.vstack(current_image)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0b_-c-6TbfTC",
        "colab": {}
      },
      "source": [
        "print(grad_G_w11_part_2.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lGK_P4habfTp",
        "outputId": "42682d27-3286-4931-a92b-ce02deecd6e6",
        "colab": {}
      },
      "source": [
        "len(current_images)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4KSfU_tGIh_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189
        },
        "outputId": "91ec3db2-9302-42cc-d189-f1db6c47322e"
      },
      "source": [
        ""
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-131-b11282365fab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrBatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3VPx9LnejKk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "79aa0414-8651-4457-e5bd-a4511a18ecc6"
      },
      "source": [
        "rBatch.shape"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(128, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CJssxefyewd2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "6a9f95ec-efb8-4f3e-858b-e1f844c56499"
      },
      "source": [
        "Dl3_rA[0]"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-4.42810727e-03,  3.14269605e-02,  4.27158606e-01,  8.89713344e-02,\n",
              "       -8.18146638e-04,  4.04001516e-02, -2.90433257e-03,  6.82628322e-02,\n",
              "        1.07329612e-01,  6.00117287e-02,  5.31471033e-02, -1.35295699e-03,\n",
              "       -1.89598130e-03,  2.32209564e-02, -2.83462584e-03, -8.50961644e-04,\n",
              "        2.09311605e-01,  7.22180040e-02,  1.11044768e-01,  2.29061059e-01,\n",
              "        8.60009851e-02, -2.29220991e-03,  1.27579713e-01, -3.57364651e-05,\n",
              "       -2.27839128e-03,  1.00151620e-01, -2.05345720e-04,  1.32098035e-01,\n",
              "       -2.60901495e-03, -3.32959583e-03,  1.50220027e-02, -1.17030874e-03,\n",
              "        1.79853066e-01, -2.58302507e-03,  1.04565859e-01,  1.03016686e-01,\n",
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              "       -5.46661494e-04,  9.99727695e-03, -4.51375186e-04,  4.07911844e-01,\n",
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              "       -2.37108092e-03, -1.23473647e-03,  2.44787789e-01,  1.39242004e-02,\n",
              "       -4.27276846e-03, -7.71568983e-04, -9.42602191e-04, -7.93243576e-04,\n",
              "       -1.00752386e-03, -8.83187716e-04, -1.27608516e-03,  1.66961420e-01,\n",
              "       -8.85916149e-04, -1.43999616e-03,  1.94988085e-01, -1.56092377e-03,\n",
              "        3.26282826e-01, -1.00327087e-03,  1.97072604e-01,  3.32734452e-01,\n",
              "       -2.23016353e-03,  9.70942641e-02,  3.04152150e-01,  6.14935600e-02,\n",
              "       -2.37129829e-03, -1.29585236e-03,  1.07868766e-02,  5.58596984e-01,\n",
              "        4.41465649e-03,  9.11788004e-02,  3.49082216e-02, -3.28864815e-04,\n",
              "        2.91619556e-01, -5.21514394e-03,  5.66267363e-02,  3.36415481e-02,\n",
              "       -1.22657603e-03,  1.35333862e-01,  1.23010562e-01,  1.72999448e-01,\n",
              "       -1.44280053e-04, -5.41327852e-04, -9.24161585e-04,  1.99284706e-01,\n",
              "        2.76447488e-02, -5.68678061e-04,  1.71305669e-01,  2.07741359e-01,\n",
              "        1.26463160e-01,  8.81646311e-02, -2.41374707e-04,  1.09437257e-01,\n",
              "        3.10950066e-01, -1.20711685e-04,  2.87821091e-01, -8.37273637e-04,\n",
              "       -9.07208721e-04,  2.36936828e-02, -2.73940342e-03,  7.53877496e-02,\n",
              "        2.80267614e-01,  2.10747389e-01,  5.50591767e-02, -1.22299403e-04,\n",
              "        5.97909213e-02, -7.76529473e-04, -4.93320224e-04, -1.06863602e-03,\n",
              "       -2.25406341e-03,  4.31545221e-01,  2.72333354e-01, -2.21796387e-03,\n",
              "       -2.01259506e-03, -2.31346595e-03, -1.10721765e-03, -2.76097317e-03,\n",
              "        1.73566653e-04,  2.34820833e-02, -1.23011250e-03, -1.14168722e-03,\n",
              "        4.14381385e-01, -3.40250335e-03, -2.67795036e-03,  9.44787504e-02,\n",
              "       -1.96947125e-04,  6.11431122e-04,  2.13876231e-01,  7.73771866e-02,\n",
              "       -7.15760639e-04,  3.69282347e-01,  1.96094831e-01,  1.44757461e-01,\n",
              "        1.82611928e-01,  1.14080151e-01,  4.47696381e-02, -2.42881143e-03,\n",
              "       -1.77047109e-03,  9.75362470e-03,  2.21781369e-02,  9.23848641e-02,\n",
              "       -3.24937786e-03, -1.66788821e-03, -2.76949890e-03, -2.77367510e-04,\n",
              "        2.53933218e-01, -2.98410554e-04, -2.59400199e-03, -1.87383736e-03,\n",
              "       -4.91545941e-04, -2.16859106e-03,  1.65145926e-01,  2.28202160e-01,\n",
              "        2.70111900e-01,  2.12098913e-01, -8.57872523e-04,  6.47902646e-02,\n",
              "       -5.43213829e-04, -1.93204809e-03, -8.92921383e-04,  8.85477916e-02,\n",
              "        4.97768755e-02,  1.17253516e-01, -1.19013737e-04,  1.13187140e-01,\n",
              "        9.67968165e-03,  4.83272590e-02,  1.01152725e-01, -1.99221385e-03,\n",
              "       -2.04417807e-04,  6.58482804e-02, -1.36333960e-03, -3.10663780e-03,\n",
              "        2.74588573e-01,  5.38470149e-02, -2.25804818e-03, -3.39327559e-03,\n",
              "        3.51447227e-01, -2.93931978e-03, -1.34349901e-03,  8.63850680e-02,\n",
              "        2.25644455e-02,  3.40859453e-02, -5.48047736e-03, -1.79078321e-03,\n",
              "        3.85180813e-02,  2.18332332e-02, -1.34374103e-04, -1.25452148e-04,\n",
              "        9.77408851e-03,  2.62465603e-01, -3.73780456e-04,  7.70217269e-02,\n",
              "        2.68529288e-01, -5.39613044e-04,  2.84388498e-02, -2.40174169e-03,\n",
              "        3.41128437e-01,  2.68719294e-01, -3.62731208e-03,  2.57199619e-01,\n",
              "        1.96923102e-01, -6.36901848e-04, -8.04085246e-04,  2.51044952e-01,\n",
              "       -2.65832484e-03, -1.85058185e-03,  1.01338553e-01, -2.23576437e-03,\n",
              "        8.71986652e-02,  7.25243848e-02, -8.66496751e-04,  6.17807199e-02,\n",
              "       -2.28080017e-03, -1.29491940e-03,  9.84688757e-02, -1.69853568e-03,\n",
              "        3.11088702e-01, -8.53192231e-04,  3.36747227e-01,  6.47782799e-02,\n",
              "       -1.88412925e-04,  9.82857424e-03,  1.69693623e-02, -1.64515939e-03,\n",
              "        8.49151769e-02,  2.67569547e-01, -1.14298950e-03, -1.17915689e-03,\n",
              "        1.43315526e-01, -2.03666594e-03,  5.91311308e-02,  3.20278625e-01,\n",
              "        4.32881838e-01, -3.61913434e-04,  1.48297515e-01,  2.45562502e-01,\n",
              "        8.48075405e-02,  7.01527447e-02,  3.25755897e-02, -3.44068906e-04,\n",
              "       -2.33004721e-03, -1.65657656e-03, -4.16886707e-03,  3.45470556e-01,\n",
              "       -6.06564086e-04, -2.62055042e-04, -2.19855568e-03,  1.87160074e-01,\n",
              "       -3.42383570e-03, -1.03336029e-03,  1.02585992e-01, -3.27281733e-03,\n",
              "        2.00287047e-02, -1.62523448e-03,  4.98858772e-01,  1.31433423e-01,\n",
              "       -2.74294867e-03, -5.59009678e-04])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iG5V4Pve9L4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}