GANWithBetterParams_batches.ipynb

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      "name": "GANWithBetterParams.ipynb",
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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",
        "\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": [
        "def ReLu(x):\n",
        "    mask = (x>0) * 1.0\n",
        "    return mask *x\n",
        "def d_ReLu(x):\n",
        "    mask = (x>0) * 1.0\n",
        "    return mask \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)\n",
        "def d_tanh(x):\n",
        "    return 1 - np.tanh(x) ** 2\n",
        "\n",
        "def plot(samples, title):\n",
        "    print(len(samples))\n",
        "    fig = plt.figure(figsize=(4, 4))\n",
        "    gs = gridspec.GridSpec(4, 4)\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",
        "        print(\"sample size\", sample.shape)\n",
        "        sample_resized = sample.reshape(5, 28, 28)\n",
        "        print(\"sample resize\", sample_resized.shape)\n",
        "        print(\"each resized sample\", sample_resized[0].shape)\n",
        "        # plt.imshow(sample.reshape(5, 28, 28), cmap='Greys_r')\n",
        "        plt.imshow(sample_resized[0], cmap='Greys_r')\n",
        "\n",
        "\n",
        "    return fig"
      ],
      "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)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ulnl3-qYbfQH",
        "outputId": "cbb2b32c-b277-41d2-adaa-09e0da7a81f6",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "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 = 0.000002\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": 205,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------- Load Data ----------\n",
            "WARNING:tensorflow:From <ipython-input-205-c0c15cddd204>: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-201-f01ec5041f04>:266: _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-201-f01ec5041f04>:268: _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-201-f01ec5041f04>:273: _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-201-f01ec5041f04>:297: _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": "32666c41-7a1f-40ad-d0a4-aba7fbe40cc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\n",
        "# 2. Declare Weights\n",
        "def ResetWeights():\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)\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)"
      ],
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--------- Declare Hyper Parameters ----------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "efhLIYTtbfQn",
        "colab": {}
      },
      "source": [
        "# 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 = 0.5,0.999,0.000001\n",
        "D_list_cost = []\n",
        "G_list_cost = []\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sIXkEq74bfQ7",
        "outputId": "7bf31c92-2ef1-4744-afb0-add19afaf96f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "print('--------- Started Training ----------')\n",
        "ResetWeights()\n",
        "# current_images = []\n",
        "# current_fake_images = []\n",
        "# batch_cost = []\n",
        "\n",
        "for iter in range(1,1000):\n",
        "\n",
        "    current_images = np.array([])\n",
        "    current_fake_images = np.array([])\n",
        "    # print(current_fake_images.shape)\n",
        "    random_int1 = np.random.randint(len(images) - 5)\n",
        "    random_int2 = np.random.randint(len(images) - 5)\n",
        "    random_int3 = np.random.randint(len(images) - 5)\n",
        "    random_int4 = np.random.randint(len(images) - 5)\n",
        "    random_int5 = np.random.randint(len(images) - 5)\n",
        "\n",
        "    current_image1 = np.expand_dims(images[random_int1],axis=0)\n",
        "    current_image2 = np.expand_dims(images[random_int2],axis=0)\n",
        "    current_image3 = np.expand_dims(images[random_int3],axis=0)\n",
        "    current_image4 = np.expand_dims(images[random_int4],axis=0)\n",
        "    current_image5 = np.expand_dims(images[random_int5],axis=0)\n",
        "    \n",
        "    current_images = np.vstack((current_image1, current_image2, current_image3, current_image4, current_image5))\n",
        "\n",
        "    # print(\"shape of current image: \", current_image1.shape)\n",
        "    print(\"shape of current image: \", current_images.shape)\n",
        "    # current_images.append(current_image)\n",
        "\n",
        "    #fig = plot(current_image,\"REAL\")\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=[5, 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",
        "    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",
        "    Dl1_r = current_images.dot(D_W1) + D_b1\n",
        "    Dl1_rA = Lrelu(Dl1_r)\n",
        "    Dl2_r = Dl1_rA.dot(D_W2) + D_b2\n",
        "    Dl2_rA = Lrelu(Dl2_r)\n",
        "    Dl3_r = Dl2_rA.dot(D_W3) + D_b3\n",
        "    Dl3_rA = Lrelu(Dl3_r)\n",
        "    Dl4_r = Dl3_rA.dot(D_W4) + D_b4\n",
        "    Dl4_rA = log(Dl4_r)\n",
        "\n",
        "    # Func: Forward Feed for Fake Data\n",
        "    Dl1_f = current_fake_data.dot(D_W1) + D_b1\n",
        "    # Dl1_f = current_fake_images.dot(D_W1) + D_b1\n",
        "    Dl1_fA = Lrelu(Dl1_f)\n",
        "    Dl2_f = Dl1_fA.dot(D_W2) + D_b2\n",
        "    Dl2_fA = Lrelu(Dl2_f)\n",
        "    Dl3_f = Dl2_fA.dot(D_W3) + D_b3\n",
        "    Dl3_fA = Lrelu(Dl3_f)\n",
        "    Dl4_f = Dl3_fA.dot(D_W4) + D_b4\n",
        "    Dl4_fA = log(Dl4_f)\n",
        "\n",
        "\n",
        "    # Func: Cost D\n",
        "    D_cost = -( np.log(Dl4_rA) +  np.log(1.0- Dl4_fA))\n",
        "    print(\"Cost: \", D_cost)\n",
        "    D_list_cost.append(D_cost[0][0])\n",
        "    # batch_cost.append(D_cost[0[0]])\n",
        "\n",
        "    # Func: Gradient\n",
        "    grad_f_w4_part_1 =  1/(1-Dl4_fA)\n",
        "    grad_f_w4_part_2 =  d_log(Dl4_f)\n",
        "    grad_f_w4_part_3 =   Dl3_fA\n",
        "    grad_f_w4 =     grad_f_w4_part_3.T.dot(grad_f_w4_part_1 * grad_f_w4_part_2) \n",
        "    grad_f_b4 = grad_f_w4_part_1 * grad_f_w4_part_2\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)\n",
        "    grad_f_w3_part_3 =   Dl2_fA\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_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)\n",
        "    grad_f_w2_part_3 = Dl1_fA\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_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)\n",
        "    grad_f_w1_part_3 = current_fake_data\n",
        "    # grad_f_w1_part_3 = current_fake_images\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",
        "    #LATER\n",
        "    grad_r_w4_part_1 =  1/(1-Dl4_rA)\n",
        "    grad_r_w4_part_2 =  d_log(Dl4_r)\n",
        "    grad_r_w4_part_3 =   Dl3_rA\n",
        "    grad_r_w4 =     grad_r_w4_part_3.T.dot(grad_r_w4_part_1 * grad_r_w4_part_2) \n",
        "    grad_r_b4 = grad_r_w4_part_1 * grad_r_w4_part_2\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)\n",
        "    grad_r_w3_part_3 =   Dl2_rA\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_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)\n",
        "    grad_r_w2_part_3 = Dl1_rA\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_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)\n",
        "    # grad_r_w1_part_3 = current_image\n",
        "    grad_r_w1_part_3 = current_images\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_w1 =grad_f_w1 + grad_r_w1\n",
        "    grad_w2 =grad_f_w2 + grad_r_w2\n",
        "    grad_w3 =grad_f_w3 + grad_r_w3\n",
        "    grad_w4 =grad_f_w4 + grad_r_w4\n",
        "\n",
        "    grad_b1 =grad_f_b1 + grad_r_b1\n",
        "    grad_b2 =grad_f_b2 + grad_r_b2\n",
        "    grad_b3 =grad_f_b3 + grad_r_b3\n",
        "    grad_b4 =grad_f_b4 + grad_r_b4\n",
        "\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",
        "    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",
        "    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",
        "    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",
        "\n",
        "    Z = np.random.normal(0., 1., size=[5, G_input]) \n",
        "    # Z1 = np.random.normal(0., 1., size=[1, G_input]) \n",
        "    # Z2 = np.random.normal(0., 1., size=[1, G_input]) \n",
        "    # Z3 = np.random.normal(0., 1., size=[1, G_input]) \n",
        "    # Z4 = np.random.normal(0., 1., size=[1, G_input]) \n",
        "    # Z5 = np.random.normal(0., 1., size=[1, G_input]) \n",
        "\n",
        "    # random_data = np.vstack((Z1, Z2, Z3, Z4, Z5))\n",
        "\n",
        "    Gl1 = Z.dot(G_W1) + G_b1\n",
        "    # Gl1 = random_data.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",
        "    print(\"5 random: \", current_fake_data.shape)\n",
        "\n",
        "    # fake_images.append(current_fake_data)\n",
        "    # current_fake_images = np.vstack((fake_images[0], fake_images[1], fake_images[2], fake_images[3], fake_images[4]))\n",
        "\n",
        "    # current_fake_images.append(current_fake_data)\n",
        "    #fig = plot(current_fake_data,\"FAKE\")\n",
        "\n",
        "    Dl1 = current_fake_data.dot(D_W1) + D_b1\n",
        "    # Dl1 = current_fake_images.dot(D_W1) + D_b1\n",
        "    Dl1_A = Lrelu(Dl1)\n",
        "    Dl2 = Dl1_A.dot(D_W2) + D_b2\n",
        "    Dl2_A = Lrelu(Dl2)\n",
        "    Dl3 = Dl2_A.dot(D_W3) + D_b3\n",
        "    Dl3_A = Lrelu(Dl3)\n",
        "    Dl4 = Dl3_A.dot(D_W4) + D_b4\n",
        "    Dl4_A = log(Dl4)\n",
        "\n",
        "    # Func: Cost G\n",
        "    G_cost = -np.log(Dl4_A)\n",
        "    G_list_cost.append(G_cost[0][0])\n",
        "    # batch_cost.append(G_cost[0][0])\n",
        "\n",
        "    # Func: Gradient\n",
        "    grad_G_w11_part_1 = (((( (-1/(Dl4_A + eps)) * d_log(Dl4).dot(D_W4.T) * (d_Lrelu(Dl3)) ).dot(D_W3.T) * (d_Lrelu(Dl2))).dot(D_W2.T) * (d_Lrelu(Dl1))).dot(D_W1.T))\n",
        "    grad_G_w11_part_2 = d_log(Gl11)\n",
        "    grad_G_w11_part_3 = Gl10A\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_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)\n",
        "    grad_G_w10_part_3 = Gl9A\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_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)\n",
        "    grad_G_w9_part_3 = Gl8A\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_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)\n",
        "    grad_G_w8_part_3 = Gl7A\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_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)\n",
        "    grad_G_w7_part_3 = Gl6A\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_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)\n",
        "    grad_G_w6_part_3 = Gl5A\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_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)\n",
        "    grad_G_w5_part_3 = Gl4A\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_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)\n",
        "    grad_G_w4_part_3 = Gl3A\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_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)\n",
        "    grad_G_w3_part_3 = Gl2A\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_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)\n",
        "    grad_G_w2_part_3 = Gl1A\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_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)\n",
        "    grad_G_w1_part_3 = Z\n",
        "    # grad_G_w1_part_3 = random_data\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",
        "    # ---- Update Gradient ----\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",
        "      \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",
        "\n",
        "    print(\"Current Iter: \",iter, \" Current D cost:\",D_cost, \" Current G cost: \", G_cost)\n",
        "  \n",
        "    if iter == 0:\n",
        "        learing_rate = learing_rate #* 0.01\n",
        "    if iter == 40:\n",
        "        learing_rate = learing_rate #* 0.01\n",
        "\n",
        "    # ---- Print to Out put ----\n",
        "# -- end code --"
      ],
      "execution_count": 209,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            " [0.79955459]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3612451 ]\n",
            " [1.35890058]\n",
            " [1.36699731]\n",
            " [1.36324192]\n",
            " [1.37720637]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  706  Current D cost: [[1.3612451 ]\n",
            " [1.35890058]\n",
            " [1.36699731]\n",
            " [1.36324192]\n",
            " [1.37720637]]  Current G cost:  [[0.80010295]\n",
            " [0.80010259]\n",
            " [0.80010314]\n",
            " [0.8001072 ]\n",
            " [0.80009814]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36063036]\n",
            " [1.36436766]\n",
            " [1.36676927]\n",
            " [1.36077891]\n",
            " [1.3673452 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  707  Current D cost: [[1.36063036]\n",
            " [1.36436766]\n",
            " [1.36676927]\n",
            " [1.36077891]\n",
            " [1.3673452 ]]  Current G cost:  [[0.80064875]\n",
            " [0.80064837]\n",
            " [0.80064875]\n",
            " [0.80065301]\n",
            " [0.80064364]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36484116]\n",
            " [1.36154428]\n",
            " [1.36747863]\n",
            " [1.36010083]\n",
            " [1.37221828]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  708  Current D cost: [[1.36484116]\n",
            " [1.36154428]\n",
            " [1.36747863]\n",
            " [1.36010083]\n",
            " [1.37221828]]  Current G cost:  [[0.80120044]\n",
            " [0.80120012]\n",
            " [0.80120027]\n",
            " [0.80120481]\n",
            " [0.80119501]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35527781]\n",
            " [1.36799998]\n",
            " [1.35848507]\n",
            " [1.35766729]\n",
            " [1.36920635]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  709  Current D cost: [[1.35527781]\n",
            " [1.36799998]\n",
            " [1.35848507]\n",
            " [1.35766729]\n",
            " [1.36920635]]  Current G cost:  [[0.80174759]\n",
            " [0.80174702]\n",
            " [0.80174728]\n",
            " [0.80175198]\n",
            " [0.8017417 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36033583]\n",
            " [1.36444966]\n",
            " [1.36020001]\n",
            " [1.36126413]\n",
            " [1.36457881]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  710  Current D cost: [[1.36033583]\n",
            " [1.36444966]\n",
            " [1.36020001]\n",
            " [1.36126413]\n",
            " [1.36457881]]  Current G cost:  [[0.80229514]\n",
            " [0.80229435]\n",
            " [0.80229476]\n",
            " [0.80229952]\n",
            " [0.80228892]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3585541 ]\n",
            " [1.36193076]\n",
            " [1.35659566]\n",
            " [1.36384536]\n",
            " [1.35974041]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  711  Current D cost: [[1.3585541 ]\n",
            " [1.36193076]\n",
            " [1.35659566]\n",
            " [1.36384536]\n",
            " [1.35974041]]  Current G cost:  [[0.80283927]\n",
            " [0.80283829]\n",
            " [0.8028389 ]\n",
            " [0.80284353]\n",
            " [0.80283286]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35945687]\n",
            " [1.36384152]\n",
            " [1.35777747]\n",
            " [1.3614744 ]\n",
            " [1.35761467]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  712  Current D cost: [[1.35945687]\n",
            " [1.36384152]\n",
            " [1.35777747]\n",
            " [1.3614744 ]\n",
            " [1.35761467]]  Current G cost:  [[0.80338292]\n",
            " [0.80338175]\n",
            " [0.8033826 ]\n",
            " [0.80338709]\n",
            " [0.80337646]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35954799]\n",
            " [1.36464877]\n",
            " [1.37487544]\n",
            " [1.36264306]\n",
            " [1.36951841]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  713  Current D cost: [[1.35954799]\n",
            " [1.36464877]\n",
            " [1.37487544]\n",
            " [1.36264306]\n",
            " [1.36951841]]  Current G cost:  [[0.8039448 ]\n",
            " [0.80394343]\n",
            " [0.80394415]\n",
            " [0.80394886]\n",
            " [0.8039381 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36718443]\n",
            " [1.36292871]\n",
            " [1.35901124]\n",
            " [1.36343723]\n",
            " [1.37270397]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  714  Current D cost: [[1.36718443]\n",
            " [1.36292871]\n",
            " [1.35901124]\n",
            " [1.36343723]\n",
            " [1.37270397]]  Current G cost:  [[0.80451396]\n",
            " [0.80451258]\n",
            " [0.80451334]\n",
            " [0.80451806]\n",
            " [0.80450701]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.37299048]\n",
            " [1.35775855]\n",
            " [1.36331258]\n",
            " [1.35536086]\n",
            " [1.37024855]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  715  Current D cost: [[1.37299048]\n",
            " [1.35775855]\n",
            " [1.36331258]\n",
            " [1.35536086]\n",
            " [1.37024855]]  Current G cost:  [[0.80508478]\n",
            " [0.80508375]\n",
            " [0.80508439]\n",
            " [0.80508931]\n",
            " [0.80507777]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35678409]\n",
            " [1.36123816]\n",
            " [1.36352236]\n",
            " [1.36109973]\n",
            " [1.3650548 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  716  Current D cost: [[1.35678409]\n",
            " [1.36123816]\n",
            " [1.36352236]\n",
            " [1.36109973]\n",
            " [1.3650548 ]]  Current G cost:  [[0.80565247]\n",
            " [0.80565153]\n",
            " [0.80565205]\n",
            " [0.80565713]\n",
            " [0.80564525]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36014206]\n",
            " [1.3604075 ]\n",
            " [1.35580421]\n",
            " [1.37182756]\n",
            " [1.35354375]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  717  Current D cost: [[1.36014206]\n",
            " [1.3604075 ]\n",
            " [1.35580421]\n",
            " [1.37182756]\n",
            " [1.35354375]]  Current G cost:  [[0.80621658]\n",
            " [0.80621567]\n",
            " [0.80621624]\n",
            " [0.80622104]\n",
            " [0.80620941]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.37285053]\n",
            " [1.36661737]\n",
            " [1.35323772]\n",
            " [1.35931998]\n",
            " [1.35852455]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  718  Current D cost: [[1.37285053]\n",
            " [1.36661737]\n",
            " [1.35323772]\n",
            " [1.35931998]\n",
            " [1.35852455]]  Current G cost:  [[0.8067856 ]\n",
            " [0.80678485]\n",
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            " [0.80679027]\n",
            " [0.80677878]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36032285]\n",
            " [1.36147955]\n",
            " [1.37318642]\n",
            " [1.35950116]\n",
            " [1.36121785]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  719  Current D cost: [[1.36032285]\n",
            " [1.36147955]\n",
            " [1.37318642]\n",
            " [1.35950116]\n",
            " [1.36121785]]  Current G cost:  [[0.80736164]\n",
            " [0.80736095]\n",
            " [0.80736175]\n",
            " [0.80736645]\n",
            " [0.80735499]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36846649]\n",
            " [1.3655495 ]\n",
            " [1.37031195]\n",
            " [1.3590285 ]\n",
            " [1.36681459]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  720  Current D cost: [[1.36846649]\n",
            " [1.3655495 ]\n",
            " [1.37031195]\n",
            " [1.3590285 ]\n",
            " [1.36681459]]  Current G cost:  [[0.80795042]\n",
            " [0.80794983]\n",
            " [0.80795046]\n",
            " [0.80795552]\n",
            " [0.80794389]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36232807]\n",
            " [1.365744  ]\n",
            " [1.35677828]\n",
            " [1.35246025]\n",
            " [1.35793381]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  721  Current D cost: [[1.36232807]\n",
            " [1.365744  ]\n",
            " [1.35677828]\n",
            " [1.35246025]\n",
            " [1.35793381]]  Current G cost:  [[0.80852901]\n",
            " [0.80852839]\n",
            " [0.80852915]\n",
            " [0.8085345 ]\n",
            " [0.80852265]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.362057  ]\n",
            " [1.3610088 ]\n",
            " [1.36199907]\n",
            " [1.35501195]\n",
            " [1.36215976]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  722  Current D cost: [[1.362057  ]\n",
            " [1.3610088 ]\n",
            " [1.36199907]\n",
            " [1.35501195]\n",
            " [1.36215976]]  Current G cost:  [[0.80910789]\n",
            " [0.80910729]\n",
            " [0.80910808]\n",
            " [0.80911374]\n",
            " [0.80910162]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36162711]\n",
            " [1.35360817]\n",
            " [1.3661904 ]\n",
            " [1.3641898 ]\n",
            " [1.35619223]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  723  Current D cost: [[1.36162711]\n",
            " [1.35360817]\n",
            " [1.3661904 ]\n",
            " [1.3641898 ]\n",
            " [1.35619223]]  Current G cost:  [[0.80968867]\n",
            " [0.80968826]\n",
            " [0.80968878]\n",
            " [0.80969465]\n",
            " [0.80968257]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36004353]\n",
            " [1.36273817]\n",
            " [1.3541746 ]\n",
            " [1.36028233]\n",
            " [1.35883879]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  724  Current D cost: [[1.36004353]\n",
            " [1.36273817]\n",
            " [1.3541746 ]\n",
            " [1.36028233]\n",
            " [1.35883879]]  Current G cost:  [[0.81026901]\n",
            " [0.81026864]\n",
            " [0.81026922]\n",
            " [0.81027505]\n",
            " [0.81026302]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35383953]\n",
            " [1.35378526]\n",
            " [1.35816152]\n",
            " [1.36097319]\n",
            " [1.36108784]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  725  Current D cost: [[1.35383953]\n",
            " [1.35378526]\n",
            " [1.35816152]\n",
            " [1.36097319]\n",
            " [1.36108784]]  Current G cost:  [[0.81084637]\n",
            " [0.81084603]\n",
            " [0.81084653]\n",
            " [0.81085228]\n",
            " [0.81084028]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36424425]\n",
            " [1.36384591]\n",
            " [1.36524799]\n",
            " [1.35531822]\n",
            " [1.36432902]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  726  Current D cost: [[1.36424425]\n",
            " [1.36384591]\n",
            " [1.36524799]\n",
            " [1.35531822]\n",
            " [1.36432902]]  Current G cost:  [[0.81143697]\n",
            " [0.81143665]\n",
            " [0.81143708]\n",
            " [0.81144304]\n",
            " [0.81143083]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36135807]\n",
            " [1.36594698]\n",
            " [1.35979213]\n",
            " [1.36631398]\n",
            " [1.35803743]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  727  Current D cost: [[1.36135807]\n",
            " [1.36594698]\n",
            " [1.35979213]\n",
            " [1.36631398]\n",
            " [1.35803743]]  Current G cost:  [[0.81203526]\n",
            " [0.81203485]\n",
            " [0.81203539]\n",
            " [0.81204129]\n",
            " [0.81202917]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36339655]\n",
            " [1.36014332]\n",
            " [1.36148086]\n",
            " [1.35387955]\n",
            " [1.35654237]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  728  Current D cost: [[1.36339655]\n",
            " [1.36014332]\n",
            " [1.36148086]\n",
            " [1.35387955]\n",
            " [1.35654237]]  Current G cost:  [[0.81263074]\n",
            " [0.81263036]\n",
            " [0.81263091]\n",
            " [0.81263698]\n",
            " [0.81262484]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35442234]\n",
            " [1.35136797]\n",
            " [1.3566463 ]\n",
            " [1.35633389]\n",
            " [1.37042657]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  729  Current D cost: [[1.35442234]\n",
            " [1.35136797]\n",
            " [1.3566463 ]\n",
            " [1.35633389]\n",
            " [1.37042657]]  Current G cost:  [[0.81322331]\n",
            " [0.81322302]\n",
            " [0.81322346]\n",
            " [0.81322962]\n",
            " [0.81321713]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36352067]\n",
            " [1.35684948]\n",
            " [1.3545434 ]\n",
            " [1.36249894]\n",
            " [1.35874377]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  730  Current D cost: [[1.36352067]\n",
            " [1.35684948]\n",
            " [1.3545434 ]\n",
            " [1.36249894]\n",
            " [1.35874377]]  Current G cost:  [[0.81381961]\n",
            " [0.81381953]\n",
            " [0.81381996]\n",
            " [0.81382598]\n",
            " [0.81381341]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36674608]\n",
            " [1.35760565]\n",
            " [1.36561944]\n",
            " [1.36061658]\n",
            " [1.35773274]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  731  Current D cost: [[1.36674608]\n",
            " [1.35760565]\n",
            " [1.36561944]\n",
            " [1.36061658]\n",
            " [1.35773274]]  Current G cost:  [[0.81442545]\n",
            " [0.81442569]\n",
            " [0.81442593]\n",
            " [0.814432  ]\n",
            " [0.81441945]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36136406]\n",
            " [1.35501294]\n",
            " [1.35265684]\n",
            " [1.35567398]\n",
            " [1.36017571]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  732  Current D cost: [[1.36136406]\n",
            " [1.35501294]\n",
            " [1.35265684]\n",
            " [1.35567398]\n",
            " [1.36017571]]  Current G cost:  [[0.81502621]\n",
            " [0.81502676]\n",
            " [0.81502695]\n",
            " [0.81503299]\n",
            " [0.81502034]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36211505]\n",
            " [1.35739585]\n",
            " [1.3598851 ]\n",
            " [1.35423469]\n",
            " [1.35506155]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  733  Current D cost: [[1.36211505]\n",
            " [1.35739585]\n",
            " [1.3598851 ]\n",
            " [1.35423469]\n",
            " [1.35506155]]  Current G cost:  [[0.81562829]\n",
            " [0.81562911]\n",
            " [0.81562923]\n",
            " [0.81563538]\n",
            " [0.81562267]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36079146]\n",
            " [1.3633317 ]\n",
            " [1.36231934]\n",
            " [1.35173263]\n",
            " [1.3553327 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  734  Current D cost: [[1.36079146]\n",
            " [1.3633317 ]\n",
            " [1.36231934]\n",
            " [1.35173263]\n",
            " [1.3553327 ]]  Current G cost:  [[0.81623513]\n",
            " [0.81623603]\n",
            " [0.81623613]\n",
            " [0.8162426 ]\n",
            " [0.81622976]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35570575]\n",
            " [1.34975774]\n",
            " [1.35878166]\n",
            " [1.3641515 ]\n",
            " [1.35731844]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  735  Current D cost: [[1.35570575]\n",
            " [1.34975774]\n",
            " [1.35878166]\n",
            " [1.3641515 ]\n",
            " [1.35731844]]  Current G cost:  [[0.81684259]\n",
            " [0.81684367]\n",
            " [0.81684354]\n",
            " [0.81685004]\n",
            " [0.8168373 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.37283579]\n",
            " [1.36478367]\n",
            " [1.35927886]\n",
            " [1.35153333]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  736  Current D cost: [[1.37283579]\n",
            " [1.36478367]\n",
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            " [1.35165499]]  Current G cost:  [[0.81745882]\n",
            " [0.81746018]\n",
            " [0.81746007]\n",
            " [0.81746678]\n",
            " [0.81745408]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36027873]\n",
            " [1.35683297]\n",
            " [1.35208813]\n",
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            "Current Iter:  737  Current D cost: [[1.36027873]\n",
            " [1.35683297]\n",
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            " [1.34859732]]  Current G cost:  [[0.81806794]\n",
            " [0.81806953]\n",
            " [0.81806954]\n",
            " [0.81807629]\n",
            " [0.81806376]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3663674 ]\n",
            " [1.35018394]\n",
            " [1.36419051]\n",
            " [1.36379217]\n",
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            "Current Iter:  738  Current D cost: [[1.3663674 ]\n",
            " [1.35018394]\n",
            " [1.36419051]\n",
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            " [1.35889589]]  Current G cost:  [[0.81869038]\n",
            " [0.81869247]\n",
            " [0.8186922 ]\n",
            " [0.81869898]\n",
            " [0.81868666]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35926466]\n",
            " [1.36027503]\n",
            " [1.36118087]\n",
            " [1.3602694 ]\n",
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            "Current Iter:  739  Current D cost: [[1.35926466]\n",
            " [1.36027503]\n",
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            " [1.35215405]]  Current G cost:  [[0.81931617]\n",
            " [0.81931848]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36310038]\n",
            " [1.35304064]\n",
            " [1.36386265]\n",
            " [1.36496357]\n",
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            "Current Iter:  740  Current D cost: [[1.36310038]\n",
            " [1.35304064]\n",
            " [1.36386265]\n",
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            " [1.3653845 ]]  Current G cost:  [[0.8199542 ]\n",
            " [0.81995687]\n",
            " [0.81995609]\n",
            " [0.81996292]\n",
            " [0.81995102]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36213475]\n",
            " [1.36282595]\n",
            " [1.35668167]\n",
            " [1.36210134]\n",
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            "Current Iter:  741  Current D cost: [[1.36213475]\n",
            " [1.36282595]\n",
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            " [1.35851558]]  Current G cost:  [[0.82059609]\n",
            " [0.82059891]\n",
            " [0.82059812]\n",
            " [0.82060482]\n",
            " [0.82059307]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36785782]\n",
            " [1.35190101]\n",
            " [1.3553744 ]\n",
            " [1.35654912]\n",
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            "Current Iter:  742  Current D cost: [[1.36785782]\n",
            " [1.35190101]\n",
            " [1.3553744 ]\n",
            " [1.35654912]\n",
            " [1.35071798]]  Current G cost:  [[0.821231  ]\n",
            " [0.82123428]\n",
            " [0.82123339]\n",
            " [0.82124   ]\n",
            " [0.82122847]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35900074]\n",
            " [1.35468809]\n",
            " [1.35667897]\n",
            " [1.36290188]\n",
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            "Current Iter:  743  Current D cost: [[1.35900074]\n",
            " [1.35468809]\n",
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            " [1.36290188]\n",
            " [1.3492012 ]]  Current G cost:  [[0.82186456]\n",
            " [0.82186818]\n",
            " [0.82186719]\n",
            " [0.82187361]\n",
            " [0.82186251]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36381148]\n",
            " [1.35480011]\n",
            " [1.35870318]\n",
            " [1.35036108]\n",
            " [1.35953435]]\n",
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            "Current Iter:  744  Current D cost: [[1.36381148]\n",
            " [1.35480011]\n",
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            " [1.35036108]\n",
            " [1.35953435]]  Current G cost:  [[0.8225017 ]\n",
            " [0.8225057 ]\n",
            " [0.82250457]\n",
            " [0.82251111]\n",
            " [0.82249999]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36373231]\n",
            " [1.36531309]\n",
            " [1.35591977]\n",
            " [1.37008277]\n",
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            "Current Iter:  745  Current D cost: [[1.36373231]\n",
            " [1.36531309]\n",
            " [1.35591977]\n",
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            " [1.35058774]]  Current G cost:  [[0.82315229]\n",
            " [0.82315644]\n",
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            " [0.82315108]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35220326]\n",
            " [1.35642118]\n",
            " [1.36303747]\n",
            " [1.3589563 ]\n",
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            "Current Iter:  746  Current D cost: [[1.35220326]\n",
            " [1.35642118]\n",
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            " [1.35747986]]  Current G cost:  [[0.82380219]\n",
            " [0.82380631]\n",
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            " [0.82380108]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35978389]\n",
            " [1.34914904]\n",
            " [1.35379276]\n",
            " [1.35603295]\n",
            " [1.35528477]]\n",
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            "Current Iter:  747  Current D cost: [[1.35978389]\n",
            " [1.34914904]\n",
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            " [1.35528477]]  Current G cost:  [[0.8244464 ]\n",
            " [0.82445076]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3589752 ]\n",
            " [1.34869807]\n",
            " [1.36559363]\n",
            " [1.36456568]\n",
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            "Current Iter:  748  Current D cost: [[1.3589752 ]\n",
            " [1.34869807]\n",
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            " [1.37218187]]  Current G cost:  [[0.8251076 ]\n",
            " [0.82511232]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36104489]\n",
            " [1.36114774]\n",
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            " [1.3548332 ]\n",
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            "Current Iter:  749  Current D cost: [[1.36104489]\n",
            " [1.36114774]\n",
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            " [1.35804867]]  Current G cost:  [[0.82576914]\n",
            " [0.82577405]\n",
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            " [0.82577841]\n",
            " [0.8257679 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35591972]\n",
            " [1.35328916]\n",
            " [1.35702168]\n",
            " [1.35756741]\n",
            " [1.35497143]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  750  Current D cost: [[1.35591972]\n",
            " [1.35328916]\n",
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            " [1.35497143]]  Current G cost:  [[0.82642725]\n",
            " [0.82643231]\n",
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            " [0.826426  ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36635068]\n",
            " [1.35402717]\n",
            " [1.35906682]\n",
            " [1.35077234]\n",
            " [1.36061053]]\n",
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            "Current Iter:  751  Current D cost: [[1.36635068]\n",
            " [1.35402717]\n",
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            " [1.36061053]]  Current G cost:  [[0.82709113]\n",
            " [0.82709655]\n",
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            " [0.82709001]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35266337]\n",
            " [1.34616626]\n",
            " [1.35063788]\n",
            " [1.34935925]\n",
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            "Current Iter:  752  Current D cost: [[1.35266337]\n",
            " [1.34616626]\n",
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            " [1.36234638]]  Current G cost:  [[0.82774543]\n",
            " [0.8277512 ]\n",
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            " [0.82774414]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3621646 ]\n",
            " [1.36711127]\n",
            " [1.35295111]\n",
            " [1.3516779 ]\n",
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            "Current Iter:  753  Current D cost: [[1.3621646 ]\n",
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            " [1.35018406]]  Current G cost:  [[0.82840792]\n",
            " [0.82841374]\n",
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            " [0.82840683]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36955479]\n",
            " [1.36226585]\n",
            " [1.34861877]\n",
            " [1.348784  ]\n",
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            "Current Iter:  754  Current D cost: [[1.36955479]\n",
            " [1.36226585]\n",
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            " [1.35462206]]  Current G cost:  [[0.82907595]\n",
            " [0.82908196]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.37056948]\n",
            " [1.3607981 ]\n",
            " [1.35032607]\n",
            " [1.35081489]\n",
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            "Current Iter:  755  Current D cost: [[1.37056948]\n",
            " [1.3607981 ]\n",
            " [1.35032607]\n",
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            " [1.35218048]]  Current G cost:  [[0.82974849]\n",
            " [0.82975483]\n",
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            " [0.82974855]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35032372]\n",
            " [1.3584709 ]\n",
            " [1.35210485]\n",
            " [1.36499878]\n",
            " [1.34413411]]\n",
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            "Current Iter:  756  Current D cost: [[1.35032372]\n",
            " [1.3584709 ]\n",
            " [1.35210485]\n",
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            " [1.34413411]]  Current G cost:  [[0.83041894]\n",
            " [0.83042524]\n",
            " [0.83042452]\n",
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            " [0.83041948]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35033653]\n",
            " [1.345475  ]\n",
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            " [1.35029355]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  757  Current D cost: [[1.35033653]\n",
            " [1.345475  ]\n",
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            " [1.35029355]]  Current G cost:  [[0.83107823]\n",
            " [0.83108462]\n",
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            " [0.83109025]\n",
            " [0.83107902]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34953335]\n",
            " [1.35341021]\n",
            " [1.35236189]\n",
            " [1.35388828]\n",
            " [1.35062415]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  758  Current D cost: [[1.34953335]\n",
            " [1.35341021]\n",
            " [1.35236189]\n",
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            " [1.35062415]]  Current G cost:  [[0.83174079]\n",
            " [0.83174712]\n",
            " [0.8317465 ]\n",
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            " [0.83174166]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35311883]\n",
            " [1.35983081]\n",
            " [1.36008238]\n",
            " [1.36325617]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  759  Current D cost: [[1.35311883]\n",
            " [1.35983081]\n",
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            " [1.35871474]]  Current G cost:  [[0.83242375]\n",
            " [0.83242989]\n",
            " [0.83242929]\n",
            " [0.83243541]\n",
            " [0.83242452]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35143731]\n",
            " [1.36370302]\n",
            " [1.34650022]\n",
            " [1.35738537]\n",
            " [1.35378441]]\n",
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            "Current Iter:  760  Current D cost: [[1.35143731]\n",
            " [1.36370302]\n",
            " [1.34650022]\n",
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            " [1.35378441]]  Current G cost:  [[0.83310791]\n",
            " [0.83311363]\n",
            " [0.83311347]\n",
            " [0.83311927]\n",
            " [0.83310856]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3512136 ]\n",
            " [1.35182008]\n",
            " [1.37145149]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  761  Current D cost: [[1.3512136 ]\n",
            " [1.35182008]\n",
            " [1.37145149]\n",
            " [1.35072208]\n",
            " [1.3530133 ]]  Current G cost:  [[0.83379692]\n",
            " [0.83380241]\n",
            " [0.83380198]\n",
            " [0.83380814]\n",
            " [0.83379746]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34164248]\n",
            " [1.35006876]\n",
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            " [1.3635849 ]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  762  Current D cost: [[1.34164248]\n",
            " [1.35006876]\n",
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            " [1.3635849 ]\n",
            " [1.3646343 ]]  Current G cost:  [[0.83448498]\n",
            " [0.83449014]\n",
            " [0.83448967]\n",
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            " [0.83448488]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34563085]\n",
            " [1.35256752]\n",
            " [1.34867261]\n",
            " [1.35830465]\n",
            " [1.35745283]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  763  Current D cost: [[1.34563085]\n",
            " [1.35256752]\n",
            " [1.34867261]\n",
            " [1.35830465]\n",
            " [1.35745283]]  Current G cost:  [[0.83517267]\n",
            " [0.83517749]\n",
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            " [0.83517196]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35224736]\n",
            " [1.35717622]\n",
            " [1.35610166]\n",
            " [1.35526962]\n",
            " [1.34945965]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  764  Current D cost: [[1.35224736]\n",
            " [1.35717622]\n",
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            " [1.34945965]]  Current G cost:  [[0.83586575]\n",
            " [0.83587027]\n",
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            " [0.83586478]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35731904]\n",
            " [1.3524693 ]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  765  Current D cost: [[1.35731904]\n",
            " [1.3524693 ]\n",
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            " [1.3475733 ]]  Current G cost:  [[0.83655998]\n",
            " [0.83656447]\n",
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            " [0.83655914]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34533705]\n",
            " [1.34816778]\n",
            " [1.3559058 ]\n",
            " [1.35081918]\n",
            " [1.36075548]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  766  Current D cost: [[1.34533705]\n",
            " [1.34816778]\n",
            " [1.3559058 ]\n",
            " [1.35081918]\n",
            " [1.36075548]]  Current G cost:  [[0.83725586]\n",
            " [0.83726026]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35301747]\n",
            " [1.35153466]\n",
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            "Current Iter:  767  Current D cost: [[1.35301747]\n",
            " [1.35153466]\n",
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            " [1.35561913]]  Current G cost:  [[0.83795306]\n",
            " [0.83795745]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35608761]\n",
            " [1.34383701]\n",
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            "Current Iter:  768  Current D cost: [[1.35608761]\n",
            " [1.34383701]\n",
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            " [1.35326838]]  Current G cost:  [[0.83865092]\n",
            " [0.8386556 ]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35444581]\n",
            " [1.34890022]\n",
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            "Current Iter:  769  Current D cost: [[1.35444581]\n",
            " [1.34890022]\n",
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            " [1.34514152]]  Current G cost:  [[0.83934835]\n",
            " [0.83935332]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34955474]\n",
            " [1.35109   ]\n",
            " [1.34863516]\n",
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            "Current Iter:  770  Current D cost: [[1.34955474]\n",
            " [1.35109   ]\n",
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            " [1.35885412]]  Current G cost:  [[0.84005081]\n",
            " [0.84005589]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34750879]\n",
            " [1.36449963]\n",
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            " [1.34806348]\n",
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            "Current Iter:  771  Current D cost: [[1.34750879]\n",
            " [1.36449963]\n",
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            " [1.3415417 ]]  Current G cost:  [[0.84075443]\n",
            " [0.84075912]\n",
            " [0.84075889]\n",
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            " [0.84075312]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36128128]\n",
            " [1.35489271]\n",
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            " [1.35228744]\n",
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            "Current Iter:  772  Current D cost: [[1.36128128]\n",
            " [1.35489271]\n",
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            " [1.35663156]]  Current G cost:  [[0.84147318]\n",
            " [0.84147783]\n",
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            "stuff:  (5, 784)\n",
            "Cost:  [[1.35844947]\n",
            " [1.35555616]\n",
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            "Current Iter:  773  Current D cost: [[1.35844947]\n",
            " [1.35555616]\n",
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            " [1.35512529]]  Current G cost:  [[0.84219226]\n",
            " [0.84219696]\n",
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            " [0.8422027 ]\n",
            " [0.84219126]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34621412]\n",
            " [1.35549405]\n",
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            "Current Iter:  774  Current D cost: [[1.34621412]\n",
            " [1.35549405]\n",
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            " [1.35922277]]  Current G cost:  [[0.84292821]\n",
            " [0.84293271]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35382039]\n",
            " [1.35326852]\n",
            " [1.34555793]\n",
            " [1.35036838]\n",
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            "Current Iter:  775  Current D cost: [[1.35382039]\n",
            " [1.35326852]\n",
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            " [1.33935855]]  Current G cost:  [[0.84365316]\n",
            " [0.84365755]\n",
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            " [0.84365214]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34948793]\n",
            " [1.3546651 ]\n",
            " [1.35743118]\n",
            " [1.35318074]\n",
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            "Current Iter:  776  Current D cost: [[1.34948793]\n",
            " [1.3546651 ]\n",
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            " [1.35172318]]  Current G cost:  [[0.8443862 ]\n",
            " [0.84439041]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3462946 ]\n",
            " [1.35407693]\n",
            " [1.34251089]\n",
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            "Current Iter:  777  Current D cost: [[1.3462946 ]\n",
            " [1.35407693]\n",
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            " [1.34407806]]  Current G cost:  [[0.84510626]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.37409277]\n",
            " [1.34610403]\n",
            " [1.34230495]\n",
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            "Current Iter:  778  Current D cost: [[1.37409277]\n",
            " [1.34610403]\n",
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            " [1.35733219]]  Current G cost:  [[0.8458396 ]\n",
            " [0.84584407]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35587434]\n",
            " [1.3404612 ]\n",
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            "Current Iter:  779  Current D cost: [[1.35587434]\n",
            " [1.3404612 ]\n",
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            " [1.35496399]]  Current G cost:  [[0.84657505]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34470056]\n",
            " [1.35712566]\n",
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            "Current Iter:  780  Current D cost: [[1.34470056]\n",
            " [1.35712566]\n",
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            " [1.34130552]]  Current G cost:  [[0.84731482]\n",
            " [0.84731999]\n",
            " [0.8473214 ]\n",
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            " [0.84731487]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34228729]\n",
            " [1.34597796]\n",
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            "Current Iter:  781  Current D cost: [[1.34228729]\n",
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            " [1.35009057]]  Current G cost:  [[0.84804181]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35100903]\n",
            " [1.35518393]\n",
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            "Current Iter:  782  Current D cost: [[1.35100903]\n",
            " [1.35518393]\n",
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            " [1.34402211]]  Current G cost:  [[0.84877834]\n",
            " [0.84878325]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34998536]\n",
            " [1.34729044]\n",
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            "Current Iter:  783  Current D cost: [[1.34998536]\n",
            " [1.34729044]\n",
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            " [1.3548823 ]]  Current G cost:  [[0.84952171]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33982729]\n",
            " [1.34845418]\n",
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            " [1.34372012]\n",
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            "Current Iter:  784  Current D cost: [[1.33982729]\n",
            " [1.34845418]\n",
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            " [1.33942365]]  Current G cost:  [[0.85025773]\n",
            " [0.85026239]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34433291]\n",
            " [1.35469601]\n",
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            "Current Iter:  785  Current D cost: [[1.34433291]\n",
            " [1.35469601]\n",
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            " [1.34480221]]  Current G cost:  [[0.85100553]\n",
            " [0.8510098 ]\n",
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            " [0.85100546]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34735399]\n",
            " [1.34330192]\n",
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            " [1.3604542 ]\n",
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            "Current Iter:  786  Current D cost: [[1.34735399]\n",
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            " [1.34769693]]  Current G cost:  [[0.85175608]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34938682]\n",
            " [1.34640809]\n",
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            "Current Iter:  787  Current D cost: [[1.34938682]\n",
            " [1.34640809]\n",
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            " [1.35218411]]  Current G cost:  [[0.85251375]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33642137]\n",
            " [1.34277393]\n",
            " [1.357731  ]\n",
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            "Current Iter:  788  Current D cost: [[1.33642137]\n",
            " [1.34277393]\n",
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            " [1.34275246]]  Current G cost:  [[0.85326437]\n",
            " [0.85326841]\n",
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            " [0.85326395]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33762415]\n",
            " [1.35484561]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  789  Current D cost: [[1.33762415]\n",
            " [1.35484561]\n",
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            " [1.34863437]]  Current G cost:  [[0.85402021]\n",
            " [0.85402372]\n",
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            " [0.85401939]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35967323]\n",
            " [1.34198171]\n",
            " [1.34685906]\n",
            " [1.34035242]\n",
            " [1.33334253]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  790  Current D cost: [[1.35967323]\n",
            " [1.34198171]\n",
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            " [1.33334253]]  Current G cost:  [[0.85477515]\n",
            " [0.85477885]\n",
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            " [0.85477481]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35110569]\n",
            " [1.3555057 ]\n",
            " [1.34317889]\n",
            " [1.34043238]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  791  Current D cost: [[1.35110569]\n",
            " [1.3555057 ]\n",
            " [1.34317889]\n",
            " [1.34043238]\n",
            " [1.34114276]]  Current G cost:  [[0.85553559]\n",
            " [0.85553928]\n",
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            " [0.85553576]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33344138]\n",
            " [1.34832216]\n",
            " [1.36121655]\n",
            " [1.35035015]\n",
            " [1.33548977]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  792  Current D cost: [[1.33344138]\n",
            " [1.34832216]\n",
            " [1.36121655]\n",
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            " [1.33548977]]  Current G cost:  [[0.85629974]\n",
            " [0.85630302]\n",
            " [0.85630396]\n",
            " [0.8563113 ]\n",
            " [0.8563001 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34629745]\n",
            " [1.34183326]\n",
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            " [1.34767346]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  793  Current D cost: [[1.34629745]\n",
            " [1.34183326]\n",
            " [1.34459874]\n",
            " [1.35163619]\n",
            " [1.34767346]]  Current G cost:  [[0.85706955]\n",
            " [0.85707274]\n",
            " [0.85707349]\n",
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            " [0.85706996]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35684976]\n",
            " [1.34611808]\n",
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            " [1.3532207 ]\n",
            " [1.34053404]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  794  Current D cost: [[1.35684976]\n",
            " [1.34611808]\n",
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            " [1.3532207 ]\n",
            " [1.34053404]]  Current G cost:  [[0.85784243]\n",
            " [0.85784586]\n",
            " [0.85784688]\n",
            " [0.85785373]\n",
            " [0.85784329]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34763245]\n",
            " [1.33889158]\n",
            " [1.33514462]\n",
            " [1.34047657]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  795  Current D cost: [[1.34763245]\n",
            " [1.33889158]\n",
            " [1.33514462]\n",
            " [1.34047657]\n",
            " [1.35371228]]  Current G cost:  [[0.85861304]\n",
            " [0.85861681]\n",
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            " [0.85861396]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34483424]\n",
            " [1.3445496 ]\n",
            " [1.34022085]\n",
            " [1.33918761]\n",
            " [1.34676289]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  796  Current D cost: [[1.34483424]\n",
            " [1.3445496 ]\n",
            " [1.34022085]\n",
            " [1.33918761]\n",
            " [1.34676289]]  Current G cost:  [[0.85938469]\n",
            " [0.85938864]\n",
            " [0.85939012]\n",
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            " [0.85938557]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35034245]\n",
            " [1.34698436]\n",
            " [1.34106477]\n",
            " [1.34469527]\n",
            " [1.34124804]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  797  Current D cost: [[1.35034245]\n",
            " [1.34698436]\n",
            " [1.34106477]\n",
            " [1.34469527]\n",
            " [1.34124804]]  Current G cost:  [[0.86016294]\n",
            " [0.86016707]\n",
            " [0.86016882]\n",
            " [0.86017493]\n",
            " [0.86016405]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34488349]\n",
            " [1.3464155 ]\n",
            " [1.33982012]\n",
            " [1.34063439]\n",
            " [1.34316658]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  798  Current D cost: [[1.34488349]\n",
            " [1.3464155 ]\n",
            " [1.33982012]\n",
            " [1.34063439]\n",
            " [1.34316658]]  Current G cost:  [[0.86094182]\n",
            " [0.860946  ]\n",
            " [0.86094805]\n",
            " [0.86095406]\n",
            " [0.86094308]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33725337]\n",
            " [1.33772854]\n",
            " [1.35207992]\n",
            " [1.34108831]\n",
            " [1.36192155]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  799  Current D cost: [[1.33725337]\n",
            " [1.33772854]\n",
            " [1.35207992]\n",
            " [1.34108831]\n",
            " [1.36192155]]  Current G cost:  [[0.86173021]\n",
            " [0.86173439]\n",
            " [0.86173622]\n",
            " [0.86174248]\n",
            " [0.86173088]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36591684]\n",
            " [1.35446313]\n",
            " [1.34463153]\n",
            " [1.34525229]\n",
            " [1.34116971]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  800  Current D cost: [[1.36591684]\n",
            " [1.35446313]\n",
            " [1.34463153]\n",
            " [1.34525229]\n",
            " [1.34116971]]  Current G cost:  [[0.8625343 ]\n",
            " [0.86253878]\n",
            " [0.86254075]\n",
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            " [0.86253532]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33441394]\n",
            " [1.33425692]\n",
            " [1.34007415]\n",
            " [1.34740532]\n",
            " [1.35043574]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  801  Current D cost: [[1.33441394]\n",
            " [1.33425692]\n",
            " [1.34007415]\n",
            " [1.34740532]\n",
            " [1.35043574]]  Current G cost:  [[0.86332893]\n",
            " [0.86333356]\n",
            " [0.86333544]\n",
            " [0.86334168]\n",
            " [0.86332969]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34608474]\n",
            " [1.34940611]\n",
            " [1.34152791]\n",
            " [1.34314219]\n",
            " [1.34845364]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  802  Current D cost: [[1.34608474]\n",
            " [1.34940611]\n",
            " [1.34152791]\n",
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            " [1.34845364]]  Current G cost:  [[0.86413144]\n",
            " [0.86413606]\n",
            " [0.8641381 ]\n",
            " [0.86414423]\n",
            " [0.86413201]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33431104]\n",
            " [1.33583798]\n",
            " [1.3552249 ]\n",
            " [1.33658829]\n",
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            "Current Iter:  803  Current D cost: [[1.33431104]\n",
            " [1.33583798]\n",
            " [1.3552249 ]\n",
            " [1.33658829]\n",
            " [1.33461433]]  Current G cost:  [[0.86492513]\n",
            " [0.86492969]\n",
            " [0.86493129]\n",
            " [0.86493788]\n",
            " [0.86492558]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33093516]\n",
            " [1.35338645]\n",
            " [1.34125333]\n",
            " [1.3682481 ]\n",
            " [1.34923012]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  804  Current D cost: [[1.33093516]\n",
            " [1.35338645]\n",
            " [1.34125333]\n",
            " [1.3682481 ]\n",
            " [1.34923012]]  Current G cost:  [[0.86573682]\n",
            " [0.86574075]\n",
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            " [0.86573671]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34685319]\n",
            " [1.34930792]\n",
            " [1.33781761]\n",
            " [1.33488323]\n",
            " [1.34536444]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  805  Current D cost: [[1.34685319]\n",
            " [1.34930792]\n",
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            " [1.34536444]]  Current G cost:  [[0.86654651]\n",
            " [0.86655005]\n",
            " [0.8665521 ]\n",
            " [0.86655805]\n",
            " [0.86654616]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33779268]\n",
            " [1.34296936]\n",
            " [1.34947021]\n",
            " [1.34724802]\n",
            " [1.33735845]]\n",
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            "Current Iter:  806  Current D cost: [[1.33779268]\n",
            " [1.34296936]\n",
            " [1.34947021]\n",
            " [1.34724802]\n",
            " [1.33735845]]  Current G cost:  [[0.86735782]\n",
            " [0.86736102]\n",
            " [0.86736308]\n",
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            " [0.86735735]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34637815]\n",
            " [1.34689907]\n",
            " [1.34543604]\n",
            " [1.34390387]\n",
            " [1.34456436]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  807  Current D cost: [[1.34637815]\n",
            " [1.34689907]\n",
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            " [1.34456436]]  Current G cost:  [[0.86817738]\n",
            " [0.8681804 ]\n",
            " [0.86818249]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33733923]\n",
            " [1.33556886]\n",
            " [1.33929419]\n",
            " [1.35611884]\n",
            " [1.33903054]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  808  Current D cost: [[1.33733923]\n",
            " [1.33556886]\n",
            " [1.33929419]\n",
            " [1.35611884]\n",
            " [1.33903054]]  Current G cost:  [[0.86899461]\n",
            " [0.86899758]\n",
            " [0.86899959]\n",
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            " [0.86899407]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33311505]\n",
            " [1.35325563]\n",
            " [1.34462694]\n",
            " [1.34285664]\n",
            " [1.33817625]]\n",
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            "Current Iter:  809  Current D cost: [[1.33311505]\n",
            " [1.35325563]\n",
            " [1.34462694]\n",
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            " [1.33817625]]  Current G cost:  [[0.86981533]\n",
            " [0.86981773]\n",
            " [0.86981992]\n",
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            " [0.86981462]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34431247]\n",
            " [1.33531933]\n",
            " [1.33830594]\n",
            " [1.33450569]\n",
            " [1.33534966]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  810  Current D cost: [[1.34431247]\n",
            " [1.33531933]\n",
            " [1.33830594]\n",
            " [1.33450569]\n",
            " [1.33534966]]  Current G cost:  [[0.87062846]\n",
            " [0.87063082]\n",
            " [0.87063302]\n",
            " [0.87063844]\n",
            " [0.8706279 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34401427]\n",
            " [1.3372115 ]\n",
            " [1.32978511]\n",
            " [1.36753492]\n",
            " [1.33780954]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  811  Current D cost: [[1.34401427]\n",
            " [1.3372115 ]\n",
            " [1.32978511]\n",
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            " [1.33780954]]  Current G cost:  [[0.87145264]\n",
            " [0.87145516]\n",
            " [0.87145757]\n",
            " [0.87146199]\n",
            " [0.87145232]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32950425]\n",
            " [1.3426625 ]\n",
            " [1.34711962]\n",
            " [1.34374273]\n",
            " [1.33916247]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  812  Current D cost: [[1.32950425]\n",
            " [1.3426625 ]\n",
            " [1.34711962]\n",
            " [1.34374273]\n",
            " [1.33916247]]  Current G cost:  [[0.87227832]\n",
            " [0.87228056]\n",
            " [0.87228294]\n",
            " [0.87228696]\n",
            " [0.87227785]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34410877]\n",
            " [1.34944086]\n",
            " [1.339516  ]\n",
            " [1.3262748 ]\n",
            " [1.3473569 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  813  Current D cost: [[1.34410877]\n",
            " [1.34944086]\n",
            " [1.339516  ]\n",
            " [1.3262748 ]\n",
            " [1.3473569 ]]  Current G cost:  [[0.87310836]\n",
            " [0.87311032]\n",
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            " [0.87311715]\n",
            " [0.87310772]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33909524]\n",
            " [1.33505235]\n",
            " [1.35927766]\n",
            " [1.33989512]\n",
            " [1.33220716]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  814  Current D cost: [[1.33909524]\n",
            " [1.33505235]\n",
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            " [1.33220716]]  Current G cost:  [[0.87394264]\n",
            " [0.87394457]\n",
            " [0.87394666]\n",
            " [0.87395149]\n",
            " [0.8739421 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33308475]\n",
            " [1.33419813]\n",
            " [1.34404394]\n",
            " [1.34673578]\n",
            " [1.33313071]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  815  Current D cost: [[1.33308475]\n",
            " [1.33419813]\n",
            " [1.34404394]\n",
            " [1.34673578]\n",
            " [1.33313071]]  Current G cost:  [[0.874775  ]\n",
            " [0.87477688]\n",
            " [0.87477843]\n",
            " [0.8747835 ]\n",
            " [0.87477451]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35397348]\n",
            " [1.33701646]\n",
            " [1.33854442]\n",
            " [1.35548911]\n",
            " [1.34742742]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  816  Current D cost: [[1.35397348]\n",
            " [1.33701646]\n",
            " [1.33854442]\n",
            " [1.35548911]\n",
            " [1.34742742]]  Current G cost:  [[0.8756261 ]\n",
            " [0.87562841]\n",
            " [0.87562965]\n",
            " [0.87563438]\n",
            " [0.8756258 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33804149]\n",
            " [1.3525012 ]\n",
            " [1.33734263]\n",
            " [1.34111267]\n",
            " [1.346833  ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  817  Current D cost: [[1.33804149]\n",
            " [1.3525012 ]\n",
            " [1.33734263]\n",
            " [1.34111267]\n",
            " [1.346833  ]]  Current G cost:  [[0.87648162]\n",
            " [0.87648376]\n",
            " [0.87648525]\n",
            " [0.87648973]\n",
            " [0.87648117]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3358975 ]\n",
            " [1.33952329]\n",
            " [1.33272975]\n",
            " [1.33352717]\n",
            " [1.34096174]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  818  Current D cost: [[1.3358975 ]\n",
            " [1.33952329]\n",
            " [1.33272975]\n",
            " [1.33352717]\n",
            " [1.34096174]]  Current G cost:  [[0.87732653]\n",
            " [0.87732847]\n",
            " [0.87733028]\n",
            " [0.87733461]\n",
            " [0.87732586]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33979854]\n",
            " [1.34543991]\n",
            " [1.34329423]\n",
            " [1.33701586]\n",
            " [1.34767432]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  819  Current D cost: [[1.33979854]\n",
            " [1.34543991]\n",
            " [1.34329423]\n",
            " [1.33701586]\n",
            " [1.34767432]]  Current G cost:  [[0.87818188]\n",
            " [0.87818357]\n",
            " [0.87818559]\n",
            " [0.87819003]\n",
            " [0.87818087]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34243707]\n",
            " [1.33283267]\n",
            " [1.33269445]\n",
            " [1.35815068]\n",
            " [1.33626596]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  820  Current D cost: [[1.34243707]\n",
            " [1.33283267]\n",
            " [1.33269445]\n",
            " [1.35815068]\n",
            " [1.33626596]]  Current G cost:  [[0.87903962]\n",
            " [0.87904146]\n",
            " [0.87904358]\n",
            " [0.87904738]\n",
            " [0.87903862]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34348646]\n",
            " [1.33550981]\n",
            " [1.33525214]\n",
            " [1.34087057]\n",
            " [1.33708986]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  821  Current D cost: [[1.34348646]\n",
            " [1.33550981]\n",
            " [1.33525214]\n",
            " [1.34087057]\n",
            " [1.33708986]]  Current G cost:  [[0.8798965 ]\n",
            " [0.87989863]\n",
            " [0.87990081]\n",
            " [0.87990415]\n",
            " [0.87989567]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33348333]\n",
            " [1.3366125 ]\n",
            " [1.33488473]\n",
            " [1.34857717]\n",
            " [1.35193795]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  822  Current D cost: [[1.33348333]\n",
            " [1.3366125 ]\n",
            " [1.33488473]\n",
            " [1.34857717]\n",
            " [1.35193795]]  Current G cost:  [[0.88076079]\n",
            " [0.88076297]\n",
            " [0.88076523]\n",
            " [0.88076796]\n",
            " [0.88075952]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33547801]\n",
            " [1.32756029]\n",
            " [1.3350848 ]\n",
            " [1.33031272]\n",
            " [1.34249291]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  823  Current D cost: [[1.33547801]\n",
            " [1.32756029]\n",
            " [1.3350848 ]\n",
            " [1.33031272]\n",
            " [1.34249291]]  Current G cost:  [[0.8816156 ]\n",
            " [0.88161804]\n",
            " [0.88162011]\n",
            " [0.88162269]\n",
            " [0.88161393]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34736349]\n",
            " [1.34096536]\n",
            " [1.33866389]\n",
            " [1.32824579]\n",
            " [1.33368112]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  824  Current D cost: [[1.34736349]\n",
            " [1.34096536]\n",
            " [1.33866389]\n",
            " [1.32824579]\n",
            " [1.33368112]]  Current G cost:  [[0.88247672]\n",
            " [0.88247946]\n",
            " [0.8824815 ]\n",
            " [0.8824843 ]\n",
            " [0.88247521]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33460384]\n",
            " [1.32047549]\n",
            " [1.33053327]\n",
            " [1.32562905]\n",
            " [1.33870363]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  825  Current D cost: [[1.33460384]\n",
            " [1.32047549]\n",
            " [1.33053327]\n",
            " [1.32562905]\n",
            " [1.33870363]]  Current G cost:  [[0.88332508]\n",
            " [0.88332837]\n",
            " [0.88333012]\n",
            " [0.88333318]\n",
            " [0.88332354]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35313789]\n",
            " [1.35277848]\n",
            " [1.33136878]\n",
            " [1.34927785]\n",
            " [1.32913678]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  826  Current D cost: [[1.35313789]\n",
            " [1.35277848]\n",
            " [1.33136878]\n",
            " [1.34927785]\n",
            " [1.32913678]]  Current G cost:  [[0.8841984 ]\n",
            " [0.88420197]\n",
            " [0.88420416]\n",
            " [0.88420686]\n",
            " [0.88419751]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33225253]\n",
            " [1.33035134]\n",
            " [1.34991657]\n",
            " [1.32942321]\n",
            " [1.32470037]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  827  Current D cost: [[1.33225253]\n",
            " [1.33035134]\n",
            " [1.34991657]\n",
            " [1.32942321]\n",
            " [1.32470037]]  Current G cost:  [[0.88506488]\n",
            " [0.88506864]\n",
            " [0.8850705 ]\n",
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            " [0.88506452]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36218288]\n",
            " [1.33936243]\n",
            " [1.33232938]\n",
            " [1.32610101]\n",
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            "Current Iter:  828  Current D cost: [[1.36218288]\n",
            " [1.33936243]\n",
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            " [1.33279509]]  Current G cost:  [[0.88593986]\n",
            " [0.88594435]\n",
            " [0.88594624]\n",
            " [0.88594973]\n",
            " [0.88594058]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33057308]\n",
            " [1.33833009]\n",
            " [1.35531075]\n",
            " [1.32951135]\n",
            " [1.31971083]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  829  Current D cost: [[1.33057308]\n",
            " [1.33833009]\n",
            " [1.35531075]\n",
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            " [1.31971083]]  Current G cost:  [[0.88681408]\n",
            " [0.88681872]\n",
            " [0.88682014]\n",
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            " [0.88681565]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33266816]\n",
            " [1.33738405]\n",
            " [1.3347275 ]\n",
            " [1.33288425]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  830  Current D cost: [[1.33266816]\n",
            " [1.33738405]\n",
            " [1.3347275 ]\n",
            " [1.33288425]\n",
            " [1.35496356]]  Current G cost:  [[0.88769859]\n",
            " [0.88770315]\n",
            " [0.88770441]\n",
            " [0.88770936]\n",
            " [0.88769994]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32468472]\n",
            " [1.33372568]\n",
            " [1.33200578]\n",
            " [1.33503013]\n",
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            "Current Iter:  831  Current D cost: [[1.32468472]\n",
            " [1.33372568]\n",
            " [1.33200578]\n",
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            " [1.34316253]]  Current G cost:  [[0.88858096]\n",
            " [0.88858523]\n",
            " [0.88858646]\n",
            " [0.88859159]\n",
            " [0.88858167]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33888929]\n",
            " [1.32766258]\n",
            " [1.32403904]\n",
            " [1.33900448]\n",
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            "Current Iter:  832  Current D cost: [[1.33888929]\n",
            " [1.32766258]\n",
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            " [1.34179064]]  Current G cost:  [[0.88946499]\n",
            " [0.88946943]\n",
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            " [0.88947555]\n",
            " [0.8894653 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33906515]\n",
            " [1.32795486]\n",
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            " [1.33486704]\n",
            " [1.33393258]]\n",
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            "Current Iter:  833  Current D cost: [[1.33906515]\n",
            " [1.32795486]\n",
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            " [1.33393258]]  Current G cost:  [[0.89035465]\n",
            " [0.89035949]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32471314]\n",
            " [1.32501389]\n",
            " [1.33542727]\n",
            " [1.3288995 ]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  834  Current D cost: [[1.32471314]\n",
            " [1.32501389]\n",
            " [1.33542727]\n",
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            " [1.3536881 ]]  Current G cost:  [[0.89124463]\n",
            " [0.89124966]\n",
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            " [0.89124402]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33210659]\n",
            " [1.33446174]\n",
            " [1.3421169 ]\n",
            " [1.32834988]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  835  Current D cost: [[1.33210659]\n",
            " [1.33446174]\n",
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            " [1.33196129]]  Current G cost:  [[0.8921382 ]\n",
            " [0.89214325]\n",
            " [0.8921433 ]\n",
            " [0.89214885]\n",
            " [0.89213716]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34687124]\n",
            " [1.34062175]\n",
            " [1.33411976]\n",
            " [1.35028449]\n",
            " [1.33769732]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  836  Current D cost: [[1.34687124]\n",
            " [1.34062175]\n",
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            " [1.33769732]]  Current G cost:  [[0.89305355]\n",
            " [0.89305878]\n",
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            " [0.89306414]\n",
            " [0.89305255]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34583529]\n",
            " [1.35280791]\n",
            " [1.34130033]\n",
            " [1.33784161]\n",
            " [1.33580941]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  837  Current D cost: [[1.34583529]\n",
            " [1.35280791]\n",
            " [1.34130033]\n",
            " [1.33784161]\n",
            " [1.33580941]]  Current G cost:  [[0.89398374]\n",
            " [0.89398887]\n",
            " [0.89398918]\n",
            " [0.89399454]\n",
            " [0.89398304]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33075262]\n",
            " [1.32319762]\n",
            " [1.33424095]\n",
            " [1.35240937]\n",
            " [1.33129118]]\n",
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            "Current Iter:  838  Current D cost: [[1.33075262]\n",
            " [1.32319762]\n",
            " [1.33424095]\n",
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            " [1.33129118]]  Current G cost:  [[0.89490428]\n",
            " [0.89490956]\n",
            " [0.89490971]\n",
            " [0.89491456]\n",
            " [0.89490371]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33991212]\n",
            " [1.33592388]\n",
            " [1.33732146]\n",
            " [1.34170361]\n",
            " [1.33666944]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  839  Current D cost: [[1.33991212]\n",
            " [1.33592388]\n",
            " [1.33732146]\n",
            " [1.34170361]\n",
            " [1.33666944]]  Current G cost:  [[0.89583169]\n",
            " [0.89583716]\n",
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            " [0.89583126]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33002349]\n",
            " [1.32907647]\n",
            " [1.34689677]\n",
            " [1.33505025]\n",
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            "Current Iter:  840  Current D cost: [[1.33002349]\n",
            " [1.32907647]\n",
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            " [1.35991775]]  Current G cost:  [[0.89676783]\n",
            " [0.89677342]\n",
            " [0.89677286]\n",
            " [0.89677751]\n",
            " [0.89676662]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3197056 ]\n",
            " [1.36057774]\n",
            " [1.32595662]\n",
            " [1.32310809]\n",
            " [1.32777643]]\n",
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            "Current Iter:  841  Current D cost: [[1.3197056 ]\n",
            " [1.36057774]\n",
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            " [1.32777643]]  Current G cost:  [[0.89769213]\n",
            " [0.89769661]\n",
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            " [0.89770157]\n",
            " [0.8976903 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32157027]\n",
            " [1.34258086]\n",
            " [1.3235256 ]\n",
            " [1.31941525]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  842  Current D cost: [[1.32157027]\n",
            " [1.34258086]\n",
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            " [1.31941525]\n",
            " [1.32592415]]  Current G cost:  [[0.89860311]\n",
            " [0.89860642]\n",
            " [0.89860746]\n",
            " [0.8986125 ]\n",
            " [0.89860083]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31628413]\n",
            " [1.32901331]\n",
            " [1.32001532]\n",
            " [1.3244849 ]\n",
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            "Current Iter:  843  Current D cost: [[1.31628413]\n",
            " [1.32901331]\n",
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            " [1.3244849 ]\n",
            " [1.33157311]]  Current G cost:  [[0.89950393]\n",
            " [0.89950628]\n",
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            " [0.89950098]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33988265]\n",
            " [1.32421984]\n",
            " [1.35586683]\n",
            " [1.31897546]\n",
            " [1.33878206]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  844  Current D cost: [[1.33988265]\n",
            " [1.32421984]\n",
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            " [1.33878206]]  Current G cost:  [[0.90042618]\n",
            " [0.9004285 ]\n",
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            " [0.90042292]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33384978]\n",
            " [1.33165976]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  845  Current D cost: [[1.33384978]\n",
            " [1.33165976]\n",
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            " [1.32715961]]  Current G cost:  [[0.90135173]\n",
            " [0.9013541 ]\n",
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            " [0.90136142]\n",
            " [0.90134851]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32437139]\n",
            " [1.3377794 ]\n",
            " [1.34070546]\n",
            " [1.33318069]\n",
            " [1.31923595]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  846  Current D cost: [[1.32437139]\n",
            " [1.3377794 ]\n",
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            " [1.31923595]]  Current G cost:  [[0.90228151]\n",
            " [0.90228351]\n",
            " [0.90228452]\n",
            " [0.90229098]\n",
            " [0.90227846]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33376526]\n",
            " [1.33239077]\n",
            " [1.31868986]\n",
            " [1.3390532 ]\n",
            " [1.31399298]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  847  Current D cost: [[1.33376526]\n",
            " [1.33239077]\n",
            " [1.31868986]\n",
            " [1.3390532 ]\n",
            " [1.31399298]]  Current G cost:  [[0.90320771]\n",
            " [0.90320957]\n",
            " [0.90321091]\n",
            " [0.90321693]\n",
            " [0.90320532]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3434034 ]\n",
            " [1.33305647]\n",
            " [1.32585983]\n",
            " [1.32262219]\n",
            " [1.33474203]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  848  Current D cost: [[1.3434034 ]\n",
            " [1.33305647]\n",
            " [1.32585983]\n",
            " [1.32262219]\n",
            " [1.33474203]]  Current G cost:  [[0.90414398]\n",
            " [0.90414607]\n",
            " [0.90414779]\n",
            " [0.90415368]\n",
            " [0.90414217]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34371453]\n",
            " [1.32155212]\n",
            " [1.32572844]\n",
            " [1.31590298]\n",
            " [1.32934272]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  849  Current D cost: [[1.34371453]\n",
            " [1.32155212]\n",
            " [1.32572844]\n",
            " [1.31590298]\n",
            " [1.32934272]]  Current G cost:  [[0.90507676]\n",
            " [0.90507961]\n",
            " [0.90508139]\n",
            " [0.90508752]\n",
            " [0.90507565]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33552131]\n",
            " [1.32492229]\n",
            " [1.34136201]\n",
            " [1.33421788]\n",
            " [1.32696086]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  850  Current D cost: [[1.33552131]\n",
            " [1.32492229]\n",
            " [1.34136201]\n",
            " [1.33421788]\n",
            " [1.32696086]]  Current G cost:  [[0.9060215 ]\n",
            " [0.90602503]\n",
            " [0.90602637]\n",
            " [0.90603283]\n",
            " [0.90602098]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33190697]\n",
            " [1.32239524]\n",
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            " [1.33327396]\n",
            " [1.33912249]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  851  Current D cost: [[1.33190697]\n",
            " [1.32239524]\n",
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            " [1.33327396]\n",
            " [1.33912249]]  Current G cost:  [[0.9069737 ]\n",
            " [0.90697786]\n",
            " [0.9069787 ]\n",
            " [0.90698528]\n",
            " [0.90697327]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3231133 ]\n",
            " [1.34290753]\n",
            " [1.33257515]\n",
            " [1.34186769]\n",
            " [1.32453074]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  852  Current D cost: [[1.3231133 ]\n",
            " [1.34290753]\n",
            " [1.33257515]\n",
            " [1.34186769]\n",
            " [1.32453074]]  Current G cost:  [[0.90793472]\n",
            " [0.90793861]\n",
            " [0.9079395 ]\n",
            " [0.90794588]\n",
            " [0.90793429]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32696189]\n",
            " [1.33242171]\n",
            " [1.32686257]\n",
            " [1.32362341]\n",
            " [1.34631146]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  853  Current D cost: [[1.32696189]\n",
            " [1.33242171]\n",
            " [1.32686257]\n",
            " [1.32362341]\n",
            " [1.34631146]]  Current G cost:  [[0.9088979 ]\n",
            " [0.90890149]\n",
            " [0.90890257]\n",
            " [0.90890894]\n",
            " [0.90889689]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33445718]\n",
            " [1.3254983 ]\n",
            " [1.31698349]\n",
            " [1.33362533]\n",
            " [1.34926632]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  854  Current D cost: [[1.33445718]\n",
            " [1.3254983 ]\n",
            " [1.31698349]\n",
            " [1.33362533]\n",
            " [1.34926632]]  Current G cost:  [[0.90986643]\n",
            " [0.90987013]\n",
            " [0.90987155]\n",
            " [0.90987744]\n",
            " [0.90986469]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32552229]\n",
            " [1.34923871]\n",
            " [1.33728737]\n",
            " [1.35062882]\n",
            " [1.32726336]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  855  Current D cost: [[1.32552229]\n",
            " [1.34923871]\n",
            " [1.33728737]\n",
            " [1.35062882]\n",
            " [1.32726336]]  Current G cost:  [[0.91085187]\n",
            " [0.91085493]\n",
            " [0.91085687]\n",
            " [0.91086214]\n",
            " [0.91084971]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3365459 ]\n",
            " [1.31895501]\n",
            " [1.32896043]\n",
            " [1.33817459]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  856  Current D cost: [[1.3365459 ]\n",
            " [1.31895501]\n",
            " [1.32896043]\n",
            " [1.33817459]\n",
            " [1.32757916]]  Current G cost:  [[0.91183086]\n",
            " [0.91183411]\n",
            " [0.91183601]\n",
            " [0.91184071]\n",
            " [0.91182875]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31693994]\n",
            " [1.33903685]\n",
            " [1.32978706]\n",
            " [1.33647464]\n",
            " [1.3360898 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  857  Current D cost: [[1.31693994]\n",
            " [1.33903685]\n",
            " [1.32978706]\n",
            " [1.33647464]\n",
            " [1.3360898 ]]  Current G cost:  [[0.91281353]\n",
            " [0.91281623]\n",
            " [0.91281837]\n",
            " [0.91282259]\n",
            " [0.91281088]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32164954]\n",
            " [1.323946  ]\n",
            " [1.33967604]\n",
            " [1.33963473]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  858  Current D cost: [[1.32164954]\n",
            " [1.323946  ]\n",
            " [1.33967604]\n",
            " [1.33963473]\n",
            " [1.34529765]]  Current G cost:  [[0.91380498]\n",
            " [0.91380733]\n",
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            " [0.91380135]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34499532]\n",
            " [1.33429682]\n",
            " [1.3197297 ]\n",
            " [1.32758779]\n",
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            "Current Iter:  859  Current D cost: [[1.34499532]\n",
            " [1.33429682]\n",
            " [1.3197297 ]\n",
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            " [1.31307743]]  Current G cost:  [[0.91478979]\n",
            " [0.91479228]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32794366]\n",
            " [1.34706865]\n",
            " [1.33142468]\n",
            " [1.3198491 ]\n",
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            "Current Iter:  860  Current D cost: [[1.32794366]\n",
            " [1.34706865]\n",
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            " [1.3198491 ]\n",
            " [1.33819849]]  Current G cost:  [[0.91578462]\n",
            " [0.91578661]\n",
            " [0.91578926]\n",
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            " [0.91578137]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32002674]\n",
            " [1.31577313]\n",
            " [1.339638  ]\n",
            " [1.32434235]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  861  Current D cost: [[1.32002674]\n",
            " [1.31577313]\n",
            " [1.339638  ]\n",
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            " [1.34142156]]  Current G cost:  [[0.91677669]\n",
            " [0.91677856]\n",
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            " [0.91677277]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3300263 ]\n",
            " [1.33481985]\n",
            " [1.33243638]\n",
            " [1.3395281 ]\n",
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            "Current Iter:  862  Current D cost: [[1.3300263 ]\n",
            " [1.33481985]\n",
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            " [1.32986248]]  Current G cost:  [[0.9177812 ]\n",
            " [0.91778286]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33106963]\n",
            " [1.31925481]\n",
            " [1.33073425]\n",
            " [1.32235156]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  863  Current D cost: [[1.33106963]\n",
            " [1.31925481]\n",
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            " [1.30976263]]  Current G cost:  [[0.91877071]\n",
            " [0.91877263]\n",
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            " [0.91876692]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31412299]\n",
            " [1.32458782]\n",
            " [1.34394602]\n",
            " [1.34637732]\n",
            " [1.3219226 ]]\n",
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            "Current Iter:  864  Current D cost: [[1.31412299]\n",
            " [1.32458782]\n",
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            " [1.34637732]\n",
            " [1.3219226 ]]  Current G cost:  [[0.91977108]\n",
            " [0.91977281]\n",
            " [0.91977367]\n",
            " [0.91977854]\n",
            " [0.91976729]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.35929024]\n",
            " [1.33013743]\n",
            " [1.3332396 ]\n",
            " [1.32241118]\n",
            " [1.32666451]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  865  Current D cost: [[1.35929024]\n",
            " [1.33013743]\n",
            " [1.3332396 ]\n",
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            " [1.32666451]]  Current G cost:  [[0.92078664]\n",
            " [0.92078914]\n",
            " [0.92078952]\n",
            " [0.92079476]\n",
            " [0.92078381]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31631279]\n",
            " [1.31409734]\n",
            " [1.30681509]\n",
            " [1.35127909]\n",
            " [1.33314899]]\n",
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            "Current Iter:  866  Current D cost: [[1.31631279]\n",
            " [1.31409734]\n",
            " [1.30681509]\n",
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            " [1.33314899]]  Current G cost:  [[0.9217921 ]\n",
            " [0.92179505]\n",
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            " [0.92178924]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33124763]\n",
            " [1.3249611 ]\n",
            " [1.32610358]\n",
            " [1.31376493]\n",
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            "Current Iter:  867  Current D cost: [[1.33124763]\n",
            " [1.3249611 ]\n",
            " [1.32610358]\n",
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            " [1.33480347]]  Current G cost:  [[0.92279878]\n",
            " [0.92280213]\n",
            " [0.92280244]\n",
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            " [0.92279579]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32945629]\n",
            " [1.32795389]\n",
            " [1.33380153]\n",
            " [1.31642555]\n",
            " [1.31664747]]\n",
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            "Current Iter:  868  Current D cost: [[1.32945629]\n",
            " [1.32795389]\n",
            " [1.33380153]\n",
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            " [1.31664747]]  Current G cost:  [[0.92380605]\n",
            " [0.92380966]\n",
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            " [0.92380339]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32314571]\n",
            " [1.33723853]\n",
            " [1.3208784 ]\n",
            " [1.32301445]\n",
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            "Current Iter:  869  Current D cost: [[1.32314571]\n",
            " [1.33723853]\n",
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            " [1.33133966]]  Current G cost:  [[0.92482149]\n",
            " [0.9248248 ]\n",
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            " [0.92481874]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32305642]\n",
            " [1.3070211 ]\n",
            " [1.31449842]\n",
            " [1.34148167]\n",
            " [1.30912183]]\n",
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            "Current Iter:  870  Current D cost: [[1.32305642]\n",
            " [1.3070211 ]\n",
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            " [1.30912183]]  Current G cost:  [[0.925825  ]\n",
            " [0.92582864]\n",
            " [0.9258291 ]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31678393]\n",
            " [1.31788371]\n",
            " [1.3443121 ]\n",
            " [1.3257586 ]\n",
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            "Current Iter:  871  Current D cost: [[1.31678393]\n",
            " [1.31788371]\n",
            " [1.3443121 ]\n",
            " [1.3257586 ]\n",
            " [1.3315908 ]]  Current G cost:  [[0.92684282]\n",
            " [0.92684659]\n",
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            " [0.92684018]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32949198]\n",
            " [1.34401978]\n",
            " [1.34178787]\n",
            " [1.3324969 ]\n",
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            "Current Iter:  872  Current D cost: [[1.32949198]\n",
            " [1.34401978]\n",
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            " [1.32575738]]  Current G cost:  [[0.92788489]\n",
            " [0.92788829]\n",
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            " [0.927892  ]\n",
            " [0.92788223]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31487477]\n",
            " [1.31686519]\n",
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            "Current Iter:  873  Current D cost: [[1.31487477]\n",
            " [1.31686519]\n",
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            " [1.33139396]]  Current G cost:  [[0.92892135]\n",
            " [0.9289245 ]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33457444]\n",
            " [1.31787569]\n",
            " [1.3335523 ]\n",
            " [1.33211761]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  874  Current D cost: [[1.33457444]\n",
            " [1.31787569]\n",
            " [1.3335523 ]\n",
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            " [1.3222687 ]]  Current G cost:  [[0.92996382]\n",
            " [0.92996736]\n",
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            " [0.92996075]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32076378]\n",
            " [1.32127005]\n",
            " [1.32710941]\n",
            " [1.33593632]\n",
            " [1.34320661]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  875  Current D cost: [[1.32076378]\n",
            " [1.32127005]\n",
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            " [1.34320661]]  Current G cost:  [[0.93101469]\n",
            " [0.9310184 ]\n",
            " [0.93101538]\n",
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            " [0.931011  ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33889713]\n",
            " [1.32338852]\n",
            " [1.30786929]\n",
            " [1.3241134 ]\n",
            " [1.33229243]]\n",
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            "Current Iter:  876  Current D cost: [[1.33889713]\n",
            " [1.32338852]\n",
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            " [1.33229243]]  Current G cost:  [[0.93206315]\n",
            " [0.93206741]\n",
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            " [0.93205933]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32294413]\n",
            " [1.31106383]\n",
            " [1.33436259]\n",
            " [1.31808945]\n",
            " [1.34976945]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  877  Current D cost: [[1.32294413]\n",
            " [1.31106383]\n",
            " [1.33436259]\n",
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            " [1.34976945]]  Current G cost:  [[0.9331163 ]\n",
            " [0.93312121]\n",
            " [0.93311772]\n",
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            " [0.93311161]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32551573]\n",
            " [1.32650931]\n",
            " [1.32703207]\n",
            " [1.32628912]\n",
            " [1.32423682]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  878  Current D cost: [[1.32551573]\n",
            " [1.32650931]\n",
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            " [1.32423682]]  Current G cost:  [[0.93417215]\n",
            " [0.93417734]\n",
            " [0.93417353]\n",
            " [0.93417876]\n",
            " [0.93416706]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32948477]\n",
            " [1.32332178]\n",
            " [1.32204906]\n",
            " [1.3436184 ]\n",
            " [1.30481622]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  879  Current D cost: [[1.32948477]\n",
            " [1.32332178]\n",
            " [1.32204906]\n",
            " [1.3436184 ]\n",
            " [1.30481622]]  Current G cost:  [[0.93522859]\n",
            " [0.93523411]\n",
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            " [0.93522406]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33535454]\n",
            " [1.3328487 ]\n",
            " [1.33519933]\n",
            " [1.32767443]\n",
            " [1.30671527]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  880  Current D cost: [[1.33535454]\n",
            " [1.3328487 ]\n",
            " [1.33519933]\n",
            " [1.32767443]\n",
            " [1.30671527]]  Current G cost:  [[0.9362941 ]\n",
            " [0.93629986]\n",
            " [0.93629579]\n",
            " [0.9363004 ]\n",
            " [0.93629074]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32450015]\n",
            " [1.32174247]\n",
            " [1.31798607]\n",
            " [1.33105893]\n",
            " [1.32103239]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  881  Current D cost: [[1.32450015]\n",
            " [1.32174247]\n",
            " [1.31798607]\n",
            " [1.33105893]\n",
            " [1.32103239]]  Current G cost:  [[0.93735795]\n",
            " [0.93736391]\n",
            " [0.9373599 ]\n",
            " [0.93736409]\n",
            " [0.93735528]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31628286]\n",
            " [1.33360096]\n",
            " [1.34943184]\n",
            " [1.31526538]\n",
            " [1.33078112]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  882  Current D cost: [[1.31628286]\n",
            " [1.33360096]\n",
            " [1.34943184]\n",
            " [1.31526538]\n",
            " [1.33078112]]  Current G cost:  [[0.93843538]\n",
            " [0.93844089]\n",
            " [0.93843643]\n",
            " [0.93844147]\n",
            " [0.93843259]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29834406]\n",
            " [1.32962739]\n",
            " [1.31230581]\n",
            " [1.34189214]\n",
            " [1.34102259]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  883  Current D cost: [[1.29834406]\n",
            " [1.32962739]\n",
            " [1.31230581]\n",
            " [1.34189214]\n",
            " [1.34102259]]  Current G cost:  [[0.93951325]\n",
            " [0.93951757]\n",
            " [0.93951342]\n",
            " [0.93951798]\n",
            " [0.93950908]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32766849]\n",
            " [1.315745  ]\n",
            " [1.3268737 ]\n",
            " [1.30880218]\n",
            " [1.31357964]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  884  Current D cost: [[1.32766849]\n",
            " [1.315745  ]\n",
            " [1.3268737 ]\n",
            " [1.30880218]\n",
            " [1.31357964]]  Current G cost:  [[0.94058047]\n",
            " [0.94058455]\n",
            " [0.94058021]\n",
            " [0.94058511]\n",
            " [0.94057604]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33974195]\n",
            " [1.32764115]\n",
            " [1.31518208]\n",
            " [1.32117302]\n",
            " [1.33705572]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  885  Current D cost: [[1.33974195]\n",
            " [1.32764115]\n",
            " [1.31518208]\n",
            " [1.32117302]\n",
            " [1.33705572]]  Current G cost:  [[0.94166342]\n",
            " [0.94166776]\n",
            " [0.94166372]\n",
            " [0.94166861]\n",
            " [0.94165895]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30579509]\n",
            " [1.30508368]\n",
            " [1.32116973]\n",
            " [1.32758195]\n",
            " [1.32591705]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  886  Current D cost: [[1.30579509]\n",
            " [1.30508368]\n",
            " [1.32116973]\n",
            " [1.32758195]\n",
            " [1.32591705]]  Current G cost:  [[0.94273578]\n",
            " [0.94274026]\n",
            " [0.94273587]\n",
            " [0.94274056]\n",
            " [0.94273065]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.36485912]\n",
            " [1.31458669]\n",
            " [1.32264517]\n",
            " [1.29774376]\n",
            " [1.34193198]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  887  Current D cost: [[1.36485912]\n",
            " [1.31458669]\n",
            " [1.32264517]\n",
            " [1.29774376]\n",
            " [1.34193198]]  Current G cost:  [[0.943824  ]\n",
            " [0.9438301 ]\n",
            " [0.94382528]\n",
            " [0.94383066]\n",
            " [0.94381924]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33692017]\n",
            " [1.35190825]\n",
            " [1.34807775]\n",
            " [1.31149285]\n",
            " [1.32667014]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  888  Current D cost: [[1.33692017]\n",
            " [1.35190825]\n",
            " [1.34807775]\n",
            " [1.31149285]\n",
            " [1.32667014]]  Current G cost:  [[0.94493834]\n",
            " [0.94494478]\n",
            " [0.94493986]\n",
            " [0.94494674]\n",
            " [0.94493407]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30422963]\n",
            " [1.30365677]\n",
            " [1.3164469 ]\n",
            " [1.31821811]\n",
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            "Current Iter:  889  Current D cost: [[1.30422963]\n",
            " [1.30365677]\n",
            " [1.3164469 ]\n",
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            " [1.32843436]]  Current G cost:  [[0.94602665]\n",
            " [0.94603327]\n",
            " [0.9460279 ]\n",
            " [0.94603548]\n",
            " [0.94602186]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32462816]\n",
            " [1.36516081]\n",
            " [1.33724918]\n",
            " [1.31786504]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  890  Current D cost: [[1.32462816]\n",
            " [1.36516081]\n",
            " [1.33724918]\n",
            " [1.31786504]\n",
            " [1.2968855 ]]  Current G cost:  [[0.94713262]\n",
            " [0.94713809]\n",
            " [0.94713335]\n",
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            " [0.94712846]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.34032122]\n",
            " [1.33826557]\n",
            " [1.30912265]\n",
            " [1.30386546]\n",
            " [1.32774659]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  891  Current D cost: [[1.34032122]\n",
            " [1.33826557]\n",
            " [1.30912265]\n",
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            " [1.32774659]]  Current G cost:  [[0.94824078]\n",
            " [0.94824573]\n",
            " [0.94824223]\n",
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            " [0.94823732]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31284555]\n",
            " [1.3286666 ]\n",
            " [1.31829554]\n",
            " [1.36237739]\n",
            " [1.31585101]]\n",
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            "Current Iter:  892  Current D cost: [[1.31284555]\n",
            " [1.3286666 ]\n",
            " [1.31829554]\n",
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            " [1.31585101]]  Current G cost:  [[0.94935966]\n",
            " [0.94936385]\n",
            " [0.94936128]\n",
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            " [0.94935644]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30443422]\n",
            " [1.33733367]\n",
            " [1.33553176]\n",
            " [1.331416  ]\n",
            " [1.3095161 ]]\n",
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            "Current Iter:  893  Current D cost: [[1.30443422]\n",
            " [1.33733367]\n",
            " [1.33553176]\n",
            " [1.331416  ]\n",
            " [1.3095161 ]]  Current G cost:  [[0.95047943]\n",
            " [0.9504822 ]\n",
            " [0.95048017]\n",
            " [0.95048795]\n",
            " [0.95047617]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32520007]\n",
            " [1.31624769]\n",
            " [1.31985695]\n",
            " [1.33980794]\n",
            " [1.30723383]]\n",
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            "Current Iter:  894  Current D cost: [[1.32520007]\n",
            " [1.31624769]\n",
            " [1.31985695]\n",
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            " [1.30723383]]  Current G cost:  [[0.95159723]\n",
            " [0.95159958]\n",
            " [0.95159769]\n",
            " [0.95160467]\n",
            " [0.95159452]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31458102]\n",
            " [1.31295463]\n",
            " [1.31273445]\n",
            " [1.31750649]\n",
            " [1.30133212]]\n",
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            "Current Iter:  895  Current D cost: [[1.31458102]\n",
            " [1.31295463]\n",
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            " [1.30133212]]  Current G cost:  [[0.95269682]\n",
            " [0.95269901]\n",
            " [0.9526972 ]\n",
            " [0.95270363]\n",
            " [0.9526948 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32090888]\n",
            " [1.30790719]\n",
            " [1.33768536]\n",
            " [1.32884396]\n",
            " [1.32690813]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  896  Current D cost: [[1.32090888]\n",
            " [1.30790719]\n",
            " [1.33768536]\n",
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            " [1.32690813]]  Current G cost:  [[0.953815  ]\n",
            " [0.95381751]\n",
            " [0.95381481]\n",
            " [0.95382125]\n",
            " [0.95381313]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.2964886 ]\n",
            " [1.35021644]\n",
            " [1.31950604]\n",
            " [1.30828576]\n",
            " [1.30436268]]\n",
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            "Current Iter:  897  Current D cost: [[1.2964886 ]\n",
            " [1.35021644]\n",
            " [1.31950604]\n",
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            " [1.30436268]]  Current G cost:  [[0.95492827]\n",
            " [0.95492925]\n",
            " [0.95492706]\n",
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            " [0.95492622]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3168573 ]\n",
            " [1.31140807]\n",
            " [1.31088347]\n",
            " [1.30963954]\n",
            " [1.33461355]]\n",
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            "Current Iter:  898  Current D cost: [[1.3168573 ]\n",
            " [1.31140807]\n",
            " [1.31088347]\n",
            " [1.30963954]\n",
            " [1.33461355]]  Current G cost:  [[0.95604276]\n",
            " [0.95604315]\n",
            " [0.95604123]\n",
            " [0.95604828]\n",
            " [0.95604005]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3312462 ]\n",
            " [1.3431095 ]\n",
            " [1.31388005]\n",
            " [1.33527943]\n",
            " [1.31812843]]\n",
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            "Current Iter:  899  Current D cost: [[1.3312462 ]\n",
            " [1.3431095 ]\n",
            " [1.31388005]\n",
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            " [1.31812843]]  Current G cost:  [[0.95718306]\n",
            " [0.95718279]\n",
            " [0.95718193]\n",
            " [0.95718842]\n",
            " [0.95718044]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32114215]\n",
            " [1.31735662]\n",
            " [1.32151618]\n",
            " [1.31206943]\n",
            " [1.3214995 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  900  Current D cost: [[1.32114215]\n",
            " [1.31735662]\n",
            " [1.32151618]\n",
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            " [1.3214995 ]]  Current G cost:  [[0.9583201 ]\n",
            " [0.95831961]\n",
            " [0.95831914]\n",
            " [0.95832567]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31751625]\n",
            " [1.32558584]\n",
            " [1.33034347]\n",
            " [1.32064239]\n",
            " [1.3255449 ]]\n",
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            "Current Iter:  901  Current D cost: [[1.31751625]\n",
            " [1.32558584]\n",
            " [1.33034347]\n",
            " [1.32064239]\n",
            " [1.3255449 ]]  Current G cost:  [[0.95946896]\n",
            " [0.95946811]\n",
            " [0.9594677 ]\n",
            " [0.95947456]\n",
            " [0.95946612]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32387348]\n",
            " [1.32283732]\n",
            " [1.33766107]\n",
            " [1.32662135]\n",
            " [1.30861828]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  902  Current D cost: [[1.32387348]\n",
            " [1.32283732]\n",
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            " [1.32662135]\n",
            " [1.30861828]]  Current G cost:  [[0.96062505]\n",
            " [0.96062406]\n",
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            " [0.96063058]\n",
            " [0.96062257]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3337874 ]\n",
            " [1.31022704]\n",
            " [1.32780725]\n",
            " [1.32192446]\n",
            " [1.29946765]]\n",
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            "Current Iter:  903  Current D cost: [[1.3337874 ]\n",
            " [1.31022704]\n",
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            " [1.29946765]]  Current G cost:  [[0.96177645]\n",
            " [0.96177614]\n",
            " [0.96177449]\n",
            " [0.96178233]\n",
            " [0.96177525]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29204057]\n",
            " [1.30709275]\n",
            " [1.33634797]\n",
            " [1.31937822]\n",
            " [1.30957993]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  904  Current D cost: [[1.29204057]\n",
            " [1.30709275]\n",
            " [1.33634797]\n",
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            " [1.30957993]]  Current G cost:  [[0.9629177 ]\n",
            " [0.96291724]\n",
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            " [0.96291656]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31045403]\n",
            " [1.32468573]\n",
            " [1.31458484]\n",
            " [1.30331821]\n",
            " [1.30430753]]\n",
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            "Current Iter:  905  Current D cost: [[1.31045403]\n",
            " [1.32468573]\n",
            " [1.31458484]\n",
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            " [1.30430753]]  Current G cost:  [[0.96405303]\n",
            " [0.96405204]\n",
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            " [0.96405212]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30503067]\n",
            " [1.36062733]\n",
            " [1.31164195]\n",
            " [1.33082048]\n",
            " [1.31817621]]\n",
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            "Current Iter:  906  Current D cost: [[1.30503067]\n",
            " [1.36062733]\n",
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            " [1.31817621]]  Current G cost:  [[0.96521492]\n",
            " [0.9652119 ]\n",
            " [0.96520996]\n",
            " [0.9652191 ]\n",
            " [0.96521368]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32430826]\n",
            " [1.31461523]\n",
            " [1.35858843]\n",
            " [1.31302112]\n",
            " [1.32519725]]\n",
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            "Current Iter:  907  Current D cost: [[1.32430826]\n",
            " [1.31461523]\n",
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            " [1.32519725]]  Current G cost:  [[0.96639543]\n",
            " [0.96639171]\n",
            " [0.96638907]\n",
            " [0.96639955]\n",
            " [0.966394  ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.3167768 ]\n",
            " [1.33821298]\n",
            " [1.30690927]\n",
            " [1.35552349]\n",
            " [1.32901627]]\n",
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            "Current Iter:  908  Current D cost: [[1.3167768 ]\n",
            " [1.33821298]\n",
            " [1.30690927]\n",
            " [1.35552349]\n",
            " [1.32901627]]  Current G cost:  [[0.96759162]\n",
            " [0.96758687]\n",
            " [0.96758489]\n",
            " [0.96759449]\n",
            " [0.9675897 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31356052]\n",
            " [1.31780622]\n",
            " [1.33777656]\n",
            " [1.32693412]\n",
            " [1.32002662]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  909  Current D cost: [[1.31356052]\n",
            " [1.31780622]\n",
            " [1.33777656]\n",
            " [1.32693412]\n",
            " [1.32002662]]  Current G cost:  [[0.9687872 ]\n",
            " [0.96878181]\n",
            " [0.96877952]\n",
            " [0.96878903]\n",
            " [0.96878482]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31044367]\n",
            " [1.3285415 ]\n",
            " [1.32641372]\n",
            " [1.29676964]\n",
            " [1.31895732]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  910  Current D cost: [[1.31044367]\n",
            " [1.3285415 ]\n",
            " [1.32641372]\n",
            " [1.29676964]\n",
            " [1.31895732]]  Current G cost:  [[0.9699703 ]\n",
            " [0.969964  ]\n",
            " [0.96996163]\n",
            " [0.96997207]\n",
            " [0.96996741]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.37861105]\n",
            " [1.30871963]\n",
            " [1.37197034]\n",
            " [1.33156736]\n",
            " [1.34448512]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  911  Current D cost: [[1.37861105]\n",
            " [1.30871963]\n",
            " [1.37197034]\n",
            " [1.33156736]\n",
            " [1.34448512]]  Current G cost:  [[0.97120528]\n",
            " [0.97120074]\n",
            " [0.97119632]\n",
            " [0.9712085 ]\n",
            " [0.97120318]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31532251]\n",
            " [1.29135362]\n",
            " [1.33424857]\n",
            " [1.29085789]\n",
            " [1.31210944]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  912  Current D cost: [[1.31532251]\n",
            " [1.29135362]\n",
            " [1.33424857]\n",
            " [1.29085789]\n",
            " [1.31210944]]  Current G cost:  [[0.97239826]\n",
            " [0.9723954 ]\n",
            " [0.97238856]\n",
            " [0.97240302]\n",
            " [0.97239667]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30194214]\n",
            " [1.30893334]\n",
            " [1.31812266]\n",
            " [1.30608209]\n",
            " [1.31625318]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  913  Current D cost: [[1.30194214]\n",
            " [1.30893334]\n",
            " [1.31812266]\n",
            " [1.30608209]\n",
            " [1.31625318]]  Current G cost:  [[0.97357669]\n",
            " [0.97357445]\n",
            " [0.97356609]\n",
            " [0.9735821 ]\n",
            " [0.97357488]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31241432]\n",
            " [1.32490459]\n",
            " [1.30734166]\n",
            " [1.30716572]\n",
            " [1.3288277 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  914  Current D cost: [[1.31241432]\n",
            " [1.32490459]\n",
            " [1.30734166]\n",
            " [1.30716572]\n",
            " [1.3288277 ]]  Current G cost:  [[0.97476175]\n",
            " [0.97475941]\n",
            " [0.97475088]\n",
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            " [0.97475929]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31187198]\n",
            " [1.307978  ]\n",
            " [1.31109631]\n",
            " [1.33175729]\n",
            " [1.30933148]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  915  Current D cost: [[1.31187198]\n",
            " [1.307978  ]\n",
            " [1.31109631]\n",
            " [1.33175729]\n",
            " [1.30933148]]  Current G cost:  [[0.97594901]\n",
            " [0.97594675]\n",
            " [0.97593804]\n",
            " [0.97595454]\n",
            " [0.9759463 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33015045]\n",
            " [1.31803429]\n",
            " [1.31668017]\n",
            " [1.33886957]\n",
            " [1.3132071 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  916  Current D cost: [[1.33015045]\n",
            " [1.31803429]\n",
            " [1.31668017]\n",
            " [1.33886957]\n",
            " [1.3132071 ]]  Current G cost:  [[0.97715653]\n",
            " [0.9771547 ]\n",
            " [0.97714596]\n",
            " [0.97716159]\n",
            " [0.97715424]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31041429]\n",
            " [1.32147401]\n",
            " [1.29973301]\n",
            " [1.32245549]\n",
            " [1.29035454]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  917  Current D cost: [[1.31041429]\n",
            " [1.32147401]\n",
            " [1.29973301]\n",
            " [1.32245549]\n",
            " [1.29035454]]  Current G cost:  [[0.97834983]\n",
            " [0.97834786]\n",
            " [0.97833981]\n",
            " [0.97835427]\n",
            " [0.97834842]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.28772964]\n",
            " [1.31768685]\n",
            " [1.3318924 ]\n",
            " [1.33786144]\n",
            " [1.3403455 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  918  Current D cost: [[1.28772964]\n",
            " [1.31768685]\n",
            " [1.3318924 ]\n",
            " [1.33786144]\n",
            " [1.3403455 ]]  Current G cost:  [[0.97956709]\n",
            " [0.97956406]\n",
            " [0.9795559 ]\n",
            " [0.97956959]\n",
            " [0.97956438]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31593331]\n",
            " [1.31265897]\n",
            " [1.33916409]\n",
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            " [1.31469412]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  919  Current D cost: [[1.31593331]\n",
            " [1.31265897]\n",
            " [1.33916409]\n",
            " [1.32982212]\n",
            " [1.31469412]]  Current G cost:  [[0.98079634]\n",
            " [0.98079289]\n",
            " [0.98078383]\n",
            " [0.98079743]\n",
            " [0.98079302]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29282075]\n",
            " [1.31258158]\n",
            " [1.30443366]\n",
            " [1.31389723]\n",
            " [1.35089929]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  920  Current D cost: [[1.29282075]\n",
            " [1.31258158]\n",
            " [1.30443366]\n",
            " [1.31389723]\n",
            " [1.35089929]]  Current G cost:  [[0.98201815]\n",
            " [0.98201383]\n",
            " [0.98200459]\n",
            " [0.98201784]\n",
            " [0.98201262]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32094663]\n",
            " [1.31899503]\n",
            " [1.32070837]\n",
            " [1.30607812]\n",
            " [1.31957093]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  921  Current D cost: [[1.32094663]\n",
            " [1.31899503]\n",
            " [1.32070837]\n",
            " [1.30607812]\n",
            " [1.31957093]]  Current G cost:  [[0.98324307]\n",
            " [0.98323839]\n",
            " [0.98322901]\n",
            " [0.98324256]\n",
            " [0.98323648]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.28002098]\n",
            " [1.30504557]\n",
            " [1.31906506]\n",
            " [1.31794408]\n",
            " [1.33259136]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  922  Current D cost: [[1.28002098]\n",
            " [1.30504557]\n",
            " [1.31906506]\n",
            " [1.31794408]\n",
            " [1.33259136]]  Current G cost:  [[0.98446128]\n",
            " [0.98445558]\n",
            " [0.98444567]\n",
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            " [0.98445241]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31050825]\n",
            " [1.29990749]\n",
            " [1.31086852]\n",
            " [1.33392224]\n",
            " [1.32776304]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  923  Current D cost: [[1.31050825]\n",
            " [1.29990749]\n",
            " [1.31086852]\n",
            " [1.33392224]\n",
            " [1.32776304]]  Current G cost:  [[0.98568903]\n",
            " [0.98568318]\n",
            " [0.98567265]\n",
            " [0.98568574]\n",
            " [0.98567845]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31628749]\n",
            " [1.28984509]\n",
            " [1.30158712]\n",
            " [1.29124803]\n",
            " [1.33683658]]\n",
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            "Current Iter:  924  Current D cost: [[1.31628749]\n",
            " [1.28984509]\n",
            " [1.30158712]\n",
            " [1.29124803]\n",
            " [1.33683658]]  Current G cost:  [[0.98690549]\n",
            " [0.98690046]\n",
            " [0.98688923]\n",
            " [0.98690234]\n",
            " [0.98689339]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32280064]\n",
            " [1.32001872]\n",
            " [1.29078184]\n",
            " [1.30227585]\n",
            " [1.31750154]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  925  Current D cost: [[1.32280064]\n",
            " [1.32001872]\n",
            " [1.29078184]\n",
            " [1.30227585]\n",
            " [1.31750154]]  Current G cost:  [[0.98812518]\n",
            " [0.98812065]\n",
            " [0.98811006]\n",
            " [0.9881228 ]\n",
            " [0.9881125 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30025279]\n",
            " [1.3199716 ]\n",
            " [1.32562018]\n",
            " [1.3061173 ]\n",
            " [1.30221328]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  926  Current D cost: [[1.30025279]\n",
            " [1.3199716 ]\n",
            " [1.32562018]\n",
            " [1.3061173 ]\n",
            " [1.30221328]]  Current G cost:  [[0.98934995]\n",
            " [0.98934502]\n",
            " [0.98933457]\n",
            " [0.98934778]\n",
            " [0.98933691]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29554135]\n",
            " [1.31864811]\n",
            " [1.29982726]\n",
            " [1.32135756]\n",
            " [1.29338991]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  927  Current D cost: [[1.29554135]\n",
            " [1.31864811]\n",
            " [1.29982726]\n",
            " [1.32135756]\n",
            " [1.29338991]]  Current G cost:  [[0.99056942]\n",
            " [0.99056352]\n",
            " [0.99055378]\n",
            " [0.9905665 ]\n",
            " [0.99055628]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31308152]\n",
            " [1.31114237]\n",
            " [1.32701098]\n",
            " [1.33263353]\n",
            " [1.32743173]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  928  Current D cost: [[1.31308152]\n",
            " [1.31114237]\n",
            " [1.32701098]\n",
            " [1.33263353]\n",
            " [1.32743173]]  Current G cost:  [[0.99182029]\n",
            " [0.99181398]\n",
            " [0.99180407]\n",
            " [0.99181637]\n",
            " [0.99180663]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29679709]\n",
            " [1.32238282]\n",
            " [1.33533653]\n",
            " [1.29175843]\n",
            " [1.31480844]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  929  Current D cost: [[1.29679709]\n",
            " [1.32238282]\n",
            " [1.33533653]\n",
            " [1.29175843]\n",
            " [1.31480844]]  Current G cost:  [[0.99307141]\n",
            " [0.99306404]\n",
            " [0.99305363]\n",
            " [0.99306718]\n",
            " [0.99305689]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31208979]\n",
            " [1.32473039]\n",
            " [1.33028895]\n",
            " [1.30936626]\n",
            " [1.2956659 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  930  Current D cost: [[1.31208979]\n",
            " [1.32473039]\n",
            " [1.33028895]\n",
            " [1.30936626]\n",
            " [1.2956659 ]]  Current G cost:  [[0.99432781]\n",
            " [0.9943195 ]\n",
            " [0.99430866]\n",
            " [0.99432353]\n",
            " [0.99431342]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30145   ]\n",
            " [1.36310501]\n",
            " [1.29835158]\n",
            " [1.31323534]\n",
            " [1.31133397]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  931  Current D cost: [[1.30145   ]\n",
            " [1.36310501]\n",
            " [1.29835158]\n",
            " [1.31323534]\n",
            " [1.31133397]]  Current G cost:  [[0.99559528]\n",
            " [0.99558448]\n",
            " [0.99557557]\n",
            " [0.9955906 ]\n",
            " [0.99558063]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33911656]\n",
            " [1.29145859]\n",
            " [1.28748632]\n",
            " [1.30245898]\n",
            " [1.28078595]]\n",
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            "Current Iter:  932  Current D cost: [[1.33911656]\n",
            " [1.29145859]\n",
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            " [1.30245898]\n",
            " [1.28078595]]  Current G cost:  [[0.9968366 ]\n",
            " [0.99682616]\n",
            " [0.99681836]\n",
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            " [0.99682379]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30318198]\n",
            " [1.31270185]\n",
            " [1.31249627]\n",
            " [1.31398163]\n",
            " [1.29937347]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  933  Current D cost: [[1.30318198]\n",
            " [1.31270185]\n",
            " [1.31249627]\n",
            " [1.31398163]\n",
            " [1.29937347]]  Current G cost:  [[0.99808487]\n",
            " [0.9980743 ]\n",
            " [0.99806706]\n",
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            " [0.99807312]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31831063]\n",
            " [1.3145407 ]\n",
            " [1.33188203]\n",
            " [1.29532075]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  934  Current D cost: [[1.31831063]\n",
            " [1.3145407 ]\n",
            " [1.33188203]\n",
            " [1.29532075]\n",
            " [1.31347986]]  Current G cost:  [[0.99935042]\n",
            " [0.99933992]\n",
            " [0.99933239]\n",
            " [0.99934783]\n",
            " [0.99933935]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31646028]\n",
            " [1.30122997]\n",
            " [1.29989117]\n",
            " [1.31209304]\n",
            " [1.31288592]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  935  Current D cost: [[1.31646028]\n",
            " [1.30122997]\n",
            " [1.29989117]\n",
            " [1.31209304]\n",
            " [1.31288592]]  Current G cost:  [[1.00061564]\n",
            " [1.0006057 ]\n",
            " [1.00059807]\n",
            " [1.00061365]\n",
            " [1.00060504]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30057244]\n",
            " [1.32643122]\n",
            " [1.29093757]\n",
            " [1.30040872]\n",
            " [1.30123777]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  936  Current D cost: [[1.30057244]\n",
            " [1.32643122]\n",
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            " [1.30123777]]  Current G cost:  [[1.00187493]\n",
            " [1.00186441]\n",
            " [1.00185795]\n",
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            " [1.00186455]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31569783]\n",
            " [1.30003697]\n",
            " [1.31653736]\n",
            " [1.30679923]\n",
            " [1.3246302 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  937  Current D cost: [[1.31569783]\n",
            " [1.30003697]\n",
            " [1.31653736]\n",
            " [1.30679923]\n",
            " [1.3246302 ]]  Current G cost:  [[1.00315071]\n",
            " [1.00314044]\n",
            " [1.003134  ]\n",
            " [1.00314952]\n",
            " [1.00314013]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29899252]\n",
            " [1.28200853]\n",
            " [1.30685521]\n",
            " [1.28932486]\n",
            " [1.30976536]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  938  Current D cost: [[1.29899252]\n",
            " [1.28200853]\n",
            " [1.30685521]\n",
            " [1.28932486]\n",
            " [1.30976536]]  Current G cost:  [[1.00440741]\n",
            " [1.00439786]\n",
            " [1.00439058]\n",
            " [1.00440681]\n",
            " [1.00439638]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30238457]\n",
            " [1.30220335]\n",
            " [1.30250043]\n",
            " [1.30829202]\n",
            " [1.30186407]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  939  Current D cost: [[1.30238457]\n",
            " [1.30220335]\n",
            " [1.30250043]\n",
            " [1.30829202]\n",
            " [1.30186407]]  Current G cost:  [[1.00566897]\n",
            " [1.0056598 ]\n",
            " [1.0056521 ]\n",
            " [1.00566848]\n",
            " [1.00565773]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30916226]\n",
            " [1.29278574]\n",
            " [1.29260074]\n",
            " [1.30845839]\n",
            " [1.33134854]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  940  Current D cost: [[1.30916226]\n",
            " [1.29278574]\n",
            " [1.29260074]\n",
            " [1.30845839]\n",
            " [1.33134854]]  Current G cost:  [[1.00694072]\n",
            " [1.00693231]\n",
            " [1.0069244 ]\n",
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            " [1.00692862]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31627658]\n",
            " [1.32526426]\n",
            " [1.33623523]\n",
            " [1.31023442]\n",
            " [1.31784822]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  941  Current D cost: [[1.31627658]\n",
            " [1.32526426]\n",
            " [1.33623523]\n",
            " [1.31023442]\n",
            " [1.31784822]]  Current G cost:  [[1.00824744]\n",
            " [1.00823912]\n",
            " [1.00823075]\n",
            " [1.00824731]\n",
            " [1.00823488]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31000043]\n",
            " [1.31273018]\n",
            " [1.31205312]\n",
            " [1.31814712]\n",
            " [1.316038  ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  942  Current D cost: [[1.31000043]\n",
            " [1.31273018]\n",
            " [1.31205312]\n",
            " [1.31814712]\n",
            " [1.316038  ]]  Current G cost:  [[1.00956173]\n",
            " [1.00955336]\n",
            " [1.00954479]\n",
            " [1.00956147]\n",
            " [1.00954872]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30454472]\n",
            " [1.32796762]\n",
            " [1.30587938]\n",
            " [1.32075214]\n",
            " [1.29657391]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  943  Current D cost: [[1.30454472]\n",
            " [1.32796762]\n",
            " [1.30587938]\n",
            " [1.32075214]\n",
            " [1.29657391]]  Current G cost:  [[1.0108766 ]\n",
            " [1.01086742]\n",
            " [1.01085951]\n",
            " [1.01087574]\n",
            " [1.01086366]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29323208]\n",
            " [1.29892022]\n",
            " [1.31730899]\n",
            " [1.31496654]\n",
            " [1.29413024]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  944  Current D cost: [[1.29323208]\n",
            " [1.29892022]\n",
            " [1.31730899]\n",
            " [1.31496654]\n",
            " [1.29413024]]  Current G cost:  [[1.01217973]\n",
            " [1.01216996]\n",
            " [1.01216174]\n",
            " [1.01217783]\n",
            " [1.01216679]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29717267]\n",
            " [1.31431556]\n",
            " [1.31771812]\n",
            " [1.31481274]\n",
            " [1.31959755]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  945  Current D cost: [[1.29717267]\n",
            " [1.31431556]\n",
            " [1.31771812]\n",
            " [1.31481274]\n",
            " [1.31959755]]  Current G cost:  [[1.01349778]\n",
            " [1.01348713]\n",
            " [1.01347866]\n",
            " [1.01349478]\n",
            " [1.01348408]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33633669]\n",
            " [1.31763995]\n",
            " [1.32635952]\n",
            " [1.29481078]\n",
            " [1.35373374]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  946  Current D cost: [[1.33633669]\n",
            " [1.31763995]\n",
            " [1.32635952]\n",
            " [1.29481078]\n",
            " [1.35373374]]  Current G cost:  [[1.01484784]\n",
            " [1.01483739]\n",
            " [1.0148285 ]\n",
            " [1.01484571]\n",
            " [1.01483318]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.33802564]\n",
            " [1.31181524]\n",
            " [1.32926765]\n",
            " [1.29609146]\n",
            " [1.33308005]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  947  Current D cost: [[1.33802564]\n",
            " [1.31181524]\n",
            " [1.32926765]\n",
            " [1.29609146]\n",
            " [1.33308005]]  Current G cost:  [[1.01620837]\n",
            " [1.01619892]\n",
            " [1.01618923]\n",
            " [1.01620814]\n",
            " [1.01619341]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29505283]\n",
            " [1.33091554]\n",
            " [1.30343038]\n",
            " [1.32918401]\n",
            " [1.32621188]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  948  Current D cost: [[1.29505283]\n",
            " [1.33091554]\n",
            " [1.30343038]\n",
            " [1.32918401]\n",
            " [1.32621188]]  Current G cost:  [[1.01756891]\n",
            " [1.01755874]\n",
            " [1.0175496 ]\n",
            " [1.01756846]\n",
            " [1.01755273]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.28985441]\n",
            " [1.28700212]\n",
            " [1.30657018]\n",
            " [1.29698396]\n",
            " [1.33223096]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  949  Current D cost: [[1.28985441]\n",
            " [1.28700212]\n",
            " [1.30657018]\n",
            " [1.29698396]\n",
            " [1.33223096]]  Current G cost:  [[1.0189062 ]\n",
            " [1.01889578]\n",
            " [1.01888623]\n",
            " [1.01890541]\n",
            " [1.01888795]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32271923]\n",
            " [1.29815814]\n",
            " [1.33071548]\n",
            " [1.31241443]\n",
            " [1.30007979]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  950  Current D cost: [[1.32271923]\n",
            " [1.29815814]\n",
            " [1.33071548]\n",
            " [1.31241443]\n",
            " [1.30007979]]  Current G cost:  [[1.02025159]\n",
            " [1.0202419 ]\n",
            " [1.02023103]\n",
            " [1.02025099]\n",
            " [1.0202331 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.27872765]\n",
            " [1.29611906]\n",
            " [1.30169605]\n",
            " [1.33462494]\n",
            " [1.31950287]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  951  Current D cost: [[1.27872765]\n",
            " [1.29611906]\n",
            " [1.30169605]\n",
            " [1.33462494]\n",
            " [1.31950287]]  Current G cost:  [[1.02159417]\n",
            " [1.02158423]\n",
            " [1.02157253]\n",
            " [1.02159175]\n",
            " [1.02157414]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29868365]\n",
            " [1.31644133]\n",
            " [1.30560426]\n",
            " [1.34795479]\n",
            " [1.31947126]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  952  Current D cost: [[1.29868365]\n",
            " [1.31644133]\n",
            " [1.30560426]\n",
            " [1.34795479]\n",
            " [1.31947126]]  Current G cost:  [[1.0229567 ]\n",
            " [1.02294604]\n",
            " [1.02293429]\n",
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            " [1.02293519]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29709921]\n",
            " [1.29984012]\n",
            " [1.29653914]\n",
            " [1.33821597]\n",
            " [1.335281  ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  953  Current D cost: [[1.29709921]\n",
            " [1.29984012]\n",
            " [1.29653914]\n",
            " [1.33821597]\n",
            " [1.335281  ]]  Current G cost:  [[1.0243239 ]\n",
            " [1.02431279]\n",
            " [1.02430115]\n",
            " [1.02431622]\n",
            " [1.02430035]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.27596498]\n",
            " [1.27123311]\n",
            " [1.30552158]\n",
            " [1.2950708 ]\n",
            " [1.30219664]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  954  Current D cost: [[1.27596498]\n",
            " [1.27123311]\n",
            " [1.30552158]\n",
            " [1.2950708 ]\n",
            " [1.30219664]]  Current G cost:  [[1.02565388]\n",
            " [1.02564273]\n",
            " [1.02562993]\n",
            " [1.0256442 ]\n",
            " [1.02562839]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31293371]\n",
            " [1.32376317]\n",
            " [1.28586008]\n",
            " [1.31978861]\n",
            " [1.28749381]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  955  Current D cost: [[1.31293371]\n",
            " [1.32376317]\n",
            " [1.28586008]\n",
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            " [1.28749381]]  Current G cost:  [[1.02699535]\n",
            " [1.02698381]\n",
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            " [1.02698445]\n",
            " [1.0269698 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31554612]\n",
            " [1.2954708 ]\n",
            " [1.30091947]\n",
            " [1.30207842]\n",
            " [1.29223718]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  956  Current D cost: [[1.31554612]\n",
            " [1.2954708 ]\n",
            " [1.30091947]\n",
            " [1.30207842]\n",
            " [1.29223718]]  Current G cost:  [[1.02833788]\n",
            " [1.02832686]\n",
            " [1.02831502]\n",
            " [1.02832687]\n",
            " [1.02831312]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.28987297]\n",
            " [1.33755736]\n",
            " [1.30642006]\n",
            " [1.31910089]\n",
            " [1.30534632]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  957  Current D cost: [[1.28987297]\n",
            " [1.33755736]\n",
            " [1.30642006]\n",
            " [1.31910089]\n",
            " [1.30534632]]  Current G cost:  [[1.02970269]\n",
            " [1.02969029]\n",
            " [1.02967962]\n",
            " [1.02969062]\n",
            " [1.0296778 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32331172]\n",
            " [1.31016445]\n",
            " [1.33492583]\n",
            " [1.32145192]\n",
            " [1.29362369]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  958  Current D cost: [[1.32331172]\n",
            " [1.31016445]\n",
            " [1.33492583]\n",
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            " [1.29362369]]  Current G cost:  [[1.0310897 ]\n",
            " [1.03107707]\n",
            " [1.03106615]\n",
            " [1.03107719]\n",
            " [1.0310658 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31195573]\n",
            " [1.30682752]\n",
            " [1.29064237]\n",
            " [1.31597334]\n",
            " [1.30637481]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  959  Current D cost: [[1.31195573]\n",
            " [1.30682752]\n",
            " [1.29064237]\n",
            " [1.31597334]\n",
            " [1.30637481]]  Current G cost:  [[1.032471  ]\n",
            " [1.03245844]\n",
            " [1.03244797]\n",
            " [1.03245815]\n",
            " [1.03244779]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.28884127]\n",
            " [1.30710638]\n",
            " [1.34502946]\n",
            " [1.28230954]\n",
            " [1.31252979]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  960  Current D cost: [[1.28884127]\n",
            " [1.30710638]\n",
            " [1.34502946]\n",
            " [1.28230954]\n",
            " [1.31252979]]  Current G cost:  [[1.03385386]\n",
            " [1.03384071]\n",
            " [1.03382916]\n",
            " [1.0338411 ]\n",
            " [1.03383018]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.2886573 ]\n",
            " [1.2722704 ]\n",
            " [1.30065368]\n",
            " [1.28524858]\n",
            " [1.29295737]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  961  Current D cost: [[1.2886573 ]\n",
            " [1.2722704 ]\n",
            " [1.30065368]\n",
            " [1.28524858]\n",
            " [1.29295737]]  Current G cost:  [[1.03520445]\n",
            " [1.0351916 ]\n",
            " [1.03517851]\n",
            " [1.03519188]\n",
            " [1.03518039]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.2832684 ]\n",
            " [1.3020053 ]\n",
            " [1.2994906 ]\n",
            " [1.33601185]\n",
            " [1.31619735]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  962  Current D cost: [[1.2832684 ]\n",
            " [1.3020053 ]\n",
            " [1.2994906 ]\n",
            " [1.33601185]\n",
            " [1.31619735]]  Current G cost:  [[1.03657707]\n",
            " [1.03656372]\n",
            " [1.03654995]\n",
            " [1.03656278]\n",
            " [1.03655167]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31507965]\n",
            " [1.29263034]\n",
            " [1.29251797]\n",
            " [1.29557779]\n",
            " [1.27284875]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  963  Current D cost: [[1.31507965]\n",
            " [1.29263034]\n",
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            " [1.27284875]]  Current G cost:  [[1.03793703]\n",
            " [1.03792423]\n",
            " [1.03791014]\n",
            " [1.03792258]\n",
            " [1.03791248]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.28204111]\n",
            " [1.3152982 ]\n",
            " [1.31149463]\n",
            " [1.28450599]\n",
            " [1.29496874]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  964  Current D cost: [[1.28204111]\n",
            " [1.3152982 ]\n",
            " [1.31149463]\n",
            " [1.28450599]\n",
            " [1.29496874]]  Current G cost:  [[1.03930227]\n",
            " [1.03928858]\n",
            " [1.03927447]\n",
            " [1.03928768]\n",
            " [1.03927769]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.28459958]\n",
            " [1.29214038]\n",
            " [1.30341962]\n",
            " [1.31828507]\n",
            " [1.31130174]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  965  Current D cost: [[1.28459958]\n",
            " [1.29214038]\n",
            " [1.30341962]\n",
            " [1.31828507]\n",
            " [1.31130174]]  Current G cost:  [[1.040681  ]\n",
            " [1.0406666 ]\n",
            " [1.04065209]\n",
            " [1.04066517]\n",
            " [1.04065547]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30901201]\n",
            " [1.28763344]\n",
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            " [1.3283409 ]\n",
            " [1.30116811]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  966  Current D cost: [[1.30901201]\n",
            " [1.28763344]\n",
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            " [1.3283409 ]\n",
            " [1.30116811]]  Current G cost:  [[1.04207982]\n",
            " [1.04206584]\n",
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            " [1.04206272]\n",
            " [1.0420541 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31413512]\n",
            " [1.27857268]\n",
            " [1.27187121]\n",
            " [1.32980182]\n",
            " [1.26896827]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  967  Current D cost: [[1.31413512]\n",
            " [1.27857268]\n",
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            " [1.26896827]]  Current G cost:  [[1.04346002]\n",
            " [1.04344753]\n",
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            " [1.04343584]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29386933]\n",
            " [1.34413059]\n",
            " [1.2663597 ]\n",
            " [1.29555974]\n",
            " [1.31081559]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  968  Current D cost: [[1.29386933]\n",
            " [1.34413059]\n",
            " [1.2663597 ]\n",
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            " [1.31081559]]  Current G cost:  [[1.04485126]\n",
            " [1.04483777]\n",
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            " [1.04483239]\n",
            " [1.04482726]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.2777821 ]\n",
            " [1.3028084 ]\n",
            " [1.33933844]\n",
            " [1.30675382]\n",
            " [1.29493771]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  969  Current D cost: [[1.2777821 ]\n",
            " [1.3028084 ]\n",
            " [1.33933844]\n",
            " [1.30675382]\n",
            " [1.29493771]]  Current G cost:  [[1.04625632]\n",
            " [1.04624144]\n",
            " [1.04622802]\n",
            " [1.04623614]\n",
            " [1.04623181]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30635458]\n",
            " [1.30364239]\n",
            " [1.32786334]\n",
            " [1.30443865]\n",
            " [1.28638167]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  970  Current D cost: [[1.30635458]\n",
            " [1.30364239]\n",
            " [1.32786334]\n",
            " [1.30443865]\n",
            " [1.28638167]]  Current G cost:  [[1.04767337]\n",
            " [1.0476579 ]\n",
            " [1.04764364]\n",
            " [1.04765262]\n",
            " [1.04764933]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.28986352]\n",
            " [1.31389648]\n",
            " [1.30078067]\n",
            " [1.31147645]\n",
            " [1.28407329]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  971  Current D cost: [[1.28986352]\n",
            " [1.31389648]\n",
            " [1.30078067]\n",
            " [1.31147645]\n",
            " [1.28407329]]  Current G cost:  [[1.04908916]\n",
            " [1.04907256]\n",
            " [1.04905836]\n",
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            " [1.04906558]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29230045]\n",
            " [1.32844623]\n",
            " [1.31062986]\n",
            " [1.312744  ]\n",
            " [1.30534377]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  972  Current D cost: [[1.29230045]\n",
            " [1.32844623]\n",
            " [1.31062986]\n",
            " [1.312744  ]\n",
            " [1.30534377]]  Current G cost:  [[1.05052469]\n",
            " [1.05050624]\n",
            " [1.05049271]\n",
            " [1.0505017 ]\n",
            " [1.05050088]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29035952]\n",
            " [1.29399543]\n",
            " [1.27317396]\n",
            " [1.29247671]\n",
            " [1.29063698]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  973  Current D cost: [[1.29035952]\n",
            " [1.29399543]\n",
            " [1.27317396]\n",
            " [1.29247671]\n",
            " [1.29063698]]  Current G cost:  [[1.05193179]\n",
            " [1.0519123 ]\n",
            " [1.05189988]\n",
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            " [1.05190787]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.28826999]\n",
            " [1.32964573]\n",
            " [1.30971368]\n",
            " [1.33226433]\n",
            " [1.33530645]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  974  Current D cost: [[1.28826999]\n",
            " [1.32964573]\n",
            " [1.30971368]\n",
            " [1.33226433]\n",
            " [1.33530645]]  Current G cost:  [[1.05338431]\n",
            " [1.05336285]\n",
            " [1.05335169]\n",
            " [1.05335882]\n",
            " [1.05335867]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.2726994 ]\n",
            " [1.31913261]\n",
            " [1.28862614]\n",
            " [1.3153243 ]\n",
            " [1.30995632]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  975  Current D cost: [[1.2726994 ]\n",
            " [1.31913261]\n",
            " [1.28862614]\n",
            " [1.3153243 ]\n",
            " [1.30995632]]  Current G cost:  [[1.05482967]\n",
            " [1.05480555]\n",
            " [1.05479613]\n",
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            " [1.05480183]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.27380022]\n",
            " [1.31491515]\n",
            " [1.30902414]\n",
            " [1.27530959]\n",
            " [1.3044348 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  976  Current D cost: [[1.27380022]\n",
            " [1.31491515]\n",
            " [1.30902414]\n",
            " [1.27530959]\n",
            " [1.3044348 ]]  Current G cost:  [[1.05626421]\n",
            " [1.05623728]\n",
            " [1.05622895]\n",
            " [1.05623502]\n",
            " [1.05623416]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30497508]\n",
            " [1.3133355 ]\n",
            " [1.29000665]\n",
            " [1.30403875]\n",
            " [1.28480493]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  977  Current D cost: [[1.30497508]\n",
            " [1.3133355 ]\n",
            " [1.29000665]\n",
            " [1.30403875]\n",
            " [1.28480493]]  Current G cost:  [[1.05770142]\n",
            " [1.05767282]\n",
            " [1.05766587]\n",
            " [1.05767168]\n",
            " [1.05767102]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29831163]\n",
            " [1.29743822]\n",
            " [1.28575393]\n",
            " [1.2952725 ]\n",
            " [1.29417517]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  978  Current D cost: [[1.29831163]\n",
            " [1.29743822]\n",
            " [1.28575393]\n",
            " [1.2952725 ]\n",
            " [1.29417517]]  Current G cost:  [[1.05913483]\n",
            " [1.05910544]\n",
            " [1.05909962]\n",
            " [1.05910496]\n",
            " [1.05910442]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30929398]\n",
            " [1.3017423 ]\n",
            " [1.30444917]\n",
            " [1.29825649]\n",
            " [1.28286213]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  979  Current D cost: [[1.30929398]\n",
            " [1.3017423 ]\n",
            " [1.30444917]\n",
            " [1.29825649]\n",
            " [1.28286213]]  Current G cost:  [[1.06057583]\n",
            " [1.06054635]\n",
            " [1.060541  ]\n",
            " [1.06054634]\n",
            " [1.0605464 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32223826]\n",
            " [1.30220482]\n",
            " [1.2922922 ]\n",
            " [1.30727442]\n",
            " [1.33675935]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  980  Current D cost: [[1.32223826]\n",
            " [1.30220482]\n",
            " [1.2922922 ]\n",
            " [1.30727442]\n",
            " [1.33675935]]  Current G cost:  [[1.0620454 ]\n",
            " [1.06201661]\n",
            " [1.06201187]\n",
            " [1.06201667]\n",
            " [1.06201596]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31685389]\n",
            " [1.30492074]\n",
            " [1.35352265]\n",
            " [1.30883238]\n",
            " [1.30939649]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  981  Current D cost: [[1.31685389]\n",
            " [1.30492074]\n",
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            " [1.30939649]]  Current G cost:  [[1.06354405]\n",
            " [1.06351606]\n",
            " [1.0635099 ]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.2938975 ]\n",
            " [1.29028265]\n",
            " [1.3061077 ]\n",
            " [1.32625084]\n",
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            "Current Iter:  982  Current D cost: [[1.2938975 ]\n",
            " [1.29028265]\n",
            " [1.3061077 ]\n",
            " [1.32625084]\n",
            " [1.3570445 ]]  Current G cost:  [[1.06505416]\n",
            " [1.06502672]\n",
            " [1.06501927]\n",
            " [1.06502534]\n",
            " [1.0650229 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.31356488]\n",
            " [1.3083069 ]\n",
            " [1.3122867 ]\n",
            " [1.27783798]\n",
            " [1.32863548]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  983  Current D cost: [[1.31356488]\n",
            " [1.3083069 ]\n",
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            " [1.32863548]]  Current G cost:  [[1.06656061]\n",
            " [1.06653365]\n",
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            " [1.06652779]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29211808]\n",
            " [1.30105008]\n",
            " [1.31091353]\n",
            " [1.30824538]\n",
            " [1.31244624]]\n",
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            "Current Iter:  984  Current D cost: [[1.29211808]\n",
            " [1.30105008]\n",
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            " [1.31244624]]  Current G cost:  [[1.06806247]\n",
            " [1.06803545]\n",
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            " [1.06802813]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.27960483]\n",
            " [1.31990549]\n",
            " [1.28970732]\n",
            " [1.32293974]\n",
            " [1.30325609]]\n",
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            "Current Iter:  985  Current D cost: [[1.27960483]\n",
            " [1.31990549]\n",
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            " [1.32293974]\n",
            " [1.30325609]]  Current G cost:  [[1.069562  ]\n",
            " [1.06953349]\n",
            " [1.06952527]\n",
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            " [1.06952606]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.26254459]\n",
            " [1.30433607]\n",
            " [1.28338106]\n",
            " [1.33624837]\n",
            " [1.30072601]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  986  Current D cost: [[1.26254459]\n",
            " [1.30433607]\n",
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            " [1.30072601]]  Current G cost:  [[1.07105237]\n",
            " [1.07102159]\n",
            " [1.07101451]\n",
            " [1.07101939]\n",
            " [1.07101423]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.27265014]\n",
            " [1.33594851]\n",
            " [1.31982458]\n",
            " [1.31040755]\n",
            " [1.29706018]]\n",
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            "Current Iter:  987  Current D cost: [[1.27265014]\n",
            " [1.33594851]\n",
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            " [1.29706018]]  Current G cost:  [[1.07255839]\n",
            " [1.07252418]\n",
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            " [1.07251825]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.32973708]\n",
            " [1.29146015]\n",
            " [1.28725138]\n",
            " [1.3059066 ]\n",
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            "Current Iter:  988  Current D cost: [[1.32973708]\n",
            " [1.29146015]\n",
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            " [1.26854921]]  Current G cost:  [[1.07405331]\n",
            " [1.0740188 ]\n",
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            " [1.07401445]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29606022]\n",
            " [1.28292772]\n",
            " [1.27697621]\n",
            " [1.31785521]\n",
            " [1.29047989]]\n",
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            "Current Iter:  989  Current D cost: [[1.29606022]\n",
            " [1.28292772]\n",
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            " [1.29047989]]  Current G cost:  [[1.07554016]\n",
            " [1.07550602]\n",
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            " [1.07550236]\n",
            " [1.07550216]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30387901]\n",
            " [1.34449056]\n",
            " [1.28324949]\n",
            " [1.28145028]\n",
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            "Current Iter:  990  Current D cost: [[1.30387901]\n",
            " [1.34449056]\n",
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            " [1.27447859]]  Current G cost:  [[1.07703197]\n",
            " [1.07699658]\n",
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            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29950247]\n",
            " [1.30263496]\n",
            " [1.29961146]\n",
            " [1.25836131]\n",
            " [1.29339806]]\n",
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            "Current Iter:  991  Current D cost: [[1.29950247]\n",
            " [1.30263496]\n",
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            " [1.29339806]]  Current G cost:  [[1.07851765]\n",
            " [1.07848154]\n",
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            " [1.07848223]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.28547565]\n",
            " [1.30600499]\n",
            " [1.29625822]\n",
            " [1.27839971]\n",
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            "5 random:  (5, 784)\n",
            "Current Iter:  992  Current D cost: [[1.28547565]\n",
            " [1.30600499]\n",
            " [1.29625822]\n",
            " [1.27839971]\n",
            " [1.30056174]]  Current G cost:  [[1.08000941]\n",
            " [1.07997221]\n",
            " [1.07997257]\n",
            " [1.0799749 ]\n",
            " [1.07997396]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30872636]\n",
            " [1.3010159 ]\n",
            " [1.32060514]\n",
            " [1.30832903]\n",
            " [1.29664773]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  993  Current D cost: [[1.30872636]\n",
            " [1.3010159 ]\n",
            " [1.32060514]\n",
            " [1.30832903]\n",
            " [1.29664773]]  Current G cost:  [[1.08153172]\n",
            " [1.08149429]\n",
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            " [1.08149672]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.24918143]\n",
            " [1.26695494]\n",
            " [1.29720934]\n",
            " [1.29569494]\n",
            " [1.27775767]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  994  Current D cost: [[1.24918143]\n",
            " [1.26695494]\n",
            " [1.29720934]\n",
            " [1.29569494]\n",
            " [1.27775767]]  Current G cost:  [[1.08301792]\n",
            " [1.08297972]\n",
            " [1.08297864]\n",
            " [1.08298266]\n",
            " [1.08298207]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29169614]\n",
            " [1.29781062]\n",
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            " [1.29881771]\n",
            " [1.2787915 ]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  995  Current D cost: [[1.29169614]\n",
            " [1.29781062]\n",
            " [1.28862824]\n",
            " [1.29881771]\n",
            " [1.2787915 ]]  Current G cost:  [[1.08451361]\n",
            " [1.08447482]\n",
            " [1.08447347]\n",
            " [1.08447742]\n",
            " [1.08447784]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.29947265]\n",
            " [1.28002831]\n",
            " [1.29977778]\n",
            " [1.29563603]\n",
            " [1.29759747]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  996  Current D cost: [[1.29947265]\n",
            " [1.28002831]\n",
            " [1.29977778]\n",
            " [1.29563603]\n",
            " [1.29759747]]  Current G cost:  [[1.08602211]\n",
            " [1.08598379]\n",
            " [1.08598156]\n",
            " [1.08598564]\n",
            " [1.08598647]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.28561459]\n",
            " [1.29860456]\n",
            " [1.32674969]\n",
            " [1.27828822]\n",
            " [1.33022154]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  997  Current D cost: [[1.28561459]\n",
            " [1.29860456]\n",
            " [1.32674969]\n",
            " [1.27828822]\n",
            " [1.33022154]]  Current G cost:  [[1.0875557 ]\n",
            " [1.08751716]\n",
            " [1.08751345]\n",
            " [1.08751941]\n",
            " [1.0875185 ]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.25651317]\n",
            " [1.33260905]\n",
            " [1.30060793]\n",
            " [1.32040764]\n",
            " [1.26191996]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  998  Current D cost: [[1.25651317]\n",
            " [1.33260905]\n",
            " [1.30060793]\n",
            " [1.32040764]\n",
            " [1.26191996]]  Current G cost:  [[1.08908629]\n",
            " [1.0890448 ]\n",
            " [1.08904154]\n",
            " [1.08904772]\n",
            " [1.08904811]]\n",
            "shape of current image:  (5, 784)\n",
            "stuff:  (5, 784)\n",
            "Cost:  [[1.30389497]\n",
            " [1.28733734]\n",
            " [1.26278321]\n",
            " [1.33791165]\n",
            " [1.28996109]]\n",
            "5 random:  (5, 784)\n",
            "Current Iter:  999  Current D cost: [[1.30389497]\n",
            " [1.28733734]\n",
            " [1.26278321]\n",
            " [1.33791165]\n",
            " [1.28996109]]  Current G cost:  [[1.09062188]\n",
            " [1.09057958]\n",
            " [1.09057748]\n",
            " [1.09058097]\n",
            " [1.09058377]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zYry6Fe6eL4m",
        "outputId": "5b34519f-c9d3-4db2-b7ed-cd8f4fe64ad4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(D_list_cost,color = 'r')\n",
        "plt.plot(G_list_cost,color = 'b')"
      ],
      "execution_count": 210,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f4fd2285710>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 210
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ckbKlFOhmNgD4E9AYeNTdby+1/l7gqNhsc2And2+dzoJK7SgogHXrtp3Wrg2B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\nYII=\n",
            "text/plain": [
              "<Figure size 432x288 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=[1, G_input])\n",
        "    print(\"input shape: \", Z.shape)\n",
        "    Gl1 = Z.dot(G_W1) + G_b1\n",
        "    print(\"bias shape: \", 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(\"shape of image: \", current_fake_data.shape)\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": 211,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "input shape:  (1, 100)\n",
            "bias shape:  (5, 128)\n",
            "shape of image:  (5, 784)\n",
            "15\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n",
            "sample size (5, 784)\n",
            "sample resize (5, 28, 28)\n",
            "each resized sample (28, 28)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+rGAEFmwRw+gbsF/jx0e9YRiSdCY/kwlhYM83YFPlXq8njcbpDzCHVSgU4Pu+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\nfQAAAABJRU5ErkJggg==\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": 212,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "bias (5, 128)\n",
            "current_fake shape:  (5, 784)\n",
            "s_data shape:  (5, 784)\n",
            "(5, 784)\n",
            "5\n",
            "sample size (784,)\n"
          ],
          "name": "stdout"
        },
        {
          "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-212-e00cf2b2be1e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     52\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"s_data shape: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms_data\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcurrent_fake_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 54\u001b[0;31m \u001b[0mfig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms_data\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mcurrent_fake_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"TEST\"\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     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-203-6a4ba1baea09>\u001b[0m in \u001b[0;36mplot\u001b[0;34m(samples, title)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sample size\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m         \u001b[0msample_resized\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m28\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     48\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"sample resize\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_resized\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"each resized sample\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_resized\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\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 784 into shape (5,28,28)"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEIAAABSCAYAAADgggJzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "sXhuAW8zbfRs",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ZA9tB9_2bfR2",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "56R3Qb35bfSC",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "4twoZKgvbfSk",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0b_-c-6TbfTC",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lGK_P4habfTp",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}