qGAN/train_qGAN.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pennylane as qml\n",
    "import numpy as np\n",
    "import sys\n",
    "from qGAN import qGAN, train_qGAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
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   "source": [
    "Here we will train the qGAN created in the file qGAN.py and train it on some simple examples.\n",
    "First, a function to create arbitrary adjacency matrices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
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   "source": [
    "def random_adjacency_matrix(n):\n",
    "    \"\"\"\n",
    "    creates an nxn symmetric adjacency matrix with 0s along the diagonal\n",
    "    used to represent an undirected graph with n nodes\n",
    "    :param n: dimension\n",
    "    :return: nxn numpy array\n",
    "    \"\"\"\n",
    "    matrix = [[np.random.randint(0, 1) for i in range(n)] for j in range(n)]\n",
    "    # No vertex connects to itself\n",
    "    for i in range(n):\n",
    "        matrix[i][i] = 0\n",
    "    # If i is connected to j, j is connected to i\n",
    "    for i in range(n):\n",
    "        for j in range(n):\n",
    "            matrix[j][i] = matrix[i][j]\n",
    "    return np.array(matrix)"
   ]
  },
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   "execution_count": null,
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   "cell_type": "code",
   "execution_count": 3,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/andrew/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/tensorflow/python/ops/array_grad.py:644: _EagerTensorBase.cpu (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.identity instead.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/andrew/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/tensorflow/python/ops/array_grad.py:644: _EagerTensorBase.cpu (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.identity instead.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gen cost: [4.75834439]\n",
      "Disc cost: [-0.17832228]\n",
      "Generated sample:\n",
      "[[1. 1. 1.]\n",
      " [0. 0. 1.]\n",
      " [1. 1. 1.]]\n",
      "Gen cost: [1.68520967]\n",
      "Disc cost: [-0.68732201]\n",
      "Generated sample:\n",
      "[[0. 0. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 1. 0.]]\n",
      "Gen cost: [0.14393633]\n",
      "Disc cost: [-0.90785427]\n",
      "Generated sample:\n",
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "Gen cost: [0.03072745]\n",
      "Disc cost: [-0.98380213]\n",
      "Generated sample:\n",
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "Gen cost: [0.00508255]\n",
      "Disc cost: [-0.9993631]\n",
      "Generated sample:\n",
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n",
      "Gen cost: [0.00369175]\n",
      "Disc cost: [-0.99998843]\n",
      "Generated sample:\n",
      "[[0. 0. 0.]\n",
      " [0. 0. 0.]\n",
      " [0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "dataset = [random_adjacency_matrix(3) for _ in range(50)]\n",
    "train_qGAN(dataset[0], dataset, 30)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Penalties: We are getting lots of matrices with 1 on the diagonal, which we ideally don't want. We also want to penalise going to a  location twice. This means we want to penalise measurements of qubits 0, n+1, 2n+2, ..., n**2 - 1.\n",
    "This is just the Z expectation value of these qubits.\n",
    "\n",
    "We also require that the qubits in each row and column have hamming weight 1. These qubits are $|0, 1, ..., n \\rangle$, $|n + 0, n + 1, ..., n \\rangle$, ... $|n^2 - n, ..., n^2 \\rangle$.\n",
    "\n",
    "And for the columns we have $|n.0, n.1, ..., n(n-1) \\rangle$, $|n.0 + 1, n.1 + 1, ..., n(n-1) + 1 \\rangle$, $|n.0 + n, n + n, ..., n(n-1) + n\\rangle$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#Training on data from DWave\n",
    "samples = np.load('../AirlineDataSets/4_qubit_adj_mat.npy')\n",
    "s = [x for x in samples]\n",
    "output = train_qGAN(s[0], s, 50, gen_dev='qiskit.aer', disc_dev='qiskit.aer')"
   ],
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  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "def stitch_sparse(in_matrix, out_matrix):\n",
    "    n = in_matrix.shape[0]\n",
    "    in_sparse = []\n",
    "    out_sparse = []\n",
    "    for i in range(n):\n",
    "        for j in range(n):\n",
    "            if (in_matrix[i][j] > 0):\n",
    "                in_sparse.append([i,j])\n",
    "            if (out_matrix[i][j] > 0):\n",
    "                out_sparse.append([i,j])\n",
    "    in_sparse, out_sparse = np.array(in_sparse), np.array(out_sparse)\n",
    "    return np.concatenate((in_sparse,out_sparse),axis=1)"
   ],
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  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [],
   "source": [
    "def decompose_sparse(in_sparse):\n",
    "    decomp = np.hsplit(in_sparse, 2)\n",
    "    n = in_sparse.shape[0]\n",
    "    count = 0\n",
    "    in_matrix = []\n",
    "    out_matrix = []\n",
    "    for i in range(int(n/2)):\n",
    "        for j in range(n):\n",
    "            if count < n:\n",
    "                in_matrix.append(decomp[i][j])\n",
    "            else:\n",
    "                out_matrix.append(decomp[i][j])\n",
    "            count += 1\n",
    "    in_matrix, out_matrix = np.array(in_matrix), np.array(out_matrix)\n",
    "    return in_matrix, out_matrix"
   ],
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  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 12 into shape (9,)",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-16-0293ded84eec>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      2\u001B[0m \u001B[0mmb\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mload\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'../AirlineDataSets/16qbMb.npy'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      3\u001B[0m \u001B[0minputs\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0mstitch_sparse\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mb\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mb\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mzip\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mmb\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 4\u001B[0;31m \u001B[0moutput\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtrain_qGAN\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0minputs\u001B[0m\u001B[0;34m[\u001B[0m\u001B[0;36m1\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minputs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;36m15\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mgen_dev\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m'qiskit.aer'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdisc_dev\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;34m'qiskit.aer'\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      5\u001B[0m \u001B[0msamples\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0mdecompose_sparse\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mx\u001B[0m \u001B[0;32min\u001B[0m \u001B[0moutput\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m      6\u001B[0m \u001B[0mprint\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msamples\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/hackathon/qGAN/qGAN.py\u001B[0m in \u001B[0;36mtrain_qGAN\u001B[0;34m(adjacency_matrix, x_samples, epochs, lr, gen_dev, disc_dev)\u001B[0m\n\u001B[1;32m    257\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mgen_weights\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdisc_weights\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    258\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 259\u001B[0;31m     \u001B[0mgen_weights\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0m_\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtraining\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx_samples\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    260\u001B[0m     \u001B[0msamples\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0msample\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m10\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mgen_weights\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    261\u001B[0m     \u001B[0;32mreturn\u001B[0m \u001B[0msamples\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/hackathon/qGAN/qGAN.py\u001B[0m in \u001B[0;36mtraining\u001B[0;34m(x_train)\u001B[0m\n\u001B[1;32m    248\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0me\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mrange\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mepochs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    249\u001B[0m             \u001B[0;32mfor\u001B[0m \u001B[0mx\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mx_train\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 250\u001B[0;31m                 \u001B[0mdisc_loss\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtrain_disc_step\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mgen_weights\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdisc_weights\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0moptimiser\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    251\u001B[0m                 \u001B[0mgen_loss\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtrain_gen_step\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mgen_weights\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdisc_weights\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0moptimiser\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    252\u001B[0m             \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0me\u001B[0m \u001B[0;34m%\u001B[0m \u001B[0;36m5\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/hackathon/qGAN/qGAN.py\u001B[0m in \u001B[0;36mtrain_disc_step\u001B[0;34m(x, gen_weights, disc_weights, optimiser)\u001B[0m\n\u001B[1;32m    211\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mtrain_disc_step\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mgen_weights\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdisc_weights\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0moptimiser\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    212\u001B[0m         \u001B[0;32mwith\u001B[0m \u001B[0mtf\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mGradientTape\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0mtape\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 213\u001B[0;31m             \u001B[0mdisc_loss\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mdisc_cost\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mx\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mgen_weights\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdisc_weights\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    214\u001B[0m         \u001B[0mgrads\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mtape\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mgradient\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdisc_loss\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0mdisc_weights\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    215\u001B[0m         \u001B[0moptimiser\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mapply_gradients\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mzip\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mgrads\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m[\u001B[0m\u001B[0mdisc_weights\u001B[0m\u001B[0;34m]\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/hackathon/qGAN/qGAN.py\u001B[0m in \u001B[0;36mdisc_cost\u001B[0;34m(sample_solution, gen_weights, disc_weight)\u001B[0m\n\u001B[1;32m    180\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mdisc_cost\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msample_solution\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mgen_weights\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdisc_weight\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    181\u001B[0m         \u001B[0;34m\"\"\"Cost function for the discriminator. P(M(1)|fake) - P(M(1)|true)\"\"\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 182\u001B[0;31m         \u001B[0mcost\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mfake_true\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mgen_weights\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdisc_weight\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m-\u001B[0m \u001B[0mreal_true\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msample_solution\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdisc_weight\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    183\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mcost\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    184\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/hackathon/qGAN/qGAN.py\u001B[0m in \u001B[0;36mreal_true\u001B[0;34m(sample_solution, disc_weights)\u001B[0m\n\u001B[1;32m    170\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mreal_true\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msample_solution\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdisc_weights\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    171\u001B[0m         \u001B[0;34m\"\"\"Probability of measuring true when given real data\"\"\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 172\u001B[0;31m         \u001B[0mdisc_output\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mreal_disc_circuits\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0msample_solution\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mdisc_weights\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    173\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0;34m(\u001B[0m\u001B[0mdisc_output\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0;36m1\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m/\u001B[0m \u001B[0;36m2\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    174\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/tensorflow/python/ops/custom_gradient.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *a, **k)\u001B[0m\n\u001B[1;32m    256\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    257\u001B[0m   \u001B[0;32mdef\u001B[0m \u001B[0m__call__\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m*\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mk\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 258\u001B[0;31m     \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_d\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_f\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mk\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    259\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    260\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/tensorflow/python/ops/custom_gradient.py\u001B[0m in \u001B[0;36mdecorated\u001B[0;34m(wrapped, args, kwargs)\u001B[0m\n\u001B[1;32m    210\u001B[0m     \u001B[0;34m\"\"\"Decorated function with custom gradient.\"\"\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    211\u001B[0m     \u001B[0;32mif\u001B[0m \u001B[0mcontext\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mexecuting_eagerly\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 212\u001B[0;31m       \u001B[0;32mreturn\u001B[0m \u001B[0m_eager_mode_decorator\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mwrapped\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mkwargs\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    213\u001B[0m     \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    214\u001B[0m       \u001B[0;32mreturn\u001B[0m \u001B[0m_graph_mode_decorator\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mwrapped\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/tensorflow/python/ops/custom_gradient.py\u001B[0m in \u001B[0;36m_eager_mode_decorator\u001B[0;34m(f, args, kwargs)\u001B[0m\n\u001B[1;32m    407\u001B[0m   \u001B[0;34m\"\"\"Implement custom gradient decorator for eager mode.\"\"\"\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    408\u001B[0m   \u001B[0;32mwith\u001B[0m \u001B[0mbackprop\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mGradientTape\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mas\u001B[0m \u001B[0mtape\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 409\u001B[0;31m     \u001B[0mresult\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mgrad_fn\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mf\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\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    410\u001B[0m   \u001B[0mall_inputs\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mlist\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m+\u001B[0m \u001B[0mlist\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mkwargs\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvalues\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    411\u001B[0m   \u001B[0;31m# The variables that grad_fn needs to return gradients for are the set of\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/pennylane/interfaces/tf.py\u001B[0m in \u001B[0;36m_TFQNode\u001B[0;34m(*input_, **input_kwargs)\u001B[0m\n\u001B[1;32m    153\u001B[0m         \u001B[0;31m# evaluate the QNode\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    154\u001B[0m         \u001B[0mqnode\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mset_trainable_args\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mtrainable_args\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 155\u001B[0;31m         \u001B[0mres\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mqnode\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\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    156\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    157\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0;32mnot\u001B[0m \u001B[0misinstance\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mres\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mndarray\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/pennylane/qnodes/base.py\u001B[0m in \u001B[0;36m__call__\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m    792\u001B[0m         \"\"\"Wrapper for :meth:`BaseQNode.evaluate`.\n\u001B[1;32m    793\u001B[0m         \"\"\"\n\u001B[0;32m--> 794\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mevaluate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mkwargs\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    795\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    796\u001B[0m     \u001B[0;32mdef\u001B[0m \u001B[0mevaluate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mkwargs\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/pennylane/qnodes/base.py\u001B[0m in \u001B[0;36mevaluate\u001B[0;34m(self, args, kwargs)\u001B[0m\n\u001B[1;32m    812\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    813\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcircuit\u001B[0m \u001B[0;32mis\u001B[0m \u001B[0;32mNone\u001B[0m \u001B[0;32mor\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mmutable\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 814\u001B[0;31m             \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_construct\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mkwargs\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    815\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    816\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdevice\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreset\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/pennylane/qnodes/jacobian.py\u001B[0m in \u001B[0;36m_construct\u001B[0;34m(self, args, kwargs)\u001B[0m\n\u001B[1;32m     88\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0meach\u001B[0m \u001B[0mpositional\u001B[0m \u001B[0mparameter\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     89\u001B[0m         \"\"\"\n\u001B[0;32m---> 90\u001B[0;31m         \u001B[0msuper\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_construct\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mkwargs\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     91\u001B[0m         \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mpar_to_grad_method\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0;34m{\u001B[0m\u001B[0mk\u001B[0m\u001B[0;34m:\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0m_best_method\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mk\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;32mfor\u001B[0m \u001B[0mk\u001B[0m \u001B[0;32min\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mvariable_deps\u001B[0m\u001B[0;34m}\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     92\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/pennylane/qnodes/base.py\u001B[0m in \u001B[0;36m_construct\u001B[0;34m(self, args, kwargs)\u001B[0m\n\u001B[1;32m    564\u001B[0m                     \u001B[0;31m# it's ok to directly pass auxiliary arguments since the circuit is re-constructed each time\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    565\u001B[0m                     \u001B[0;31m# (positional args must be replaced because parameter-shift differentiation requires Variables)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m--> 566\u001B[0;31m                     \u001B[0mres\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mfunc\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0marg_vars\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\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    567\u001B[0m                 \u001B[0;32melse\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    568\u001B[0m                     \u001B[0;31m# TODO: Maybe we should only convert the kwarg_vars that were actually given\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/hackathon/qGAN/qGAN.py\u001B[0m in \u001B[0;36mreal_disc_circuits\u001B[0;34m(adjaceny_matrix, disc_weights)\u001B[0m\n\u001B[1;32m    151\u001B[0m         \"\"\"Builds the discrimination circuits when given real data.\n\u001B[1;32m    152\u001B[0m         Probability of the data being real is given by measurement of first qubit\"\"\"\n\u001B[0;32m--> 153\u001B[0;31m         \u001B[0mqgan\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mcreate_real\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0madjacency_matrix\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0madjaceny_matrix\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    154\u001B[0m         \u001B[0mqgan\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mdiscriminator\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mdisc_weights\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    155\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mqml\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mexpval\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0mobservable\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;36m0\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/hackathon/qGAN/qGAN.py\u001B[0m in \u001B[0;36mcreate_real_qubit\u001B[0;34m(self, adjacency_matrix)\u001B[0m\n\u001B[1;32m     83\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0msum\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0madjacency_matrix\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mshape\u001B[0m\u001B[0;34m)\u001B[0m \u001B[0;34m>\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mn_qubits\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     84\u001B[0m             \u001B[0;32mraise\u001B[0m \u001B[0mValueError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m'The adjacency matrix provided is too large'\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 85\u001B[0;31m         \u001B[0madj_vec\u001B[0m \u001B[0;34m=\u001B[0m \u001B[0mnp\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mreshape\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0madjacency_matrix\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mself\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mn_qubits\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     86\u001B[0m         \u001B[0;32mfor\u001B[0m \u001B[0mi\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mconnection\u001B[0m \u001B[0;32min\u001B[0m \u001B[0menumerate\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0madj_vec\u001B[0m\u001B[0;34m)\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     87\u001B[0m             \u001B[0;32mif\u001B[0m \u001B[0mconnection\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m<__array_function__ internals>\u001B[0m in \u001B[0;36mreshape\u001B[0;34m(*args, **kwargs)\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001B[0m in \u001B[0;36mreshape\u001B[0;34m(a, newshape, order)\u001B[0m\n\u001B[1;32m    297\u001B[0m            [5, 6]])\n\u001B[1;32m    298\u001B[0m     \"\"\"\n\u001B[0;32m--> 299\u001B[0;31m     \u001B[0;32mreturn\u001B[0m \u001B[0m_wrapfunc\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0ma\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m'reshape'\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0mnewshape\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0morder\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0morder\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    300\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    301\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/Documents/PhD/CDLQuantum/CDLenv/lib/python3.7/site-packages/numpy/core/fromnumeric.py\u001B[0m in \u001B[0;36m_wrapfunc\u001B[0;34m(obj, method, *args, **kwds)\u001B[0m\n\u001B[1;32m     56\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     57\u001B[0m     \u001B[0;32mtry\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m---> 58\u001B[0;31m         \u001B[0;32mreturn\u001B[0m \u001B[0mbound\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwds\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     59\u001B[0m     \u001B[0;32mexcept\u001B[0m \u001B[0mTypeError\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m     60\u001B[0m         \u001B[0;31m# A TypeError occurs if the object does have such a method in its\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 12 into shape (9,)"
     ]
    }
   ],
   "source": [
    "ma = np.load('../AirlineDataSets/16qbMa.npy')\n",
    "mb = np.load('../AirlineDataSets/16qbMb.npy')\n",
    "inputs = [stitch_sparse(a, b) for a, b in zip(ma, mb)]\n",
    "output = train_qGAN(inputs[1], inputs, 15, gen_dev='qiskit.aer', disc_dev='qiskit.aer')\n",
    "samples = [decompose_sparse(x) for x in output]\n",
    "print(samples)"
   ],
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   "cell_type": "code",
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    "\n"
   ],
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     "name": "#%%\n"
    }
   }
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