TestUse.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8.0.dev20201206\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from tensornetworks_pytorch.TNModels import PosMPS, Born\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "flare\n",
      "\tdata shape: (1065, 13)\n",
      "\trange of X values: 0 -- 7\n",
      "lymphography\n",
      "\tdata shape: (148, 19)\n",
      "\trange of X values: 0 -- 7\n",
      "spect\n",
      "\tdata shape: (187, 23)\n",
      "\trange of X values: 0 -- 1\n",
      "tumor\n",
      "\tdata shape: (339, 17)\n",
      "\trange of X values: 0 -- 3\n",
      "votes\n",
      "\tdata shape: (435, 17)\n",
      "\trange of X values: 0 -- 2\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "for dataset in [#'biofam',\n",
    "    'flare','lymphography','spect','tumor','votes']:\n",
    "    with open('datasets/'+dataset, 'rb') as f:\n",
    "            a=pickle.load(f)\n",
    "    X=a[0].astype(int)\n",
    "    print(dataset)\n",
    "    print(\"\\tdata shape:\", X.shape)\n",
    "    print(f\"\\trange of X values: {X.min()} -- {X.max()}\")\n",
    "\n",
    "def load_dataset(dataset):\n",
    "    with open('datasets/'+dataset, 'rb') as f:\n",
    "            a=pickle.load(f)\n",
    "    X=a[0]\n",
    "    X=X.astype(int)\n",
    "\n",
    "    print(\"\\tdata shape:\", X.shape)\n",
    "    print(f\"\\trange of X values: {X.min()} -- {X.max()} ==> d={X.max()+1}\")\n",
    "    d = X.max()+1\n",
    "    return X, d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset: tumor\n",
      "\tdata shape: (339, 17)\n",
      "\trange of X values: 0 -- 3 ==> d=4\n",
      "Initializing models:\n",
      "\ttorch.Size([4, 8, 8]) model type: Positive MPS, Homogeneous + log_stability\n",
      "\ttorch.Size([17, 4, 8, 8]) model type: Born (torch.float32), Non-homogeneous\n",
      "\ttorch.Size([17, 4, 8, 8]) model type: Born (torch.complex64), Non-homogeneous\n",
      "\ttorch.Size([17, 4, 8, 8]) model type: Born (torch.float32), Non-homogeneous + log_stability\n",
      "\ttorch.Size([17, 4, 8, 8]) model type: Born (torch.complex64), Non-homogeneous + log_stability\n",
      "\ttorch.Size([4, 8, 8]) model type: Positive MPS, Homogeneous + log_stability\n",
      "\ttorch.Size([4, 8, 8]) model type: Born (torch.float32), Homogeneous\n",
      "\ttorch.Size([4, 8, 8]) model type: Born (torch.complex64), Homogeneous\n",
      "\ttorch.Size([4, 8, 8]) model type: Born (torch.float32), Homogeneous + log_stability\n",
      "\ttorch.Size([4, 8, 8]) model type: Born (torch.complex64), Homogeneous + log_stability\n"
     ]
    }
   ],
   "source": [
    "dataset = 'tumor'\n",
    "print(\"dataset:\", dataset)\n",
    "X,d = load_dataset(dataset)\n",
    "\n",
    "D = 8\n",
    "#mps       = PosMPS(X, d, D, homogeneous=False)\n",
    "mps_hom   = PosMPS(X, d, D, homogeneous=True, log_stability=True)\n",
    "\n",
    "rBorn     = Born(X, d, D, dtype=torch.float, homogeneous=False, log_stability=False) \n",
    "rBorn_hom = Born(X, d, D, dtype=torch.float, homogeneous=True, log_stability=False) \n",
    "\n",
    "cBorn     = Born(X, d, D, dtype=torch.cfloat, homogeneous=False, log_stability=False)\n",
    "cBorn_hom = Born(X, d, D, dtype=torch.cfloat, homogeneous=True, log_stability=False)\n",
    "\n",
    "rBorn_s   = Born(X, d, D, dtype=torch.float, homogeneous=False, log_stability=True) \n",
    "rBorn_s_hom = Born(X, d, D, dtype=torch.float, homogeneous=True, log_stability=True) \n",
    "\n",
    "cBorn_s   = Born(X, d, D, dtype=torch.cfloat, homogeneous=False, log_stability=True)\n",
    "cBorn_s_hom = Born(X, d, D, dtype=torch.cfloat, homogeneous=True, log_stability=True)\n",
    "\n",
    "models     = (mps_hom, rBorn, cBorn, rBorn_s, cBorn_s)\n",
    "models_hom = (mps_hom, rBorn_hom, cBorn_hom, rBorn_s_hom, cBorn_s_hom)\n",
    "print(\"Initializing models:\")\n",
    "for model in (*models, *models_hom):\n",
    "    print(f\"\\t{model.core.shape} model type: {model.name}\")\n",
    "    for p in model.parameters():\n",
    "                if torch.isnan(p).any():\n",
    "                    print(\"Model weights contain a NaN value!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def register_clip_hook(model, clip_val, verbose=False):\n",
    "    \"\"\"\"Set up model parameters to clip gradient at `clip_val`\"\"\"\n",
    "    for p in model.parameters():\n",
    "        if p.dtype==torch.cfloat:\n",
    "            p.register_hook(lambda grad: torch.complex(\n",
    "                grad.real.clamp(-clip_val, clip_val),grad.imag.clamp(-clip_val, clip_val)))\n",
    "            if verbose:\n",
    "                p.register_hook(lambda grad: print('real grad range',grad.real.min(),grad.real.max()))\n",
    "                p.register_hook(lambda grad: print('imag grad range',grad.imag.min(),grad.imag.max()))\n",
    "        else:\n",
    "            p.register_hook(lambda grad: torch.clamp(grad, -clip_val, clip_val))\n",
    "            if verbose:\n",
    "                p.register_hook(lambda grad: print('grad range',grad.min(),grad.max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(self, dataset, batchsize, max_epochs, plot=True, **optim_kwargs):\n",
    "    trainloader = DataLoader(dataset, batch_size=batchsize, shuffle=True)\n",
    "    #register_clip_hook(self, clip_val=1000, verbose=True)\n",
    "    optimizer = torch.optim.Adadelta(self.parameters(), **optim_kwargs)\n",
    "    early_stopping_threshold = 0.000001 # min difference in epoch loss\n",
    "    loss_values = [] # store by-epoch avg loss values\n",
    "    print('╭───────────────────────────')\n",
    "    print(f\"│Training {self.name}.\")\n",
    "    av_batch_loss_running = -1000\n",
    "    with tqdm(range(max_epochs), leave=True) as tepochs:\n",
    "        for epoch in tepochs:\n",
    "    #         print(\"epoch\", epoch)\n",
    "            batch_loss = []\n",
    "            with tqdm(trainloader, unit=\"batch\", leave=False, desc=f\"epoch {epoch}\") as tepoch:\n",
    "                for batch in tepoch:\n",
    "                    for p in self.parameters():\n",
    "                        if torch.isnan(p).any():\n",
    "                            print(\" loss values:\", *(f\"{x:.3f}\" for x in loss_values))\n",
    "                            print(\"└────Stopped. Model weights contain a NaN value!\")\n",
    "                            if plot:\n",
    "                                plt.plot(loss_values)\n",
    "                                plt.show()\n",
    "                            return loss_values\n",
    "                    self.zero_grad()\n",
    "                    neglogprob = 0\n",
    "                    for i,x in enumerate(batch):\n",
    "                        out = self(x)\n",
    "                        neglogprob -= out\n",
    "                    loss = neglogprob / len(batch)\n",
    "                    loss.backward()\n",
    "                    optimizer.step()\n",
    "                    tepoch.set_postfix(loss=loss.item())\n",
    "                    with torch.no_grad():\n",
    "                        batch_loss.append(loss.item())\n",
    "    #                     n=5\n",
    "    #                     if batch_idx % n == 0: # print every nth batch loss\n",
    "    #                         print(\"\\tbatch\", batch_idx, \"size\", len(batch), \"loss\", loss.item())\n",
    "            av_batch_loss = torch.Tensor(batch_loss).mean().item()\n",
    "    #         print(f\"ep{epoch} av_batch_loss\\t {av_batch_loss}\")\n",
    "            loss_values.append(av_batch_loss)\n",
    "            tepochs.set_postfix(av_batch_loss=av_batch_loss)\n",
    "            if abs(av_batch_loss_running - av_batch_loss) < early_stopping_threshold:\n",
    "                print(\"└────Early stopping.\")\n",
    "                break\n",
    "            av_batch_loss_running = av_batch_loss\n",
    "    print(\" loss values:\", *(f\"{x:.3f}\" for x in loss_values))\n",
    "    if plot:\n",
    "        plt.plot(loss_values)\n",
    "        plt.show()\n",
    "    print('╰────────Finished─training──\\n')\n",
    "    return loss_values\n",
    "\n",
    "# train(rBorn, X, batchsize=30, max_epochs = 10, lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch.autograd.anomaly_mode.set_detect_anomaly at 0x11f01da00>"
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     "execution_count": 21,
     "metadata": {},
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   ],
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    "torch.autograd.set_detect_anomaly(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╭───────────────────────────\n",
      "│Training Positive MPS, Homogeneous + log_stability.\n"
     ]
    },
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      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m<ipython-input-20-2db3f626e9eb>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, dataset, batchsize, max_epochs, plot, **optim_kwargs)\u001b[0m\n\u001b[1;32m     28\u001b[0m                         \u001b[0mneglogprob\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m                     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mneglogprob\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m                     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m                     \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\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     32\u001b[0m                     \u001b[0mtepoch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_postfix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\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~/anaconda3/envs/torch_nightly/lib/python3.8/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    231\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 233\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\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    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\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~/anaconda3/envs/torch_nightly/lib/python3.8/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    142\u001b[0m         \u001b[0mretain_graph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    143\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 144\u001b[0;31m     Variable._execution_engine.run_backward(\n\u001b[0m\u001b[1;32m    145\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    146\u001b[0m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "modelhom_loss_values={}\n",
    "for model in models_hom:\n",
    "    loss_values = train(model, X, batchsize=20, plot=False, max_epochs = 20)\n",
    "    plt.plot(loss_values, label=model.name)\n",
    "    plt.ylabel('avg loss (NLL)')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.title(f\"dataset: {dataset} (d={d}), bond dim={D}\")\n",
    "    modelhom_loss_values[\"model.name\"]=loss_values\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╭───────────────────────────\n",
      "│Training Born (torch.float32), Non-homogeneous.\n"
     ]
    },
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      "\n",
      " loss values: 38.342 35.146 32.758 30.851 29.264 27.905 26.713 25.672 24.744 23.901 23.169 22.518 21.963 21.454 21.010 20.582 20.199 19.851 19.551 19.255 18.970 18.729 18.493 18.281 18.082 17.900 17.741 17.580 17.433 17.307 17.171 17.040 16.935 16.846 16.733 16.660 16.543 16.461 16.414 16.319 16.255 16.199 16.132 16.085 16.032 15.988 15.930 15.888 15.858 15.834 15.781 15.772 15.723 15.672 15.666 15.655 15.605 15.571 15.552 15.540 15.522 15.495 15.484 15.476 15.427 15.435 15.403 15.380 15.382 15.361 15.354 15.328 15.321 15.309 15.312 15.294 15.266 15.256 15.243 15.227 15.224 15.203 15.203 15.186 15.185 15.188 15.173 15.157 15.141 15.140 15.133 15.117 15.120 15.123 15.107 15.092 15.087 15.079 15.075 15.054\n",
      "╰────────Finished─training──\n",
      "\n",
      "╭───────────────────────────\n",
      "│Training Born (torch.complex64), Non-homogeneous.\n"
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      " loss values:\n",
      "└────Stopped. Model weights contain a NaN value!\n",
      "\n",
      "╭───────────────────────────\n",
      "│Training Born (torch.float32), Non-homogeneous + log_stability.\n"
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      "\n",
      " loss values: 36.953 33.643 31.054 28.994 27.398 25.994 24.702 23.697 22.876 21.856 21.226 20.434 19.920 19.317 18.874 18.447 18.102 17.736 17.404 17.152 16.815 16.591 16.429 16.161 15.938 15.791 15.620 15.443 15.285 15.147 15.044 14.905 14.793 14.681 14.596 14.558 14.402 14.329 14.222 14.160 14.085 14.031 13.992 13.935 13.853 13.820 13.751 13.717 13.668 13.610 13.571 13.529 13.515 13.466 13.434 13.387 13.340 13.321 13.311 13.268 13.239 13.210 13.167 13.168 13.138 13.120 13.069 13.101 13.034 13.008 13.010 12.975 12.972 12.924 12.907 12.891 12.881 12.870 12.839 12.812 12.804 12.782 12.764 12.746 12.734 12.733 12.708 12.685 12.677 12.656 12.651 12.618 12.621 12.604 12.592 12.562 12.562 12.553 12.528 12.526\n",
      "╰────────Finished─training──\n",
      "\n",
      "╭───────────────────────────\n",
      "│Training Born (torch.complex64), Non-homogeneous + log_stability.\n"
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      " loss values:\n",
      "└────Stopped. Model weights contain a NaN value!\n",
      "\n",
      "╭───────────────────────────\n",
      "│Training Positive MPS, Non-homogeneous.\n"
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      "\n",
      " loss values: 37.381 34.589 32.234 30.227 28.487 26.968 25.641 24.482 23.486 22.599 21.820 21.147 20.542 20.021 19.569 19.171 18.810 18.494 18.217 17.977 17.729 17.536 17.354 17.207 17.052 16.925 16.777 16.663 16.548 16.457 16.368 16.281 16.204 16.131 16.080 15.999 15.947 15.890 15.827 15.776 15.731 15.699 15.649 15.616 15.580 15.541 15.495 15.461 15.426 15.410 15.385 15.360 15.322 15.305 15.273 15.273 15.229 15.231 15.179 15.163 15.154 15.140 15.111 15.093 15.076 15.055 15.052 15.025 15.022 15.000 14.989 14.990 14.978 14.956 14.945 14.945 14.926 14.905 14.899 14.902 14.876 14.878 14.854 14.867 14.842 14.841 14.831 14.823 14.807 14.803 14.800 14.788 14.781 14.792 14.758 14.745 14.733 14.743 14.722 14.709\n",
      "╰────────Finished─training──\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_loss_values={}\n",
    "for model in models:\n",
    "    loss_values = train(model, X, batchsize=30, plot=False, max_epochs = 100, lr=0.1)\n",
    "    plt.plot(loss_values, label=model.name)\n",
    "    plt.ylabel('avg loss (NLL)')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.title(f\"dataset: {dataset} (d={d}), bond dim={D}\")\n",
    "    model_loss_values[\"model.name\"]=loss_values\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z:\n",
      "tensor([4.], requires_grad=True)\n",
      "\n",
      "loss grad_fn:\n",
      "<LogBackward object at 0x12df51370>\n",
      "\n",
      "z.grad:\n",
      "tensor([0.2500])\n",
      "\n",
      "L: tensor([1.3863], grad_fn=<LogBackward>)\n"
     ]
    }
   ],
   "source": [
    "# testing complex functions\n",
    "real = torch.tensor([4], dtype=torch.float32)\n",
    "#imag = torch.tensor([[3, 4],[1, 2]], dtype=torch.float32)\n",
    "#z = torch.complex(real, imag)\n",
    "# z = torch.randn(2,2, dtype=torch.cfloat)\n",
    "z = real\n",
    "z.requires_grad=True\n",
    "b = (2.7j*z + 3.4*z + 18)\n",
    "L = z.log()\n",
    "L.backward()\n",
    "\n",
    "print(\"z:\\n\",z,'\\n', sep='')\n",
    "print(\"loss grad_fn:\\n\", L.grad_fn,'\\n', sep='')\n",
    "print(\"z.grad:\\n\", z.grad,'\\n', sep='')\n",
    "print(\"L:\", L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(7.6344+0.j, grad_fn=<CopyBackwards>)\n",
      "tensor(7.6344, grad_fn=<MaxBackward1>)\n"
     ]
    }
   ],
   "source": [
    "print(matnorm(z))\n",
    "print(torch.max(torch.sum(abs(z), dim=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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