Generative Adversarial Network for Normal Generation.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import torch.nn\n",
    "import torch.utils.data\n",
    "import torch.optim\n",
    "import tqdm\n",
    "\n",
    "np.random.seed(0)\n",
    "torch.manual_seed(0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_SAMPLES = 10000\n",
    "IN_SHAPE = 5\n",
    "OUT_SHAPE = 1\n",
    "\n",
    "EPOCHS = 100\n",
    "BATCH_SIZE = 100\n",
    "LEARNING_RATE = 5E-4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniform_dataset = datasets.UniformRVDataset(num_samples=NUM_SAMPLES,\n",
    "                                            shape=IN_SHAPE)\n",
    "uniform_dataloader = torch.utils.data.DataLoader(uniform_dataset,\n",
    "                                                 batch_size=BATCH_SIZE)\n",
    "\n",
    "normal_dataset = datasets.NormalRVDataset(num_samples=NUM_SAMPLES,\n",
    "                                          shape=OUT_SHAPE,\n",
    "                                          static_sample=False)\n",
    "normal_dataloader = torch.utils.data.DataLoader(normal_dataset,\n",
    "                                                batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define generator model (simple fully-connected with leaky ReLUs).\n",
    "generator = torch.nn.Sequential(\n",
    "    torch.nn.Linear(IN_SHAPE, 5),\n",
    "    torch.nn.LeakyReLU(),\n",
    "    torch.nn.Linear(5, 5),\n",
    "    torch.nn.LeakyReLU(),\n",
    "    torch.nn.Linear(5, 5),\n",
    "    torch.nn.LeakyReLU(),\n",
    "    torch.nn.Linear(5, OUT_SHAPE),\n",
    ").to(device)\n",
    "\n",
    "# Define discriminator model (simple fully-connected with leaky ReLUs).\n",
    "discriminator = torch.nn.Sequential(\n",
    "    torch.nn.Linear(OUT_SHAPE, 5),\n",
    "    torch.nn.LeakyReLU(),\n",
    "    torch.nn.Linear(5, 5),\n",
    "    torch.nn.LeakyReLU(),\n",
    "    torch.nn.Linear(5, 5),\n",
    "    torch.nn.LeakyReLU(),\n",
    "    torch.nn.Linear(5, 1),\n",
    "    torch.nn.Sigmoid()\n",
    ").to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define loss criterion (binary cross-entropy).\n",
    "criterion = torch.nn.BCELoss()\n",
    "# Define optimizer for generator and discriminator (Adam).\n",
    "optimizerG = torch.optim.Adam(generator.parameters(), lr=LEARNING_RATE)\n",
    "optimizerD = torch.optim.Adam(discriminator.parameters(), lr=2 * LEARNING_RATE)"
   ]
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      "\n",
      "Epoch: 98\n"
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     "text": [
      "\n",
      "Epoch: 99\n"
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    {
     "name": "stdout",
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     "text": [
      "\n"
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   ],
   "source": [
    "real_label = 0.9  # single-side label smoothing.\n",
    "fake_label = 0\n",
    "for epoch in range(EPOCHS):\n",
    "    print('Epoch:', epoch)\n",
    "    progress_bar = tqdm.tqdm_notebook(zip(uniform_dataloader, normal_dataloader),\n",
    "                                      total=len(uniform_dataloader))\n",
    "    for input_, target in progress_bar:\n",
    "        input_, target = input_.float().to(device), target.float().to(device)\n",
    "        # Train discriminator with real batch\n",
    "        optimizerD.zero_grad()\n",
    "        # Format batch\n",
    "        label = torch.full_like(target[:, 0, 0], real_label, device=device)\n",
    "        \n",
    "        ############################\n",
    "        # (1) Update D network: maximize log(D(x)) + log(1 - D(G(z)))\n",
    "        ###########################\n",
    "        # Forward pass real batch through D\n",
    "        output = discriminator(target).view(-1)\n",
    "        # Calculate loss on all-real batch\n",
    "        errD_real = criterion(output, label)\n",
    "        # Calculate gradients for D in backward pass\n",
    "        errD_real.backward()\n",
    "        D_x = output.mean().item()\n",
    "\n",
    "        # Train with fake batch\n",
    "        # Generate fake batch with G\n",
    "        fake = generator(input_)\n",
    "        label.fill_(fake_label)\n",
    "        # Forward pass fake batch through D\n",
    "        output = discriminator(fake.detach()).view(-1)\n",
    "        # Calculate D's loss on the all-fake batch\n",
    "        errD_fake = criterion(output, label)\n",
    "        # Calculate the gradients for this batch\n",
    "        errD_fake.backward()\n",
    "        D_G_z1 = output.mean().item()\n",
    "        # Add the gradients from the all-real and all-fake batches\n",
    "        errD = errD_real + errD_fake\n",
    "        # Update D\n",
    "        optimizerD.step()\n",
    "\n",
    "        ############################\n",
    "        # (2) Update G network: maximize log(D(G(z)))\n",
    "        ###########################\n",
    "        optimizerG.zero_grad()\n",
    "        label.fill_(real_label)  # fake labels are real for generator cost\n",
    "        # Since we just updated D, perform another forward pass of all-fake batch through D\n",
    "        output = discriminator(fake).view(-1)\n",
    "        # Calculate G's loss based on this output\n",
    "        errG = criterion(output, label)\n",
    "        # Calculate gradients for G\n",
    "        errG.backward()\n",
    "        D_G_z2 = output.mean().item()\n",
    "        # Update G\n",
    "        optimizerG.step()\n",
    "\n",
    "        progress_bar.set_postfix({'Discriminator loss': errD.item(), 'Generator loss': errG.item()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_ = torch.tensor([uniform_dataset[i] for i in range(1000)], device=device, dtype=torch.float)\n",
    "with torch.no_grad():\n",
    "    # Model forward pass.\n",
    "    output = generator(input_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1000, 1, 1])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(output[:,0].transpose(1,0).tolist(), np.random.normal(0, 0.1, size=output.size(0)));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1bfb19b0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Iz3VNidRZM75By135nJrGVu56fjupcRH85prJehXqAEmOCScnJUbnu/sILXflU4wxfO/VfI43tvD7L00jNkKX8h1IF4xNZOuhWupP6ZRIb6flrnzKcxsP8e6eSu69ZBwT0nQ++0BbMCaJDqdhXZGuEuntXCp3EblYRApFpEhE7u3l8TtFZKeI7BCRT0Qkx/1RVaDbW9HAL1bu4YIxiXxlbobtOAFp6vA4YsKD+UB3Z/J6fZa7iDiAx4BLgBzghl7K+wVjzERjzBTgQeC/3Z5UBbQOZ+dwTEx4MA/pOLs1wY4g5o9OZO0+nRLp7Vw5c58JFBljDhhjWoGXgMu7H2CMaeh2MwrQf3XlVs+sLyG/vJ77LhtPwqAw23EC2gVjkqg60ULBkYa+D1bWuFLuaUBZt9vlXff9ExG5S0SK6Txzv8c98ZSCw3Wn+M2aQhaMSeSySSm24wS880cnAPDRfp0S6c1cKffefv/9lzNzY8xjxpgs4PvAj3t9IpFlIpInInlVVfqFoVzz0zcKMAZ+ccUEHY7xAknR4YwdGs0n+/VNVW/mSrmXA8O73R4GHDnD8S8BV/T2gDHmCWNMrjEmNzEx0fWUKmBtPljDu3sq+beF2QwbHGk7juoyLzuBvJJaTrXqxtneypVy3wKMEpFMEQkFrgdWdD9ARLrvjHApsN99EVWgMsbw4Nt7SY4J47Y5mbbjqG7mjUqgtcPJlpIa21HUafRZ7saYduBuYDWwB3jZGFMgIveLyNKuw+4WkQIR2QF8B7jFY4lVwHh/7zHyDtXyzYWjiQjVzTe8yczMIYQ6gvhE57t7rWBXDjLGrAJW9bjvvm4ff9PNuVSA63AaHny7kMyEKK7JHWY7juohMjSYaSPi+FjH3b2WXqGqvNLqggoKK0/wnQtHE6JrtHul+aMS2XO0gaoTLbajqF7od43ySk9+fIAR8ZF8YaJOffRW87I7p0SuL9azd2+k5a68zrbSWraV1nHbnAwcQTr10VtNSIslNiJEh2a8lJa78jpPfXKQ6PBgrskd3vfByhpHkDA3O551RdW6FIEX0nJXXqWspom3dh7lxlnpRIW59H6/smhudgJH65sprmq0HUX1oOWuvMqzG0oIEuHWORm2oygXzM/uvBjxE12KwOtouSuv0druZPm2w1yYk0xKbITtOMoF6fGRpA+J1PnuXkjLXXmN9/dWUtPYyrUzdKzdl8wblcDGAzW0dThtR1HdaLkrr/FyXjlDY8I5f5SuO+RL5mcncLKlnR1ldbajqG603JVXqGxoZm3hMb44PU2nP/qY87LiEUGnRHoZLXflFZZvK8dp4JrpOiTja+IiQ5mUFqv7qnoZLXdlnTGGV/LKmZk5hIyEKNtxVD/MG5XAjrI6GprbbEdRXbTclXX55fUcrG7k6mm6QJivmpudQIfTsPmALgHsLbTclXVv5h8hxCEsHj/UdhTVT9PSBxMWHMT64uO2o6guWu7KKqfT8Gb+UT43OpHYyBDbcVQ/hYc4yM0YrIuIeREtd2XVttJajtY3s2RSqu0o6hzNyUpgb8UJqk/qEsDeQMtdWfVm/lHCgoNYlJNsO4o6R3Oy4gHYeECHZryBlruypsNpWLnzKBeMSWKQLhLm8yamxRIdFsy6Ii13b6DlrqzZdPA4VSdaWDJZN+TwB8GOIGaNHMIGHXf3Clruypq3d1UQHhLE58cm2Y6i3OS8rARKjjdxuO6U7SgBT8tdWWGMYU1BJeePSiQyVIdk/MVn4+7r9WpV67TclRX55fVUNDTr3HY/MyY5mvioUDbofHfrtNyVFWt2V+AIEh2S8TNBQcLsrHjWFevWe7ZpuSsr1hRUMjNjCIOjQm1HUW42NyuByoYWDlTr1ns2abmrAXeg6iT7j51k8Xid2+6PdNzdO2i5qwG3ZnclABfqeLtfGhEfSVpchK4zY5mWuxpwawoqmJAWQ1qc7pPqj0SE87Li2XDgOE6njrvbouWuBtSxhma2l9WxOEfP2v3ZnKx46pra2H20wXaUgKXlrgbUO3sqMQYu0iEZvzYnKwFAp0RapOWuBtSagkpGxEcyOnmQ7SjKg4bGhjMyMUqXALZIy10NmBPNbawvrmbx+KGI6CbY/m5uVgKbD9bQ1uG0HSUgabmrAfNBYRVtHYaLdHnfgDAnK57G1g7yy+tsRwlILpW7iFwsIoUiUiQi9/by+HdEZLeI5IvIeyIywv1Rla9bU1BBwqBQpqYPth1FDYDZI+MRQZcAtqTPchcRB/AYcAmQA9wgIjk9DtsO5BpjJgGvAg+6O6jybS3tHawtrOLCnGQcQTokEwgGR4WSkxKj4+6WuHLmPhMoMsYcMMa0Ai8Bl3c/wBjzgTGmqevmRkC3sVf/ZEPxcU62tHORToEMKHOy4tl2qI7mtg7bUQKOK+WeBpR1u13edd/p3A68dS6hlP9ZXVBJVKiD87ouTVeBYU52Aq0dTvJKam1HCTiulHtvv0P3etmZiNwE5AIPnebxZSKSJyJ5VVVVrqdUPs3pNLyzu5IFY5MID3HYjqMG0MyMIQQHiQ7NWOBKuZcDw7vdHgYc6XmQiCwCfgQsNcb0uv25MeYJY0yuMSY3MTGxP3mVD9peVkf1yRadJROAosKCmTI8jnV6MdOAc6XctwCjRCRTREKB64EV3Q8QkanAH+ks9mPuj6l82ZqCCkIcwgW6dntAmpMVz87yOhqa22xHCSh9lrsxph24G1gN7AFeNsYUiMj9IrK067CHgEHAKyKyQ0RWnObpVIAxxrC6oILzshKICQ+xHUdZcF5WAk4Dmw/U2I4SUFzavNIYswpY1eO++7p9vMjNuZSfKDp2kpLjTdwxf6TtKMqSaSPiCAsOYl1xNYt0aG7A6BWqyqNWF1QAcKF+UwessGAHMzKG6CJiA0zLXXnUW7sqmJoeR3JMuO0oyqI52fHsrThB9cle51ooD9ByVx5TUt1IwZEGLp2YYjuKskyXAB54Wu7KY1buPArAF7TcA96E1Biiw4N1670BpOWuPGbVzqNMTY8jVbfTC3jBjiBmZcbrxUwDSMtdeYQOyaie5mTFc+h4E+W1TX0frM6ZlrvyiM+GZC7Rcldd5mbruPtA0nJXHvHZkEyaDsmoLqOTB5EwKJR1RTo0MxC03JXbFR07qUMy6l+ICPOyE/h4fzVOZ69rDyo30nJXbvf69nKCBJZOSbUdRXmZBWOSON7Yys7D9baj+D0td+VWTqfh9W2HOX90IknReuGS+mfnj05EBNYW6pLfnqblrtxq48HjHKlv5qppuhmX+ldDokKZNCyOtft08VhP03JXbvXatsNEhwXr2u3qtBaMTmRHWR21ja22o/g1LXflNk2t7by18yhfmJiiOy6p01owJhFj4KP9OjTjSVruym1WF1TQ2NrBVdPOtMWuCnSThsUxODKED3Xc3aO03JXbPLfhEJkJUczIGGI7ivJijiDh/NGJfLivSqdEepCWu3KLXYfr2VZax02zRxAU1Nue6kr9n8+P7ZwSuaO8znYUv6XlrtziuQ2HiAhxcPV0nSWj+rZgdBLBQcI7uyttR/FbWu7qnNU3tfHGp4e5YmoqsRG6T6rqW2xkCLNHxrOma6cu5X5a7uqcvbK1jOY2JzfPzrAdRfmQC3OSKa5qpOjYSdtR/JKWuzonbR1O/ry+hNwRg8lJjbEdR/mQz/bV1aEZz9ByV+fkb9sPU157ijs/l2U7ivIxqXERTEyL5Z3dOjTjCVruqt/aO5z8fm0xOSkxLByXZDuO8kEX5SSzvayOYw3NtqP4HS131W9v5h/lYHUj9yzMRkSnP6qzd+H4ZIyBd/bo0Iy7abmrfulwGv7fB0WMSY7mopyhtuMoHzUmOZrMhChW5h+1HcXvaLmrflm+rZyiYye5+/PZetGS6jcR4bLJqWw4cFyHZtxMy12dtYbmNh58u5Bp6XG625I6Z0snp2BM5zCfch8td3XWHn1vP8cbW/jZ0vF61q7OWXZSNONSYvh7/hHbUfyKlrs6K0XHTvL0uhKuyx3OpGFxtuMoP7F0cirbS+soq2myHcVvaLkrl3U4DT98bScRoQ6+u3iM7TjKjyyZ1Dm8t+JTPXt3Fy135bI/fFjM5pIafr50PAmDwmzHUX5k+JBIpqXHsWLHEYzRZYDdQctduSS/vI6H39nHkkkpXDlVN+NQ7nfVtGEUVp5g5+F621H8gpa76lNDcxvfemkHidFhPHDFRL1gSXnE0imphIcE8dKWMttR/IJL5S4iF4tIoYgUici9vTx+vohsE5F2Ebna/TGVLe0dTu56fhulNU387ropxEbqkr7KM2LCQ7h0YiordhyhqbXddhyf12e5i4gDeAy4BMgBbhCRnB6HlQK3Ai+4O6Cy6/43d/Px/moeuHICs0bG246j/Nz1M4dzsqVdr1h1A1fO3GcCRcaYA8aYVuAl4PLuBxhjSowx+YDTAxmVJY+vLebZDYdYdv5IrpuRbjuOCgC5IwYzMjGKv+rQzDlzpdzTgO7/p8u77jtrIrJMRPJEJK+qSnc+92b/+9EBfv32Xi6bnMr3Lx5rO44KECLC9TOGk3eolv2VJ2zH8WmulHtv7571a66SMeYJY0yuMSY3MTGxP0+hBsCTHx/ggVV7uHRiCg9fOxmHXoWqBtAXpw0jNDiIP68vsR3Fp7lS7uXA8G63hwF6pYEfMsbw67f38ouVe7hkwlB+d/0Ugh06oUoNrPhBYVw5JY3l28qpbWy1HcdnufKduwUYJSKZIhIKXA+s8GwsNdDaOpx879V8Hl9bzA0z03n0hqmEaLErS74yL5PmNicvbC61HcVn9fnda4xpB+4GVgN7gJeNMQUicr+ILAUQkRkiUg5cA/xRRAo8GVq5V11TK7f8aTOvbC3nnoWj+K8rJ+gZu7JqzNBo5o9K4NkNJbS26zyN/gh25SBjzCpgVY/77uv28RY6h2uUjyk6dpI7ntnCkbpmfnvNZL44Xf8ZlXe4fV4mtz69hZU7j3DlVP26PFt6ehbAPtxXxZW/X8fJlnZeXDZLi115lc+NTmRU0iD+sPYATqeuN3O2tNwDkDGGp9cd5LanN5MWF8Hf7prL9BFDbMdS6p+ICHd/PpvCyhOsLqiwHcfnaLkHmLYOJz98fRc///tuFo1LZvnX5zBscKTtWEr1asmkVEYmRvHIe/v17P0sabkHkLqmVm5+ahMvbi7lGwuy+MNN04kKc+ltF6WscAQJ31w4ir0VevZ+trTcA0Tp8Sauenw92w7V8fB1k/nexWN1izzlE/TsvX+03APAp2V1XPX4OmoaW3n+q7N05oHyKd3P3l/ffth2HJ+h5e7n3tldyfVPbCQi1MHyr89hRoa+cap8z2WTUpk8LJaHVhdyqrXDdhyfoOXux57bUMLXnstjVPIgXvv6XLISB9mOpFS/BAUJP16SQ0VDM//78QHbcXyClrsfMsbw8Dv7+MkbBVwwJomXls0mMVr3PFW+bUbGEL4wcSiPry2msqHZdhyvp+XuZ5xOw/1v7uaR9/Zz9fRh/PHm6USG6owY5R/uvXgcHU7Df63aYzuK19Ny9yPtHU6+tzyfp9eVcNvcDB784iRdI0b5lfT4SO5ckMUbO47w8X7dE+JM9DvfT7S0d3D3C9t5dWs531o0ivuW5OhUR+WXvrEgi5EJUfzo9V365uoZaLn7gabWdu54Jo+3Cyr4yZIcvrVoNCJa7Mo/hYc4+MWVEyitaeJ/3t9vO47X0nL3cfWn2rj5qc2sK6rmwasncfu8TNuRlPK4OVkJXDN9GE98dIDtpbW243glLXcfVnWiheuf2Eh+eR2P3TiNa3OH9/2XlPITP7ksh6Ex4Xz7rztobGm3HcfraLn7qJLqRr74+HoOVp/kyVtmcMnEFNuRlBpQMeEh/PbayRyqaeIXK3X2TE9a7j5oZ3k9V/9hPSea23jxq7P53GjdbFwFptkj41k2fyQvbi7l7V1HbcfxKlruPuajfVVc98QGwoIdvPr1OUxNH2w7klJWfeei0UweHsd3X8mnuOqk7TheQ8vdh/xt+2G+8uctpA+J5LVvzNHlBJQCwoIdPP6laYQGB3Hnc1t1/L2LlrsPcDo7lxP41l93MH3EYP76tfNIjgm3HUspr5EaF8GjN0yluOok//7yp7qQSZadAAAInElEQVQ0MFruXq+xpZ1vPL+NR97bz1XT0njmKzOJjQixHUsprzM3O4EffmEcbxdU6PIEgC464sX2VjRw1/PbOFjdyI8vHcft8zL14iSlzuD2eZmU157iyU8OkjY4gtvmBu51H1ruXsgYw0tbyvjZigJiIkL4y+2zmJOdYDuWUl5PRPjJkhyO1J3i/jd3ExMewhenB+bmNFruXuZI3SnufW0nH+2rYl52Ag9fN0WX61XqLDiChP+5YSpffTaP7776KUFBBOTuY1ruXqKtw8mzGw7x8Dv7cBrD/ZeP56ZZI3TxL6X6ITzEwRM353LHs1v495c/pa3dcO2MwLqCW8v9HGTcu5KSX1162seA0z7e/binb53BbX/eAsD5oxP5aF8VXz4v45+ep+dzdf/c3Y/pruRXl572se6P93VcX39fKVf19jXj6n097+/5/dD9GICIUAdPfnkGX/vLVr63PJ/Ddad45L39fX5P+gudLWOJMYa1hccA/lHsT92SyzO3zbAZSym/EhHq4KlbcgF45L3OFSQDZZlgLfcBdqq1g5c2l7L4dx9x69Odpf7LqyYCsHBcss6GUcrNQro2rPn2otEAXPn7dQFxJauW+wBwOg1bSmr4wWv5zHzgXe59bSeOoCAevHoSADfMTLecUCn/981FowCobGhm6aOf8JeNh/z6Yicdc/ewH7y2k3d2V1J9soWIEAeXTBzKdbnDmZk5BBHhe6/m246oVEBZec98/uPVT/nx33axYscRHrhyAqOSo23HcjstdzcxxnCgupH1xcdZX1T9j/tX7DjMgrFJXJSTzMJxyQwK0//lStmUGhfBX26fxStby3lg5R4W/+4jrpk+nG9dOIqU2Ajb8dxGm6afPvt17sXNpWw5WMP64uNUNDQDkBr7f+u+bP3JhYSHOKxkVEr1TkS4Nnc4i8Yl89gHRTy34RCvbz/MZZNTuW1uBhPSYm1HPGculbuIXAw8AjiAJ40xv+rxeBjwLDAdOA5cZ4wpcW9Ue4wxHK1vZveRBnYermdbaS07yuqAzmGX+KhQZmfFMzcrgTlZ8YyIjyTzB6sAtNiV8mJDokL5yZIcbpubwZMfH+TlvDKWbytnQloMl09O45KJQxk2ONJ2zH7ps9xFxAE8BlwIlANbRGSFMWZ3t8NuB2qNMdkicj3wa+A6TwT2JKfTcLShmZLqRg52/dlb0cDuIw3UNrUBIAJjkqO5bHIqL2wq5YPvLiAjPlJnuSjlw4YNjuRnS8fz7QtH8+rWct7YcZgHVu3hgVV7GJkYxfzsBKaNGMykYXGMGBLpExcXunLmPhMoMsYcABCRl4DLge7lfjnws66PXwX+n4iIMcajb0UbY3Aa6HAanKbzT+fHnUXd0u6ksbWdppaOzv+2ttPY0kFTazu1TW1UnWjh2IkWqk40c+xEC+W1p2htd/7j+cOCgxgzNJqLJwwlJyWGnNQYxg6NIapr3PyFTaVkJkR58iUqpQZQbEQIt8/L5PZ5mRysbuS9PZV8vL+av+aV8cyGQwCEBgcxbHAEwwdHkj4kkuFDIhgcGUpMRAgx4SHERAQTHRZCSLAQHBREiEMIdgQRHCSEOIJwDNAPBlfKPQ0o63a7HJh1umOMMe0iUg/EA9W42f9+dIBfv72XDmM41x8dESEOkmLCSIoOY+zQaBaOTSIjIYrM+CgyEqIYGhPuEz+hlVLul5kQxR3zR3LH/JG0dzjZV3mS/PI6DlQ3UlbTRFltE9tLa2loPrvNQUTgPy+fwE2zR3goedfn6evkWkSuARYbY+7oun0zMNMY82/djinoOqa863Zx1zHHezzXMmBZ180xQKG7XoibJOCBH0heKBBeZyC8RtDX6W9ceZ0jjDF9bpzsypl7OdB9xZ1hwJHTHFMuIsFALFDT84mMMU8AT7jwOa0QkTxjTK7tHJ4WCK8zEF4j6Ov0N+58na5coboFGCUimSISClwPrOhxzArglq6Prwbe9/R4u1JKqdPr88y9awz9bmA1nVMh/2SMKRCR+4E8Y8wK4CngOREpovOM/XpPhlZKKXVmLs1zN8asAlb1uO++bh83A9e4N5oVXjtk5GaB8DoD4TWCvk5/47bX2ecbqkoppXyPrgqplFJ+SMu9GxH5TxHJF5EdIrJGRFJtZ/IEEXlIRPZ2vdbXRSTOdiZPEJFrRKRARJwi4nczLUTkYhEpFJEiEbnXdh5PEJE/icgxEdllO4snichwEflARPZ0fc1+81yfU8v9nz1kjJlkjJkCvAnc19df8FHvABOMMZOAfcAPLOfxlF3AVcBHtoO4W7dlQS4BcoAbRCTHbiqP+DNwse0QA6Ad+HdjzDhgNnDXuf57arl3Y4xp6HYzCvDLNySMMWuMMZ9dVreRzmsX/I4xZo8xxtsulHOXfywLYoxpBT5bFsSvGGM+opdrZvyNMeaoMWZb18cngD10Xvnfb7rkbw8i8gDwZaAeuMBynIHwFeCvtkOos+bKsiDKB4lIBjAV2HQuzxNw5S4i7wJDe3noR8aYN4wxPwJ+JCI/AO4GfjqgAd2kr9fZdcyP6Px18PmBzOZOrrxOP9Xbokd++ZtmIBGRQcBy4Fs9RhLOWsCVuzFmkYuHvgCsxEfLva/XKSK3AEuAhb58NfFZ/Hv6G1eWBVE+RERC6Cz2540xr53r8+mYezciMqrbzaXAXltZPKlr85XvA0uNMU2286h+cWVZEOUjpHNDiKeAPcaY/3bLc/rwSZvbichyOlerdAKHgDuNMYftpnK/rmUiwujcNQtgozHmTouRPEJErgQeBRKBOmCHMWax3VTuIyJfAH7H/y0L8oDlSG4nIi8CC+hcLbES+Kkx5imroTxAROYBHwM76ewfgB92rQ7Qv+fUcldKKf+jwzJKKeWHtNyVUsoPabkrpZQf0nJXSik/pOWulFJ+SMtdKaX8kJa7Ukr5IS13pZTyQ/8f4DNSqL77Wt4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(output[:,0].cpu().numpy().T, rug=True, hist=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.1445697695016861\n",
      "-0.94377201795578\n",
      "0.209812730550766\n",
      "-0.04212966561317444\n",
      "0.6230037212371826\n",
      "-0.30220603942871094\n",
      "-0.18258601427078247\n",
      "-0.7333046793937683\n",
      "0.060207635164260864\n",
      "-1.9463856220245361\n",
      "-0.08323836326599121\n",
      "-0.4789586663246155\n",
      "0.226782888174057\n",
      "-0.1313982456922531\n",
      "-1.184600591659546\n",
      "0.5962585210800171\n",
      "-0.915986955165863\n",
      "0.5802477598190308\n",
      "-0.3475506901741028\n",
      "-1.4434077739715576\n",
      "-1.4340472221374512\n",
      "-0.8789086937904358\n",
      "-1.4306843280792236\n",
      "0.3750802278518677\n",
      "-0.6668277978897095\n",
      "-1.1915581226348877\n",
      "-0.2097284197807312\n",
      "-1.3489634990692139\n",
      "-0.19207853078842163\n",
      "0.9151989817619324\n",
      "0.01597316563129425\n",
      "-1.3078348636627197\n",
      "-0.17467863857746124\n",
      "-1.3467063903808594\n",
      "0.13392138481140137\n",
      "-1.0306637287139893\n",
      "-0.7853797674179077\n",
      "-0.2723104655742645\n",
      "-1.2092812061309814\n",
      "0.2314237356185913\n",
      "-0.5079944133758545\n",
      "0.309506893157959\n",
      "0.19039997458457947\n",
      "0.18810096383094788\n",
      "0.20682498812675476\n",
      "0.08629223704338074\n",
      "-0.46258577704429626\n",
      "-1.5967390537261963\n",
      "-1.6916859149932861\n",
      "-0.5291053056716919\n",
      "-1.4125611782073975\n",
      "0.4778289198875427\n",
      "0.05977639555931091\n",
      "-0.0781719982624054\n",
      "0.06702768802642822\n",
      "-0.8931838870048523\n",
      "-0.5363810062408447\n",
      "-0.41039609909057617\n",
      "0.7350262999534607\n",
      "-0.44384557008743286\n",
      "-0.0031420141458511353\n",
      "-0.34273040294647217\n",
      "-0.45960885286331177\n",
      "-0.9563617706298828\n",
      "-0.8326727747917175\n",
      "0.004823312163352966\n",
      "0.44042015075683594\n",
      "-1.9909110069274902\n",
      "0.033349066972732544\n",
      "-0.667502224445343\n",
      "-1.041213035583496\n",
      "-1.0236220359802246\n",
      "0.038940295577049255\n",
      "-0.3880654573440552\n",
      "0.4819874167442322\n",
      "-1.1088943481445312\n",
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      "0.35597890615463257\n",
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      "-0.1995030790567398\n",
      "0.23873847723007202\n",
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      "-0.49183574318885803\n",
      "-2.3401715755462646\n",
      "-1.3634679317474365\n",
      "-0.48125940561294556\n",
      "-0.8352958559989929\n",
      "0.48324310779571533\n",
      "0.34404462575912476\n",
      "-0.13018617033958435\n",
      "0.560546338558197\n",
      "-0.22616681456565857\n",
      "-0.5848371982574463\n",
      "0.5537983179092407\n",
      "-0.3914182782173157\n",
      "-0.9467567801475525\n",
      "1.2980115413665771\n",
      "0.18429121375083923\n",
      "0.22540801763534546\n",
      "-0.4965130090713501\n",
      "1.146756887435913\n",
      "-0.8379137516021729\n",
      "-0.6061457395553589\n",
      "0.2349034547805786\n"
     ]
    }
   ],
   "source": [
    "for o in output:\n",
    "    print(o.item())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1a1ce4e550>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats                                                         \n",
    "\n",
    "ax = sns.distplot(output[:,0].cpu().numpy().T, rug=True, hist=False)\n",
    "\n",
    "# calculate the pdf over a range of values\n",
    "xx = np.arange(-3, +3, 0.001)                                                   \n",
    "yy = stats.norm.pdf(xx)                                                         \n",
    "# and plot on the same axes that seaborn put the histogram\n",
    "ax.plot(xx, yy, 'r', lw=2)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "GAN_PoC",
   "language": "python",
   "name": "gan_poc"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}