opt_tutorial_1.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jfialho/.conda/envs/ml_default/lib/python3.7/site-packages/torchvision/io/image.py:11: UserWarning: Failed to load image Python extension: libtorch_cuda_cu.so: cannot open shared object file: No such file or directory\n",
      "  warn(f\"Failed to load image Python extension: {e}\")\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import torch.utils.data\n",
    "from torchvision import datasets\n",
    "from torchvision import transforms\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEVICE = torch.device(\"cpu\")\n",
    "BATCHSIZE = 128\n",
    "CLASSES = 10\n",
    "DIR = os.getcwd()\n",
    "EPOCHS = 1000\n",
    "LOG_INTERVAL = 10\n",
    "N_TRAIN_EXAMPLES = BATCHSIZE * 30\n",
    "N_VALID_EXAMPLES = BATCHSIZE * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dropout_l0 = 0.2083603371473296\n",
    "dropout_l1 = 0.29475991600696116\n",
    "lr = 0.004143220502481869\n",
    "n_layers = 2\n",
    "n_units_l0 = 119\n",
    "n_units_l1 = 89\n",
    "optimizer = Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# hyperparameters set\n",
    "optimizer_name = \"Adam\"\n",
    "lr = 0.004143220502481869\n",
    "n_layers = 2\n",
    "p = 0.29475991600696116\n",
    "out_features = 89"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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     "name": "stdout",
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      "epoch: 0 | acc: 0.66640625\n",
      "epoch: 1 | acc: 0.71015625\n",
      "epoch: 2 | acc: 0.7546875\n",
      "epoch: 3 | acc: 0.79296875\n",
      "epoch: 4 | acc: 0.7796875\n",
      "epoch: 5 | acc: 0.81484375\n",
      "epoch: 6 | acc: 0.79921875\n",
      "epoch: 7 | acc: 0.81171875\n",
      "epoch: 8 | acc: 0.83828125\n",
      "epoch: 9 | acc: 0.821875\n",
      "epoch: 10 | acc: 0.8296875\n",
      "epoch: 11 | acc: 0.81328125\n",
      "epoch: 12 | acc: 0.81328125\n",
      "epoch: 13 | acc: 0.82265625\n",
      "epoch: 14 | acc: 0.82421875\n",
      "epoch: 15 | acc: 0.8140625\n",
      "epoch: 16 | acc: 0.8359375\n",
      "epoch: 17 | acc: 0.83671875\n",
      "epoch: 18 | acc: 0.840625\n",
      "epoch: 19 | acc: 0.83828125\n",
      "epoch: 20 | acc: 0.83359375\n",
      "epoch: 21 | acc: 0.8234375\n",
      "epoch: 22 | acc: 0.83125\n",
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      "epoch: 24 | acc: 0.84921875\n",
      "epoch: 25 | acc: 0.83203125\n",
      "epoch: 26 | acc: 0.83359375\n",
      "epoch: 27 | acc: 0.82890625\n",
      "epoch: 28 | acc: 0.83828125\n",
      "epoch: 29 | acc: 0.84921875\n",
      "epoch: 30 | acc: 0.8421875\n",
      "epoch: 31 | acc: 0.83671875\n",
      "epoch: 32 | acc: 0.83046875\n",
      "epoch: 33 | acc: 0.8328125\n",
      "epoch: 34 | acc: 0.84296875\n",
      "epoch: 35 | acc: 0.8359375\n",
      "epoch: 36 | acc: 0.84921875\n",
      "epoch: 37 | acc: 0.8421875\n",
      "epoch: 38 | acc: 0.86171875\n",
      "epoch: 39 | acc: 0.85546875\n",
      "epoch: 40 | acc: 0.8734375\n",
      "epoch: 41 | acc: 0.85703125\n",
      "epoch: 42 | acc: 0.8609375\n",
      "epoch: 43 | acc: 0.8625\n",
      "epoch: 44 | acc: 0.84296875\n",
      "epoch: 45 | acc: 0.84921875\n",
      "epoch: 46 | acc: 0.86953125\n",
      "epoch: 47 | acc: 0.84375\n",
      "epoch: 48 | acc: 0.84296875\n",
      "epoch: 49 | acc: 0.840625\n",
      "epoch: 50 | acc: 0.84765625\n",
      "epoch: 51 | acc: 0.84921875\n",
      "epoch: 52 | acc: 0.83828125\n",
      "epoch: 53 | acc: 0.86328125\n",
      "epoch: 54 | acc: 0.859375\n",
      "epoch: 55 | acc: 0.84453125\n",
      "epoch: 56 | acc: 0.8625\n",
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      "epoch: 58 | acc: 0.8578125\n",
      "epoch: 59 | acc: 0.859375\n",
      "epoch: 60 | acc: 0.86171875\n",
      "epoch: 61 | acc: 0.8453125\n",
      "epoch: 62 | acc: 0.85078125\n",
      "epoch: 63 | acc: 0.85234375\n",
      "epoch: 64 | acc: 0.85625\n",
      "epoch: 65 | acc: 0.8625\n",
      "epoch: 66 | acc: 0.853125\n",
      "epoch: 67 | acc: 0.8671875\n",
      "epoch: 68 | acc: 0.83984375\n",
      "epoch: 69 | acc: 0.846875\n",
      "epoch: 70 | acc: 0.86015625\n",
      "epoch: 71 | acc: 0.8515625\n",
      "epoch: 72 | acc: 0.865625\n",
      "epoch: 73 | acc: 0.8640625\n",
      "epoch: 74 | acc: 0.85\n",
      "epoch: 75 | acc: 0.85390625\n",
      "epoch: 76 | acc: 0.86015625\n",
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      "epoch: 91 | acc: 0.85625\n",
      "epoch: 92 | acc: 0.8640625\n",
      "epoch: 93 | acc: 0.86953125\n",
      "epoch: 94 | acc: 0.8484375\n",
      "epoch: 95 | acc: 0.8609375\n",
      "epoch: 96 | acc: 0.83359375\n",
      "epoch: 97 | acc: 0.85390625\n",
      "epoch: 98 | acc: 0.87265625\n",
      "epoch: 99 | acc: 0.85546875\n",
      "epoch: 100 | acc: 0.85703125\n",
      "epoch: 101 | acc: 0.86796875\n",
      "epoch: 102 | acc: 0.8421875\n",
      "epoch: 103 | acc: 0.8578125\n",
      "epoch: 104 | acc: 0.86640625\n",
      "epoch: 105 | acc: 0.8703125\n",
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      "epoch: 114 | acc: 0.84921875\n",
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      "epoch: 179 | acc: 0.85546875\n",
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      "epoch: 181 | acc: 0.875\n",
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      "epoch: 194 | acc: 0.85546875\n",
      "epoch: 195 | acc: 0.85390625\n",
      "epoch: 196 | acc: 0.865625\n",
      "epoch: 197 | acc: 0.85859375\n",
      "epoch: 198 | acc: 0.853125\n",
      "epoch: 199 | acc: 0.87265625\n",
      "epoch: 200 | acc: 0.86796875\n",
      "epoch: 201 | acc: 0.8703125\n",
      "epoch: 202 | acc: 0.86328125\n",
      "epoch: 203 | acc: 0.86484375\n",
      "epoch: 204 | acc: 0.8671875\n",
      "epoch: 205 | acc: 0.85859375\n",
      "epoch: 206 | acc: 0.87421875\n",
      "epoch: 207 | acc: 0.85546875\n",
      "epoch: 208 | acc: 0.86875\n",
      "epoch: 209 | acc: 0.87265625\n",
      "epoch: 210 | acc: 0.86640625\n",
      "epoch: 211 | acc: 0.87421875\n",
      "epoch: 212 | acc: 0.85546875\n",
      "epoch: 213 | acc: 0.83828125\n",
      "epoch: 214 | acc: 0.86640625\n",
      "epoch: 215 | acc: 0.86953125\n",
      "epoch: 216 | acc: 0.87734375\n",
      "epoch: 217 | acc: 0.8375\n",
      "epoch: 218 | acc: 0.87265625\n",
      "epoch: 219 | acc: 0.86015625\n",
      "epoch: 220 | acc: 0.884375\n",
      "epoch: 221 | acc: 0.859375\n",
      "epoch: 222 | acc: 0.8640625\n",
      "epoch: 223 | acc: 0.8625\n",
      "epoch: 224 | acc: 0.86328125\n",
      "epoch: 225 | acc: 0.878125\n",
      "epoch: 226 | acc: 0.871875\n",
      "epoch: 227 | acc: 0.865625\n",
      "epoch: 228 | acc: 0.86484375\n",
      "epoch: 229 | acc: 0.86171875\n",
      "epoch: 230 | acc: 0.84609375\n",
      "epoch: 231 | acc: 0.83828125\n",
      "epoch: 232 | acc: 0.8515625\n",
      "epoch: 233 | acc: 0.8890625\n",
      "epoch: 234 | acc: 0.85390625\n",
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      "epoch: 236 | acc: 0.86953125\n",
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      "epoch: 238 | acc: 0.85546875\n",
      "epoch: 239 | acc: 0.8578125\n",
      "epoch: 240 | acc: 0.8453125\n",
      "epoch: 241 | acc: 0.8484375\n",
      "epoch: 242 | acc: 0.865625\n",
      "epoch: 243 | acc: 0.878125\n",
      "epoch: 244 | acc: 0.87578125\n",
      "epoch: 245 | acc: 0.8734375\n",
      "epoch: 246 | acc: 0.86015625\n",
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      "epoch: 248 | acc: 0.859375\n",
      "epoch: 249 | acc: 0.871875\n",
      "epoch: 250 | acc: 0.8640625\n",
      "epoch: 251 | acc: 0.84765625\n",
      "epoch: 252 | acc: 0.8609375\n",
      "epoch: 253 | acc: 0.8578125\n",
      "epoch: 254 | acc: 0.85\n",
      "epoch: 255 | acc: 0.86328125\n",
      "epoch: 256 | acc: 0.878125\n",
      "epoch: 257 | acc: 0.8640625\n",
      "epoch: 258 | acc: 0.8625\n",
      "epoch: 259 | acc: 0.875\n",
      "epoch: 260 | acc: 0.8546875\n",
      "epoch: 261 | acc: 0.86328125\n",
      "epoch: 262 | acc: 0.865625\n",
      "epoch: 263 | acc: 0.87109375\n",
      "epoch: 264 | acc: 0.84453125\n",
      "epoch: 265 | acc: 0.8765625\n",
      "epoch: 266 | acc: 0.87890625\n",
      "epoch: 267 | acc: 0.8609375\n",
      "epoch: 268 | acc: 0.86953125\n",
      "epoch: 269 | acc: 0.865625\n"
     ]
    }
   ],
   "source": [
    "layers = []\n",
    "acc = []\n",
    "\n",
    "# Generate the model.\n",
    "in_features = 28 * 28\n",
    "for i in range(n_layers):        \n",
    "    layers.append(nn.Linear(in_features, out_features))\n",
    "    layers.append(nn.ReLU())\n",
    "    layers.append(nn.Dropout(p))\n",
    "    in_features = out_features\n",
    "layers.append(nn.Linear(in_features, CLASSES))\n",
    "layers.append(nn.LogSoftmax(dim=1))\n",
    "\n",
    "model =  nn.Sequential(*layers).to(DEVICE)\n",
    "\n",
    "\n",
    "# Generate the optimizers.\n",
    "optimizer = getattr(optim, optimizer_name)(model.parameters(), lr=lr)\n",
    "\n",
    "# Load FashionMNIST dataset.\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    datasets.FashionMNIST(DIR, train=True, download=True, transform=transforms.ToTensor()),\n",
    "    batch_size=BATCHSIZE,\n",
    "    shuffle=True,\n",
    ")\n",
    "valid_loader = torch.utils.data.DataLoader(\n",
    "    datasets.FashionMNIST(DIR, train=False, transform=transforms.ToTensor()),\n",
    "    batch_size=BATCHSIZE,\n",
    "    shuffle=True,\n",
    ")\n",
    "\n",
    "\n",
    "# Training of the model.\n",
    "for epoch in range(EPOCHS):\n",
    "    model.train()\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        # Limiting training data for faster epochs.\n",
    "        if batch_idx * BATCHSIZE >= N_TRAIN_EXAMPLES:\n",
    "            break\n",
    "\n",
    "        data, target = data.view(data.size(0), -1).to(DEVICE), target.to(DEVICE)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.nll_loss(output, target)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "    # Validation of the model.\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    with torch.no_grad():\n",
    "        for batch_idx, (data, target) in enumerate(valid_loader):\n",
    "            # Limiting validation data.\n",
    "            if batch_idx * BATCHSIZE >= N_VALID_EXAMPLES:\n",
    "                break\n",
    "            data, target = data.view(data.size(0), -1).to(DEVICE), target.to(DEVICE)\n",
    "            output = model(data)\n",
    "            # Get the index of the max log-probability.\n",
    "            pred = output.argmax(dim=1, keepdim=True)\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "    accuracy = correct / min(len(valid_loader.dataset), N_VALID_EXAMPLES)\n",
    "    acc.append(accuracy)\n",
    "\n",
    "    print(\"epoch:\",epoch,\"| acc:\",accuracy)\n",
    "    \n",
    "#print best value of acc    \n",
    "print(\"--------------------------\")\n",
    "print(\"best acc:\", np.max(acc))"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(acc)\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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