08_ CV -Convolutional Neural Networks - LAB3.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"gpuClass":"standard","accelerator":"GPU"},"cells":[{"cell_type":"markdown","source":["<h1 style=\"text-align: center;\"><center>Master&nbsp;in&nbsp;Interdisciplinary&nbsp;and&nbsp;Innovation&nbsp;Engineering Computer&nbsp;Vision</center></h1>\n","<h2 style=\"text-align: center;\"><center><strong>Convolutional Neural Networks - Practice 3</strong></center></h2>\n","<hr />\n","<p style=\"text-align: center;\"><center><br />Departament de Matem&agrave;tiques (DMAT)<br />Escola d&rsquo;Enginyeria de Barcelona Est (EEBE)<br />Universitat Polit&egrave;cnica de Catalunya (UPC)<br />2023</center></p>\n","<ul>\n","<li>Introduction</li>\n","<li>Convolution</li>\n","<li>Subsampling</li>\n","<li>Multilayer CNN structure</li>\n","</ul>\n","\n"],"metadata":{"id":"kkpyYCggLKQL"}},{"cell_type":"markdown","source":["NOTES\n","CNN is used to clasify images whereas the MLNN is used for data classification"],"metadata":{"id":"WL51nZhGn7pb"}},{"cell_type":"markdown","source":["<h1>Practice 4</h1>\n","\n","The **CIFAR-10 database** is a dataset of 60000 images, the purpose of the database is to enable the development and evaluation of image recognition algorithms. Database features:\n","- Size of the images of **32 x 32** pixels.\n","- The images are labeled in 10 different categories (labels): **airplane, automobile, bird, cat, deer, dog, frog, horse, ship and truck**.\n","- **Training set**: This set consists of 50000 images. Each category has 5000 images.\n","- **Test Set**: This set consists of 10000 images. Each category has 1000 images.\n","![image.png](data:image/png;base64,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)\n","---"],"metadata":{"id":"zh_7bDcBwoB3"}},{"cell_type":"markdown","source":["# 1. Data set\n","---"],"metadata":{"id":"oGslg_y71NwD"}},{"cell_type":"markdown","source":["- **PyTorch:** It is a deep learning library for Python, it is used for the creation of neural networks. It has great flexibility to create custom models.\n","\n","- **Torchvision** is a PyTorch library that provides pre-trained data sets, transformations and models for computer vision.\n","\n","- **Transforms:** Is a PyTorch library used to transform input images into a format that can be used by machine learning models."],"metadata":{"id":"sOQQ0lV54Bfx"}},{"cell_type":"code","source":["import torch\n","import torchvision\n","import torchvision.transforms as transforms"],"metadata":{"id":"Xqn2h5sn4D9f","executionInfo":{"status":"ok","timestamp":1681910760353,"user_tz":-120,"elapsed":4500,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","import numpy as np"],"metadata":{"id":"yOCXkh6Nk0-s","executionInfo":{"status":"ok","timestamp":1681910762098,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":["**1.1 Data Normalization** is used to scale data values, helps improve performance of learning models, and reduces the influence of outliers.\n","*   **Tensors** are the basic data structure used to represent input data, model weights, and model outputs."],"metadata":{"id":"OGqDl7oo4NDo"}},{"cell_type":"code","source":["# transform the image to a tensor (like a matrix for GPU), to normalizate it\n","transform = transforms.Compose([\n","                                transforms.ToTensor(), \n","                                transforms.Normalize((0.5, 0.5, 0.5),\n","                                                     (0.5, 0.5, 0.5))\n","                                ])"],"metadata":{"id":"9cn18uab4UYD","executionInfo":{"status":"ok","timestamp":1681910921831,"user_tz":-120,"elapsed":449,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":["**1.2 Load the CIFAR10 train and test data sets**\n","*   **trainloader and testloader:** We load the data in sets of 128 images, the images are loaded randomly. It is a useful technique to reduce the amount of memory needed to process the entire set of images. This technique makes the training process more efficient and effective."],"metadata":{"id":"RhAXnG0v54h1"}},{"cell_type":"code","source":["# Train set\n","trainset =  torchvision.datasets.CIFAR10(root = './data', train = True, download = True, transform = transform)\n","trainloader = torch.utils.data.DataLoader(trainset, batch_size = 128, shuffle = True, num_workers = 2)\n","\n","# Test set\n","testset = torchvision.datasets.CIFAR10(root = './data' , train = False , download = True , transform = transform)\n","testloader = torch.utils.data.DataLoader(testset, batch_size = 128, shuffle = False, num_workers = 2)"],"metadata":{"id":"31dej5b8qq9H","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681911408863,"user_tz":-120,"elapsed":19136,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"51c90f37-d73f-44f3-fbca-dc4c45d5a41b"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 170498071/170498071 [00:13<00:00, 12402084.58it/s]\n"]},{"output_type":"stream","name":"stdout","text":["Extracting ./data/cifar-10-python.tar.gz to ./data\n","Files already downloaded and verified\n"]}]},{"cell_type":"markdown","source":["---\n","# 2. Inputs and Outputs\n","---"],"metadata":{"id":"yhXMJxSFVh0b"}},{"cell_type":"markdown","source":["**2.1 Inputs**"],"metadata":{"id":"fXcCTpd8H8FF"}},{"cell_type":"code","source":["# Get the first 10 elements of the training set\n","images, labels = next(iter(trainloader))\n","classes = trainset.classes\n","\n","# Display the first 10 elements of the training set\n","fig, axes = plt.subplots(nrows=2, ncols=5, figsize=(10, 4))\n","for i, ax in enumerate(axes.flat):\n","    # show the image\n","    ax.imshow(images[i].permute(1,2,0))\n","    # show the label\n","    ax.set_title(\"label:{}\".format(classes[labels[i]]))\n","    ax.axis('off')\n","\n","plt.show()"],"metadata":{"id":"8xrmDt3F6kKa","colab":{"base_uri":"https://localhost:8080/","height":533},"executionInfo":{"status":"ok","timestamp":1681911788876,"user_tz":-120,"elapsed":2272,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"cd171642-d1a4-43e5-c7c9-9f8f5537ccc0"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n","WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x400 with 10 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+EAgEZMaMGfLoo4/KyZMn5cwzz5QVK1bY/nb62muvle9///vyhS98QW6//XaJRCLy85//XEaPHm0EySdPniwrV66Un/70p1JQUCBnnXWWnH/++fLggw/Km2++KdOnT5dbbrlF3G63PPfcc3LixAl59NFH2/Iy20r3eDpx4kSZO3euuvWtiMh9992X1nrb+/5kkkzYv9PBc/D/r5PvfmXAW56988471tSpU61+/fpZBQUF1l133WUtX77cEhFr9erVLY+bOXOmNWHCBGvDhg3WBRdcYHm9Xmv48OHWM888Y6wjFotZjzzyiDVhwgQrOzvbGjRokDV58mTrvvvus44cOdIyXzq3POvfv7+xHrtb9R09etT69re/bRUUFFh9+/a1Ro0aZT322GPqtnuW9cktz775zW8ay/zTn/5kzZkzx8rLy7M8Ho9VVFRkLV682Nq/f7+xnh/+8IdWSUmJ5fF4rNzcXGvatGnW448/3nL7N7LHffAT3Ae7n0zeN3ft2mXNmTPH8vl81pAhQ6wlS5ZYzc3Nal75lFvfnn573FOam5ut++67zxo6dKjVr18/a9asWdaWLVtst6unycT9oKamxvrCF75gBYNBa+DAgdaXvvQlq7a21njPLcuyVqxYYU2cONHyeDzWmDFjrJdeesn2WLZ9+3ZrxowZVr9+/YxbHm/atMmaO3eu5ff7LZ/PZ1188cXW+vXrbV/Hf/7zn2r6p+13dsfZdI+nL730kjVq1CgrOzvbOvfcc9X7c/o2tXbrW8tK/f3JRJm4f/McnJ4sy+oG6SciIiIiIupxulVmg4iIiIiIeg5ebBARERERkSN4sUFERERERI7gxQYRERERETmCFxtEREREROQIXmwQEREREZEjUm7q19kt3ql76At1wRBdV9V1zp2T53z3KlWHGz16Bk/UeEwCdm93Aq6tXQmY3wU/1j93i64/mQmv1/EjZfMYvYAUJrW+DBduAz5PEYm7Ybtc+Fxb/bEp4bPZkCbYjJj+cRy6+Lr1/B6bDqfRmFfVsViFqtf/Sjf9clJvPgZ+ce5wVZdOmqTqzZvfU3U0an4eg/6xqp44frqqv//wY+3ZRBERGYTrPEPXuw9J6+zeYjjEZUHj50S8c46BY8efo+odH38Ec2SbD+p7QpV9cvQr1HzgcEdsGnWhzupe8B+v6A9HuFH/PKYP5yIi4nUPVLUbzhsN9Q2qDnpz9OO9+jzv8pnHFZ9+iEQSekOios9D4tLnwoTrpLFMPOXi6TCR0J+1aFh/ztzw9cRmkZKA010OnB7zgroOwDLD9eY66ut0ja9WPbxnTSFdu2yajMfg5cOvEr/9RWr7H/+yQUREREREjuDFBhEREREROYIXG0RERERE5IiUMxvUO+FoRpcbUxydwyt6jHhdpFjVcWN0okjCHVK1J+HXy4TxiS4ILsRgbGdUzJyCG3MhSTMayXlh4KQxXtSlB2/iMMuE29wGFywzgdvphgwHrANDHIm4fi0/WUQIaj3YM+HSg2tjElF13Ob1dbl1ZiPa9IoxDznPF9D7i8ev9yePT+8fTSH93oqIxF16QHFl2QpVj4MsRFWzrs+DQIbNbi5xmGbEtPrruvaYro+nMPzYak4+jxPiNpmmZAYG9BMOh2FguHR8ZmOCjvfI7Im6LoZvHXmF5jLi8L654XCTB4eKKIwrj9n8GrWyVtfrtul6c6Wud+83l9Gb/epXuoaPvOThriUihTn6vFxffUTV2zbr+QN5B1RdPF7/3G0XFYR9oxEOPRH82EBGw2uTr8BjC35RLsjVC80N6gyHx6UzHCLmsckLCw3C64kvJ9YT4bUREWmA/Mp6iDTGIOcRD8E2BM1l5sDrW1FtzpMK/mWDiIiIiIgcwYsNIiIiIiJyBC82iIiIiIjIEcxsUFp277O5J3Un8PlgxKK3RJUJDGCISMKjB+km4nrApysOmYEGPRgx4Quq2p1TZKwjjvkKyCkY25RInumIw+8A3JBlSIjOMQiu02Xe8DxSt0XVjXVV+iEePTDTn5Or6kBOvl6gN89YB+Y+JKZv6h2DnhniD+qHe83XLh7Sg0zra8rN9ZLjaur2qtpdrt/rYFDvH/GQ+Xus+qoqVdfB211Y3E/Vnvrjqg7D/GHIW4iIYKQsGta1F8YfT5+g6/e2mss8ak7qEqkcO1BJgT5OVlWEVJ2s7Ugq/vxLXZ83Rdfb9aFH8DBaaB5WJc9oSqDLzTBufH2ZrrdAHkNEpAIyGV6I+U2CQxz2Gtq1z1xmb+JpHKDq/AI4T8V0HkNEpK5aZxfi8BmeAvtKGN93+IY6dqwYPJDj2K5bMck2OGXU1Oj6EMwvIkaDigsu0YGxRL1+7ltC+pMELXFExMxs5OtTrBQGdW1k0uC1q7XJTmAOZATkofAUHYHnaXeIaYDeHONtsiKp4F82iIiIiIjIEbzYICIiIiIiR/Big4iIiIiIHMGLDSIiIiIicgQD4pQRXNCgz+XXNfR++2QapApdLp2GynHrJmPxyHuqXremTNXnXH2zuQ7oSBU3Wuylz+2CIHtCf0w9kLLzeXXQvbp6rbHMj/76vJ4Qg2RYANJq3qAqzyzRybwRpbONdcTCOnn3/prXVV0yQj8mD5Jm1dv/aixz19q/6wkdkWiltK1+W9eLx+t9MhjUyevq9yEVLCJ1tdAxD84+TUd0IBxvc2DGT20kuX9FP0h750KTq7HQkE5E5J97Ulmx89zuZKdr8+c+aKToPn7AmCcdv3nCnFZSoOth57ZrFSIi8sQDur7zJ+1fJrXPtbP1+TTu0h+mqM2prwACyvmwr2BDvaYGXWOgHMPfImZTv1kTdXfQSSP0DFVV+ty3RswGfFv/oespI3RT4Wuv1d8FHnr0QVWveOVDY5nz/mWoqnP8+vV0J/RnMwI3t8DXdws07BMRyYf7toyFoHoAfl4Gh+m1a8xl1lTp+qqrzHlSwb9sEBERERGRI3ixQUREREREjuDFBhEREREROaLHZzYuOHuMqq+9ZoGq//OnzxiP2XXksKPbROlzQYcbj1d3mvHi4E8R8cVg94bmd41b1qh6RJMeBDk6AV2cKnQGQUQkJ/fLqo66gnqV+ADoFJXAblUiEofNdkMOxJvQY04jNe+rur5sqbFMgfHwWf31j/0+vdKj0A0tHtO5kEj9ZmMVW9a/pOoTu/RY2EhCv/5Rn34Pd/0F8hnUbT333C5VL75O79eNleY46Hzoy+nPP0PVFVt0IKcB8hd67rbFd45Dvf2grmdPHiSorlGfDxqixiydIlnDUKPznYjkRfU48LeNOVr32WG6XjDPnKffuDQXmgJmNLqfkvG6i2YMMgT1Oh4kIiI+OC3DaUYaIKNRDQ34MIPgsfnGimf+oKdZ1YV5eo4RBfpzNH6EuczXIVuyvXyDqletLVO131eqardA4EJEIvU69+Eu1DnJnFx9jg34daCipmqjqnNhG0XMI0A9NOTzQb4lAPV4m6aJM6bqGrMkqeJfNoiIiIiIyBG82CAiIiIiIkfwYoOIiIiIiBzR4zIbi780X9U3XP81VY8eoQfoFedBfwER+fw3vtXxG0bt4oJGGh6PHmzocZk3+Q4k9IDFRG2ZqitW/V9V18Ew8+ln6bqmYp2xjoao3q7C865Wdcybo+o4XN77Peb1fhSyDYl4SNVN1TpbsmHlc6o+BuPQ7Vgw/PsoDsD16AH2udDHpOG9PxnLxIwG2v3xTlXH4HlR1xg82Jx2MIV96HR1G/R7646Y8+A+VAgDhkdcom/KX7lF36s+AstcbxOtO2ZOahV2nfjTRnOh+An19k1zJR3E6/UlmQMTKSKVMcygpJdH/C18zB98Nq2HUw9SqdsoSQAyWBVV5mNcMK0Qcgb1tbqOYugA1lE6w1xHUWGWXmdcf8drqNchg8aQ/pzUQK8dEZGrFpyvaq/7ClWvWavDJk0xveF+DEOISPm27Xoe/xRVRxM6oDK2ZJqq3R69oXHZa6zDD69XExwz8RiKX5uKbXIgQf0VRnzJDkOfgn/ZICIiIiIiR/Big4iIiIiIHMGLDSIiIiIickTGZzbOPkvfff23L+t79a984w1VP3jv/aqedckl5jKH6ZuLf7TXHBtHnQ36bMT0vbN9Yt783tW0TdXhst+qegTcoDsMkYNNu3UdF8tYR/2+N/VWxqtVHckdr+oY3BW8JM8c2+nxBVVdWaF7Wny0Xq/TZqh2ctDDQMIhXfv1dlZt+B9VH+uAVjT7dqYZDCBHNLbhbbhxjB4nvWGH+dlAC8YOVPX/eWuPql2i6xnQv+E9aHszFnrFiIhE4TON7XeC/j6qbqzX9+QXyJWIiGyB1+cwfnY6idejB0uPGjVU1Tt37jce88Hu9D6oTz6gazgMyL8/ndbiqAfZrts+SC70wAg1mY/BjEAMTtOby3Tthm+k1ZAT2QbbICISDOpjTyyqewA1QCajFvqBJGy+Bc+7TB8IamsrVf37petVfeLgDnMhSbghg+Xx64YWjXV6QyeO1VmUvALzeym+B00hXUOLLsHIaF6+uZ0ReIzbbGmWEv5lg4iIiIiIHMGLDSIiIiIicgQvNoiIiIiIyBEZn9koHqHvzf7R7kOq3nVUj8n9P//nF6rOw4GHIhKPxoxp9P/LSj6LE1ww+NMfrW+1FhHZvPZ1Vdft0OEGuH20YGsAGCIudrfXx6v1f2zEsZu67psNP7VpTXEG3Bo/jBvWejuLtjmiB6IPKdADMxt0yxLqQZptps3VUTgp14dV2QQZDZ1UErl5pLnMFW8fUfUH8PP5UDd+rOsq+Hmx2VpHCkbopiFun85EVdboLS0sHa7q2kadGxEROdlNokXRiD4v2WU00jX3bF1jL5T7f9zuVVAP8cYaXR80Pyrd0tAzdZ2A48YBbLYjIlWVb6na5dbfL04chAYhbdAU0SfVUEj3A6mv1QGMygq9ztlzoBGYiDQ06qBpblD/3At1sf76LEH4uYiZAwmFzHlSwb9sEBERERGRI3ixQUREREREjuDFBhEREREROSLjMxsxuHHzmQP0z/cd1fW2j/W9iV/966vGMmsO2gzi66WyYVig3Zi+TtGk7zntDekfh+p0LwoRka07dhnTTneo1Z+aMOMhYt6WH24LboyHP5lC3uJQB/SwaK8D26HJSPI2CpShhtpMw4wGZjIw1XYNHHc32Xz0lkM9DOpCyDOtg88Kfv5CNp+lxo90wCJviK4Lcvup+ryJpaourzFvIv/B1p3mirpAzCajcroxNm/k9Em6njJW1+dN0/V2OIBVh1LaNOoFvAGYAJ9XR7KEHWA/9NWwDamBwxDYPGMUzDAYsr4H4Yumjb46TibFI/QyIjGdyXD7dB+OSjgIb680/1YwoljX2NckP1fXXrgCiOrYiIiYvTgKbHpxpIJ/2SAiIiIiIkfwYoOIiIiIiBzBiw0iIiIiInIELzaIiIiIiMgRWZZlpRT9zMrqom5uaYKcoiSL7Vw08kxjWk7BCFW/9vbb7duoHmToGF3Xbu+c5HAgR+9/rohusfe1a+cZj3niN6916DZ8xmZaAuoGqDFIi/357DKfJ22m9QaDbaYl66eW4uGrQ2TKMTAZfJ0x9ykigjFp/K0UttCE3LHxuRARwTZY+JicfjABVlo8Vn/m6xvNT0oMAo5VsAMloDPn1f92sV5lrvlq/OAxfRzBHGxn7YPnTJmu6gUz3lH1zYvMx+TojKm48I3FQCj8POLSKeBbfmCmgH/zsrne1mF71N56xOsYnbX/4fFvAN48xiY4XFig63ffgBmOtX+70ED4SncEu/O2QZ+hutNucxjO3GH4pmnzlvSD7SqdqBuKxhP6w5hI6Bc0EtVH1YICs9Puvy3QTUzjTfpOHT44plZX6tpj3h9D3DANm/zd82Bq+x//skFERERERI7gxQYRERERETmCFxtEREREROSIHpfZwHH1OIY+leF77R1Reubw4ca0BQuuVvXY0cWq9nj1wLhwWHdj2bBpi6rXrVtvrGPXxzvS2MqO0VXjRdHlF5gdrVa+u1/V7R0ZfKnNJjTB08eMRi40PgrBUM/tNg2G2juMFXstiXTbfktKW7abmY3k8Hg2GuqQzWN8NtNOh0P9MXt0Tn/zMYWw0HLIUwRhfsyF+KG2GV5sNNXErrUwhFz8sJ1zbvicsczH//CmqnfCdnfWPjhtxnmqfunuDaouzDO3Iw7hGTf8etEFL5AL5o/Dixy3aQM8dY6uP9pjzkPO6S7nYDtjLtL1jFm6DkJEaqyOy8raFbr+6yvmOg4nC/a1QfawPqo+sTeFToAOG3au3qYcf54xz9Wzdde+y2ZVqbqiUmdL/vQn/fjt+mumiIjsSPJ5TnX/4182iIiIiIjIEbzYICIiIiIiR/Big4iIiIiIHGEzAjOzXTFT36x99Pipqn78/6xW9Uc2A/mTje3/zS+fVvWsOXrQamFRsfEYl3Fdh6P7NbxPvSvpKGqRWEwvs7GhQtWbN+ucx9zLv550mZliGeQznBC2GZqId+XPGaLr8aW6jsInLrDNXObq3elumWaXc8AWBgV6+Kd4YLsaYSEheHyOzZj8OgibpDuSOBNyJZkA35qJUMNt0m1PAlGo8Wh1AGq7HikGCHbgMnG7NqayzCTMLkraNUW6TkRwK0QKYGg0ZjY6S36O7tITDOhPWMLujTRPJEoMfu5NkuFw4Y4hImVrdf176KWwZpWuV76n6z0dkPGYDGHNm79szjOxRNc5EAJqgrYFCQx8YpMkGw0wTwUEj176g67/eTj5MjPZjrdbr4eM0nUpHKxKinU9dry5jhC8b42h1mvMMflzzGUe7YSMRl8IKZ5McgLcu0VvU+EMM7NRvkV/QKeM1U+uskpnNt6H7x/7Hcxb8S8bRERERETkCF5sEBERERGRI3ixQUREREREjuhWmQ28i/PEUWep+qOd5mB2vId8eeVxVSc8laq+467Fqn7o8eeMZe6CsXM/ue1GVV//ta/BI/TLGIuZgzsTCT1Q0AWDaePw83hUj2h2JUL65zEz8+Hz6VyH36vrGVNnqfpnj/xE1d/6/gPGMruLs/SuILthV7C7A3iyzACObbcZjqyYo7lFArDiXH2bayksGKQneLy6dMOAUxEpyNU7YAPMEoMmBziu2mb3E79erZx3jq4jIV3X4PhlCKds1nEgEREJQnuZqlpdH29voxOydQbkby4bqyds36rH+mJ/Crv9HvtoHIJ6DNTFcCAutDmzFMLY6ADsk9Ww0vmwYQnYf+rMVRgRhQL4fPrgySd8egY3flBsltlV3HEdInDj62PzRsLhRlzw/N24M8CvH+E0JGGbY4sXsjj/dpmuF1yl63vu1fX7ZeYyr7hC19N09FJKYF/yYxYFd2ARIyTkg2U0BHVdD69NY7WuI5jpEBE37PdjYVj9jyFLUgsnlbXrzGX+dzszfN3ZgZ26Xo51J2zD0fY2tmojFzYOShZahONfVQV2FRIJjMhX9fvv64PC0tf1/Ps7cd/iXzaIiIiIiMgRvNggIiIiIiJH8GKDiIiIiIgc0a0yG4u++AVVl5XbDAwHOAy8DsaaXzvjElXPumqBqq+4+hpjmbG4HoSaV6Rvxh4P6ZtnuyAb4cGBsiIiLnip4WbPHlhn3KUHmMYjerBsImIOSo2E9CBSTwAGncJN1C+bM1vVfW0yG91lmD2+pJjR8MK4dRGR43ApnQVPBoYaS7I7a5vpChG4Tb8U6iGTEvDrgZnhKOxbQezUIeLzwMDoAr0vuF16GTFoAFIDY4tFROphgHsIhnuG4cnVwSB9FzTqcOlo1CfbAZt97b/oLgev/3GfqrFXAyVns5vLeaP1p6G+Tu/JbnhQAnZ0HLYvIoJHsC8M03XDXl2vh8/WZUfMZTbBIRAjBh7YEPxNWAImeGzGOOfBvetzYcx8U0jXdSH92clLmAmNkN34/y7QVKc/MX9bqn8+fZL5GIjxGa+xG8aNu+CAFof37KW/mesoCur6qhm6TsA6v3MLrNPmV574vmEGIwE19sho1FHNTx4D+YjcAqihD0d+qa5rCnV9K0Y3xczXPf+8rr2we+XDVxzsMyEicj1s96oycx7KPAkPdr+yOam2Yv8eTNKJNMC0Svgc7HSwj0Yy/MsGERERERE5ghcbRERERETkCF5sEBERERGRI3ixQUREREREjuhWAfG6kE6xbkshIA55QJk9Z5Sqx4/X3ctiER229fvMMLffqxNt0bBOaGGgMhHTUUeP3wz9utx6PQlIJxtvBATGo5iAi9o0DoRGf2Go4z69ltxgUNWzZ55tLHPZWx8Z07pCGAKB2LDvuF26G6bhY5L10EH1NtOKIVRYXDxA1Ym4fg8isC+5jK5aIkG3fu/DYZ2+9cKvCHy480CzKhGREDTYq6nStQeWEYLHV0N2rdhchXkPBEh1lkDTv0YMq9mln+E97C43LOgqdrv5uo/1nj0Wfo7NKPHGCHYngbHQ8XILBMKxn9l5UAcHiKEM8ozlSbYrCPUI2Ca/Tbc9bFoXg3ncEJguLBqs6vqQ2bqzwuaGC12hDjbtQehH+8IS8zH50GQ0jAFxWKYXGt8FxuvaJj8vQQhaYwNQbKaXC8cvu2XG4IYWGFSHe7JILgTdc+BGHSIiTXAAj0Bdt0XX/iCsA57X/d8x14E3E/DDTo2vVRDenyabfa0opOuSQnMeyjxun95JT0r7O+yNHnOGqsMRM0Su4Dk32V1y2oF/2SAiIiIiIkfwYoOIiIiIiBzBiw0iIiIiInJEt8psLFvzrp4A48cG2jwmf5CutzfqbEPZ9k2qHg9ZiITN4E4/NlpL6JcpEoOBrtAZyW13DQeDYRMwUDUe1YNpXTFo8geZjVADdGUTc/y/N6BzIvGIzpb48vXPx481B4N2l8zGvrrk8zjN7sNSApkNr1u/93X1B6DW88cTZiMfH45phu5n2CQLm+lhHkhExAePKTuqaxeEIXAYNSZLzL1PRPbrsr5Gd3bD7fL01XXU5gVuxs5vGLwhgbfSyFPA8HYcQi9mwszM3+yEn58FNe5y63CjRKQK6mQtrDAjFTqm61I49ouI1BzWdTlsB0QQ5LxpOuBUVl1lLPN4N9nn4i59Brx2rv58NZkxPoH4l8ThzQ7gBx1fdNgPbrrCXIcPMmJuPH7BDoj7XxwniJlLi+PzgPlxf8V1ioj4R+jaBw0MI3COCcNBLlSl6zyb41VBUNdx+DCG8asDNC8shFrEfK42T63HGgkHmru++31jnpwc/cY2NIZUvW79ClWvXPU/qj4A5y07g6H/3sH0+u/ZisUgL5xmfqIP9gQUER8E2bzQ7XjPXliogxkNxL9sEBERERGRI3ixQUREREREjuDFBhEREREROaJrMxtw3/QBcK/sIkhp+OM2vSW8erB5LdwnvXz7ZlV7YWBrrGi0scySsXpaUzSka+jVUQDLcOHN3kUk4YOMBgxgd4dhJGZUryMGjRIiTWaIIZCjB6F6YHSnN663wRUOqbpkRNBYZreBL2kXNFzIsekDgf0paqv1OOoaHAcMGYR6aJ8iIpKD46rhuWOLlUZYRxTu9S4iEoFfK2yHn+NvHbBvAiabtpmrkANQ18OYZxeMzY7Ae2g7NL6bjJfvzoZBje8lDonHGMw/7Raqd2M5E36cAw2OyqBpjdmtIvlbiT2TEO5fyw+b82CurxjqsfBE4pBzq2mCJ96NeEQP6N9eobd1LOTHRER+/5Kuf71R1/96vq7vWaTrBPR98Nocr1yQS3DBUHQPhoLgPO81W12JG7IkmMmAiKPxc2OnFxEXfDDwcxKEyGIAemBAGzBptOmJgcc4PDbje+SCY7Vd1kRgO7x2H64e6gd3P6Hq0tJpxjyNDfqINna8/h50zTU36flDOphUV2smELdv0U1XFt/2reQbm0yfIapsxn00zfyE8XgRqajSB8WxRZiua38vj7biXzaIiIiIiMgRvNggIiIiIiJH8GKDiIiIiIgckXJmo/9ndY3jHxN4v24R8eC4SpgnEoIHQF21X49Jhdusi4jIGBjom5ujsxC12/TodH+DHvAeDuEIZhG/X78s9Q06L+GCgfp50JcjijcFFxHx6GkxuHt2wK1HxTfU63X+7W+/1+vMNW/IPWmSHqsYx8YF2C8EeneUjIDBtCIycKSuj+wyZukcmNmw2xkcFrUZU1kP96WvrdQ13vu+sFjXlTgQXWz6ZsB97DGTgfduD9nkWTBjgePn8akFoYeBB9bpTiEzUwn3Ly+A+4IbY/i7IIeTac62mYbDvPE3SHiEszk6JbUP6ghkNNrSDiUL6hO2c306m9vMy798RtcBeDHCsB/XRfQxsAablHQjbgg/bIFjzYgq8zGY0UC/+4eur5+n6yI47mL/CxGzT0YcvxvAa4oZDZdNTiEGpyI3zIOxSOOLjM12xmA7jbwE/DwG6/TDKdcXNNdRWwHLgIwLfk3y4XO3+UYWg2m+XHOenuLC+RepekOlDr38/m//aTwmGCxQ9SWXzFZ1SUmxqisr9XfCqip400Rk04YNSbe1dXh0E5FCaOwC3yvTZrOPHw7p2neO3lm+/vVSVf/yl39p3zakgX/ZICIiIiIiR/Big4iIiIiIHMGLDSIiIiIickTKmQ1o0SBuvGe1zZJCIZgHfo49Co624f7RO2Cg746P9GjhfjAS+PpxO1UdLNLj/UREtpWXqbquTg+OHT1a3yy7oVaPiHfbDUJ16yfrhsGaFY16HOGP7/13Vb+5Ry9uJt74XkT+7Vo9Gnvs+OmqzoOGDd4cPWg132Ys6KQpul5ttjrpHDaZoM5mdwf+9RCGaDqqa9wTtm3V9cc2y/TjMuBzUgf9BXDk5w6bZaZrGaxjXBuWga+X93hbt6b3+gz0drHLDWEGA/MTGEOA3atNcJ14y3e7nhmYyUi3hcoQqEsGmPM0VOm6Co5XedBWKZYIqXrn3jQ3qhM1hfUnfQdknPyr2r+OVZt0Pe8cXcdsfj2ZBwc5zFfgoRv7BLnM2KSZp4CcB+ZA8PuH3SkYM6TQYkUSkBMxumXBE8FtEhEpgNcrB86pQXwMfm+y+R6FSUpo+yU2m9F9DdblhbN0KDQS1TvYc089Dwsw39jPX3+Lqusj+o3avuY9VVdWlau60SY7EYaGWNkjz1W11x9U9ZE6aKhi+4UYdtpjmIRLAg+qdt+J4PywYbt+roVjdTMZeFqy6wNzkQOgVccUbMKVIv5lg4iIiIiIHMGLDSIiIiIicgQvNoiIiIiIyBG82CAiIiIiIkekHBA/AeFtowGTXbgbGpZhtvH4GboeBGGqwx3QuA0zqeFgX1UXlUJiUETWrl2h6pUrdDr7mqtDqm4K685uCexqJCLhsJ72fpkOnf/87fTCQm/ZzB76/cuq/pd5Ovg0aYRuKpNTqsPxXj+k8sRmB7EJ83WGfvCSdpes8eokaduZUNsFwlEZ1O9DWLsrnjtut002Nymb/oUEIIsnbggB2v12CD+1OE9HBMIxmxiAGteRboO+VODz3GTzxPAeF0Go82HC++Vd0B20jRqa9rf683c74MDwVwiZT4E+ZFGbjpAxmBaE2oPhbkhe441iRER8ECKPw5uPQXVcBzYOFBFxw3PBJn542o7CTo6B8YTNuRBD49Ggrp99VNdV0BS23qbP2+sf6fog/NxK904LbdQPGr0eP2w/3+n6DNN1CXzdKtukQ9PHdkHX4DOGqvL8WdB1UkQKCnXoedMGfZeD+gYd3o7F9JdVj8dMWhdDI0AvJP1DEb3zuOCGP4dtGgXKkRpzWjpS+bYO+/AR6Cq85r0yvUiX/j5s11n36G5dr27DjZxsNo2IiIiIiKhj8GKDiIiIiIgcwYsNIiIiIiJyRMqZDWOQLg5zsxnLmS7okyLjYZ3vwNjFtnAX6ZVEfOZ4vZVbdEbj3UP65+/+lw5MDO6v6xybhkJ1MNDSrkFce1VAzxi3Xw8qzc3XA0rjcf0AV9zs2BfHcakpjNN0grebZjaSwZGbWVDbDbd1Yt/oaHY5gJFQ77KZp7c7A8IuCXghC+Dn4RQCFzlQf5j2ViWHGYw021F1CExX9LeZJx/qnOG6roWx/zsyaCedcs4YVS9f3RHtO7WPYX8LwzklYXOej0P2AbMQOdCVDhvuhbEjpIiEYT0uOP57sXEgBipsGtSu3KJriCyKMXQf+rRhPzk3dtsTMYIdv/29rn/4os1jMkQqGQ3UDE0yK+PwqXWN1XW/alWOmahDHueM1Q2VRUSqK3Rn3cqKKlXH4HuNy613Lpff/J27x6O/fObAlzoXBI2i4UZVH260Cd/Y5CHSkkK8rM8QHYT2QJipsVF/2KJmTDe5Q8lnscO/bBARERERkSN4sUFERERERI7gxQYRERERETki9cxGNdR4H2u7zMaZUNtkGU7XBJc+eTDu8qx+5mN247hJfctlyYbxoC6XHr9XWQMDOUWkEfMpSRw81nrdWY7BkMD6hpCqi4rHq9ob3azqSEyPOxQRKcBB0IOMWTpFvAMyQV0BIy94dY+9ZzIZPlf8uGZKzqYj4V3Mi/De/vBzzGjkD9F1NYyhFxH5sCftRGmw+00ZjpR2wxlui3m4zxhNFeWdvs5QSNdRm3xFAkJDccg+xOAxbuyZYbNMD7y5ATgnB2EdmBMZbbZjkP1J8k9/+ZmuZ0FfDvywJuz6DYR0ueW91teZSYZP0HUYIp6HoB+DnbygzlxE3TrkEoGMQVGB/hIYDmGQRqSifLuqY1F9JkrAmckLx4SoTRApHMV8q35QU50+EO8r26AXcKIDuhthc6MUmhc1N+hARQL6yiXc+kv4ycZ25kjSwL9sEBERERGRI3ixQUREREREjuDFBhEREREROSL1zAa2YMDLFLzZu93SsYbcB/Z0cMPPJ55jrqIOYgbHIcPhg2UGfXqh4SbzfshuvGf3Z3EGqHHsps0YVMGx1nDP6jPgx0XQtOCDGpjBLsMAY7f/sErfRH7WJD1geeoI/UQiOPBVbF6LYpv1doKjduNjM8B0GHO/9oCuu6htiSOw9wI89R6f2cAhtiIicBd5ScC4WzyUBOBFWw77C/0vu1HRJZATjMDY/qMZnG95d69dVx5nlVXqeuxoc55wSNd+OI1E8BwC3Da/8swJ6hrjnjH4PlIPOZBk+Qw7X/gWLOPvuq57VdfYP0REJDAHtitDz1t2fv9XXWOOsrbKfMyqFYN1vVZnLhrD8MUGwjrYzyKMb7yIJFx6h4u78HuM3tAYHBO8Nr9zb2wMqboBvmjuKYPw1zEHDiwpZDQMsBknDulMRmwgdPHCz2Yq67TJTqeCf9kgIiIiIiJH8GKDiIiIiIgcwYsNIiIiIiJyROqZDYSXKWGbeXDpmDOAe2fHYJneoK6Lcvsbq1ixBppahHTph3GVhYW6EUddyLx3eR3GOHBcG47VxLyK3TjNINSQJSmAdUyCe3zXwKDVgzarwPfgADyPLVXbVD2j9DK9SY0VxiJr8LbWmB3pLBnaoMIHn4HxfXT9ToY8j7bobXEDu+Gu2HsEDw14SDzY2160djDPBiIfYHCI2uU3O3Q9bIc5z7Qxuh5brOsI9uSCMfOYzxAR8cH5rhHObZglXK9PbR0iAue+KmilsBmeh4jIeMi0rNrYsdvUlSJwMPPCexCzyZGWl+tvKruwzw2e16EvRG2dbvAWzDHDwQGY5IKgVhSaw0Rj+omEG82sqquxXs8TgiO3ExmNTmDB5ygLvmdaNvmqYcW6LsL+MyniXzaIiIiIiMgRvNggIiIiIiJH8GKDiIiIiIgckXpm41iSOhUwXh3zFSdhbGcYxj9OLNF5CxGREw0wiBQyBb7zdO0P6KBIRbl59/9DNuNSlYFQQ/bEtgcGToPxnk1w++g66MvRhIO/cRysiJkLgXc36tELjUMjk9r6kLHIBpyE29FZMnOIpFTAGHIPNmPI0OdFqUl2KKHUjRmq65zCLGOed//Z+b0oepO9NtP+iDs51J+F834pnNfPsemfhf2xonD+9MA59z3MAnSAldt1PeMWXdtlFN5YqeuedHgvh1wM5mG32bwHb62CCfh1qy+UHh0aqK7UQZlIOGisw23XqOU0roT+soVfehMJ80uN16u/G0XddqHkzJMV1HUgT9eYkxYRyYeMht9mnlTwLxtEREREROQIXmwQEREREZEjeLFBRERERESO4MUGERERERE5ou1N/doC01JHW6+3V+raFbKJWyYJqkNPGNmyTae+/rq29cfbOpKkxiC8HXgtsHdeBLb7BDQ5sg2hI+hD8+qq3aqekr9e1bVNupGNiEhjCCbgdlCrsNeU367zWw+FTdfack8JolNyS3Q97+p/M+Z5958vdtLWdL5+UGPWFu89YSfnzAmq9gb07xt3f/xR2tuVzD/hXPfPj3U9AWoRkel6M+Wc8TADnP+2mb15222FPj3KZXB/GrfZC04qzL64PQZ+FwjDTW0q4fuaiCQ/6J+EslE/4Ah8tzqy07adcauyB+vaBzfXido0Z2zy6i+jx+0aNWegkhG6zp2oa5fN9zsX/Ekinsp3Txv8ywYRERERETmCFxtEREREROQIXmwQEREREZEjOjezkabaRl03NdjP15oj0FRt3To95q8Zft4h2tDJJ3e4rhtxjOD+Nm9Ni3COrutCelBqVd1h4zF7ITsiPaO3TafBIZC96eoeh3Z+BrJMH/akjlekDMPGpyKyF7NtaXrnbV3/yxXmPF9ffJGqf/nc2+ZMGeqKC3Vqw+3RR5cm7AwrIis26mTH/n1b9QxOnP/StNVuGkz8DNTjz9J1pQNZuFXv6Lr+Kl3HzJdbGtrwHSVTbINgaTCo6wQ0+RMROWOMrpM2TE5yjMjCIKCIWPgtFpZxAmIeKe0qmLv12M7lqMFn6/pgB8SpauE9jEFTP5sYkuzHQHEb9abvPkRERERE1Il4sUFERERERI7gxQYRERERETmiW2c2EjBOLtqGS6O+cHPyiN2gtG5gH2Y0zPhEuzVCBqauSa+kzmYMquAYVI6zbxebW3r3HnB/czbe6Lnq2pnPSMWvnjd7ajz17M9VPbrkHFX/+Af/r6pPZNDxLBIvULXfowN0Pj9+wESC2TqzcTBD+/x8iPVu29naJatvX1UfPqmbQLwOfTeqbXp7bNvb0VvVfVRBG65gVNcuu/4L7fx19rgLdF0yyZynCXKk76/R9fE96a93VKmui6DHz//8Mf1lXjxf18FcXTfClwN/QNer4PvZ8V3pb8MxeK38sEyb2JfZBK6N7yn/skFERERERI7gxQYRERERETmCFxtEREREROSIbp3ZiMEYtojdeLJkYNxbWxbhCLyPcydc9h09pOvf/g02weY+2d0mo4H3186Q8f7J2pJk2UyznNiQLmDEo+CzmCnvIaXvpM20yyfrg96WjfrgAkPABW6Pb9hqM2b5ty+9oOprr/2aqn/84+tU/Z/P/LdeJxwju5PtdfoT5XXpo4vbbSbCxo8doOq3Pjza8RvWQ1gn7fba//UnOF9W2Jwbj5uTeowQjN2PQgY0ZtNjpKnenNaaQdBbIpGv68215mMScKIZrWNaEoJmV3s+Tr4dGH8qK0v+mNMNGGJOu+euJ1Udj+sX7Nk//LuqK6v0473wHbFN+xocIop0DEwCeI4WkcREXfva2HOEf9kgIiIiIiJH8GKDiIiIiIgcwYsNIiIiIiJyRLfObJzAIaih9JfhgnsZ77UZ89duOPA+lUH3OO4t2eB+B+zGe5X3tZ2te+ghDSrwbe/JY3zxY/Dh/i7ZDOomauv1IHc4NBsno2SZDTvr1/9D1REI+k2fNkvVL730hKoffPRuY5lvr+4ezSnCbv1cYjE9GL2+xnzFvD3kuNkdfNRd8otdBCNBLshKhG0yGyeTZKAGnKnrkhG6rofvRflF5jJiEPaKQbYkhj3MgN3XHj/kPNL9/pGPBzcRKcofDevQTUOuuUw3cnlv82pVvw7fXdvSim3mHF1PhDxGTo75GMyv5NjkOlLBv2wQEREREZEjeLFBRERERESO4MUGERERERE5ghcbRERERETkiG4dEBcMMKciW5cnsItfW1I1ybSlC1t3TAa33tOoS2VBGC1TGt/h25w3UNdHjnTaphB1qQ/3tv5zOHTLYKhTCYzvgkZ/8chHql75N13ffOtMVT/z1CvGMp/6zwdV/etfvZvClnS8SINOusbc+vQ9YvRZxmNiMX3gdIdCqj50kJ01KTVTpug6Dt+t3qtJvow+0Jx3zmW6zgnq2gdB7Vy7gHhc1+Vlun51XevbdMml5rTLrtX1FAidP/mj1peJDfhERFwunWQPBvWTuWTGAlWPhvB2Q40OjO/6oPVtEBEZMlzXN92saz/csSYOr6WIiAuuEtxec55U8C8bRERERETkCF5sEBERERGRI3ixQUREREREjujemY22wP5Lbcl9ULdTPFbX1eW69thcNsdhX0h2Ze2B5oxuGM8Ytunt5YcxqNj8xw0rdcMY1KE229GUpAkRjqFECZsGRAloSOWC5+rBboP4PODnLrsXE6ZFYUxvPEljJHxt7OD4XMpc5w/R9ZRJo1RdtmKnqg+2oananiSNJB954i09IWB2rLr1O3eoeuJ5k4x5OsPRJh2q6+/XH6iaGnPQfGFhoap9MAg+MLL1Dl1e6OSWiJndZ/FY0NiocyAHnMhJUqcrgbxEqF7XTVDbGQvn8dElui6EpnLYXC8E2U0RkUZo6rcBMhon4Lz9mXG6/revmcssgO2UzeY8rRlRjAk0kVikUtWRkP7s+QP6s+oR3QRQZLWkq6BY1y44B8fgHB3BjLOIcZXgSeE8bYd/2SAiIiIiIkfwYoOIiIiIiBzBiw0iIiIiInJElmVZmdKygIiIiIiIMgj/skFERERERI7gxQYRERERETmCFxtEREREROQIXmwQEREREZEjeLFBRERERESO4MUGERERERE5ghcbRERERETkCF5sEBERERGRI3ixQUREREREjuDFBhEREREROYIXG0RERERE5AhebBARERERkSO6/cXGCy+8IFlZWVJVVZXW42bNmiUTJ07s0G0pLi6WRYsWtfnxixYtkuLi4g7bHnJeJu5/ixYtEr/fn9Iys7Ky5N577+2U7aL0ZOK+l+qyrrjiiqTzrVmzRrKysmTNmjUdsl5qm560H/IcnBm4z/U83f5ig4iIiIiIMpO7qzeAiLrO8ePHxe3mYYC6nxkzZsjx48fF4/F09aYQEVE78C8bRL2Y1+tNerFx7NixTtoaov/lcrnE6/WKy8XTFBFROrrbeTvjjuKvvfaazJ8/XwoKCiQ7O1tGjhwpDzzwgDQ3N9vOv3HjRpk2bZr069dPzjrrLPnFL35hzHPixAlZsmSJlJSUSHZ2tgwbNkzuuusuOXHiRNLt2bVrl+zatcuY/uqrr8rEiRPF6/XKxIkT5S9/+Yvt448dOyZ33nmnDBs2TLKzs2XMmDHy+OOPi2VZar7jx4/L7bffLrm5uTJgwAC56qqrZN++fR0y5p5Slyn7n4hIZWWlzJ07V/r37y8FBQVy//33G/sV7j/33nuvZGVlybZt2+TLX/6yDBo0SKZPny4iIpZlyYMPPiiFhYXi8/nk4osvlq1btybdRuoYmbDv1dXVyVe/+lUpLCyU7OxsGTp0qHz+85+3HXu9bt06Oe+888Tr9cqIESPkt7/9rfq5XWbj1JjsVJ4bOSMT9kMRnoN7kp62zyUSCXnqqadkwoQJ4vV6ZciQIbJ48WI5fPiwMe+yZcvkoosukv79+8uAAQNk/vz5xnn3VE5z165dMm/ePBkwYID867/+a9Ln0ZkybvzECy+8IH6/X77zne+I3++XVatWyT333CNNTU3y2GOPqXkPHz4s8+bNkwULFsh1110nS5culW984xvi8XjkhhtuEJFP3vSrrrpK1q1bJzfddJOMGzdOPvroI3nyySelvLxcXn311Va359JLLxURUSfTFStWyBe/+EUZP368PPzww3Lo0KGWE/DpLMuSq666SlavXi033nijlJaWyvLly+V73/ue7Nu3T5588smWeRctWiRLly6Vr3zlKzJ16lR56623ZP78+e14JaktMmH/ExFpbm6Wyy67TKZOnSqPPvqovPHGG7JkyRKJx+Ny//33J32eX/rSl2TUqFHy0EMPtZx077nnHnnwwQdl3rx5Mm/ePNm0aZPMmTNHYrFYiq8etUcm7Htf/OIXZevWrXLbbbdJcXGx1NfXy5tvvinV1dUqJFlRUSHXXHON3HjjjbJw4UL51a9+JYsWLZLJkyfLhAkTWl1vKs+NnJMJ+yHPwT1LT9rnREQWL14sL7zwgnz1q1+V22+/XXbv3i3PPPOMfPDBB/LOO+9I3759RUTkxRdflIULF8rcuXPlkUcekUgkIj//+c9l+vTp8sEHH6hjajwel7lz58r06dPl8ccfF5/P14ZX2kFWN/frX//aEhFr9+7dlmVZViQSMeZZvHix5fP5rGg02jJt5syZlohYTzzxRMu0EydOWKWlpVZeXp4Vi8Usy7KsF1980XK5XNbbb7+tlvmLX/zCEhHrnXfeaZk2fPhwa+HChWq+4cOHW8OHD1fTSktLraFDh1qhUKhl2ooVKywRUfO++uqrlohYDz74oHr8NddcY2VlZVkVFRWWZVnWxo0bLRGx7rjjDjXfokWLLBGxlixZYrwm1DEycf9buHChJSLWbbfd1jItkUhY8+fPtzwej3Xw4MGW6bj/LFmyxBIR67rrrlPLrK+vtzwejzV//nwrkUi0TL/77rstETG2i9ov0/a9w4cPWyJiPfbYY60+r+HDh1siYq1du7ZlWn19vZWdnW3deeedLdNWr15tiYi1evXqtJ8bdZxM2w8ti+fgTNeT97m3337bEhHrd7/7nXr8G2+8oaYfPXrUCgaD1te//nU1X11dnTVw4EA1/dQ5/wc/+IHxOnUXGTeMql+/fi3/P3r0qDQ0NMhFF10kkUhEtm/fruZ1u92yePHiltrj8cjixYulvr5eNm7cKCIiL7/8sowbN07Gjh0rDQ0NLf8uueQSERFZvXp1q9tTVVWlrm73798vZWVlsnDhQhk4cGDL9M997nMyfvx49djXX39d+vTpI7fffruafuedd4plWbJs2TIREXnjjTdEROSWW25R8912222tbht1vO6+/53u1ltvbfl/VlaW3HrrrRKLxWTlypVJn+fNN9+s6pUrV0osFpPbbrtNsrKyWqbfcccdSZdFHaO773v9+vUTj8cja9assR0OcLrx48fLRRdd1FIPHjxYxowZI5WVla2/CCk+N3JOd98PeQ7ueXrSPvfyyy/LwIED5XOf+5xa9+TJk8Xv97es+80335RQKCTXXXedmq9Pnz5y/vnn227jN77xjVa3uytl3DCqrVu3yo9//GNZtWqVNDU1qZ8dOXJE1QUFBdK/f381bfTo0SLyyc4ydepU2blzp3z88ccyePBg2/XV19entX179uwREZFRo0YZPxszZoxs2rRJzVtQUCADBgxQ840bN04ta8+ePeJyueSss85S85WUlKS1bdR+3X3/O8XlcsmIESM+dd3J4L72afv14MGDZdCgQW3aRkpPd9/3srOz5ZFHHpE777xThgwZIlOnTpUrrrhCrr/+esnPz1fzFhUVGY8fNGhQ0osUkdSeGzmnu++HPAf3PD1pn9u5c6ccOXJE8vLyWl33zp07RURaLoBQIBBQtdvtth2y1V1k1MVGKBSSmTNnSiAQkPvvv19GjhwpXq9XNm3aJN///vclkUikvcxEIiFnn322/PSnP7X9+bBhw9q72dRD9Kb97/TfJFHXy5R974477pArr7xSXn31VVm+fLn85Cc/kYcfflhWrVol5557bst8ffr0sX28BaFc6l4yZT+knqOn7XOJRELy8vLkd7/7ne3PT10AnXpeL774ovHLGhEx7iKZnZ3dre/cl1EXG2vWrJFDhw7JK6+8IjNmzGiZvnv3btv5a2tr5dixY+oqt7y8XESkJVgzcuRI+fDDD+XSSy9Vw0Paavjw4SLyv1elp9uxY4cx78qVK+Xo0aPqNyun/ix4alnDhw+XRCIhu3fvVlfOFRUV7d5eSl0m7H+nJBIJqaysbPmNjt2603H6fn36X0wOHjyY0m+jqX0yad8bOXKk3HnnnXLnnXfKzp07pbS0VJ544gl56aWXOmT5qTw3ckYm7Ic8B/csPW2fGzlypKxcuVIuvPDCVn+pN3LkSBERycvLk9mzZ7d7G7ta970MsnHqt2Gn//YrFovJs88+azt/PB6X5557Ts373HPPyeDBg2Xy5MkiIrJgwQLZt2+f/PKXvzQef/z48aT3KsZboA0dOlRKS0vlN7/5jfrz3ptvvinbtm1Tj503b540NzfLM888o6Y/+eSTkpWVJZdffrmIiMydO1dExHieTz/9dKvbRh0rE/a/052+X1mWJc8884z07du35U4a6Zg9e7b07dtXnn76afX8n3rqqbSXRenLhH0vEolINBpV84wcOVIGDBiQ0u0kU5XKcyNnZMJ+yHNwz9LT9rkFCxZIc3OzPPDAA7bbHgqFROSTfS4QCMhDDz0kJ0+eNOY9ePBgq9vY3WTUXzamTZsmgwYNkoULF8rtt98uWVlZ8uKLL37qn94LCgrkkUcekaqqKhk9erT88Y9/lLKyMnn++edbbi32la98RZYuXSo333yzrF69Wi688EJpbm6W7du3y9KlS2X58uUyZcqUT90mu1ugPfzwwzJ//nyZPn263HDDDdLY2ChPP/20TJgwQcLhcMt8V155pVx88cXyox/9SKqqquQzn/mMrFixQl577TW54447Wq5sJ0+eLF/84hflqaeekkOHDrXcdu/U1XpH/laSPl2m7H8inzTre+ONN2ThwoVy/vnny7Jly+Tvf/+73H333Z86TrU1gwcPlu9+97vy8MMPyxVXXCHz5s2TDz74QJYtWya5ublpL4/Skwn7Xnl5uVx66aWyYMECGT9+vLjdbvnLX/4iBw4ckGuvvbbDXotUnhs5IxP2QxGeg3uSnrbPzZw5UxYvXiwPP/ywlJWVyZw5c6Rv376yc+dOefnll+VnP/uZXHPNNRIIBOTnP/+5fOUrX5FJkybJtddeK4MHD5bq6mr5+9//LhdeeKFxkdytdcEdsNKCt0B75513rKlTp1r9+vWzCgoKrLvuustavny57S0SJ0yYYG3YsMG64IILLK/Xaw0fPtx65plnjHXEYjHrkUcesSZMmGBlZ2dbgwYNsiZPnmzdd9991pEjR1rmS/UWaJZlWX/+85+tcePGWdnZ2db48eOtV155xVq4cKEx79GjR61vf/vbVkFBgdW3b19r1KhR1mOPPaZuL2pZlnXs2DHrm9/8ppWTk2P5/X7r6quvtnbs2GGJiPUf//Efab2mlLpM3P8WLlxo9e/f39q1a5c1Z84cy+fzWUOGDLGWLFliNTc3q3nlU259e/rtcU9pbm627rvvPmvo0KFWv379rFmzZllbtmyx3S5qv0zb9xoaGqxvfvOb1tixY63+/ftbAwcOtM4//3xr6dKlxuPmz59vbMvMmTOtmTNnttSfduvbVJ8bdYxM2w9P4Tk4c/X0fc6yLOv555+3Jk+ebPXr188aMGCAdfbZZ1t33XWXVVtbq+ZbvXq1NXfuXGvgwIGW1+u1Ro4caS1atMjasGFDyzynzvndWZZlMZGXqcrKyuTcc8+Vl156qdt1iyQi6mizZs2ShoYG2bJlS1dvChHPwUQpyqjMRm92/PhxY9pTTz0lLpdLhaaIiIioY/EcTNR2GZXZ6M0effRR2bhxo1x88cXidrtl2bJlsmzZMrnpppt4a0AiIiIH8RxM1Ha82MgQ06ZNkzfffFMeeOABCYfDUlRUJPfee6/86Ec/6upNIyIi6tF4DiZqO2Y2iIiIiIjIEcxsEBERERGRI3ixQUREREREjkg5s4FNaybAzyM2jymB/kpboAni/lRX3g79k/zc08eclheAFvK+hCrrIrob7pEmWEBz8u06YyiswqPXuXePeeeL000YN9yYFonrDckLelVdvUW/4ueU6HWWjCgxltnUpN/ZWDSu6j+sr2p1OztKVtZ5MCUBddDmUWOhngh1gS4HNuraD+twFZuriOboOgQ7w8k6eEBIl4NsPoIu/RqLz6froO7SLALrbKo3l7lnM0wIQz0a6iJdZsE6grXmOryw3XhQ8OSpcni+fv3zgx5jkfF4QNUbq/R6rdp7ze1wCBt3kZ3OGolcW12lan9AfzaisZjxGI9HnwPwqIm/bUzAHHFXrNWffzKTsRC9DnxIstqG2wVbinUCtjuOG2WzTLc+9rqgTsCrk8B12krhyaiVwqNtttsL72FTkz4WFxYWprfONjKOf/jdKYXvPT0GPnf4GmC7Gxzq4G2w6V/aJ6jrwkm6nj5d13E4ZMTxu6yI5MBXlMYGXf/phdSOf/zLBhEREREROYIXG0RERERE5AhebBARERERkSPa3GdjK9R22YjdJ20mdjJ8gvlDs1Wdl4OD7UQCosdIilcPwMtP6IFtjdHDqsZh+yIifj28VkIwZD5ZRgM1RBuMaQfqj6k6DOMGD+IqavWERBDH8YtEInpQ3zsb96n6D0m2s+MUQ41ZCLtdGd9bzCEEdZ2AzAYOYPSZr4944Y2FnIzEIG8Rhu2OYf5CRFwYdoDfCXhgOzF/YTe2uC+8PidxvfjcYOywC/IUCfNzY4zddsNjfPoxrmCxrn3me5hwwbS8oLleol7An5uva58+1nhi5nh/j6f1U7yZ2Wh9QsL4kItIIg4lzGMMDIf5E+YyXXAMc8NxIBHXGxaDZcbEzK/gMr3w2ri9+niV8MKxPIXfzbpgHlxnMnaZDcySpLvMDpMN9QmoIeoqIiJ4mkk33oTrtIPbgdG6Ebq8cJqu8RQjIlIHX692lsEM+LxwdztiLrPdIKNxbqk5i8ev6xBsJ379wF26KWQu0wVfYcorPmX7kuBfNoiIiIiIyBG82CAiIiIiIkfwYoOIiIiIiBzR5swGOpZ8li4xdsIgVcfh+mrdVrPbhxN3TZ+cO0DVBWODqt7fsFc/IEmE48Du5K/4wV2t/3w/3Pd5/9u7ky6z68BgRGPssDlG19y9IWdgYb4CxxpXQ21z82w3THPDdnpgmVHIgURqzGVi3ww3PjfoceHGzIZNnsIY54vBoiqo4bVphoGbEZux21F4jAfyLAldN7r0a+WJm302mvAtdAeMeYh6g1ii9TqeMI9Prljr+Qjjt424DKMnhrmOWFQff+IxnTlzwbHaLUkyHmL2wMDzdjSqB6NHoI7b5Fcw6xCDjIYbMxrQx8Tj1ccru+yEkdkQT6s/lwRsU8KmV4pPz5NKDxEnDBuv6zBEB4M2h2aIfEoutLYK5uo6D2rs0RKxiU3iW10GmYLRpbougW1Yu9JcZi3GIkNQHzUfo9j0b0u3D8nAIbr2w2vjKTYfUwjz+OGrQgN83WiE1zNskzcug7Zdh/eY86SCf9kgIiIiIiJH8GKDiIiIiIgcwYsNIiIiIiJyBC82iIiIiIjIER0WEO8u+kMTmLLtuuHeiTRDOh1l44eYKNL1Zy7QQfYP39XbTQiDdKlcN0MAMAvC3Jh9jGNKzCaEjo3rPBDOhoaQ4oUuO3GzOaM0QyLrJKS2whBcF9hX/JAsExGzwSEmwfC5YggxD36MgX0REUwIBnUZ1e/RkZg+/Hhd5jKbEthYiwFx6p0wj4xHPLfZks8IY4sRQE7Aj/XxyQU/twtex0L62BGFm2B4cLuxmZ7b/BriSeDNImAhkRDUers9bpvwNgbqm/SECN6XIxrUy8zTTRXdHrhphoi4sPmp8dRgAm5m3Ga74Y3vqqZ+Udh1Cot1XQTBaxGReggkl56j6zCcDhvhPXh/k67t7tFyFO+xAtv57haosQlgW8D3zKEluvabu4bAxyTpDXxy4FSHn7w49v4VkT//vPVl9h2n65P49eNg648XEZEzUpjHBv+yQUREREREjuDFBhEREREROYIXG0RERERE5IgOy2z0t5mGbecGQd0RqYR+sNBjGRp1wIxGf90DUI4layLT42E3H5vuMwYYEIqDOXF8LV57N8M6YjYdhRKQhYDmUpLAjxgM3GyuM5cpOAgVBlaeSNLQMXHAnHYyBBNw7Da2ssT5i3Xpthmgi69fHNYRhVGn0JUs5MN8hsgJV5GeELYZqErUC2Amw2iOl8DjnYgLMhpx6IqWgAZ80Yg+PiXgOOG2yUJIHBrq4TpgoL3Lqwe0e/w2+S9oPpiI4fPQx2Y3NrpLmNsZw2XAYzAPEG7Ur0VTvc7SBfPMY2AgV+c6XNCQLwGvX8KFGY7u+/vf6bN1XVysa7st9wd1XQtxxC3lut5TBQuAxsO2ID9hfKvFBsnQcK8/NCsUMfMRJ+AUPBweg313d242lylpnrp272z953u3prc8EZGTH6f/GNSvsG2P6757NhERERERZTRebBARERERkSN4sUFERERERI7osMyG3Sjys6De3VErO83xDM1oJIMZjTP66vrQyc7blu4BbwiNfSHsdmXMWCQZNIn3e0/AIF6XTWYD14HvSxPeGByzJjCIVUREsI8G5imSsN030r25OC4Et8nm9xQeHH8MeZY4ZGRCOq9ywmPTQwN7nbjNcelEvQHmK2JxfSyJhfGYKCLQ8yIa0sdRF+QtxAWj1T26jtn08ohCs4RIE/S8cOkslhcOHXZROMwuJBLYD6T1Y3kCMxwiEoceIo2QyQg16Q2BiIfE4RwTzDfzdkWj9WD+YNEIPQOcYxLYQ8Pm9e0ucMteeV3X9SHzMcfx9IY1LrQtPTCwpwWcZvpA7M8XhNkxuikih+CtzYI2UzHch6HuD+sUETlWqesBo3V9tA0ZjLRhuDpJ/NPO8Q/btmr+ZYOIiIiIiBzBiw0iIiIiInIELzaIiIiIiMgRHZbZsONERqO3wozGELy3tIgcaMt4x4wBAx6NPhBmjwYzowGDgxOwjATOj70obPICMRwnDWOFm/EBmNnALIpI2hmNTgE7VwJ7gYiIC8d/42sDGY4IDOCtNcdADynWg189hXb9PSgj9Rml6+YkN5bv5cJR6IGR0MezeMjmMwk5jgQEJFyQGfBgrxvoRRSJ4DFRJB7FjIb+OUa5omF9DGyKmrm1SFivxwXbkXBB7w5YR9zolCASgfxKfYM+3jRBZsMPg/u9Xv3aREPma9FUq2s/vJzeXN2kwO3Tx0SbqIltz5Cu8DfIaDTjKdk419mA/mEShHpvksf3s1mkbm0iJcW69kMUsLxC1wnzbZTJE3XthVYwHnhfoe2LVNu0ATsG7+3R7eY8HS0b+tCdwF0pCLVdfgqfi03GJRXdYy8mIiIiIqIehxcbRERERETkCF5sEBERERGRIxzNbJBzenY+w06y3hN2913HaSFYBAw+9EBmIA7zx23ugW4l6/+BOY/1ULfhRtfdQbPNdsdgWgLes5Mw0NUTUuWEYvP1/ZfLxqp6Q6XNuHTKSH3yc1XdvK87ZDb6pDBPKoPTOx5mI+KQMXPFzEyZCwekQ+1162OgC0IDUVhnNGQO6k5ApsALA9wx51FRoQf7b9liDl4Ph/RA8cJCnXUoLNZ1IKgH5nt85lebBuirsWW7Xm9tvT6W5+boMEBRgc6LBTDfImKGLmK6DuaHoNaZNBc2gRAR8UDWLWEX7HBeM2YbIMdg2yIENt34xolPBZuzwTL72Dx1N2xXCCJAblhGDrxthcXmMqddomvYNWT9Kl1/iKd16JMmIjLubH1sicX1cWTXx+Zj2isf8iwN8PpDWxjjK4+ISBRePz++pyniXzaIiIiIiMgRvNggIiIiIiJH8GKDiIiIiIgcwcxGDzIchhvj/c7roFeHzbDCbixZ74mQzTS8QTTmPuAxUbgBd3MVzG/XE+OkzbSO9YWBum6EqMkWGLN66Iiz2/OpjByRfs+yh/tUff2cc1TtiZtjzrdveFXV2+rz2rp11M0079vWCWvBDAbuPzgQ3e4m8naD0TufF07XPqgTCfN07orrk0ACavxtYyKqMxlNDXoAfFW1mZnyB/XAcH+Ozja8X6azEXff/0tV77M5tJ8J9R1fv1zVubk674OZDa9fH2tERCJR/V7X1ukMx2//ogfNu7N0fdklY1RdUmAei/Jz9HbEojpXE26CvifQoMGXp7MoIjbvkZFbaOMg+nRhXAc/KjZ5ir4wDfMVuMvm6AiL1EP+otlsxSSHYR0hOI1UwWN88IJirkFE5L01ul7+ojlPuj7+qPOzXnvKdf2563WdA7vbFsyeiBhfo+pDbdsW/mWDiIiIiIgcwYsNIiIiIiJyBC82iIiIiIjIEbzYICIiIiIiRzAg3oNEIX/khnriMF2/u9fZ7elcdgHyWqgrWq9Pvt2B29NxXoXAdwB+XtxP14cc3ZrUXfTVC1R99fTZqi5bs1bV72/C90ukujGo6sNxmzQfdUPZNtMwaK2Th1n99QEqH5KbPmiiVlNjhpVPHN4NUzCUud9muzKD3wsh6Jh+PSM2Ad0YTHQl4DERHWCOhEOqboSEritmhuW9Lp0Urq3Rn+M//OkVVdsFwhHe2qOwSCeHR4wdrbfBpwPh0RgG/0V80CgwGtO/azVumALb+d//s0PVCy821+FJ6LB2AEPUEMD3xPUy3E0hY5l50DTRHcg15ukMAyAPD7uSRGz66gaDusYGe9gntxHuwYL3aOkDyxMRyYPtwrc+iqF0CLqv/rO5zB4DDn/FcC+BPKhD+OVCRNa9p+uS8eY8qeBfNoiIiIiIyBG82CAiIiIiIkfwYoOIiIiIiBzBzEYKBkBtDMOE+piD29KaA1B7++q6IAgz9KjMhp0qqHF3/2cnbUf74BBn7Nn34fHO2pLWDZs5SNULrv2aqu+5+zuqPrwRnkn/LGOZWflT9YTu0V+Nss/Q9Qk9YDv7TLM52QkYMy/H9BHLOqYPSPt36fnPHKabxZn5jB7Orc88LpfOsIRxcLqINDXqJnICjTPxt41xWIbb41d1fsDMTEWhM1ssptcx9Tz9GV679e+q9pofe7n7O/+q6lmzL1G13w+Dy924DWaAJQ7H/5hNE8R01NZUGdOKc3V2JB7Vr4UPtjMCGY1Y1Nxujz+olwE/76SWfuKHFUEfRcG3RMSIqEg0pGvMGeXq3U18I3SdY3P8D8BjgrCdBXAoCsJOf9+PzGX2VO7IOFUX+3XHw1DwsPGYwjm6LprUtnXzLxtEREREROQIXmwQEREREZEjeLFBRERERESOYGbDxsUXfk7V8+bNU3Ue3Ng5HtcDD+tqzX4BG9auUPVf3lrdnk1MSQzGUEbw5uU9Ho4/3NIlW+G0/3ryPlV7g+Z92H/wg/9Q9d4DaQZ2zoTaa84yderVqr5t0V16hv2YNgHHzBvwW9Wb9ARPkTEPdb6+QT1y3OXWA6cnjtd9EEREmsL6ALTzXUyZIb2/7NubZP/pEH1spmGvjq4Rd+vfDbogTyFe80PZhE0HjMyGHgSfgD4abvigJ2LmVwYvDJIfX6AH2p8zbYaqv3zDDXqZxhJFfB491QW/Ft28TR/L163XzQCqqs1zcF6+Hrx/3nnT9Trc+rmuXPGuqmGTpLgAGjyISC40lghCHciBY7MbX1/z1YiEdO4mBqlRMx3lDGjzIg3QVyNi86vriXC4zp+o6xr4ThKDQGwQ6rDZWkdyIaMRCMIyoN62zlxGb7H0tx+rur5S/7zIpoULtHmR3Da2uuJfNoiIiIiIyBG82CAiIiIiIkfwYoOIiIiIiBzR6zMb53/2bGPaFV9eoOocnx6smJ+nR0l6AvrnhWPPMZaZk6MHFq6BzIZ5d+P2C0MDkA2HdI23NzdHzPc02JACx2d3j7HZ6cot0vvbsz/9T2OetDMaqF7vLedeM8uY5eVHft2+ddg5eQLqv3X8Oro1DMvsS+Ex0GBHTnbQtpy2xCT7U3DqNGNaYYHuk7HzXRw83R2OQN33GOBywenaq2t/nn59RUR8DfWqbmrQWQZXQucN/blBVQd9+rwVtenl4fLo31l6/TrP44M66NUD8cNN0IxBREKNers3V+rB5c/+6gVV//0fqXwutDMH6vrWm76i6vNKi1UdhtfO7zG/PhXl6wHtBfCeBCBPF4MB8aGY+VqEoReF26aHSKeA7xPFkMeI22yWD3aXPIgV5eAyIAdSvw3WWWKuowh6ceBmbNms69o66bUOQRO4NWt1ff/95mPw9arcZs6TCv5lg4iIiIiIHMGLDSIiIiIicgQvNoiIiIiIyBG9PrMxYux4YxrertwleuBhPKYHFsbDcG9yrx6jKiISjerHhNLYxrY6CuPzjsLPz+yv630wf8/Xfcdnp+PzX/yC8ys5qcfTf/BX5/vE2OttO2kk+SwGvBF6srxOP2PKRfN1bi0Y0OPsq6uqVD112lRV5+WaPQheffUVmNIdMhqZw+3G07WuvTlmx4V8aHeSgB4NkSbdw8Hj15kCty+o6ni8wViHywU9MeD86YYGFdGIziXEYxAGEPN8Wd+gt7Oq3sw2pGsftG3ZUPa+qqeW6jBAIEdnM72YoRERf47e771+nXnBuEUkCn1PAja9OyADmrBZb2eYUqxrzE8EoO2LiAjEecQf1LUX6gTsCiO8rf9cRAR3SdydCqBXR64+VIlNTEb+udGc1hMdghir12/2GfK6O+Z7Ev+yQUREREREjuDFBhEREREROYIXG0RERERE5AhebBARERERkSN6fUB8w/vvGdMK8nRILhcaG4U8urlP/ggdJLMLcFVBJ5TuEI3sfYFw6jCp7DvDoc4doOtauGWBXbOl7vBB6VKptPuE7mRJA+EIm12KvP+eDsuOLSlW9Yf/eFPX7+r6rDGfNZa5e8eGNLeLTpfA07ULkq9u88YkwfxiPYtHp3ibGuBDBwnmaETfHCVm8/tJPzQXTMA8cej2hkuI2zSpawo1qdrt0c910pTzVL11t97/2iIW08+1CZoNeoJBVefm2YS583WXOpcRyIdlQsPgohFjjWXmwTQMlXeWc+BeOm5423w2mxWAe1XkQF1bAT/XX73EFdR1PGSuA3fhmmpdj4AgO940od7mvNNbAuKooc4Mg+NuHvUYs6SEf9kgIiIiIiJH8GKDiIiIiIgcwYsNIiIiIiJyRK/PbDSFQsa0qko9kHBLnW4oNH60brIzvrRU1Zu26XyGiMimMo5Xpl5mD9SNOqMxEJpAhfVwZxERKdJxKWm0y3VQO2UZUxIJPX79w38sS2uJddU1xrShQ/Wg7/37t6a1zN4u5oIsBGYjsJueiHjcepo/Vw+49gX1IPkonA/roXlj3GX+fjKa0E37EmGdS0jAzyWhl9FQr8+vIiLVtToXWbZFn1M32WQt0zUQdvugV+cnJKFfq1hMb7fbA/OL+R5EozrY0BTVr8WIwgJV5xWYB0GvVx8oY/Gu+R1xQZFu/lm7XWe9ysvNxwTrdT3JNUzVgajOl1XDV6dC/VVLGnSUR0REGqEpXz70tszRL7EEArqbcdAH+6eI2OXYegOvzUtRsV3XHuwZmyL+ZYOIiIiIiBzBiw0iIiIiInIELzaIiIiIiMgRvT6z4Q+Y4y6jcT1wLb9Q99EohBs3V9fqgeRef9BYZhzviU6UwS4425xW0aDrg/thBmircQTvy27ebl8EhqHnm7e271GGn3uhqieO1zmHKaWTjMfU1+sXPgp9C/LhRQsEICxj8yunmpoqvY4qfYyrq9b122//RdXHj+Obbz+N0gDjqfHjYvThEJEYToBMgQv6VwiMX8feEp5ExFhHOKz3Pzf01YjF9DLDIT3Ivq4GBvaLSAgG4m/brgeOb93T/jH1HujhE4DMht+n8yw+n/7cuD1BY5nhiH6uUcho+KHRRKBAf7dw+8zvIy43fncw34PO4I7p13zadH2sip5nZobqq3WQwxXT2140VucnCqB5x7r3T+htsNmuKeOzVV1ZpR/TCB+CSEg3iaqusllomvpAbXar6J6GD9K119PXmMefd1LVgQJjlpTwLxtEREREROQIXmwQEREREZEjeLFBRERERESO6PWZjVjcvLFwE4wlLynWN26uqQ+p+s4ld6v6xm//2FhmfrHOecg//pH6RhJ1sXGDde3FvIXYZDSyodZDaWWoT9duiBKIiFTjvdtt5ukuLvx/vmFMKy2dqOppU3RdUqDHcLvg9z9h6HsQgR4GIiJNMM2IwiT0OOgEDP6PRMwx4EVFemBuYZ4+Br4Xe1/VY86eqer6OnMcfj4811hMD6ZuiustHzFW51W2Qe+Oox+uNtbRk7nhfXRDHwh8X0VE3HiKx+wg/LrRC5/JvCLo++C2OV9GQqqOw/sai+r9KwTNEiIRI1liZEmmTpuq6qIivX9VlFeqOhoxD1BeeO7F8NzycnS2ye3SB5vcvGJVjxgLTSBExAXrcLn165+bpz9XgVydC0m44Q2w4XJ1zde20SW6R4bbo1+f/AA0uBCR3AK9v5RX6P4oEehnkZc7RtUF+hAgI3LMdeRBr5hAsc73RPzQ8yd+SJX15qEqqc9DZnHzR7renf4iu8SUGbquD5005tkG5+ARdtnKFPAvG0RERERE5AhebBARERERkSN4sUFERERERI7o9ZmNuDlcVOoa9ZjSDVu2qdpt3Pda37C7pqZOUF6OHqs5ZMhQVR84wHvQU9tceOHnVT12bNCY57/+6zftWkcu1DU7zXkwopEPH5PZMP62CT4m/lJzmeuh3rHPfvs6w+Xf+ndVzz7vPFVPKYUnKCJxlz7A1FfrseVr16zRP6/TL0oMx54nzDHzMeir0RjTj/H69P3v3TCO3Oczx4lj/yE/9BS4ZPZsVV9z7bWq9hjHSJE1a1apess2fVz1NDaquq6mVtXzYJ3bS0Yb6/jwz88Z03oKN3TNwDxGImGTfYBfJ+Le44IJCZjf5dH7TiAnx1hHoEkfHZrq9fsWi+r9ETMHfr9NbwnIPgTz9XpHFOq8xXkTdRYqETM/J3HoeeGFgIrbA58Dr96GvHzIeECPDBGbvl3wXN1e6HMCn5N43Pz9L0R1JGEzT6fwQmAOeq6E6rcYD6ks19nURshHTDpvuKrxnFBaPFbVUZt9POTR00LQK6YxpL/P1W4YoOp3N0IDKBvnjtT1ljZkNL5wpa7H6tOHvPF7XX/wcQoLbacC/fJKk933YXhPIHKVMv5lg4iIiIiIHMGLDSIiIiIicgQvNoiIiIiIyBG82CAiIiIiIkf0+oB4cVGxMc0FTWJqa3VCxu3RL9sFl+uAbiE0rxIR8UCznvySSao+cODvSbeVeok+fVV5fqlOcZ1zjg7K1oaCqv6v//qPDt+khoO6tuutd7W+54HUQbY5ENQ19sT66yZzmRhg29HGcFpHWPazH+m6i7aj58iCGn/3pY+Zeyp1uH4CBPRFRIZ8dq6qD5RX6BmO7EpnA7sXvDkA1GYkWsx0MbzGGAg3FgIz+L1mQDzXr5vh1W2vUjU29fPADQo8XvNrCG6GxwePwXsaBPQjMAz+yUS9DAyheyHc7SvQzyu3UDeU89uE5T0QojZeTuMrl359sQGniIjLeJ/b2FWtneoaQqquLdcJ5qYG8zF4n4g8+GoUgn0Du8Wu3/ya3oZqcx2N8CJXQQi9RN+bR9571Wxchz47Tte33qpPbgu/2foNfS6YbE6bcpmuGyF4/S9f1nXTr3S9y4FOgdhP06YXppQ3wjy15jyp4F82iIiIiIjIEbzYICIiIiIiR/Big4iIiIiIHNHrMxsebMIj5tjN0VB7PHogYn6+HoiYnweDBEUk1BiGdeCo9/5QH7PZWuoN+jTrMaVNMFB1RXiFqvfs0GPZRWw686RpGNQ4Stgus+HHhmBwdKmDsbX1MLa2yewDJx9uhgm9/ojVk1hQN0MNzbaO6Hrrm8kHMQ8667OqPpzBmY047PxxyFMkbH53iA30kkmW4bDpKSkNjXrc/aZNulljQ4Me5O3P0UePnDxsGSrSCN3ffHDOzcvV59w4DD73ec3zuh+b+MHrmZOvz9tFpbpRoAfO2W6b1xbfg7jRRbH19yxhm7zRR18XdmLsJPGwfo3zgvo7iy9hfmfxwFsb8/ZRdUNchwG9sP/5dGxGRpj9RyUAOb5CWGccch6VH5jLQNfeCsuI64zGIJj/MNRfvglb3IoUFuoAYo3nQ1U3QTZiCmQaU8lsTD5b1xuh+eDwMboeX6Jr/S31E3OCuva08RzMv2wQEREREZEjeLFBRERERESO4MUGERERERE5otePgK6qM28afA5kLoLBoKrjcT2GEjMc8YQ5Zj4U1jehNsdmMqNBn3j2kSdVvfj734Y5jiRZwpk20zAQ0foA0EkDdR2FwZx+fct5EREpg/uG+4p1vQ3Gzn4ImzB4pM2GwG3YxeY+4ESf5vDuf3b1JjjGiZH7CcgDxN26rqkxGx289OofVL150/uqjsLBIxzF/KI5EL+xUZ8v3fBr0cKCIj1/vR7wnp9r9roqgPM69tmYFNcHm5zRxbr2elUdt8lOxDHd5sLeHq606u6kuu6QqnPglOL1mjmF+uoTqm5s0rksH0RrgtC6BPt0xELmduXA7uMJ6rpSR4hEPwuRL33dXKYbsiL1EIuc+Bldv63jFxJ36ectIlJVvUXVwXyd/MjL1yc3X/y4qv+oW47YKtXt24zMxkRoTTSiWNcbbHpoYE+bKeOTb4ed7rtnExERERFRRuPFBhEREREROYIXG0RERERE5IhOzWx0x04ScZvrrUgExm7m6IGEoVBI1UZmI45dCUSqqvWgv01bytLYShLJghrv0Z+Zvnj5Z41pf1u7vp1LtRvNPRZqPT70DNH3EXdDLAT36Pf3mGvwwAfcDUeXqgpdXwj3BC+D+6WLiAiM4ZW9NvMQdZgBNtOO2kzLDAlojJEsExCP6eNCuElnJ7ZseM94zCsv/VbVsbAea16QP1jVkYjObFSV62OPiEiTbjUkOAL+w4/1YHQ8O3hln7HMuGxUdcmAvqreXlam6pqaKlVPnTZN1fmFZnAtt0DnQnwQQsCXH7Mo9n02uocm2NZKOJ5742ZOAfMSccjclcBpCduPYR22yez54BxRCzm/Gqj79YNtsvkWDF8BxQcRoBw8nUI/qHIz2iTFRTqv4nHpbK/Lp9/7HB1LMnpk7NlhriPgNacpsHvhV1WfTa+rIEyL2OQ6UsG/bBARERERkSN4sUFERERERI7gxQYRERERETmiUzMb3SGjgcaPn2hMq9iuByNu26YHHvr9eiCh16tvRBxq0vf8FhF5e9UbesLRTB0HbN5Lu3Pg4H28W3ZmOFMPE5Y8f54xz89ffrmdawnbTMOGFXq8aCn01ciBI0MjPDyih2WLiMhe+IDv2qrroTAc/h24B7icYS7T6KvR12aeHg0zBHbvLf7OqNlmHocNhQDOfnxzM0X3PS5j/iJZncoycBB3rEkHp+LQh8rXGDKWmRPVA78b4djgb9LHmoZDega7qJY5+r91mOCzOTwZKo/qYEjTx7v0DHG93RF4bUqnTDGWORY+iwXQm8MDIQ0zM2q+h6m8r51hG/SaCMPXHI/NZsahj4Y3Fx4DWQg3/ByPdoFzhpjbVXdA1VtgOxvqdZ0Dp1zYRBERCUHWJAq9JnJhO/vDz8OQZxEx+1VEY/qEGYLtgmiTkW+xy2yUm5EqJQ69sJrKdT0asygiEoMcSM1mc55U8C8bRERERETkCF5sEBERERGRI3ixQUREREREjujUzEZ3sPjOO1Xt8fiNed787xfTWuaMadNVXYkNBUQ6PqNhDl0UOWAzrcOlO5q2o/SMzMaia+er+t9f/LsDa7Hb1/Q+eeYZ+vUbC8OPcbyoFzIbhz5If6v2J/sI2MURYGysze3ze7hUjhtdkNFAGZvR6Ensxvbr3yfi+P94TOcSYvDBj4Z0nw2v0XFHZMrE8aquLq9SdTAQVHWkCdZx0tx/MceB0S38LWkQajM1aZ65YlDjl6FEVB/0GmprVB1qgEYIIhKP6ufmEuxzAg9w6deze6Qz7DVCfwUv9F/INeOHxjR8TAzOK1Xwxrkh5xC0+ZKDaUT4OiZboroBVKhOZyWmTzIWKR7YOXz+PqoughYrZWv1Prx5g7nMnKCuS0t1XZCjv9TVhfVzTeAOayMHtuss6PcRgJ18M+QvSsyvwyIwzYNfxVLEv2wQEREREZEjeLFBRERERESO4MUGERERERE5ghcbRERERETkiF4XEK+t0IkZn9+upUt6RhcVqHrTpjZ2PUlDf0zMSfdsmthxglBjs7Pu2Yxr7md0t7y/bbNJjnWCUSN1ymsKBK/LdQ5U1kNTo2OpdMlC/aFOtoPa3Xug1wXCidoq+e8OjYB4XAeUo3F9YmmK6/htzGMGxCfNOE/VRSUjVF1TpZPFRQGd+g2EMeIr0hTRaVgPNMerqYHka0IHdF0290yA3m5GM7cANAzFcHc0rGPrTSEzhh7BgD2EzF0+/Txc0OTPTJB3H1P02ypBOIcEg+ZjIvDWhuE8kwsN47wQPk7Az8ObzHXgLpnr0d8NGivgNYdmenYvOW5HIKCT7olGvTcVwPyr1pvLvKpYd631FuibtEQjOhDuh2VOPEfX5TZfJYpn6xoy/ZID71npPFimvgeCiIjUwdfZwqA5Tyq6755NREREREQZjRcbRERERETkCF5sEBERERGRI3pdZuP/vvZy+xcCYzsjAT0m8IPNNgML22nI8MGqPrDnYIevo3uDAYtGcym7Nk57HNqWT4dJknu/+x1VL3jwP9q9Dtj95IoLYILNp7outEPV7zXpBkK7P3agIyRmNIZCvb8Ny8xq47YQZbzWW74lUugIl4jrLEQ8ElJ1DHIJCTjMev1BY5l5xbpTmy+oB5v7cvVAcV+tHknuD2ELP5HyikpV1zfWqboBGgFCrzjx4EFSRIInde2HTJkvqCck4FexYXhtqquqjHX4N5ep2u3X+ZQSn669br3ltu9hN/mVMLxtkpev64BN/DXo1gf9aFyHNqpr9JsCERej6VzU3FVkxGhdr3hD5zf//H91Pe4sPX/MJv+KKSIfdCOs2K73v+rtev5jsK+JiAREv/fuuM5sVEJT2yA0RJw4Q9crV5rrwAZ82FSxAYJL9bDO9+F5iIjUQzakyq7xXwq6yW5MREREREQ9DS82iIiIiIjIEbzYICIiIiIiR/S6zEaHgPF49931XT3hSPt7PvQbPkjVvS+jgXBULtyE2va6GXfvXR23OZ/i0W9/Q9X+4imqvvqqf9M/D8KAUxGpqdfjWt9//1VVj/ZUqNod1uNHK2zulf3BXtvNPU1HBCqSwEUOgtpuvHIB1NU28xD1Atj3Acf3J1LpswGZjURMD9p2x/TPvXDcdfnwuCsiCT1PJKyX4Q/qwfy5sA3+gDkIPAD9KKqqdIbDHYdjuVuHuaJxy1imB04hHl8fVbvc+nzhhnH6MdjuehwALyIJCBm4oD9IDgyiL/Tr5+5K5fe/qYRzHBCF8f2NEJMMme1SJB7SB33su9EItbsOashT5Bea63DD7rMGsgzZcGq77Gp94vG44ImJSHWl/pJXW6Xzn9VwHmqC7TxDt9cSEZH3NuuT8HrIQjTq3Uvq4PXNg49eCF4rEZE3XtG1CzIutfqrg7wHy9i30VwmmvDF5PPY4V82iIiIiIjIEbzYICIiIiIiR/Big4iIiIiIHNHzMxvDoE46dr0NDqef0cgapQcS5kb0oL+Dew63a5N6nlyovVCb4y5Fam2mna79GY6vfPYiVVfHdcjg7Bm36wdYep3Zw84wljlpvF5GbYXukbGjTfEdXA/cgFvM8ccdrg/UuIuPs3kMjMfNMiMuRPRpYHx/HDIZsShkOGB+N+QYjOCDiAQgd5CI6+YcuIxoNNrqz0VEcn16mX7oR+GG2EINNoGIHzeW6fX309uJM8B2u1z6d7HYdyNuk52IwzKqKnXWZHOZznT4/DrP4g/ieU7Mb2murvkd8axL9HeWsk06j2G3VflwmlkHfRwaIOsApz4Jwkuca3NKL4fsgx+yDj+9VdfnlJaquilhnvs8AR12eOkl/cWxCrIPMfg6UjjW3M46yGRgVKQRciD4trvhBW7aZ64jFIR5QrqOwet5uMxcRjJb/5z+Y0T4lw0iIiIiInIILzaIiIiIiMgRvNggIiIiIiJH9PjMRjbUniG6Pgrj6EREJA51upEM7B8gIv0TfVV9bKce79jbu2gkNx7qANR2mQ0YvGnkPvDNbz3QM26QefPs0bN0H42fPPYgzHGi1WWe2HvImPYuTBvQLwvmMO8hr/WzmYYZDXy97F6//9UHnnrzkSSbYAd7ZoSghnuCi4gI9Ayx8G0n6q1g7L7Llbz/AuYMMGNgriL57yNxnkAAcgg+n6pjkBsJhULGMgMu6O+Rq7e7vlY3CGj06GO922V+tXF79bQYPPdYQtcJeF5uyKskbBoD4XPDTExdjT6g1VRWqbp4tH6tREQ8HsjEdFGfjS2Q0SiAXSNs0wMpAsfvAuirEYBlRCA+UQ/1qg/MdeyG+owBuq7YpGtXVG/o+41VxjJ//ZLuXYVv9YXn6Tq8TdcRm9fCDafYKVNghmJdYu+OVSt0fcxchbghO3JAt+ySbMw84teCrTYL7SD8ywYRERERETmCFxtEREREROQIXmwQEREREZEjeLFBRERERESO6PEB8ROQ+W09rttBbPrxHZOTnbHmHiwH6iKoIXkmImYCGeuVaW3B9Vdfa0z74W//AFNwD4O0Wtp3GxA5ehwD4cmWaTa0kjEhXe+A1ys7qMsiHVT0NOhbGMSwQZ+InGg2pykQeDM2O5WXJv2Xj6hHwCC2G+q43e8OMUwMNS7TlaSJn91vJ7FJn9EY0KtTqxggtwtz50KoPArN72rqdHe3pqhO3zaFzRte+KD5IAbCIzH9PDx+OAbC8zBeK7EL4OuAeDSsj7tNDTrYHs4375Jh2+ivCxRC+HgbhKLjNqfgJpgGWXejdsFTbQzqejc0BRQROQNOd/Mm6toHXxXWVuvU9NK/2py44L4Jc+foenyxrqfA/Wu2lJmLhF1UauG5xOHeAJXQwPAANPHro+85JCIi27foejAEwictgAfAe+qzuUlLeSXMY/b1TAn/skFERERERI7gxQYRERERETmCFxtEREREROSIHp/ZoJ4Cmx0VQo2BABGRINTYaQcGLIIlC7+u6j9tDpkzHdgPE3AgZRsHOLbKrhNl6/pGQqo+mQ3P/YR+fU+E9eBZb1xnNkrM3lOyNVmeAjtXtj/OQtRrxKFhnMsNTeliNscFyCVgEz+jqR/mLXAbbJoARiJ6YD5mOLCxHQoEzU6dLvg9aCQR0tvl08evvALdMdQbMTMbcejM5oLn4oZzjDegazdkNGIxu+Z6elo0ol+LSBNkS6ChYbjJHDQfgyxJNMnr6RQ/nMpwM1w2p1N8TCXmPGB+H2QM/vEWzHCmuY5J+bpuDOn69Vd1fWiP7kg7bLi5zNEjdO2Gt7oKmuXlwDZ4sFmeiGyr0PX7H+raSPVCR+o+cL5sTuF8GcWPN/x5IR+20wsZGhGRIMRlbeJQKeFfNoiIiIiIyBG82CAiIiIiIkfwYoOIiIiIiByRZVkW3sSfiIiIiIio3fiXDSIiIiIicgQvNoiIiIiIyBG82CAiIiIiIkfwYoOIiIiIiBzBiw0iIiIiInIELzaIiIiIiMgRvNggIiIiIiJH8GKDiIiIiIgcwYsNIiIiIiJyxP8HC13uymHwCeQAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"markdown","source":["**2.2 Outputs**"],"metadata":{"id":"0OFz54lbl087"}},{"cell_type":"code","source":["classes = trainset.classes\n","print(classes)"],"metadata":{"id":"vF-wEN3_BTuS","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681911819071,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"7d041b9f-0336-4677-8221-2db495515130"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n"]}]},{"cell_type":"markdown","source":["---\n","# 3. CNN structure\n","---"],"metadata":{"id":"I-SHgbmLKiGm"}},{"cell_type":"markdown","source":["**3.1 Define the CNN architecture**\n","*   import **torch**.nn as **nn**: library to build neural networks"],"metadata":{"id":"Xo-YDtBkg9pj"}},{"cell_type":"markdown","source":["$$\n","\\begin{aligned}\n","&\\text{Conv1: } &&\\mathbf{u}_1 = \\text{ReLU}(\\mathbf{W}_1 * \\mathbf{x} + \\mathbf{b}_1) \\\\\n","&\\text{Pool1: } &&\\mathbf{v}_1 = \\text{MaxPool}(\\mathbf{u}_1) \\\\\n","&\\text{Conv2: } &&\\mathbf{u}_2 = \\text{ReLU}(\\mathbf{W}_2 * \\mathbf{v}_1 + \\mathbf{b}_2) \\\\\n","&\\text{Pool2: } &&\\mathbf{v}_2 = \\text{MaxPool}(\\mathbf{u}_2) \\\\\n","&\\text{Flatten: } &&\\mathbf{v}_3 = \\text{Flatten}(\\mathbf{v}_2) \\\\\n","&\\text{FC1: } &&\\mathbf{u}_3 = \\text{ReLU}(\\mathbf{W}_3 \\mathbf{v}_3 + \\mathbf{b}_3) \\\\\n","&\\text{FC2: } &&\\mathbf{u}_4 = \\text{ReLU}(\\mathbf{W}_4 \\mathbf{u}_3 + \\mathbf{b}_4) \\\\\n","&\\text{FC3: } &&\\mathbf{y} = \\mathbf{W}_5 \\mathbf{u}_4 + \\mathbf{b}_5\n","\\end{aligned}\n","$$"],"metadata":{"id":"IxZ5RhXEjbBD"}},{"cell_type":"code","source":["import torch.nn as nn"],"metadata":{"id":"LXnaQOKzg2Vw","executionInfo":{"status":"ok","timestamp":1681911903641,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["class Net(nn.Module):\n","    def __init__(self):\n","        super(Net, self).__init__()\n","        # We define the structure Conv1(in, out, kernel)\n","        self.conv1 = nn.Conv2d(3, 6, 5)\n","        self.pool = nn.MaxPool2d(2, 2) # define pool (2x2)\n","\n","        # We define seconf convolution in 6, out 16, 5x5\n","        self.conv2 = nn.Conv2d(6, 16, 5)\n","        self.fc1 = nn.Linear (16*5*5, 120) # FC1 (120)\n","        self.fc2 = nn.Linear(120, 84) # FC2 84 neurons\n","        self.fc3 = nn.Linear(84, 10) # FC3 10 output neurons\n","\n","    def forward(self, x):\n","        # Conv1 -> Relu -> MaxPool 1\n","        x = self.pool(nn.functional.relu(self.conv1(x)))\n","\n","        # Conv2 -> Relu -> MaxPool 2\n","        x = self.pool(nn.functional.relu(self.conv2(x)))\n","        x = x.view(-1, 16*5*5) #Flatten\n","        x = nn.functional.relu(self.fc1(x)) # FC1-> Relu\n","        x = nn.functional.relu(self.fc2(x)) # FC2-> Relu\n","        x = self.fc3(x) # FC3\n","        return x"],"metadata":{"id":"hoNLJaODqq_4","executionInfo":{"status":"ok","timestamp":1681914955123,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":36,"outputs":[]},{"cell_type":"markdown","source":["**__init__** define the layers of the neural network.\n","1. There is a convolution layer (**conv1**) with 3 input channels, 6 output channels and a kernel size of 5x5.\n","> First convolution\n","> *   3 input channels. In this case, the input is a color image (RGB), so it has 3 channels: red, green, and blue.\n","> *   6 output channels, that is, the number of filters to be applied to the input image. Each filter will produce a different \"feature\" of the image.\n","> *   Size of the kernel or filter to be used to convolve the input image. The kernel is 5x5 pixels in size.\n","\n","2. A pooling layer (**pool**) is defined with a kernel size of 2x2.\n","3. There is another convolution layer (**conv2**) with 6 input channels, 16 output channels and a kernel size of 5x5.\n","4. There are three fully connected layers (fc1, fc2 and fc3) with 120, 84 and 10 neurons, respectively."],"metadata":{"id":"enRY9lt8nwVE"}},{"cell_type":"markdown","source":["\n","**Forward** defines how the data is propagated through the neural network.\n","1. The **conv1** convolution layer is applied, the **ReLU** activation function is applied, and then the **pooling** layer is applied.\n","\n","2. The **conv2** convolution layer is applied, the **ReLU** activation function is applied, and then the **pooling** layer is applied.\n","\n","3. **Flatten** the data with x.view(-1, 16 * 5 * 5) and step through the fully connected neuron layers **fc1**, **fc2**, and **fc3**, each followed by the ReLU activation function, except for the last layer .\n","\n","4. Finally, the **output** of the neural network is returned.\n"],"metadata":{"id":"kD0EoaWOnFQr"}},{"cell_type":"code","source":["# we call the class\n","net = Net()"],"metadata":{"id":"8HJWHxIs72fO","executionInfo":{"status":"ok","timestamp":1681914958957,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":37,"outputs":[]},{"cell_type":"markdown","source":["**3.2 Initial parameters**\n"],"metadata":{"id":"3I_iPkyKpgzw"}},{"cell_type":"markdown","source":["*  Loss function"],"metadata":{"id":"xoz2mlBtrnOl"}},{"cell_type":"code","source":["loss_function = nn.CrossEntropyLoss()"],"metadata":{"id":"X66FAC5YryUg","executionInfo":{"status":"ok","timestamp":1681914961219,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":38,"outputs":[]},{"cell_type":"markdown","source":["*   import **torch.optim** as **optim**: provides different optimizers that are used to tune the weights of a neural network during training. The most common optimizers are SGD (stochastic gradient descending), Adam, and Adagrad.\n","*  Optimizer (stochastic gradient descending)"],"metadata":{"id":"Xx5PN70mrz47"}},{"cell_type":"code","source":["import torch.optim as optim"],"metadata":{"id":"H8wCMXl4pp6-","executionInfo":{"status":"ok","timestamp":1681914963223,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":39,"outputs":[]},{"cell_type":"markdown","source":["*   **.cuda()** Moves the tensors and models from the CPU to the GPU. The GPU is designed to process large amounts of data in parallel, optimizing training time."],"metadata":{"id":"dgB5-1e0qkQy"}},{"cell_type":"code","source":["net = Net().cuda() # net is the model name, Net() is the architecture"],"metadata":{"id":"04D5HHefr4P8","executionInfo":{"status":"ok","timestamp":1681914965113,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":40,"outputs":[]},{"cell_type":"code","source":["optimizer = optim.SGD(net.parameters(), lr = 0.01, momentum = 0.9)\n","optimizer = optim.Adam(net.parameters(), lr = 0.01)\n","optimizer = optim.Adagrad(net.parameters(), lr = 0.01, lr_decay = 0.001)"],"metadata":{"id":"ORCy73NyBnz7","executionInfo":{"status":"ok","timestamp":1681915098562,"user_tz":-120,"elapsed":874,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":44,"outputs":[]},{"cell_type":"markdown","source":["---\n","# 4. Train the CNN\n","---\n","\n","*   **import time** The library is used for various timing-related functions, such as measuring execution time, waiting for a specified time before continuing program execution, etc.\n","\n","\n"],"metadata":{"id":"6FkT5jWcuKrZ"}},{"cell_type":"code","source":["import time"],"metadata":{"id":"3_0_D6M1Vdeg","executionInfo":{"status":"ok","timestamp":1681915100863,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":45,"outputs":[]},{"cell_type":"code","source":["for epoch in range(20):\n","    running_loss = 0.0\n","    start_time = time.time() # Start time for epoch\n","    for i, data in enumerate(trainloader, 0):\n","        inputs, labels = data\n","        inputs, labels = inputs.cuda(), labels.cuda() # Send the data to GPU\n","        optimizer.zero_grad()\n","        outputs = net(inputs)\n","        loss = loss_function(outputs, labels)\n","        loss.backward() # backwardpropagation\n","        optimizer.step()\n","        running_loss += loss.item()\n","        if i % 100 == 99:\n","            print('Epoch: %d, Batch: %5d, loss: %.3f' %\n","                  (epoch+1, i+1, running_loss /100))\n","            running_loss = 0.0\n","    end_time = time.time() # End time for epoch\n","    epoch_time = end_time - start_time\n","    print('Epoch: %d, Time: %d seconds' % (epoch + 1, epoch_time))\n","\n","print('Finished Training')"],"metadata":{"id":"32Uhn9vIqrE_","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681915412501,"user_tz":-120,"elapsed":309709,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"cd91628c-1353-4c0e-8a9f-750e0b9f65b3"},"execution_count":46,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch: 1, Batch:   100, loss: 1.769\n","Epoch: 1, Batch:   200, loss: 1.649\n","Epoch: 1, Batch:   300, loss: 1.637\n","Epoch: 1, Time: 13 seconds\n","Epoch: 2, Batch:   100, loss: 1.614\n","Epoch: 2, Batch:   200, loss: 1.609\n","Epoch: 2, Batch:   300, loss: 1.617\n","Epoch: 2, Time: 13 seconds\n","Epoch: 3, Batch:   100, loss: 1.596\n","Epoch: 3, Batch:   200, loss: 1.593\n","Epoch: 3, Batch:   300, loss: 1.588\n","Epoch: 3, Time: 13 seconds\n","Epoch: 4, Batch:   100, loss: 1.569\n","Epoch: 4, Batch:   200, loss: 1.580\n","Epoch: 4, Batch:   300, loss: 1.586\n","Epoch: 4, Time: 17 seconds\n","Epoch: 5, Batch:   100, loss: 1.564\n","Epoch: 5, Batch:   200, loss: 1.559\n","Epoch: 5, Batch:   300, loss: 1.568\n","Epoch: 5, Time: 16 seconds\n","Epoch: 6, Batch:   100, loss: 1.557\n","Epoch: 6, Batch:   200, loss: 1.559\n","Epoch: 6, Batch:   300, loss: 1.565\n","Epoch: 6, Time: 17 seconds\n","Epoch: 7, Batch:   100, loss: 1.555\n","Epoch: 7, Batch:   200, loss: 1.557\n","Epoch: 7, Batch:   300, loss: 1.552\n","Epoch: 7, Time: 15 seconds\n","Epoch: 8, Batch:   100, loss: 1.543\n","Epoch: 8, Batch:   200, loss: 1.543\n","Epoch: 8, Batch:   300, loss: 1.548\n","Epoch: 8, Time: 14 seconds\n","Epoch: 9, Batch:   100, loss: 1.544\n","Epoch: 9, Batch:   200, loss: 1.537\n","Epoch: 9, Batch:   300, loss: 1.531\n","Epoch: 9, Time: 14 seconds\n","Epoch: 10, Batch:   100, loss: 1.522\n","Epoch: 10, Batch:   200, loss: 1.546\n","Epoch: 10, Batch:   300, loss: 1.533\n","Epoch: 10, Time: 15 seconds\n","Epoch: 11, Batch:   100, loss: 1.533\n","Epoch: 11, Batch:   200, loss: 1.541\n","Epoch: 11, Batch:   300, loss: 1.530\n","Epoch: 11, Time: 15 seconds\n","Epoch: 12, Batch:   100, loss: 1.530\n","Epoch: 12, Batch:   200, loss: 1.535\n","Epoch: 12, Batch:   300, loss: 1.524\n","Epoch: 12, Time: 22 seconds\n","Epoch: 13, Batch:   100, loss: 1.522\n","Epoch: 13, Batch:   200, loss: 1.528\n","Epoch: 13, Batch:   300, loss: 1.523\n","Epoch: 13, Time: 16 seconds\n","Epoch: 14, Batch:   100, loss: 1.515\n","Epoch: 14, Batch:   200, loss: 1.534\n","Epoch: 14, Batch:   300, loss: 1.517\n","Epoch: 14, Time: 14 seconds\n","Epoch: 15, Batch:   100, loss: 1.519\n","Epoch: 15, Batch:   200, loss: 1.522\n","Epoch: 15, Batch:   300, loss: 1.528\n","Epoch: 15, Time: 14 seconds\n","Epoch: 16, Batch:   100, loss: 1.497\n","Epoch: 16, Batch:   200, loss: 1.532\n","Epoch: 16, Batch:   300, loss: 1.527\n","Epoch: 16, Time: 15 seconds\n","Epoch: 17, Batch:   100, loss: 1.512\n","Epoch: 17, Batch:   200, loss: 1.514\n","Epoch: 17, Batch:   300, loss: 1.517\n","Epoch: 17, Time: 14 seconds\n","Epoch: 18, Batch:   100, loss: 1.513\n","Epoch: 18, Batch:   200, loss: 1.525\n","Epoch: 18, Batch:   300, loss: 1.517\n","Epoch: 18, Time: 14 seconds\n","Epoch: 19, Batch:   100, loss: 1.532\n","Epoch: 19, Batch:   200, loss: 1.499\n","Epoch: 19, Batch:   300, loss: 1.518\n","Epoch: 19, Time: 14 seconds\n","Epoch: 20, Batch:   100, loss: 1.523\n","Epoch: 20, Batch:   200, loss: 1.504\n","Epoch: 20, Batch:   300, loss: 1.515\n","Epoch: 20, Time: 14 seconds\n","Finished Training\n"]}]},{"cell_type":"markdown","source":["---\n","# 5. Evaluate the CNN on the test set\n","---\n","**5.1 Accuracy**"],"metadata":{"id":"LaTuorsquZA0"}},{"cell_type":"code","source":["with torch.no_grad():\n","    predictions = []\n","    for data in testloader:\n","        images , labels = data\n","\n","        # Send the data to the GPU\n","        images , labels = images.cuda () , labels.cuda ()\n","        outputs = net ( images )\n","\n","        # Get the predicted class\n","        _, predicted = torch.max(outputs.data, 1)\n","\n","        # Store the predicted class\n","        predictions += predicted.tolist ()\n","print(predictions)\n"],"metadata":{"id":"xRWvOEhdYchw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681915425160,"user_tz":-120,"elapsed":2837,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"d507d56e-454d-44e9-db49-49342a42a562"},"execution_count":47,"outputs":[{"output_type":"stream","name":"stdout","text":["[3, 8, 8, 0, 6, 6, 9, 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9, 0, 8, 7, 9, 5, 3, 6, 9, 9, 3, 6, 8, 3, 1, 2, 8, 6, 1, 6, 1, 1, 8, 0, 8, 0, 1, 8, 8, 4, 9, 6, 6, 5, 6, 0, 9, 8, 2, 9, 8, 7, 1, 7, 4, 7, 6, 1, 7, 5, 4, 9, 9, 0, 6, 4, 1, 5, 8, 7, 9, 6, 3, 4, 7, 8, 2, 9, 7, 6, 9, 6, 1, 3, 8, 3, 6, 1, 9, 2, 7, 8, 1, 5, 1, 5, 2, 1, 3, 6, 1, 3, 0, 5, 6, 1, 1, 1, 5, 6, 5, 1, 0, 3, 5, 6, 7, 9, 0, 8, 4, 4, 5, 7, 7, 7, 4, 0, 8, 3, 8, 2, 3, 5, 1, 6, 9, 7, 9, 4, 5, 2, 8, 4, 5, 1, 9, 7, 8, 9, 7, 4, 0, 8, 4, 8, 5, 7, 0, 2, 6, 7, 8, 7, 7, 2, 1, 3, 6, 8, 8, 8, 0, 9, 8, 1, 4, 6, 0, 5, 0, 6, 3, 6, 4, 4, 0, 8, 7, 1, 6, 8, 3, 1, 0, 7, 9, 9, 9, 9, 6, 6, 5, 4, 2, 8, 5, 0, 9, 5, 6, 8, 0, 4, 3, 7, 2, 9, 0, 5, 5, 6, 6, 9, 9, 6, 4, 5, 5, 6, 8, 1, 7, 7, 6, 4, 5, 8, 9, 7, 0, 6, 2, 9, 0, 9, 2, 8, 3, 7, 6, 8, 7, 1, 5, 3, 0, 5, 4, 8, 7, 5, 8, 3, 7, 3, 9, 1, 5, 1, 1, 1, 9, 9, 7, 3, 9, 9, 7, 8, 5, 8, 7, 8, 7, 9, 7, 0, 0, 9, 8, 2, 4, 6, 6, 7, 8, 1, 0, 7, 6, 2, 9, 2, 2, 1, 2, 9, 1, 5, 7, 4, 0, 1, 3, 4, 3, 5, 2, 0, 7, 0, 1, 9, 1, 6, 7, 5, 5, 9, 7, 9, 5, 7, 8, 0, 5, 9, 6, 5, 5, 9, 8, 8, 3, 7, 9, 7, 9, 1, 7, 7, 1, 9, 7, 0, 6, 4, 3, 8, 2, 5, 6, 6, 2, 1, 1, 0, 9, 4, 6, 0, 6, 6, 0, 8, 8, 7, 9, 8, 4, 7, 9, 4, 5, 7, 7, 9, 9, 5, 6, 5, 8, 9, 5, 5, 8, 1, 8, 8, 3, 5, 7, 0, 8, 9, 0, 2, 3, 7, 6, 3, 7, 2, 9, 8, 5, 4, 1, 6, 7, 9, 1, 9, 3, 5, 8, 3, 8, 2, 1, 9, 3, 9, 5, 4, 7, 7, 8, 3, 6, 6, 1, 3, 4, 0, 4, 7, 8, 5, 6, 4, 9, 7, 3, 2, 6, 5, 6, 7, 5, 3, 6, 0, 8, 7, 8, 1, 4, 0, 3, 6, 3, 8, 4, 1, 1, 8, 7, 1, 3, 0, 1, 3, 0, 8, 1, 1, 2, 1, 9, 1, 1, 8, 2, 1, 9, 0, 3, 1, 6, 0, 6, 9, 4, 0, 4, 1, 5, 1, 0, 4, 7, 0, 3, 2, 7, 3, 3, 6, 8, 6, 1, 2, 9, 8, 0, 2, 2, 4, 6, 0, 6, 9, 5, 7, 1, 2, 7, 2, 6, 3, 6, 9, 8, 0, 8, 8, 2, 7, 1, 9, 0, 7, 8, 0, 3, 4, 3, 3, 1, 1, 3, 9, 8, 4, 3, 8, 0, 0, 2, 5, 7, 8, 4, 6, 8, 0, 7, 7, 8, 1, 6, 0, 9, 0, 6, 9, 1, 7, 9, 8, 7, 8, 2, 0, 2, 9, 2, 4, 0, 6, 7, 0, 4, 6, 8, 9, 8, 3, 8, 0, 7, 4, 9, 1, 7, 9, 5, 7, 1, 9, 9, 7, 6, 0, 2, 9, 3, 5, 0, 5, 5, 8, 8, 0, 8, 1, 4, 0, 3, 6, 8, 2, 4, 4, 6, 3, 3, 4, 9, 3, 0, 1, 7, 5, 3, 4, 9, 0, 4, 6, 0, 2, 5, 1, 8, 8, 4, 7, 9, 3, 3, 6, 9, 5, 5, 1, 4]\n"]}]},{"cell_type":"code","source":["# Create a list of true labels from the test set\n","true_labels = []\n","for data in testloader:\n","    _, labels = data\n","    true_labels += labels.tolist()\n","print(true_labels)"],"metadata":{"id":"LK1D6sqPXbMg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681915430450,"user_tz":-120,"elapsed":2763,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"4df8f911-00c1-45c3-b9a1-3efa02803047"},"execution_count":48,"outputs":[{"output_type":"stream","name":"stdout","text":["[3, 8, 8, 0, 6, 6, 1, 6, 3, 1, 0, 9, 5, 7, 9, 8, 5, 7, 8, 6, 7, 0, 4, 9, 5, 2, 4, 0, 9, 6, 6, 5, 4, 5, 9, 2, 4, 1, 9, 5, 4, 6, 5, 6, 0, 9, 3, 9, 7, 6, 9, 8, 0, 3, 8, 8, 7, 7, 4, 6, 7, 3, 6, 3, 6, 2, 1, 2, 3, 7, 2, 6, 8, 8, 0, 2, 9, 3, 3, 8, 8, 1, 1, 7, 2, 5, 2, 7, 8, 9, 0, 3, 8, 6, 4, 6, 6, 0, 0, 7, 4, 5, 6, 3, 1, 1, 3, 6, 8, 7, 4, 0, 6, 2, 1, 3, 0, 4, 2, 7, 8, 3, 1, 2, 8, 0, 8, 3, 5, 2, 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8, 2, 8, 7, 7, 5, 5, 0, 1, 2, 9, 6, 1, 0, 1, 0, 6, 5, 0, 5, 7, 4, 4, 8, 3, 5, 3, 7, 2, 5, 8, 3, 5, 7, 3, 7, 0, 5, 4, 5, 6, 9, 3, 5, 3, 8, 2, 4, 2, 2, 6, 7, 6, 0, 5, 5, 8, 4, 5, 6, 1, 3, 5, 4, 7, 3, 0, 7, 2, 0, 1, 2, 4, 9, 6, 4, 5, 9, 7, 7, 6, 7, 4, 3, 1, 6, 9, 4, 8, 0, 3, 1, 6, 4, 4, 2, 1, 4, 6, 3, 0, 8, 9, 7, 6, 8, 4, 8, 1, 4, 5, 9, 4, 7, 1, 4, 5, 3, 1, 2, 6, 4, 7, 1, 5, 9, 3, 1, 4, 8, 7, 7, 0, 4, 3, 4, 6, 7, 7, 5, 4, 4, 4, 3, 6, 6, 1, 9, 9, 7, 9, 4, 3, 2, 9, 8, 5, 0, 6, 9, 0, 9, 7, 8, 5, 4, 4, 9, 4, 2, 6, 6, 9, 9, 7, 9, 9, 0, 5, 1, 1, 8, 6, 9, 9, 5, 6, 5, 5, 9, 7, 9, 4, 5, 4, 3, 3, 9, 8, 8, 3, 4, 8, 4, 0, 4, 0, 7, 2, 8, 0, 2, 0, 7, 4, 0, 6, 3, 3, 1, 7, 4, 6, 5, 0, 4, 9, 5, 6, 6, 3, 0, 5, 2, 6, 3, 3, 8, 0, 8, 5, 4, 2, 9, 5, 0, 5, 4, 8, 2, 7, 6, 5, 7, 8, 2, 0, 1, 8, 2, 4, 8, 4, 3, 0, 4, 0, 1, 9, 3, 0, 3, 6, 5, 5, 2, 8, 5, 0, 8, 7, 5, 2, 4, 3, 7, 0, 2, 3, 1, 0, 0, 2, 1, 2, 1, 4, 6, 7, 1, 0, 5, 2, 5, 9, 2, 5, 6, 7, 1, 9, 3, 6, 1, 0, 2, 9, 4, 2, 9, 3, 8, 2, 9, 8, 5, 1, 0, 1, 5, 2, 5, 6, 8, 8, 3, 5, 0, 4, 1, 3, 7, 0, 3, 3, 9, 2, 3, 9, 9, 8, 5, 9, 7, 2, 0, 9, 4, 7, 1, 7, 6, 4, 4, 8, 9, 0, 7, 1, 2, 4, 0, 3, 7, 5, 1, 9, 3, 5, 2, 4, 1, 1, 3, 3, 9, 2, 5, 8, 0, 8, 1, 5, 1, 5, 9, 0, 9, 2, 4, 0, 6, 9, 1, 1, 3, 4, 9, 5, 3, 3, 3, 3, 4, 1, 5, 9, 8, 7, 4, 8, 7, 3, 5, 2, 5, 8, 9, 7, 2, 0, 0, 7, 5, 3, 5, 7, 4, 6, 2, 8, 2, 7, 7, 6, 1, 3, 8, 6, 2, 0, 4, 4, 8, 6, 4, 0, 2, 5, 4, 1, 2, 5, 4, 6, 9, 5, 0, 5, 2, 0, 8, 2, 6, 4, 7, 9, 5, 7, 2, 6, 2, 1, 9, 9, 7, 2, 5, 1, 8, 1, 6, 3, 0, 8, 8, 5, 6, 9, 9, 9, 6, 3, 0, 8, 8, 5, 3, 3, 1, 1, 0, 7, 0, 6, 0, 4, 4, 6, 5, 3, 2, 9, 6, 6, 7, 9, 7, 7, 0, 8, 4, 2, 7, 7, 7, 4, 0, 1, 6, 0, 5, 3, 0, 2, 4, 9, 7, 3, 3, 2, 8, 8, 5, 3, 8, 0, 8, 0, 0, 0, 2, 2, 7, 0, 7, 7, 7, 2, 2, 0, 4, 0, 3, 7, 8, 4, 7, 9, 6, 1, 1, 6, 9, 3, 8, 9, 5, 8, 3, 9, 4, 3, 7, 5, 5, 2, 7, 5, 7, 9, 6, 3, 0, 8, 4, 5, 6, 1, 0, 4, 8, 4, 7, 1, 3, 2, 0, 9, 1, 8, 8, 6, 8, 1, 7, 1, 0, 9, 7, 6, 4, 5, 8, 2, 1, 2, 5, 8, 1, 1, 8, 8, 0, 9, 0, 0, 4, 3, 3, 5, 6, 6, 3, 1, 4, 4, 7, 8, 0, 1, 4, 8, 4, 6, 2, 2, 2, 6, 3, 4, 0, 8, 1, 3, 8, 3, 8, 9, 1, 6, 1, 9, 8, 2, 0, 4, 7, 4, 0, 0, 7, 6, 8, 9, 8, 6, 2, 8, 8, 0, 1, 1, 0, 7, 7, 4, 4, 7, 7, 2, 6, 6, 7, 4, 5, 4, 0, 9, 4, 7, 2, 5, 3, 2, 2, 7, 6, 2, 6, 4, 9, 6, 8, 3, 8, 5, 5, 2, 3, 4, 5, 2, 7, 5, 1, 5, 3, 7, 8, 8, 9, 6, 0, 9, 2, 6, 0, 0, 1, 4, 1, 3, 5, 1, 8, 9, 6, 2, 6, 9, 5, 6, 5, 9, 1, 4, 2, 8, 9, 5, 4, 7, 0, 3, 7, 1, 6, 9, 1, 1, 4, 0, 5, 1, 6, 3, 0, 2, 5, 5, 5, 2, 8, 2, 3, 1, 6, 2, 7, 0, 8, 9, 2, 6, 4, 7, 5, 5, 8, 8, 7, 8, 5, 7, 4, 4, 0, 0, 7, 0, 4, 0, 4, 4, 2, 5, 4, 0, 7, 7, 7, 0, 4, 6, 2, 9, 1, 1, 5, 0, 9, 1, 0, 6, 5, 3, 0, 9, 0, 6, 2, 0, 4, 1, 6, 7, 6, 1, 0, 4, 6, 0, 4, 4, 2, 7, 6, 5, 3, 5, 9, 4, 5, 2, 0, 5, 7, 2, 3, 9, 4, 5, 8, 1, 9, 3, 1, 8, 5, 8, 6, 6, 3, 4, 0, 2, 3, 2, 6, 1, 8, 2, 1, 3, 6, 2, 2, 3, 3, 5, 0, 2, 1, 2, 8, 0, 8, 2, 2, 7, 3, 5, 9, 8, 3, 6, 1, 0, 8, 6, 3, 5, 8, 9, 4, 8, 0, 0, 2, 9, 1, 8, 3, 7, 2, 8, 0, 9, 4, 8, 9, 4, 8, 9, 5, 4, 8, 1, 5, 5, 7, 2, 5, 8, 8, 1, 2, 3, 1, 5, 0, 2, 3, 0, 8, 2, 2, 8, 7, 3, 5, 3, 9, 6, 3, 5, 1, 1, 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3, 8, 6, 9, 8, 5, 5, 8, 3, 5, 9, 0, 4, 2, 0, 5, 9, 9, 3, 8, 0, 6, 0, 2, 3, 7, 0, 0, 6, 9, 1, 5, 2, 2, 7, 9, 3, 9, 3, 5, 2, 2, 2, 8, 8, 5, 0, 0, 7, 0, 3, 1, 6, 5, 0, 9, 2, 6, 7, 4, 2, 0, 0, 5, 3, 3, 2, 6, 4, 2, 7, 8, 0, 1, 0, 1, 3, 6, 4, 8, 0, 6, 0, 9, 1, 6, 2, 1, 0, 1, 3, 0, 3, 7, 9, 9, 3, 7, 5, 7, 1, 6, 6, 1, 1, 1, 4, 4, 2, 1, 7, 0, 1, 2, 1, 1, 3, 2, 6, 9, 1, 3, 2, 0, 8, 7, 3, 4, 6, 4, 6, 9, 4, 8, 9, 3, 0, 5, 7, 2, 0, 2, 9, 9, 3, 8, 7, 1, 1, 2, 2, 2, 5, 9, 1, 2, 6, 1, 2, 1, 8, 9, 2, 3, 1, 9, 9, 4, 4, 5, 5, 0, 3, 3, 7, 1, 7, 7, 9, 8, 6, 3, 5, 5, 1, 6, 2, 7, 8, 4, 3, 3, 5, 9, 6, 8, 5, 1, 0, 6, 7, 0, 9, 6, 2, 0, 8, 7, 1, 2, 6, 9, 1, 6, 4, 7, 1, 9, 0, 0, 5, 2, 8, 3, 8, 2, 8, 4, 0, 8, 3, 6, 1, 2, 3, 0, 5, 0, 4, 1, 7, 2, 0, 1, 3, 9, 1, 6, 1, 6, 1, 2, 8, 7, 0, 9, 5, 8, 0, 0, 1, 7, 9, 7, 1, 9, 2, 1, 9, 4, 3, 6, 0, 2, 0, 0, 9, 9, 8, 9, 4, 5, 4, 3, 0, 7, 8, 7, 3, 7, 5, 4, 6, 5, 9, 5, 2, 4, 2, 7, 9, 6, 7, 4, 7, 1, 2, 3, 1, 1, 3, 6, 8, 3, 7, 7, 0, 8, 1, 5, 2, 5, 5, 3, 6, 3, 5, 9, 8, 4, 6, 8, 0, 4, 1, 7, 8, 4, 9, 7, 2, 6, 3, 8, 8, 6, 7, 3, 8, 1, 1, 5, 3, 7, 9, 9, 9, 8, 5, 9, 9, 6, 8, 8, 4, 8, 1, 0, 1, 8, 8, 6, 5, 9, 0, 6, 1, 2, 6, 6, 3, 5, 6, 5, 5, 3, 3, 6, 8, 9, 4, 4, 3, 8, 3, 6, 0, 8, 5, 7, 8, 4, 6, 5, 3, 1, 8, 3, 0, 4, 4, 3, 3, 5, 3, 3, 2, 7, 7, 8, 2, 0, 5, 5, 6, 1, 1, 3, 9, 9, 3, 6, 7, 1, 8, 3, 9, 8, 7, 8, 8, 0, 3, 2, 4, 8, 7, 5, 7, 1, 8, 7, 3, 5, 4, 4, 3, 8, 6, 1, 1, 6, 3, 2, 2, 8, 9, 8, 3, 3, 8, 1, 1, 0, 5, 2, 0, 5, 0, 5, 8, 6, 3, 9, 6, 6, 3, 9, 4, 8, 3, 4, 3, 1, 1, 2, 9, 7, 8, 5, 5, 8, 6, 9, 0, 8, 3, 5, 1, 3, 3, 5, 5, 3, 8, 1, 4, 6, 4, 2, 4, 3, 8, 9, 6, 6, 6, 5, 5, 2, 7, 7, 4, 4, 9, 3, 8, 3, 7, 7, 8, 5, 3, 9, 1, 6, 7, 3, 3, 3, 8, 5, 6, 1, 3, 7, 9, 6, 1, 6, 4, 9, 0, 5, 7, 4, 9, 1, 0, 0, 2, 7, 8, 3, 2, 7, 6, 1, 6, 6, 4, 4, 2, 1, 8, 4, 4, 7, 8, 5, 8, 9, 4, 3, 2, 2, 9, 3, 0, 1, 1, 1, 1, 6, 8, 7, 1, 3, 0, 4, 5, 0, 3, 0, 6, 2, 0, 9, 8, 6, 8, 5, 8, 2, 9, 6, 0, 6, 1, 8, 7, 3, 2, 2, 6, 5, 6, 9, 2, 5, 4, 9, 8, 5, 3, 9, 3, 5, 7, 1, 8, 5, 8, 6, 2, 5, 1, 3, 0, 4, 1, 9, 5, 4, 9, 2, 1, 4, 2, 5, 9, 8, 7, 2, 1, 6, 9, 7, 3, 6, 5, 8, 0, 2, 2, 4, 9, 0, 5, 5, 4, 2, 3, 1, 0, 3, 0, 4, 4, 9, 1, 3, 3, 0, 2, 1, 1, 0, 4, 1, 9, 4, 8, 1, 1, 1, 5, 9, 8, 1, 6, 7, 2, 9, 9, 7, 8, 8, 1, 9, 7, 8, 9, 1, 6, 6, 3, 3, 1, 3, 0, 8, 8, 3, 6, 6, 7, 1, 0, 7, 6, 4, 8, 2, 0, 6, 6, 8, 6, 3, 1, 1, 8, 6, 7, 0, 5, 4, 3, 2, 1, 7, 8, 2, 0, 1, 7, 4, 8, 7, 6, 6, 7, 0, 6, 4, 9, 2, 3, 2, 1, 3, 8, 6, 8, 1, 9, 6, 7, 6, 5, 0, 6, 0, 1, 3, 1, 1, 5, 3, 4, 3, 3, 8, 1, 6, 6, 1, 8, 8, 6, 2, 7, 4, 6, 8, 9, 3, 3, 5, 5, 3, 1, 4, 1, 9, 5, 1, 7, 7, 7, 5, 2, 3, 3, 6, 6, 6, 4, 0, 7, 1, 7, 3, 0, 8, 1, 2, 4, 1, 2, 2, 4, 9, 8, 2, 8, 7, 6, 3, 4, 7, 2, 3, 1, 5, 3, 0, 4, 3, 4, 9, 4, 7, 9, 1, 7, 8, 3, 1, 8, 3, 2, 5, 7, 2, 7, 9, 6, 8, 6, 8, 6, 5, 9, 0, 4, 8, 5, 6, 4, 3, 8, 0, 4, 8, 6, 7, 2, 7, 1, 7, 4, 1, 2, 4, 2, 2, 8, 7, 2, 2, 6, 7, 5, 6, 8, 9, 9, 6, 5, 2, 9, 8, 7, 2, 3, 3, 7, 4, 3, 2, 1, 9, 0, 1, 5, 3, 2, 6, 5, 9, 1, 5, 4, 3, 6, 2, 8, 9, 7, 8, 0, 8, 9, 1, 2, 5, 7, 2, 0, 6, 6, 1, 6, 3, 1, 5, 0, 3, 7, 4, 6, 5, 6, 4, 8, 3, 4, 5, 5, 0, 6, 6, 7, 5, 0, 9, 9, 6, 1, 0, 4, 6, 3, 6, 9, 3, 6, 6, 8, 2, 1, 0, 1, 9, 3, 7, 4, 3, 0, 2, 7, 6, 4, 3, 0, 8, 2, 0, 0, 9, 6, 6, 6, 8, 7, 4, 1, 8, 1, 2, 2, 4, 8, 5, 2, 6, 5, 3, 9, 1, 0, 7, 2, 4, 4, 0, 0, 6, 2, 2, 4, 0, 5, 9, 7, 1, 8, 4, 5, 5, 9, 8, 5, 7, 8, 0, 9, 8, 9, 1, 6, 3, 8, 0, 3, 4, 4, 8, 4, 8, 9, 8, 6, 0, 0, 8, 2, 7, 4, 2, 5, 6, 0, 5, 8, 4, 1, 9, 0, 1, 4, 4, 8, 4, 9, 6, 0, 7, 7, 6, 8, 9, 6, 2, 0, 4, 9, 4, 9, 3, 9, 6, 6, 7, 0, 9, 7, 1, 8, 6, 0, 6, 7, 4, 1, 9, 4, 6, 7, 9, 8, 3, 9, 2, 1, 2, 7, 6, 1, 0, 0, 5, 6, 0, 4, 3, 2, 8, 8, 0, 6, 9, 5, 2, 8, 7, 0, 6, 5, 9, 7, 2, 3, 6, 9, 6, 2, 2, 4, 1, 0, 5, 0, 8, 9, 3, 5, 9, 3, 8, 1, 6, 3, 7, 5, 6, 2, 0, 2, 8, 2, 8, 7, 7, 8, 1, 0, 8, 9, 7, 0, 3, 8, 0, 5, 9, 5, 8, 4, 2, 0, 9, 2, 2, 4, 4, 9, 2, 2, 2, 5, 1, 3, 2, 0, 0, 4, 0, 6, 5, 8, 0, 5, 8, 6, 4, 8, 5, 2, 9, 7, 9, 7, 1, 0, 1, 9, 6, 9, 2, 7, 9, 4, 4, 0, 6, 2, 4, 1, 3, 7, 2, 8, 5, 9, 0, 3, 2, 3, 2, 7, 6, 3, 2, 5, 9, 0, 5, 9, 9, 8, 7, 7, 4, 8, 6, 5, 2, 3, 1, 0, 4, 1, 8, 8, 4, 9, 4, 4, 3, 3, 9, 2, 0, 1, 1, 8, 4, 4, 8, 3, 2, 9, 5, 7, 6, 2, 5, 4, 7, 3, 3, 9, 0, 1, 5, 9, 3, 7, 6, 0, 4, 2, 2, 5, 6, 3, 8, 9, 5, 6, 1, 4, 5, 4, 6, 7, 2, 1, 0, 2, 0, 4, 9, 8, 8, 9, 1, 1, 5, 0, 0, 8, 7, 1, 7, 4, 5, 4, 3, 3, 2, 0, 6, 6, 0, 1, 3, 9, 8, 3, 7, 8, 9, 4, 8, 9, 0, 9, 7, 1, 6, 2, 9, 5, 6, 9, 3, 9, 8, 7, 7, 1, 6, 5, 3, 1, 3, 1, 2, 7, 1, 8, 2, 0, 9, 7, 9, 8, 8, 6, 7, 3, 7, 1, 3, 9, 0, 9, 3, 6, 7, 2, 7, 3, 0, 5, 9, 7, 5, 5, 0, 6, 5, 1, 8, 2, 7, 5, 9, 0, 0, 0, 8, 8, 7, 3, 7, 8, 9, 3, 7, 9, 7, 8, 7, 9, 8, 5, 4, 8, 3, 7, 6, 3, 8, 2, 1, 9, 5, 7, 3, 9, 5, 5, 8, 7, 3, 5, 3, 5, 9, 7, 6, 7, 3, 6, 4, 3, 9, 4, 2, 1, 9, 6, 0, 2, 6, 7, 4, 7, 9, 0, 7, 4, 3, 5, 3, 1, 1, 2, 6, 8, 2, 1, 7, 8, 5, 9, 6, 1, 1, 5, 0, 6, 0, 9, 2, 6, 5, 8, 9, 5, 5, 6, 2, 9, 1, 5, 8, 8, 7, 1, 7, 3, 5, 4, 9, 7, 5, 2, 9, 9, 4, 7, 4, 1, 3, 8, 7, 9, 0, 4, 5, 7, 5, 2, 8, 7, 6, 9, 6, 9, 3, 8, 5, 6, 6, 9, 5, 7, 8, 0, 5, 0, 7, 4, 8, 2, 5, 1, 3, 2, 2, 6, 2, 1, 7, 4, 6, 3, 1, 3, 7, 2, 1, 3, 7, 0, 8, 4, 4, 5, 7, 9, 5, 4, 3, 9, 6, 8, 2, 3, 3, 1, 6, 1, 7, 0, 3, 4, 2, 9, 4, 5, 8, 2, 7, 0, 9, 6, 8, 0, 8, 2, 8, 5, 7, 7, 2, 2, 0, 0, 0, 7, 4, 1, 6, 6, 8, 8, 9, 0, 9, 0, 1, 3, 3, 0, 9, 6, 6, 2, 6, 3, 4, 0, 8, 4, 1, 4, 0, 6, 5, 0, 9, 9, 9, 9, 1, 2, 3, 5, 4, 2, 9, 6, 0, 9, 6, 6, 8, 0, 6, 1, 4, 6, 8, 0, 5, 4, 1, 2, 0, 9, 6, 4, 2, 4, 6, 5, 9, 7, 7, 4, 6, 5, 0, 1, 9, 0, 3, 1, 9, 0, 9, 7, 8, 6, 7, 6, 8, 2, 4, 5, 3, 0, 3, 2, 1, 7, 5, 9, 3, 4, 5, 7, 1, 5, 0, 1, 1, 1, 9, 7, 5, 4, 9, 7, 8, 1, 0, 2, 8, 5, 6, 7, 0, 1, 4, 8, 4, 4, 6, 6, 5, 8, 1, 8, 4, 6, 5, 9, 2, 2, 1, 4, 9, 1, 6, 7, 2, 0, 1, 7, 6, 5, 2, 2, 5, 6, 0, 9, 0, 1, 5, 3, 3, 5, 8, 7, 5, 6, 5, 8, 0, 5, 9, 4, 6, 5, 1, 1, 0, 3, 3, 9, 4, 8, 1, 7, 7, 9, 9, 4, 3, 6, 3, 2, 8, 2, 7, 6, 7, 0, 2, 1, 2, 9, 4, 6, 9, 6, 1, 0, 1, 8, 7, 0, 0, 4, 7, 4, 2, 6, 9, 5, 9, 0, 7, 4, 5, 8, 1, 4, 7, 9, 9, 8, 8, 6, 3, 7, 0, 8, 9, 6, 2, 4, 6, 2, 9, 7, 4, 6, 8, 5, 6, 1, 3, 5, 9, 9, 1, 3, 2, 0, 3, 0, 2, 0, 7, 3, 9, 3, 5, 7, 6, 5, 9, 5, 6, 1, 2, 4, 0, 2, 7, 8, 4, 4, 0, 9, 5, 9, 2, 9, 3, 2, 4, 3, 2, 2, 8, 8, 6, 8, 1, 6, 8, 9, 8, 2, 1, 4, 9, 1, 5, 7, 1, 6, 0, 1, 5, 2, 8, 1, 1, 3, 8, 1, 3, 1, 8, 5, 1, 9, 0, 3, 0, 4, 0, 5, 1, 2, 0, 4, 9, 5, 1, 2, 2, 7, 8, 7, 2, 4, 5, 3, 4, 0, 6, 1, 5, 9, 8, 0, 2, 0, 7, 6, 0, 5, 9, 7, 5, 0, 4, 6, 0, 6, 3, 6, 8, 1, 1, 8, 9, 7, 2, 9, 0, 2, 2, 9, 0, 6, 5, 7, 7, 9, 1, 7, 9, 8, 4, 5, 0, 8, 0, 2, 5, 2, 1, 4, 4, 8, 9, 7, 8, 3, 6, 6, 0, 1, 1, 1, 8, 1, 4, 4, 0, 7, 8, 2, 1, 2, 5, 4, 6, 0, 5, 7, 4, 4, 3, 9, 5, 8, 8, 0, 8, 7, 4, 1, 8, 4, 9, 5, 4, 1, 7, 7, 7, 7, 0, 3, 8, 3, 3, 0, 5, 7, 0, 8, 0, 0, 9, 2, 2, 3, 4, 8, 2, 2, 6, 3, 3, 6, 2, 9, 4, 0, 1, 7, 5, 5, 7, 3, 0, 4, 2, 0, 7, 5, 8, 0, 8, 2, 7, 0, 3, 5, 3, 8, 3, 5, 1, 7]\n"]}]},{"cell_type":"markdown","source":["$$\\text{accuracy}(y_{\\text{true}}, y_{\\text{pred}}) = \\frac{\\sum_{i=1}^{n} [y_{\\text{true}, i} = y_{\\text{pred}, i}]}{n}$$\n"],"metadata":{"id":"v7WG99dTZ_yC"}},{"cell_type":"code","source":["def accuracy(y_true, y_pred):\n","    correct_predictions = np.sum (y_true == y_pred)\n","    total_predictions = len (y_true)\n","    accuracy = correct_predictions / total_predictions\n","    return accuracy"],"metadata":{"id":"vPeDIiwifCSR","executionInfo":{"status":"ok","timestamp":1681915450302,"user_tz":-120,"elapsed":390,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":50,"outputs":[]},{"cell_type":"code","source":["acc = accuracy(np.array (true_labels) , np.array (predictions))\n","print('Accuracy of test set: {:.2f}%'.format(acc*100))"],"metadata":{"id":"J2_HiEW3fcS-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681915453019,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"2978c3ba-f295-4b77-c6ce-af992053cb93"},"execution_count":51,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy of test set: 43.20%\n"]}]},{"cell_type":"code","source":["# Using the sklearn library\n","from sklearn.metrics import accuracy_score\n","accuracy = accuracy_score(true_labels, predictions)\n","print('Accuracy of test set: {:.2f}%'.format(accuracy*100))"],"metadata":{"id":"uLQvohMQXjbh","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681915456845,"user_tz":-120,"elapsed":523,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"33a78790-be4c-47f7-c8d8-e1636dbae3df"},"execution_count":52,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy of test set: 43.20%\n"]}]},{"cell_type":"markdown","source":["---\n","# 6. Convolutional neural network with a pretrained architecture\n","---"],"metadata":{"id":"mBKzXiRyxjnc"}},{"cell_type":"markdown","source":["**6.1 Define the CNN architecture with a pretrained architecture**\n","*   import **torchvision.models** as **models**: library to build neural networks. This library contains a variety of pre-trained models of convolutional neural networks for image classification, object detection, semantic segmentation, among others."],"metadata":{"id":"yAC2Y6krxwmx"}},{"cell_type":"code","source":["import torchvision.models as models"],"metadata":{"id":"uCwkgbHOynal","executionInfo":{"status":"ok","timestamp":1681915472850,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":53,"outputs":[]},{"cell_type":"markdown","source":["*   The **ResNet-18 model** is a pretrained convolutional neural network (CNN) model that has proven to be very effective in image classification tasks. ResNet-18 consists of 18 layers between basic convolution and pooling blocks, as well as a final softmax classification layer.\n","\n","*   **ResNet-18** was pre-trained on the ImageNet dataset, which contains millions of images labeled in 1000 different classes. That is, the output of this pre-trained network will be **1000 labels**.\n","\n","$$\\begin{equation}\n","    \\textbf{Input} \\rightarrow \n","    \\textbf{Conv1} \\rightarrow \n","    \\textbf{MaxPool} \\rightarrow \n","    \\textbf{ResNet Block 1} \\rightarrow \n","    \\textbf{ResNet Block 2} \\rightarrow \n","    \\textbf{ResNet Block 3} \\rightarrow \n","    \\textbf{ResNet Block 4} \\rightarrow \n","    \\textbf{AvgPool} \\rightarrow \n","    \\textbf{FC} \\rightarrow \n","    \\textbf{Output}\n","\\end{equation}$$"],"metadata":{"id":"tRkzD6ifyqV0"}},{"cell_type":"code","source":["net = models.resnet18(pretrained=True)"],"metadata":{"id":"Zla7KZlazyDx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681915488207,"user_tz":-120,"elapsed":506,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"ee0c3df9-e5d1-4728-dfef-f247eaa36759"},"execution_count":54,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n","  warnings.warn(\n","/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n","  warnings.warn(msg)\n","Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n","100%|██████████| 44.7M/44.7M [00:00<00:00, 177MB/s]\n"]}]},{"cell_type":"markdown","source":["*  Modify the output layer, adding one more layer that connects the **1000 labels** to the **10 labels** of this practice"],"metadata":{"id":"OoY24Kma0Q4Z"}},{"cell_type":"code","source":["num_ftrs = net.fc.in_features\n","net.fc = nn.Linear(num_ftrs, 10)"],"metadata":{"id":"qSB2N4NDqrMi","executionInfo":{"status":"ok","timestamp":1681915513102,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":55,"outputs":[]},{"cell_type":"code","source":["net = net.cuda() # CPU -->> GPU"],"metadata":{"id":"o6xn31LoXTN8","executionInfo":{"status":"ok","timestamp":1681915515490,"user_tz":-120,"elapsed":1,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":56,"outputs":[]},{"cell_type":"markdown","source":["**6.2 Initial parameters**"],"metadata":{"id":"2Mo747zM0rdg"}},{"cell_type":"code","source":["# Define the loss function and optimizer\n","criterion = nn.CrossEntropyLoss()"],"metadata":{"id":"pcPpgE29XTRW","executionInfo":{"status":"ok","timestamp":1681915539589,"user_tz":-120,"elapsed":1,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":57,"outputs":[]},{"cell_type":"code","source":["optimizer = optim.SGD(net.parameters(), lr = 0.001, momentum = 0.9)\n","optimizer = optim.Adam(net.parameters(), lr = 0.001)\n","optimizer = optim.Adagrad(net.parameters(), lr = 0.01, lr_decay = 0.001)"],"metadata":{"id":"GqYKAJgn0vU3","executionInfo":{"status":"ok","timestamp":1681915583293,"user_tz":-120,"elapsed":822,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":58,"outputs":[]},{"cell_type":"markdown","source":["**6.3 Train the CNN**\n","\n","*   **6.3.1  CNN schematic training**"],"metadata":{"id":"9Cn-AyuG0zDd"}},{"cell_type":"code","source":["# Train the CNN\n","for epoch in range(10):\n","    running_loss = 0.0\n","    start_time = time.time()\n","    for i, data in enumerate(trainloader, 0):\n","        inputs, labels = data\n","        inputs, labels = inputs.cuda(), labels.cuda() # Send the data to GPU\n","        optimizer.zero_grad()\n","        outputs = net(inputs)\n","        loss = loss_function(outputs, labels)\n","        loss.backward() # backwardpropagation\n","        optimizer.step()\n","        running_loss += loss.item()\n","        if i % 100 == 99:\n","            print('Epoch: %d, Batch: %5d, loss: %.3f' %\n","                  (epoch + 1, i + 1, running_loss /100))\n","            running_loss = 0.0\n","            \n","    end_time = time.time() # End time for epoch\n","    epoch_time = end_time - start_time\n","    print('Epoch: %d, Time: %d seconds' % (epoch + 1, epoch_time))\n","\n","print('Finished Training')"],"metadata":{"id":"14WjGJrlXTUw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681915912385,"user_tz":-120,"elapsed":213538,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"9fe1e4e8-7202-42eb-bc1e-0b06e68bf243"},"execution_count":59,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch: 1, Batch:   100, loss: 1.895\n","Epoch: 1, Batch:   200, loss: 1.130\n","Epoch: 1, Batch:   300, loss: 0.873\n","Epoch: 1, Time: 18 seconds\n","Epoch: 2, Batch:   100, loss: 0.611\n","Epoch: 2, Batch:   200, loss: 0.582\n","Epoch: 2, Batch:   300, loss: 0.558\n","Epoch: 2, Time: 24 seconds\n","Epoch: 3, Batch:   100, loss: 0.369\n","Epoch: 3, Batch:   200, loss: 0.398\n","Epoch: 3, Batch:   300, loss: 0.388\n","Epoch: 3, Time: 26 seconds\n","Epoch: 4, Batch:   100, loss: 0.241\n","Epoch: 4, Batch:   200, loss: 0.257\n","Epoch: 4, Batch:   300, loss: 0.254\n","Epoch: 4, Time: 25 seconds\n","Epoch: 5, Batch:   100, loss: 0.152\n","Epoch: 5, Batch:   200, loss: 0.156\n","Epoch: 5, Batch:   300, loss: 0.159\n","Epoch: 5, Time: 22 seconds\n","Epoch: 6, Batch:   100, loss: 0.097\n","Epoch: 6, Batch:   200, loss: 0.095\n","Epoch: 6, Batch:   300, loss: 0.102\n","Epoch: 6, Time: 18 seconds\n","Epoch: 7, Batch:   100, loss: 0.062\n","Epoch: 7, Batch:   200, loss: 0.058\n","Epoch: 7, Batch:   300, loss: 0.056\n","Epoch: 7, Time: 18 seconds\n","Epoch: 8, Batch:   100, loss: 0.037\n","Epoch: 8, Batch:   200, loss: 0.039\n","Epoch: 8, Batch:   300, loss: 0.038\n","Epoch: 8, Time: 18 seconds\n","Epoch: 9, Batch:   100, loss: 0.028\n","Epoch: 9, Batch:   200, loss: 0.030\n","Epoch: 9, Batch:   300, loss: 0.028\n","Epoch: 9, Time: 19 seconds\n","Epoch: 10, Batch:   100, loss: 0.019\n","Epoch: 10, Batch:   200, loss: 0.023\n","Epoch: 10, Batch:   300, loss: 0.024\n","Epoch: 10, Time: 20 seconds\n","Finished Training\n"]}]},{"cell_type":"markdown","source":["**6.4 Evaluate the CNN on the test set**"],"metadata":{"id":"cwtCgjfF055U"}},{"cell_type":"code","source":["with torch.no_grad():\n","    predictions = []\n","    for data in testloader:\n","        images, labels = data\n","        images, labels = images.cuda(), images.cuda() # Send the data to the GPU\n","        outputs = net(images)\n","        _, predicted = torch.max(outputs.data, 1) # Get the predicted class\n","        predictions += predicted.tolist () # Store the predicted class\n","        \n","true_labels = []\n","for data in testloader:\n","    _, labels = data\n","    true_labels += labels.tolist()"],"metadata":{"id":"ikHRNSeh9gNJ","executionInfo":{"status":"ok","timestamp":1681915944877,"user_tz":-120,"elapsed":4856,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}}},"execution_count":60,"outputs":[]},{"cell_type":"code","source":["def accuracy(y_true, y_pred):\n","    correct_predictions = np.sum ( y_true == y_pred )\n","    total_predictions = len ( y_true )\n","    accuracy = correct_predictions / total_predictions\n","    return accuracy\n","    \n","acc = accuracy ( np.array ( true_labels ) , np.array ( predictions ))\n","print('Accuracy of test set: {:.2f}%'.format(acc*100))"],"metadata":{"id":"DAG1dkPI9sIr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681915948133,"user_tz":-120,"elapsed":915,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"849c1c6a-36b3-44fc-b9b5-88e77aa9d1a1"},"execution_count":61,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy of test set: 80.25%\n"]}]},{"cell_type":"code","source":["accuracy = accuracy_score(true_labels , predictions)\n","print('Accuracy of test set: {:.2f}%'.format(accuracy*100))"],"metadata":{"id":"gvdXXWWO9w_T","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1681915966609,"user_tz":-120,"elapsed":2,"user":{"displayName":"Anna Pallares Lopez","userId":"02802121497963548737"}},"outputId":"4de45c7c-50dd-4f0f-c9d8-509ebfe00e62"},"execution_count":62,"outputs":[{"output_type":"stream","name":"stdout","text":["Accuracy of test set: 80.25%\n"]}]}]}