PC04_MLP_CIFAR10_PartB.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"xMsNyA3WPrER"},"source":["# **Notebook PC#04 (Part B)**\n","# MLP classifier for the CIFAR-10 database.\n","#### Grid search for the hyperparameters. Code produced by previous students of IA353. Thank you for making PC#04 still more complete and informative.\n","**Professor:** Fernando J. Von Zuben <br>\n","**Aluno(a):** Gabriel Toffanetto França da Rocha - 289320 <br>\n","**Aluno(a):** Maria Fernanda Paulino Gomes - 206745 <br>"]},{"cell_type":"code","execution_count":null,"metadata":{"executionInfo":{"elapsed":12812,"status":"ok","timestamp":1712248799316,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"},"user_tz":180},"id":"feKIgQlnaT7U","colab":{"base_uri":"https://localhost:8080/"},"outputId":"344272a0-717e-4eb5-94a0-60f7aa355721"},"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n","170498071/170498071 [==============================] - 2s 0us/step\n"]}],"source":["import matplotlib.pyplot as plt\n","from tqdm.notebook import tqdm\n","import tensorflow as tf\n","import pandas as pd\n","import numpy as np\n","import os\n","import gc\n","\n","cifar10 = tf.keras.datasets.cifar10\n","\n","(x_train, y_train),(x_test, y_test) = cifar10.load_data()\n","x_train, x_test = x_train / 255.0, x_test / 255.0"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MH_I88sRYMQR"},"outputs":[],"source":["def model_and_evaluate(n_neurons = 512,\n","                       n_layers  = 1,\n","                       dropout   = 0.25,\n","                       epochs    = 5,\n","                       optimizer = 'adam',\n","                       verbose   = 1):\n","    gc.collect()\n","    tf.keras.backend.clear_session()\n","\n","    model = tf.keras.models.Sequential([\n","          tf.keras.layers.Flatten(),\n","          tf.keras.layers.Dense(n_neurons,activation=tf.nn.relu),\n","          tf.keras.layers.Dropout(dropout)])\n","\n","    if n_layers > 1:\n","        for _ in range(n_layers-1):\n","            model.add(tf.keras.layers.Dense(n_neurons,activation=tf.nn.relu))\n","            model.add(tf.keras.layers.Dropout(dropout))\n","\n","    model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))\n","    model.compile(optimizer=optimizer,\n","                  loss='sparse_categorical_crossentropy',\n","                  metrics=['accuracy'])\n","\n","    model.fit(x_train, y_train, epochs=epochs, verbose=verbose)\n","\n","    loss, accuracy = model.evaluate(x_test, y_test,verbose=verbose)\n","\n","    if verbose:\n","        model.summary()\n","        # Evaluate the model on the test data using `evaluate`\n","        print(\"Evaluate on test data\")\n","        print(\"test loss, test acc:\", loss, accuracy)\n","\n","    train_result = dict(n_neurons = n_neurons,\n","                        n_layers  = n_layers,\n","                        optimizer = optimizer,\n","                        dropout   = dropout,\n","                        epochs    = epochs,\n","                        loss      = loss,\n","                        accuracy  = accuracy)\n","    return loss, accuracy"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"YdFzu9J-WgWO"},"outputs":[],"source":["n_repetitions = 5\n","DEFAULT = {\n","    'n_neurons' : 512,\n","    'n_layers'  : 1,\n","    'dropout'   : 0.25,\n","    'epochs'    : 5,\n","    'optimizer' : 'adam'\n","}\n","\n","df = pd.DataFrame(columns=['n_neurons', 'n_layers', 'dropout', 'epochs','optimizer'] + \\\n","                          [f\"{metric} {iteration}\" for iteration in range(1, n_repetitions+1) for metric in ['loss', 'accuracy']])"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["93c8f2aad3c1487f8aded17a4a77d72e","04781081708d4906bbc03b97ab545d49","6afc74a4af5542dea67c90e4b084fad8","ebf7d9f082f4496cb2b75a86496cd27e","70cd7f314de04bcfb29ea46905932eb2","5628b89a81ba4a07b6d2efdc880b468f","9d1834fff22b4861b18bfb788a13498a","796562497351406383bef87180cec2bb","a13d191f965a495c918584fbb6e4d61d","6d8642b0d4fb4be1904d2563a1754719","4fa72e293871420b8cdebf1625063657"]},"executionInfo":{"elapsed":147146,"status":"ok","timestamp":1712248946454,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"},"user_tz":180},"id":"_gnl-yKziHBk","outputId":"3cb21895-7778-4bc7-c1b2-ef9f5884653b"},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"93c8f2aad3c1487f8aded17a4a77d72e"}},"metadata":{}}],"source":["args = DEFAULT.copy()\n","row = list(args.values())\n","for iter in tqdm(range(n_repetitions)):\n","        loss, accuracy = model_and_evaluate(verbose=0)\n","\n","        row.append(loss)\n","        row.append(accuracy)\n","df.loc[0] = row"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["7cb0f78ecd8848b6b3e3f42a843943eb","d07378a3a63b428e934aa8f9f1a09e80","6820dd5169ab4f4f92d7308c869e5cc5","dce9633e924d4d9e8bdbe3a11e451228","0801e684bfa546a89a2dc87ac349fbe4","a0d4042ab7b544088d8b9024a30cc7be","5cc8fc7795b040a996a1335eed0ca625","fc74ba286bb04645b747f01afe2998eb","307227b3364e46cba827d25128426c60","f88f63e52ae54ca38ea9bdd34d0fd563","34db7f23d94b4194972183fb9981e2f7","c88e017a6a374eab824e67a84ea15ce6","bf291a945a2e4639a45ec471b54f33bb","021e41c48ba14806ab9df8048b69e884","50046764f345436b89cf30cfe11b09cb","e049aac41cd64921948015cb9bc08947","86d4378ded5f4e14b9a2d9f660dc791b","b198b635bddb4adc9c92de2a96295e2e","cf5b6d671cd048e4b1d13a72e5824c85","498b93c9071246b0b0d19cbf0a6c5d7f","07c22d9e96d345ceb7adfb9ff8a9923c","6ea669105304433381b9d3ce33a20cee","ef69f7b2753b4b3ebe5afd4359c31470","8f726205ba324ef99b3dd266155033fe","5e813889fe494222b9df03d85fccc057","462a669238b644968c8791db4b85bbce","3349eda3c3014d848857e51461a18d2e","23c301f75d5748fb89821dac5ecfda6a","f4a0512670344ec4a4b98b3464c34837","8c8e4b3081a64a82a00ba0f1cc2d420a","9dedb172e41847249c6bac21b5af6bd4","753a290df2cc4b67a50834bab8add7b3","07e616967bac4c3c8e8bb29f9122df12"]},"id":"xZ-lbPruWgWO","executionInfo":{"status":"ok","timestamp":1712249507153,"user_tz":180,"elapsed":560709,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"50e0ddff-6581-4fd4-e67d-fe36b1908e40"},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7cb0f78ecd8848b6b3e3f42a843943eb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c88e017a6a374eab824e67a84ea15ce6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ef69f7b2753b4b3ebe5afd4359c31470"}},"metadata":{}}],"source":["for n_neurons in [1024, 2048, 4096]:\n","    args = DEFAULT.copy()\n","    args['n_neurons'] = n_neurons\n","\n","    row = list(args.values())\n","\n","    for iter in tqdm(range(n_repetitions)):\n","        loss, accuracy = model_and_evaluate(n_neurons=n_neurons, verbose=0)\n","\n","        row.append(loss)\n","        row.append(accuracy)\n","\n","    df.loc[f'n_neurons={n_neurons}'] = row\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"42sb--VnWgWP","colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["83d350d4a9b54d1e9892d77820e142b1","32992c9072bf4e39b33e736386ef13d6","9631cf5b779548d3b39a104c28d793a2","16b90b45b1e445e79044ac3604f09d5d","8c96da13e21f44db97a8de0b8b3b30d7","dc0e7c8ca79b4ef4af184151971a4e19","73837498238c46d49c370038e9940ba6","0a65c461be6b4bf3bfc51c18f55c2b32","5c49fadbbacc4d9195a8adf2f35171b4","38274837095b426f832d1f4c9919de62","82d4a9733eed4998af05001f9e86c6fa","bdb5ddab4740497ba17d20319d99b4ce","5e0dd14932b64db4bc83665c2d21d5b3","a0ec5de345134bec836d52d484fd0b06","6f1e679a85c142068484b061aa23a888","a2c73a4da0a94beda9af5c3464e9c0b8","f9e9e6a054b242119c01fc0afa0c7ccb","cc37845a07af46f685d8942e3e10146d","c9cb2d15d88e4e08acad43a33cfa83e2","4eeb98dca1a3437d97f08ad2e128e4e8","88212b92e9cd4708ba8ead3ce546fdfa","acf3470b679949febc1a46bfa28d92d9","d65464fc32e74ca295c8f14760f8c3e2","628c9ef42c654c08beec4e426c1a60fb","2221bfa59d17472a8f39ab206992352b","068e963b51d1436cae6fc2521f73f199","9b7a6272abcf4ef8a29b639f5be367c0","4e2d3708b30841849ea8fffb8d389b83","406634ab2dce417ea728f9c203d4eacc","e4f3129a8b3c439a9e1c17e7bad9d5b8","03d27b8c79f746518f0cc7412384a0c9","042b0eed8641452892b2201b49be3221","2cb062748a524287996c2045ec686572"]},"executionInfo":{"status":"ok","timestamp":1712250085928,"user_tz":180,"elapsed":578783,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"a6330c59-594b-435f-d66c-9ae6fbb5b51d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"83d350d4a9b54d1e9892d77820e142b1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bdb5ddab4740497ba17d20319d99b4ce"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d65464fc32e74ca295c8f14760f8c3e2"}},"metadata":{}}],"source":["for n_layers in [2, 3, 4]:\n","    args = DEFAULT.copy()\n","    args['n_layers'] = n_layers\n","\n","    row = list(args.values())\n","\n","    for iter in tqdm(range(n_repetitions)):\n","        loss, accuracy = model_and_evaluate(n_layers=n_layers, verbose=0)\n","\n","        row.append(loss)\n","        row.append(accuracy)\n","\n","    df.loc[f'n_layers={n_layers}'] = row"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-DPXsJa1WgWP","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["b14a5032232e41ca9925424a2fe3be28","1bfa2542c7df4028bdd1eecb339748c4","837e635dcfd34f29a3e23a39557c0e39","beb60a6a933f4beb8df0895faea8f9f5","0a26566afb614e6e90fd371b69515b17","269e7592166e4c818d91be9dab97a963","a27b90d0da7b4fb4a6dd8a913872ecf8","49e5c92213c04413bebb9f5c664f1e50","9bcf937f87794c29b12bc2d12e69c25b","68de3026afd2428d800e54dfbbf56ad0","d41f618df9c84b9d946ceab6bbea504c","3d586154ea5a4c0295ee15103d6e9c1f","b3880a0e88964117afaefcf674712e87","1cf06e59d6c348f8a00a4104e49ab1b2","dd73c27833da4a5cbc6294745c3ac0c8","881acaf84b5e416abd1024e7854bca66","dc968639e02d4098b1fc953e1e29d603","6934e9386d934605b25b7666dcc28503","169c701daa5a4d23b0ba02417e06dae9","d6b7aa43e2644b889c8328125368819c","f43db85c782d4edbb817571e9bc8857f","74c3d8c3c4224935b9af1f05bdcf3866"]},"executionInfo":{"status":"ok","timestamp":1712250325612,"user_tz":180,"elapsed":239694,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"25cdc389-ab5f-489a-e8f9-8102c4557f9e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b14a5032232e41ca9925424a2fe3be28"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3d586154ea5a4c0295ee15103d6e9c1f"}},"metadata":{}}],"source":["for dropout in [0, 0.5]:\n","    args = DEFAULT.copy()\n","    args['dropout'] = dropout\n","\n","    row = list(args.values())\n","\n","    for iter in tqdm(range(n_repetitions)):\n","        loss, accuracy = model_and_evaluate(dropout=dropout, verbose=0)\n","\n","        row.append(loss)\n","        row.append(accuracy)\n","\n","    df.loc[f'dropout={dropout}'] = row"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"g7sL1VTbWgWQ","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["1392f97f17604f15a9e914cf8fb2c71e","c62b9ffebc274381a96e941baf31526e","1a1e33413ae74901b953a7204ff92cff","be88be9c766f4e79918dc3bc0ea37069","fa75d32fb0c644a3b18189b16e656600","a26e9d38d126478588221a12884615e4","7e195381a2cc487e858423983ef732bb","5074391bdfe84a2f9a425f8d9b498bb7","c1b6d53162d143e885a11eddf5c07a24","3f8d60c199274371b203b49736b6e310","8ba687a64ad64661a092c4ac354f2af1","dcdcbf5d0381458e95803ff934711a91","308805244704412abc336d21b342611f","9bf77cc14d1a43338b3711836bb26713","763326b61b864a059f7f329f490792ee","6cf8bc6b2b974685bdbfc4720988c332","84ae49d6e65c4b7ab2f5257a93e27ba4","4fe2ca8331c34d7e9820604ce4f96648","4eb85742489e40dfbc09b675ce2408e8","422f88fb3d604a6db97f3864ff908a25","a5ca90343dcd4629bccf0eb18597a434","7fdcd30e0b164fd2a4a74a1ca9e13fec"]},"executionInfo":{"status":"ok","timestamp":1712250561851,"user_tz":180,"elapsed":236247,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"ee6e7ae2-259d-47bb-87b3-778c256ca735"},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1392f97f17604f15a9e914cf8fb2c71e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dcdcbf5d0381458e95803ff934711a91"}},"metadata":{}}],"source":["for optimizer in ['RMSprop', 'SGD']:\n","    args = DEFAULT.copy()\n","    args['optimizer'] = optimizer\n","\n","    row = list(args.values())\n","\n","    for iter in tqdm(range(n_repetitions)):\n","        loss, accuracy = model_and_evaluate(optimizer=optimizer, verbose=0)\n","\n","        row.append(loss)\n","        row.append(accuracy)\n","\n","    df.loc[f'optimizer={optimizer}'] = row"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"UjqkU0jSWgWQ","colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["0d3430115c5c4f71af2d2b20a0d8429a","7841564ac0a4498eb0656052a4a0ac9d","33cac0984da74760b9d42e0f56b9346a","319ec1bb65b5415abb5899ef6751db40","f6f4691ac46a41a088b88a924998eda1","2c5f35a761c746889b22ee5d28a92eb7","556b05e1465c4c3786fe97c4996136ba","a281e901ce5e409899ed685bcd269f21","0bf0c60341014a2a91bc33602d894239","3a024ec20b464fb095d76e54b9a27e91","55cd9b199bbc4d9599461e3c851f116b","afe035bdbd914b60b0b567cc3fc8823c","28297e20dd834638bcee0df25a4b42b2","559be1317cd2486c8e28b58e491b9493","40ef4835455d4f1aac478b172b946544","5c567e402a9f46c3884f057b3e912515","0f51ec54ddf449069ae5af5d19639562","bdaf867e77af4835a4850f38836d4457","81d471881bd04b43867e4ad5c9b65e3c","af78b98a47774d848423c166131127fd","3b3b5d8d11124ea7a45000f76d36efb3","496f4e79e5484f6d80301abf9be1615c","db9cb244cb2f47be804f8a45f21d8141","e96d1e31b77c46a1b707ecc5ccfe3e13","85bc1e8c60a54c939ba3ac7c6e48693f","f58468e3ed91464d9de5eabe503ac166","86a62df8bd294be0bf3df0075f5e7bd1","8c335a1e18694c2ab3f5e04a4d74a623","2d2b4ba1fabd4d7bb781140c8b28a322","755a655d4450495e913a942cc1c06c46","5f9b37b741b04e4d90da4f603103aa24","f0b800a78f4745e6910156e4bfc84df4","dbd7845ed1bb4ef6b593b082c318da4a"]},"executionInfo":{"status":"ok","timestamp":1712251803858,"user_tz":180,"elapsed":1242017,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"3858d400-6f09-44bd-9e42-a42e8b38671d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0d3430115c5c4f71af2d2b20a0d8429a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"afe035bdbd914b60b0b567cc3fc8823c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/5 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"db9cb244cb2f47be804f8a45f21d8141"}},"metadata":{}}],"source":["for epochs in [10, 20, 30]:\n","    args = DEFAULT.copy()\n","    args['epochs'] = epochs\n","\n","    row = list(args.values())\n","\n","    for iter in tqdm(range(n_repetitions)):\n","        loss, accuracy = model_and_evaluate(epochs=epochs, verbose=0)\n","\n","        row.append(loss)\n","        row.append(accuracy)\n","\n","    df.loc[f'epochs={epochs}'] = row"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fwjImmahSJV6","scrolled":true,"colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1712251803859,"user_tz":180,"elapsed":13,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"13337578-7401-4466-87ce-908d4cf24bca"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                n_neurons  n_layers  dropout  epochs optimizer    loss 1  \\\n","0                     512         1     0.25       5      adam  1.724917   \n","n_neurons=1024       1024         1     0.25       5      adam  1.654128   \n","n_neurons=2048       2048         1     0.25       5      adam  1.632170   \n","n_neurons=4096       4096         1     0.25       5      adam  1.587692   \n","n_layers=2            512         2     0.25       5      adam  1.792163   \n","\n","                accuracy 1    loss 2  accuracy 2    loss 3  accuracy 3  \\\n","0                   0.3909  1.679064      0.3942  1.675461      0.4117   \n","n_neurons=1024      0.4121  1.669571      0.4111  1.682325      0.3903   \n","n_neurons=2048      0.4145  1.626898      0.4290  1.612662      0.4207   \n","n_neurons=4096      0.4396  1.669261      0.3907  1.609528      0.4278   \n","n_layers=2          0.3357  1.783335      0.3589  1.769958      0.3610   \n","\n","                  loss 4  accuracy 4    loss 5  accuracy 5  \n","0               1.643609      0.4086  1.685976      0.3917  \n","n_neurons=1024  1.633816      0.4135  1.640561      0.4078  \n","n_neurons=2048  1.636626      0.4148  1.609891      0.4267  \n","n_neurons=4096  1.629496      0.4115  1.617242      0.4331  \n","n_layers=2      1.743491      0.3905  1.736534      0.3851  "],"text/html":["\n","  <div id=\"df-e9d66c4f-5b6f-40b1-907b-759b88058d83\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>n_neurons</th>\n","      <th>n_layers</th>\n","      <th>dropout</th>\n","      <th>epochs</th>\n","      <th>optimizer</th>\n","      <th>loss 1</th>\n","      <th>accuracy 1</th>\n","      <th>loss 2</th>\n","      <th>accuracy 2</th>\n","      <th>loss 3</th>\n","      <th>accuracy 3</th>\n","      <th>loss 4</th>\n","      <th>accuracy 4</th>\n","      <th>loss 5</th>\n","      <th>accuracy 5</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>512</td>\n","      <td>1</td>\n","      <td>0.25</td>\n","      <td>5</td>\n","      <td>adam</td>\n","      <td>1.724917</td>\n","      <td>0.3909</td>\n","      <td>1.679064</td>\n","      <td>0.3942</td>\n","      <td>1.675461</td>\n","      <td>0.4117</td>\n","      <td>1.643609</td>\n","      <td>0.4086</td>\n","      <td>1.685976</td>\n","      <td>0.3917</td>\n","    </tr>\n","    <tr>\n","      <th>n_neurons=1024</th>\n","      <td>1024</td>\n","      <td>1</td>\n","      <td>0.25</td>\n","      <td>5</td>\n","      <td>adam</td>\n","      <td>1.654128</td>\n","      <td>0.4121</td>\n","      <td>1.669571</td>\n","      <td>0.4111</td>\n","      <td>1.682325</td>\n","      <td>0.3903</td>\n","      <td>1.633816</td>\n","      <td>0.4135</td>\n","      <td>1.640561</td>\n","      <td>0.4078</td>\n","    </tr>\n","    <tr>\n","      <th>n_neurons=2048</th>\n","      <td>2048</td>\n","      <td>1</td>\n","      <td>0.25</td>\n","      <td>5</td>\n","      <td>adam</td>\n","      <td>1.632170</td>\n","      <td>0.4145</td>\n","      <td>1.626898</td>\n","      <td>0.4290</td>\n","      <td>1.612662</td>\n","      <td>0.4207</td>\n","      <td>1.636626</td>\n","      <td>0.4148</td>\n","      <td>1.609891</td>\n","      <td>0.4267</td>\n","    </tr>\n","    <tr>\n","      <th>n_neurons=4096</th>\n","      <td>4096</td>\n","      <td>1</td>\n","      <td>0.25</td>\n","      <td>5</td>\n","      <td>adam</td>\n","      <td>1.587692</td>\n","      <td>0.4396</td>\n","      <td>1.669261</td>\n","      <td>0.3907</td>\n","      <td>1.609528</td>\n","      <td>0.4278</td>\n","      <td>1.629496</td>\n","      <td>0.4115</td>\n","      <td>1.617242</td>\n","      <td>0.4331</td>\n","    </tr>\n","    <tr>\n","      <th>n_layers=2</th>\n","      <td>512</td>\n","      <td>2</td>\n","      <td>0.25</td>\n","      <td>5</td>\n","      <td>adam</td>\n","      <td>1.792163</td>\n","      <td>0.3357</td>\n","      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adam  1.792163      0.3357   \n","\n","     loss 2  accuracy 2    loss 3  accuracy 3    loss 4  accuracy 4    loss 5  \\\n","0  1.679064      0.3942  1.675461      0.4117  1.643609      0.4086  1.685976   \n","1  1.669571      0.4111  1.682325      0.3903  1.633816      0.4135  1.640561   \n","2  1.626898      0.4290  1.612662      0.4207  1.636626      0.4148  1.609891   \n","3  1.669261      0.3907  1.609528      0.4278  1.629496      0.4115  1.617242   \n","4  1.783335      0.3589  1.769958      0.3610  1.743491      0.3905  1.736534   \n","\n","   accuracy 5  \n","0      0.3917  \n","1      0.4078  \n","2      0.4267  \n","3      0.4331  \n","4      0.3851  "],"text/html":["\n","  <div id=\"df-278d2f94-86dd-4ada-abd0-29b07b4a8778\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    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pd.read_csv('cifar10_PC04_MLP_analysis.csv')\n","df.head()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"b1XF7sp6wfyF","colab":{"base_uri":"https://localhost:8080/","height":484},"executionInfo":{"status":"ok","timestamp":1712251804517,"user_tz":180,"elapsed":665,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"366313e5-0a7d-4b4b-a11a-c8dd072adb98"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 480x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["losses = np.zeros((n_repetitions, 4))\n","accurs = np.zeros((n_repetitions, 4))\n","\n","for i, n_neurons in enumerate([512,1024,2048,4096]):\n","    sample = df[(df['n_neurons'] == n_neurons) & (df['n_layers'] == 1) & (df['dropout'] == 0.25) & (df['optimizer'] == 'adam') & (df['epochs'] == 5)]\n","    loss   = sample[[f'loss {i}' for i in range(1, n_repetitions+1)]].to_numpy()\n","    accy   = sample[[f'accuracy {i}' for i in range(1, n_repetitions+1)]].to_numpy()\n","\n","    losses[:, i] = loss\n","    accurs[:, i] = accy\n","\n","fig,ax=plt.subplots(2,1,figsize=(4,4),dpi=120,sharex=True)\n","\n","ax[0].boxplot(accurs, labels=[512,1024,2048,4096])\n","ax[1].boxplot(losses, labels=[512,1024,2048,4096])\n","\n","ax[0].set_ylabel('Accuracy')\n","ax[1].set_ylabel('Loss')\n","ax[1].set_xlabel('Number of artificial neurons')\n","fig.align_ylabels()\n","plt.tight_layout()\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WJakFAknziv0","colab":{"base_uri":"https://localhost:8080/","height":485},"executionInfo":{"status":"ok","timestamp":1712251804966,"user_tz":180,"elapsed":452,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"3bf4555f-7100-4435-b8a4-e30bf9c57647"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 480x480 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAdQAAAHUCAYAAACDJ9lsAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAABJ0AAASdAHeZh94AABPHElEQVR4nO3deVxU9f4/8BfgyCKxCQgmo8CoKW6VglvilmshLoipmV65klhqtwVRQsUsvJKWGlcNBVHsUghp6rXMhZRraKYlYOqAOKgQKCIuCAOc3x9+md+dhn0OzACv5+Ph4975nM/5nPccjRefsxoIgiCAiIiItGKo6wKIiIhaAgYqERGRCBioREREImCgEhERiYCBSkREJAIGKhERkQgYqERERCJgoBIREYmAgUpERCQCBioREZEI2ui6gNassLAQSUlJcHJygrGxsa7LISKi/1NSUoLs7Gx4enrCysqqTuswUHUoKSkJ3t7eui6DiIiq8e2332LSpEl16stA1SEnJycAT//CZDKZjqshIqJKcrkc3t7eqp/TdcFA1aHKw7wymQxubm46roaIiP6qPqfjeFESERGRCBioREREImCgEhERiYCBSkREJAJelEQAAC8vL2RkZNTaT6FQAACkUmmtfV1dXXHgwAGtayMiag4YqFQvSqVS1yUQEeklBioBQJ1nkpW396SlpTVmOUREzQ7PoRIREYmAgUpERCQCHvJt4UJDQxEXFyfaeJUXLon5ZCdfX1+EhISINh4RkS5whtrCxcXFQS6XizaeRCKBRCIRbTy5XC5q4BMR6QpnqK2ATCbT24uI+AxjImopOEMlIiISAQOViIhIBAxUIiIiETBQiYiIRMBAJSIiEgEDlYiISAR6HaglJSUIDAxEx44dYWpqCg8PDxw9erTe47z88sswMDDAW2+9VeXyHTt2oEePHjAxMUHXrl2xefPmKvvdunUL06dPh5WVFSwsLDBp0iRkZmbWux4iImp59DpQ586diw0bNmDWrFn4/PPPYWRkhAkTJuD06dN1HiMhIQFnzpypdvm2bdvg5+cHNzc3bN68GYMGDcLixYuxbt06tX4PHz7EiBEjkJSUhOXLl2P16tW4cOECPD09cffu3QZ/RyIiaiEEPZWSkiIAENavX69qKy4uFlxdXYVBgwbVaYzi4mKhS5cuQmhoqABAWLRokdryx48fC+3btxcmTpyo1j5r1iyhXbt2QkFBgapt3bp1AgDh7NmzqrbLly8LRkZGQlBQUEO+opCamioAEFJTUxu0fl307NlT6NmzZ6ONry19r4+IWqeG/HzW2xlqfHw8jIyMsGDBAlWbiYkJ5s+fjzNnziA7O7vWMf75z3+ioqIC7733XpXLT5w4gbt37yIgIECtfdGiRXj06BEOHTqkVs+AAQMwYMAAVdtzzz2HUaNG4euvv67v1yMiohZGbwP1woUL6NatGywsLNTa3d3dAQAXL16scX2FQoGwsDCsW7cOpqam1W4DAPr376/W/uKLL8LQ0FC1vKKiAr///rtGv8p6MjIy8ODBgzp9LyIiapn09lm+OTk5cHR01GivbLt9+3aN67/77rt4/vnnMWPGjBq3YWRkBHt7e7X2tm3bon379qptFBQUoKSkpNZ6unfvXu228vLykJ+fr9Ym5kPriYhIt/Q2UIuLi2FsbKzRbmJiolpenRMnTmDfvn1ISUmpdRtt27atcpmJiYlqG5X/29B6ACAiIgKrV6+usQ8RETVfehuopqamKCkp0Wh/8uSJanlVysrKsHjxYrz++utq5zur20ZpaWmVy548eaLaRuX/NqSeSgEBAfDx8VFrk8vl8Pb2rnE9IiJqHvQ2UB0dHXHr1i2N9pycHABAx44dq1wvJiYGV65cwbZt25CVlaW27MGDB8jKyoK9vT3MzMzg6OiI8vJy5OXlqR32LS0txd27d1XbsLGxgbGxsWrb9amnkr29vcahZSIiajn09qKkfv364erVqygqKlJrrzyM269fvyrXUygUUCqVGDJkCJydnVV/gKdh6+zsjB9++EFtjF9++UVtjF9++QUVFRWq5YaGhujdu7dGv8p6XFxc8MwzzzT0qxIRUQugt4E6bdo0lJeXY/v27aq2kpISREVFwcPDA05OTgCeBugff/yh6jNjxgwkJiZq/AGACRMmIDExER4eHgCAkSNHwsbGBv/617/Utv2vf/0LZmZmmDhxolo9586dUwvVK1eu4Pjx4xqHcomIqPXR20O+Hh4e8PHxQVBQEPLy8iCTybBr1y5kZWVhx44dqn5z5sxBUlISBEEA8PTe0Oeee67KMZ2dndXOWZqammLNmjVYtGgRfHx8MHbsWJw6dQp79uzB2rVrYWNjo+obEBCAL7/8EhMnTsR7770HiUSCDRs2oEOHDnj33XcbZycQEVGzobeBCjw9RPvhhx9i9+7duHfvHvr06YODBw9i2LBhom0jICAAEokEn376KQ4cOAAnJyds3LgRS5YsUev3zDPP4OTJk3jnnXfw0UcfoaKiAsOHD8fGjRthZ2cnWj1ERNQ8GQiVUztqcmlpaejVqxdSU1Ph5ubWKNuoHDctLa1RxteWvtdHRK1TQ34+6/UMlbS3ZcgdOLUrA77w0HUpVdo/JhfZj/jPkIiaP729KImIiKg54dSghXsr2RYAkLax5qdG6cqkykO+Oq6DiEhbnKESERGJgDPUFi49PR0ARLvoSaFQAACkUqko48nlcshkMlHGIiLSJQYq1YtSqRR1PJlMBl9fX1HHJCLSBQZqC1fXu6K8vLyQkZEh6rZdXV1x4MABUcds7uq6n+tzJID7mUg/MFCpXiQSia5LaBXEPhJARI2PgUoAwBlOE6nrfuYDL4iaH17lS0REJAIGKhERkQgYqERERCJgoBIREYmAgUpERCQCBioREZEIeNsMkQhCQ0MRFxcn2niVD38Q8z25vr6+CAkJEW08IlLHGSqRCOLi4iCXy0UbTyKRiPoQDblcLmrgE5EmzlCJRCKTyfT2QQxiznSJqGqcoRIREYmAgUpERCQCrQI1JSVFrDqIiIiaNa0CddCgQejWrRvWrFmDzMxMsWoiIiJqdrQK1D179qBr165Ys2YNunbtiiFDhmDr1q0oKCgQqz4iIqJmQatAnTlzJg4dOoTbt2/j888/hyAICAgIQMeOHeHt7Y34+HiUlpaKVSsREZHeEuWiJFtbW7z11lv473//i2vXrmHFihX4448/4OvrCwcHByxYsACnT58WY1NERER6SfSrfE1NTWFmZgYTExMIggADAwPs378fnp6eGDBgANLT08XeJBERkc6JEqgPHjxAVFQURo8ejc6dO2P58uXo0qUL4uPjkZubi9u3byMuLg55eXmYN29encctKSlBYGAgOnbsCFNTU3h4eODo0aO1rpeYmIixY8eiY8eOMDY2RqdOnTBt2jSkpqaq9Tt58iQMDAyq/bN27VpV3+jo6Gr75ebm1n1nERFRi6TVk5L279+P2NhYHDx4EE+ePMGAAQPw2WefYcaMGWjfvr1a32nTpuHevXtYtGhRncefO3cu4uPjsXTpUnTt2hXR0dGYMGECTpw4gaFDh1a73qVLl2BtbY0lS5bA1tYWubm52LlzJ9zd3XHmzBn07dsXANCjRw/s3r1bY/3du3fjhx9+wJgxYzSWhYaGwtnZWa3Nysqqzt+JiIhaKEELBgYGglQqFZYvXy788ccftfZPSUkR5s6dW6exU1JSBADC+vXrVW3FxcWCq6urMGjQoHrXmpubK7Rp00bw9/evta9MJhO6du2q1hYVFSUAEM6dO1fvbVcnNTVVACCkpqaKNibpRs+ePYWePXvquoxq6Xt9RPqmIT+ftZqhHj9+HMOHD69zf3d3d7i7u9epb3x8PIyMjLBgwQJVm4mJCebPn4/ly5cjOzsbTk5Odd62vb09zMzMUFhYWGO/s2fPQi6XY9WqVdX2efDgAczMzGBkZFTn7RMRUcum1TnU+oRpfV24cAHdunWDhYWFWntlIF+8eLHWMQoLC5Gfn49Lly7Bz88PRUVFGDVqVI3rxMbGAgBmzZpV5fIRI0bAwsICZmZm8PLywrVr1+rwbYiIqKXTaoYaHByMgwcPVhtuzz//PLy9vbFy5cp6j52TkwNHR0eN9sq227dv1zrGwIEDceXKFQCAubk5goODMX/+/Gr7l5eXIy4uDu7u7pDJZGrLzMzMMHfuXFWgnj9/Hhs2bMDgwYPx66+/1jpbzsvLQ35+vlqbmK/7IiIi3dIqUOPj4zF58uRql0+YMAFxcXENCtTi4mIYGxtrtJuYmKiW1yYqKgpFRUXIzMxEVFQUiouLUV5eDkPDqifmx44dw59//only5drLJs+fTqmT5+u+uzt7Y2xY8di2LBhWLt2LbZu3VpjLREREVi9enWtNRMRUfOkVaAqFAq4urpWu9zZ2Rk3btxo0NimpqYoKSnRaH/y5IlqeW0GDRqk+v8zZsxAjx49AADh4eFV9o+NjYWRkRF8fX3rVOPQoUPh4eGBH3/8sda+AQEB8PHxUWuTy+Xw9vau07aIiEi/aRWo5ubmNQbm9evXVTPK+nJ0dMStW7c02nNycgAAHTt2rNd41tbWGDlyJGJjY6sM1OLiYiQmJmL06NHo0KFDncd1cnJSHVauib29Pezt7etVMxERNR9aX5S0bdu2KoMvOzsb27dvx4gRIxo0dr9+/XD16lUUFRWptVe+Mq5fv371HrO4uBj379+vctmBAwfw4MGDai9Gqk5mZibs7OzqXQsREbUsWgXqmjVrUFJSAjc3N7z77rvYuXMndu7ciX/84x/o3bs3SktLsWbNmgaNPW3aNJSXl2P79u2qtpKSEkRFRcHDw0N1EZBCocAff/yhtm5eXp7GeFlZWTh27Bj69+9f5fb27t0LMzOzas8J//WCIgA4fPgwzp8/j3HjxtX5exERUcuk1SHf7t2749SpU3j77bexceNGtWXDhg3Dpk2bVOct68vDwwM+Pj4ICgpCXl4eZDIZdu3ahaysLOzYsUPVb86cOUhKSoIgCKq23r17Y9SoUejXrx+sra1x7do17NixA0qlEmFhYRrbKigowH/+8x9MnToV5ubmVdYzePBgPP/88+jfvz8sLS3x66+/YufOnXBycqryIiYiImpdtApUAOjTpw+SkpJw584d1UvGXVxcYGtrq3VxMTEx+PDDD7F7927cu3cPffr0wcGDBzFs2LAa11u4cCEOHTqEI0eO4MGDB7C3t8eYMWOwfPly9O7dW6P/N998A6VSiZkzZ1Y7pq+vLw4dOoQffvgBjx8/hqOjI/7+979j5cqV9TrnSkRELZOB8L9TO2pSaWlp6NWrF1JTU+Hm5qbrckgLlX9/aWlpOq6kavpeH5G+acjPZ61nqABw8+ZNXLhwAffv30dFRYXG8jlz5oixGSIiIr2lVaA+efIEb7zxBvbt24eKigoYGBiozmUaGBio+jFQiYiopdPqKt/ly5cjISEBa9euxcmTJyEIAnbt2oUffvgB48ePR9++ffHbb7+JVSsREZHe0ipQ4+PjMW/ePAQGBqqOMT/77LMYPXo0Dh48CCsrK3zxxReiFEpERKTPtArUvLw81dtfKh8F+OjRI9XyqVOnIiEhQZtNEBERNQtaBWqHDh1w9+5dAE/fxmJtba32GL6ioiLVs3eJiIhaMq0uSvLw8MDp06cRGBgIAHj11Vexfv16ODo6oqKiAhs3bsTAgQNFKZSIqK68vLyQkZFRaz+FQgEAkEqltfZ1dXXFgQMHtK6NWi6tZqiLFy+Gi4uL6q0wa9asgZWVFV5//XW88cYbsLS0xKZNm0QplIhIbEqlEkqlUtdlUAuh1Qx16NChGDp0qOqzk5MTLl++jEuXLsHIyAjPPfcc2rQR5VZXIiKEhoYiLi5OJ9vOyMio0w3+vr6+CAkJaYKKSN80eIb6+PFjTJkyBbGxseoDGhqib9++6NWrF8OUiEQVFxcHuVwu2ngSiQQSiUS08eRyuc4Cn3SvwYlnZmaGH3/8EePHjxezHiKiGslkMr19hCIfIdq6aXUOdejQoThz5oxYtRARETVbWgXqli1bcOrUKQQHB+PmzZti1URERNTsaBWoffv2xc2bN/HJJ5+gc+fOMDY2hoWFhdofS0tLsWolIiLSW1pdNTR16lS1h+ATtVZbhtyBU7sy4AsPXZdSpf1jcpH9iBcJEjUmrf4Li46OFqkMIiKi5o2/shKJ4K1kWwBA2sYUHVdStUmVLxjXcR1ELZlWgRoTE1OnfnwfKrV06enpAMS7baI+j8SrC7lcDplMJspYRFQ1rQJ17ty51S7jC8aJGk7sx+HJZDL4+vqKOiYRqdMqUK9fv67RVl5ejqysLEREREChUGDXrl3abIKoWRAEQdTxKme6+voAAyLSpFWgdu7cucp2FxcXjBw5EhMnTsSWLVv4knEiImrxtLoPtTavvPIKn2tJREStQqMGakZGhurVbkRERC2ZVod8f/rppyrbCwsL8dNPP2HTpk3w9vbWZhNERETNglaBOnz48CqflCQIAoyMjODj44PNmzdrswkiImrGlEolkpOTUVBQABsbGwwZMkTUV+bpE60C9cSJExptBgYGsLa2RufOnWFhYaHN8ERE1EwplUqEhYUhIiICubm5qnZHR0csXLgQy5Yta3HBqlWgenp6ilVHlUpKShASEoLdu3fj3r176NOnDz766CO8/PLLNa6XmJiIrVu34tKlS7h79y7s7OwwcOBArFq1Cr169VLr26VLF9y4cUNjDH9/f2zdulWtrbCwEB988AESExPx+PFjuLu749NPP8ULL7yg/ZclolrxmcnNg1KphLe3Nw4fPgypVIrQ0FC4uroiIyMDkZGRCAkJQUpKChITE1tUqGp9H2pqaipeffXVKpd/99136N27N7p06dKg8efOnYv4+HgsXboUXbt2RXR0NCZMmIATJ05g6NCh1a536dIlWFtbY8mSJbC1tUVubi527twJd3d3nDlzBn379lXr369fP7z77rtqbd26dVP7XFFRgYkTJ+K3337D+++/D1tbW0RERGD48OE4f/48unbt2qDvSETU0oSFheHw4cOYM2cOIiMj1UJz2bJl8PPzQ0xMDNatW4fg4GAdVioyQQtTpkwRRo8eXe3ysWPHCr6+vg0aOyUlRQAgrF+/XtVWXFwsuLq6CoMGDar3eLm5uUKbNm0Ef39/tfbOnTsLEydOrHX9uLg4AYDwzTffqNry8vIEKysr4bXXXqt3PYIgCKmpqQIAITU1tUHrU8vVs2dPoWfPnrouQ+/o+37R9/qaQmlpqeDg4CBIpVKhtLS02j5SqVRwdHSsto+uNeTns1a3zZw5c6bGw6+jRo3CqVOnGjR2fHw8jIyMsGDBAlWbiYkJ5s+fjzNnziA7O7te49nb28PMzAyFhYVVLi8tLcWjR49qrKdDhw6YMmWKqs3Ozg7Tp0/H/v37eXsQERGA5ORk5Obmws/Pr9rDuRKJBH5+fsjJyUFycnITV9h4tArUe/fu4Zlnnql2ubm5Oe7evdugsS9cuIBu3bppXNjk7u4OALh48WKtYxQWFiI/Px+XLl2Cn58fioqKMGrUKI1+x48fh5mZGczNzdGlSxd8/vnnVdbzwgsvwNBQfZe5u7vj8ePHuHr1aj2+HRFRy1RQUAAAcHV1rbGfi4uLWv+WQKtzqFKpFMnJyVi4cGGVy0+dOoVOnTo1aOycnBw4OjpqtFe23b59u9YxBg4ciCtXrgB4Gu7BwcGYP3++Wp8+ffpg6NCh6N69O+7evYvo6GgsXboUt2/fxrp169TqGTZsWI319O7du9pa8vLykJ+fr9Yml8tr/Q5ERM2JjY0NgKcP9qlJZmamWv+WQKtAfe2117BmzRq4u7vjrbfeUs3eysvLsWXLFsTFxWHFihUNGru4uBjGxsYa7SYmJqrltYmKikJRUREyMzMRFRWF4uJilJeXq80yDxw4oLbOvHnzMH78eGzYsAFvv/226hcCbeuJiIjA6tWra62ZiKg5GzJkCBwcHBAZGVntrTFKpRKRkZFwdHTEkCFDdFBl49DqkG9QUBBGjBiBpUuXwtHREcOGDcOwYcPQsWNHvPPOO/D09GxwoJqamlZ5XvLJkyeq5bUZNGgQxo4di4ULF+L777/Hnj17EBQUVOM6BgYGeOedd1BWVoaTJ0+KVk9AQABSU1PV/nz77be1fgciouZEIpEgICAACoUCfn5+Gq8iVCqVmD9/PhQKBQICAnjbTCVjY2P88MMP2LVrFxISElRTfHd3d0ydOhVz5szROOdYV46Ojrh165ZGe05ODgCgY8eO9RrP2toaI0eORGxsLMLDw2vs6+TkBED92L6jo6Nq2w2px97eHvb29vWqmYioOVq2bBlSUlIQExODkydPws/PDy4uLsjIyMCOHTugUCgwceJEBAYG6rpUUWl9B7KhoSHmzZuHefPmiVGPSr9+/XDixAkUFRWpXZiUkpKiWl5fxcXFuH//fq39Ko/t29nZqdVz6tQpVFRUqP2SkJKSAjMzM437VomIWiuJRILExESsW7cOERERCAkJUS1zdHTEmjVrEBgY2KJmp4CWh3wLCgrw+++/V7v80qVLuHfvXoPGnjZtGsrLy7F9+3ZVW0lJCaKiouDh4aGaRSoUCvzxxx9q6+bl5WmMl5WVhWPHjqF///5q9ZeXl6v1q3xcVtu2bTFixAi1ev78808kJCSo2u7cuYNvvvkGr776apXnV4mIWiuJRILg4GDcuHEDJ06cwL59+3DixAncuHEDwcHBLS5MAS1nqO+88w6uXLmCn3/+ucrl/v7+6NGjB3bs2FHvsT08PODj44OgoCDk5eVBJpNh165dyMrKUhtvzpw5SEpKgiAIqrbevXtj1KhR6NevH6ytrXHt2jXs2LFDFZaVDhw4gI8++gjTpk2Ds7MzCgoKsHfvXqSmpuLjjz+Gg4ODqu+0adMwcOBAzJs3D+np6aonJZWXl/NiIyKiakgkEgwfPlzXZTQJrQL1+PHj1d4yAwCvvvqqxvNw6yMmJgYffvih2rN8Dx48WOXtK/9r4cKFOHToEI4cOYIHDx7A3t4eY8aMwfLly9Vubenduzd69uyJPXv2ID8/H23btkW/fv3w9ddfw8fHR21MIyMjHD58GO+//z42bdqE4uJiDBgwANHR0ejevXuDvyMREbUMBsL/Tu3qycTEBJs2bVJ7mtH/2r59O5YsWVKnW1xao7S0NPTq1Qupqalwc3PTdTmkRyr/PaSlpem4Ev1S+brInj171thPoVBoXF2qLYlEAqlUWmMfuVwOmUzGv7cWoCE/n7U6h+ro6IgLFy5Uu/z8+fNqF/YQEbVkMpkMvr6+ui6DdESrQ77e3t744osvMH78eHh5eakt279/P6Kiomo8JExEVB9aHFAjanRaBeqqVavw448/YvLkyejbt6/qXaOpqam4ePEievbsyQt2iIioVdDqkK+lpSV+/vlnBAcHQ6lUIj4+HvHx8VAqlQgJCcHZs2f5GyUREbUKWgUqALRr1w6rV6/GpUuX8PjxYzx+/Bjnzp2Dm5sbZs6cWeUD7omIiFoarZ+UVEkQBBw7dgyxsbFITEzEgwcPYGtri5kzZ4q1CSIiIr2ldaCeP38esbGx+Pe//43c3FwYGBhgxowZeOuttzBw4EDVZe5EREQtWYMCNTMzE7GxsYiNjcW1a9fw7LPPYtasWXB3d4evry+mTp2KQYMGiV0rERGR3qp3oA4aNAhnz56Fra0tpk2bhsjISAwdOhRA7S+UJSIiaqnqHagpKSlwdnbGhg0bMHHiRLRpI9ppWCIiomar3lf5btmyBY6Ojpg8eTIcHBzg7++PEydO8PYYIiJq1eodqAEBATh9+jQyMjKwdOlSnDp1CqNGjcKzzz6LkJAQGBgY8EIkIiJqdRp8H6qzszOCg4ORnp6Oc+fOYcaMGTh58iQEQUBAQAAWLFiAgwcP4smTJ2LWS0REpJe0frADALz44ovYsGEDsrOz8cMPP2Ds2LGIi4uDl5cXbG1txdgEERGRXhP1iiJDQ0OMHj0ao0ePxtatW7F//37s3btXzE0QNWteXl51uhq+sk9dXhvl6uqKAwcOaF0bEWlHlBlqVUxMTODr64v9+/c31iaIWiyJRAKJRKLrMoioHnjPC1ET4kySqOVqtBkqERFRa8JAJSIiEgEDlYiISAQMVCIiIhEwUImIiETAQCUiIhIBA5WIiEgEDFQiIiIRMFCJiIhEwEAlIiISgV4HaklJCQIDA9GxY0eYmprCw8MDR48erXW9xMREjB07Fh07doSxsTE6deqEadOmITU1Va3f3bt3sX79egwbNgx2dnawsrLCwIEDERcXpzHmyZMnVe96/eufn3/+WbTvTEREzZNeP8t37ty5iI+Px9KlS9G1a1dER0djwoQJOHHiBIYOHVrtepcuXYK1tTWWLFkCW1tb5ObmYufOnXB3d8eZM2fQt29fAMCZM2ewYsUKTJgwAcHBwWjTpg327duHGTNmID09HatXr9YYe/HixRgwYIBam0wmE/eLExFR8yPoqZSUFAGAsH79elVbcXGx4OrqKgwaNKje4+Xm5gpt2rQR/P39VW2ZmZlCVlaWWr+Kigph5MiRgrGxsfDw4UNV+4kTJwQAwjfffNOAb1O11NRUAYCQmpoq2phERKS9hvx81ttDvvHx8TAyMsKCBQtUbSYmJpg/fz7OnDmD7Ozseo1nb28PMzMzFBYWqtqcnZ3RuXNntX4GBgbw9vZGSUkJMjMzqxzrwYMHKCsrq9f2iYioZdPbQL1w4QK6desGCwsLtXZ3d3cAwMWLF2sdo7CwEPn5+bh06RL8/PxQVFSEUaNG1bpebm4uAMDW1lZj2bx582BhYQETExOMGDECv/zySx2+DRERtXR6ew41JycHjo6OGu2Vbbdv3651jIEDB+LKlSsAAHNzcwQHB2P+/Pk1rlNQUIDIyEi89NJLattv27Ytpk6digkTJsDW1hbp6ekIDw/HSy+9hP/+9794/vnnaxw3Ly8P+fn5am1yubzW70BERM2D3gZqcXExjI2NNdpNTExUy2sTFRWFoqIiZGZmIioqCsXFxSgvL4ehYdUT84qKCsyaNQuFhYXYvHmz2rLBgwdj8ODBqs9eXl6YNm0a+vTpg6CgIBw5cqTGWiIiIqq8yImIiFoGvQ1UU1NTlJSUaLQ/efJEtbw2gwYNUv3/GTNmoEePHgCA8PDwKvu//fbbOHLkCGJiYlRXAtdEJpNh0qRJSEhIQHl5OYyMjKrtGxAQAB8fH7U2uVwOb2/vWrdDRET6T28D1dHREbdu3dJoz8nJAQB07NixXuNZW1tj5MiRiI2NrTJQV69ejYiICISFheH111+v87hOTk4oLS3Fo0ePNM73/i97e3vY29vXq2YiIl0IDQ2t8n78v1IoFFAqlaJuWyKRQCqV1trP19cXISEhom5bW3p7UVK/fv1w9epVFBUVqbWnpKSoltdXcXEx7t+/r9H+xRdfYNWqVVi6dCkCAwPrNWZmZiZMTExgbm5e73qIiPRRXFycXl/jIZfL6xT4TU1vZ6jTpk1DeHg4tm/fjvfeew/A0ycnRUVFwcPDA05OTgCe/ob0+PFjPPfcc6p18/LyNGaDWVlZOHbsGPr376/WHhcXh8WLF2PWrFnYsGFDtfXk5+fDzs5Ore23337DgQMHMH78+GrPyxIRNUcymQxpaWm6LqNKbm5uui6hSnobqB4eHvDx8UFQUBDy8vIgk8mwa9cuZGVlYceOHap+c+bMQVJSEgRBULX17t0bo0aNQr9+/WBtbY1r165hx44dUCqVCAsLU/U7e/Ys5syZg/bt22PUqFGIjY1Vq2Hw4MFwcXEB8PTwgqmpKQYPHgx7e3ukp6dj+/btMDMzUxuTiIhaJ70NVACIiYnBhx9+iN27d+PevXvo06cPDh48iGHDhtW43sKFC3Ho0CEcOXIEDx48gL29PcaMGYPly5ejd+/eqn7p6ekoLS1Ffn4+/va3v2mMExUVpQpUb29vxMbGYsOGDSgqKoKdnR2mTJmClStX8tGDREQEA+F/p3bUpNLS0tCrVy+kpqbq7SEMImp9Kn8e6fsh38asryE/n3nij4iISAQMVCIiIhHo9TlUIiJqeluG3IFTuzLgCw9dl1Kl/WNykf1I/+KLM1QiIiIR6F/EExGRTr2V/PRNW2kbU3RcSdUmVV6UpOM6/oozVCIiIhEwUImIiETAQCUiIhIBA5WIiEgEDFQiIiIRMFCJiIhEwEAlIiISAe9DJaJWSalUIjk5GQUFBbCxscGQIUMgkUh0XRY1YwxUImpVKt+LHBERgdzcXFW7o6MjFi5ciGXLlrX6YE1PTwcg3ou8FQoFAEAqlYoynlwu18vXZjJQiajVUCqV8Pb2xuHDhyGVShEaGgpXV1dkZGQgMjISISEhSElJQWJiYqsPVTEplUpRx5PJZPD19RV1TDEwUImo1QgLC8Phw4cxZ84cREZGqoXmsmXL4Ofnh5iYGKxbtw7BwcE6rFS3xH5Ntr6/X1UsvCiJiFoFpVKJiIgISKVSjTAFAIlEgsjISEilUkRERIg+q6KWj4FKRK1CcnIycnNz4efnV+3hXIlEAj8/P+Tk5CA5ObmJK6Tmjod8iahVKCgoAAC4urrW2M/FxUWtP1XPy8sLGRkZtfar7FOXi5xcXV1x4MABrWvTBQYqEbUKNjY2AFBrAGRmZqr1J+21lgu8DASxzz5TnaWlpaFXr15ITU0V7fJ0IqqaUqmEVCpF27ZtIZfLq/whr1QqIZPJoFQqcePGjVYTBKSpIT+feQ6ViFoFiUSCgIAAKBQK+Pn5aVx0pFQqMX/+fCgUCgQEBDBMqd54yJeIWo1ly5YhJSUFMTExOHnyJPz8/ODi4oKMjAzs2LEDCoUCEydORGBgoK5LpWaIgUpErYZEIkFiYiLWrVuHiIgIhISEqJY5OjpizZo1CAwM5OyUGoSBSkStikQiQXBwMAIDA/ksXxIVA5WIWiWJRILhw4frugxqQRioOlRSUgLg6YOeiYhIf1T+XK78OV0XDFQdys7OBgB4e3vrthAiIqpSdnY2XnjhhTr15X2oOlRYWIikpCQ4OTnB2NhY1+XUyfHjx7F48WJs2rQJI0eO1HU5LRb3c9Pgfm4azXE/l5SUIDs7G56enrCysqrTOpyh6pCVlRUmTZqk6zLqpfIwiFQq5cMoGhH3c9Pgfm4azXU/13VmWokPdiAiIhIBA5WIiEgEDFQiIiIRMFCpXmQyGTw9PSGTyXRdSovG/dw0uJ+bRmvZz7zKl4iISAScoRIREYmAgUpERCQCBioREZEIGKhEREQiYKASERGJgIFKREQkAgYqERGRCBioREREImCgEhERiYCBSkREJAIGKhERkQgYqERERCJgoBIREYmAgUpERCQCBioREZEI2ui6gNassLAQSUlJcHJygrGxsa7LISKi/1NSUoLs7Gx4enrCysqqTuswUHUoKSkJ3t7eui6DiIiq8e2332LSpEl16stA1SEnJycAT//CZDKZjqshIqJKcrkc3t7eqp/TddHqAvXhw4dYv349UlJScPbsWdy7dw9RUVGYO3dundY/evQoVq9ejV9//RXGxsYYNWoUwsPD0aVLl3rXUnmYVyaTwc3Nrd7rExFR46rP6bhWd1HSnTt3EBoaisuXL6Nv3771WvfgwYMYN24cSkpKEBYWhnfffRdJSUkYOnQo8vPzG6liIiJqDlrdDNXR0RE5OTlwcHDAL7/8ggEDBtR53cDAQLi4uCA5ORlt27YFALz66qt44YUXEBYWhk8//bSxyiYiIj3X6maoxsbGcHBwqPd6BQUFSE9Px+TJk1VhCgB9+/ZFjx498O9//1vMMomIqJlpdYHaUCUlJQAAU1NTjWVmZma4ffs2cnNzm7osIiLSE63ukG9DdejQAVZWVkhOTlZrv3v3LtLT0wEAt27dqnb2m5eXp3GeVS6XN06xRERNwMvLCxkZGbX2UygUAACpVFprX1dXVxw4cEDr2nSBgVpHhoaG8Pf3x7p16xAUFIS//e1vKCoqwgcffIDS0lIAQHFxcbXrR0REYPXq1U1VLhGR3lAqlbouoUkwUOshNDQUd+7cwT//+U+EhYUBAMaMGYP58+dj69atMDc3r3bdgIAA+Pj4qLVV3udERNQc1XUmWXlbYFpaWmOWo3MM1Hpo27YtIiMjsXbtWly9ehUdOnRAt27dMHPmTBgaGtb4cAZ7e3vY29s3YbVERNSUGKgN0KFDB3To0AEAUF5ejpMnT8LDw6PGGSoREbVsvMq3Gjk5Ofjjjz9qPfYfHh6OnJwcvPvuu01UGRER6aNWOUPdsmULCgsLcfv2bQDAd999h5s3bwIA3n77bVhaWiIoKAi7du3C9evXVY8V3LNnD/bt24dhw4bB3NwcP/74I77++mv4+flh6tSpuvo6RESkB1ploIaHh+PGjRuqzwkJCUhISAAAzJ49G5aWllWu161bNxQUFGDNmjUoLi5G9+7dsXXrVixYsKBJ6iYiIv3VKgM1Kyur1j7R0dGIjo5Wa3N3d0dSUlLjFEVERM1aqwxUIiKqXmhoKOLi4kQbr/LhD2K+VcvX1xchISGijScGXpRERERq4uLiRH2Sm0QigUQiEW08uVwuauCLhTNUIiLSIJPJ9PZBDPr6/mjOUImIiETAQCUiIhIBA5WIiEgEDFQiIiIRMFCJiIhEwEAlIiISAQOViIhIBAxUIiIiETBQiYiIRMBAJSIiEgEDlYiISAQMVCIiIhEwUImIiETAQCUiIhIBA5WIiEgEDFQiIiIRMFCJiIhE0EbXBRARkX7ZMuQOnNqVAV946LqUKu0fk4vsR/oXX5yhEhERiUD/Ip6IiHTqrWRbAEDaxhQdV1K1SW5uAIA0HdfxV5yhEhERiYCBSkREJAIGKhERkQgYqERERCLgRUlERKQmPT0dAOD2fxf/aEuhUAAApFKpKOPJ5XLIZDJRxhITA5WIiBqVUqkUdTyZTAZfX19RxxQDA5WIiNQIgiDqeJUz3bQ0fbvRRVw8h0pERCQCBioREZEIGKhEREQiYKASERGJgBclEVGL4+XlhYyMjFr71ed2DldXVxw4cEDr2qjlYqASUasl9u0c1LoxUImoxanrTLK13M5BTYPnUImIiETAQCUiIhIBA5WIiEgEDFQiIiIRMFCJiIhEwEAlIiISAQOViIhIBAxUIiIiETBQiYiIRMBAJSIiEgEfPUhERA1S15cQVPapfNRjTZrzSwgYqERE1KgkEomuS2gSDFQiajZCQ0MRFxcn2nj1mTnVla+vL0JCQkQbT58115lkY+E5VCJqNuLi4iCXy0UbTyKRiDp7ksvlogY+NS+tbob68OFDrF+/HikpKTh79izu3buHqKgozJ07t07rnz9/HitXrsQvv/yChw8fwsXFBX5+fli0aBGMjIwat3gigkwm09vXrYk506Xmp9XNUO/cuYPQ0FBcvnwZffv2rde658+fx+DBg5GVlYXAwEB8+umncHFxwZIlS/CPf/yjkSomIqLmoNXNUB0dHZGTkwMHBwf88ssvGDBgQJ3X3bZtGwDgp59+go2NDQDA398fnp6eiI6Oxueff94oNRMRkf5rdTNUY2NjODg4NGjdoqIimJiYwMrKSq3d0dERpqamIlRHRETNVasLVG0MHz4cRUVF8Pf3x+XLl3Hjxg1s3boVCQkJCAoK0nV5RESkQ63ukK82/v73vyMtLQ3btm1DZGQkAMDIyAhbtmzBm2++WeO6eXl5yM/PV2sT82pFIiLSLQZqPRgZGcHV1RVjx46Fj48PTExM8NVXX+Htt9+Gg4MDvL29q103IiICq1evbrpiiYioSTFQ6yEsLAyff/45rl27BnNzcwDA9OnTMWLECCxatAivvPIK2rSpepcGBATAx8dHrU0ul9cYwkRE1HwwUOshIiICI0eOVIVpJS8vL/zjH/9AVlYWZDJZleva29vD3t6+KcokIiId4EVJ9fDnn3+ivLxco12pVAIAysrKmrokIiLSE6LMUBUKBRQKBYYOHapq++233/Dpp5+ipKQEr732WrM7tJmTk4P79+/D1dVV9Wiybt264ejRo7h79y7at28PACgvL8fXX3+NZ555Bq6urrosmZqBur6dQ6FQAACkUmmtfZvz2zmIWhJRAnXx4sV4+PAhfvzxRwBPZ3IjRoxAaWkpnnnmGcTHx+Obb77BlClTxNic1rZs2YLCwkLcvn0bAPDdd9/h5s2bAIC3334blpaWCAoKwq5du3D9+nV06dIFALBs2TLMnj0bHh4eWLBgAUxNTfHVV1/h/Pnz+Oijj1rNGxWo8VUe9SB1W4bcgVO7MuALD12XUqX9Y3KR/Yhn0lorUf7mz549iyVLlqg+x8TEoLi4GKmpqXB2dsa4ceMQHh6uN4EaHh6OGzduqD4nJCQgISEBADB79mxYWlpWud6sWbNga2uLTz75BOvXr0dRURG6d++OrVu3wt/fv0lqp+atrjPJymfC6usza4lIkyiBWlBQoHbBzcGDB+Hp6ak6BDplyhQsX75cjE2JIisrq9Y+0dHRiI6O1mgfO3Ysxo4dK35RRFSrt5JtAQBpG1N0XEnVJlX+IqTjOkg3RLkoyc7OTjXjKywsxM8//6wWOmVlZbxgh4iIWjRRZqijR4/Gpk2bYGFhgZMnT6KiokLtIqT09HQ4OTmJsSkiIiK9JEqghoWF4erVq3jvvffQtm1bhIeHw9nZGQBQUlKCr7/+GjNnzhRjU0R6KTQ0VNQXS1deCSzm+zV9fX0REhIi2nhEpE6UQO3QoQOSk5Nx//59mJqaom3btqplFRUVOHbsGGeo1KLFxcVBLpdX+2CP+hL7inG5XI64uDgGKlEjEvX67qqujjU1Na33i7yJmiOZTKa3V+WKOdMloqqJEqjHjh3Dr7/+ivfff1/VtnPnTqxatQolJSWYOXMmwsPDYWRkJMbmiKiVSk9PByDeLwj1eYBGXYh5lIKaH1Gu8l21ahV+++031edLly7B398fdnZ2GD58ODZt2oTw8HAxNkVEJBqlUinqQzRkMhl8fX1FG4+aF1FmqJcvX8bUqVNVn3fv3g0LCwucOnUKZmZmePPNNxETE4PAwEAxNkdErZQgCKKOxwdokJhEmaE+evQIFhYWqs9HjhzBuHHjYGZmBgAYMGCA2pOJiIiIWhpRZqhOTk44d+4c/va3v0EulyM1NRXvvvuuanlBQQGMjY3F2BQRUa3q+hKC+tyexJcQUG1ECdRZs2YhNDQUt27dQlpaGqytrTFp0iTV8vPnz6Nbt25ibIqISDR8oQWJSZRAXbFiBUpLS3H48GFIpVJER0fDysoKwNPZ6cmTJ9Uenk/U0vAtKPqFM0nSBVH+C2vTpg3Wrl2LtWvXaiyzsbFBbm6uGJshIiLSW6L/yvrw4UNkZ2cDeHpu1dzcXOxNEOkdvgWFiES5yhcAzp07hxEjRsDa2hq9evVCr169YG1tjZEjR+KXX34RazNERER6SZQZakpKCoYPH462bdvCz88PPXr0APD0/tSvvvoKw4YNw8mTJ+Hu7i7G5oiIiPSOaBclPfvsszh9+jQcHBzUlq1atQpDhgzBihUrcPToUTE2R0REpHdEOeSbkpICf39/jTAFnr6JZsGCBfj555/F2BQREZFeEiVQDQ0NUVZWVu3y8vJyGBqKdrqWiIhI74iScoMHD8YXX3xR5eMFFQoFIiIiMGTIEDE2RUREpJdEOYf68ccfY9iwYXjuuecwefJk1VORrly5gv3798PIyAiffPKJGJsiIiLSS6IE6vPPP4+UlBSsWLECBw4cwOPHjwEAZmZmGDduHFatWgVbW1sxNkVERKSXRDux2bNnTyQmJqKoqAg5OTnIyclBUVEREhIS8N1338HJyUmsTREREekd0Z+UZGhoiA4dOog9LBERkV7jpbdEREQiYKASERGJgIFKREQkggafQ/3111/r3Pf27dsN3QwREVGz0OBA7d+/PwwMDOrUVxCEOvclao7S09MBAG7/95o0bSkUCgCAVCoVZTy5XA6ZTCbKWERUtQYHalRUlJh1ENH/UCqVoo4nk8ng6+sr6phEpK7BgfrGG2+IWQdRsyYIgqjjVc5009L4SnCi5oIXJREREYlA9Ac7EFH1vLy8kJGRUWu/yj51OSfr6uqKAwcOaF0bEWmHgUqkhyQSia5LIKJ6YqASNSHOJIlaLgYq1ZlSqURycjIKCgpgY2ODIUOGcCZFRPR/GKhUK6VSibCwMERERCA3N1fV7ujoiIULF2LZsmUMViJq9RioVCOlUglvb28cPnwYUqkUoaGhcHV1RUZGBiIjIxESEoKUlBQkJiYyVImoVWOgUo3CwsJw+PBhzJkzB5GRkWqhuWzZMvj5+SEmJgbr1q1DcHCwDislItIt3odK1VIqlYiIiIBUKtUIU+DplaiRkZGQSqWIiIgQ/ek+RETNCQOVqpWcnIzc3Fz4+flVezhXIpHAz88POTk5SE5ObuIKiYj0BwOVqlVQUADg6YMDauLi4qLWn4ioNWKgUrVsbGwAoNYn+2RmZqr1JyJqjRioVK0hQ4bAwcEBkZGR1Z4fVSqViIyMhKOjI4YMGdLEFRIR6Q8GKlVLIpEgICAACoUCfn5+GqGqVCoxf/58KBQKBAQE8LYZImrVeNsM1WjZsmVISUlBTEwMTp48CT8/P7i4uCAjIwM7duyAQqHAxIkTERgYqOtSiYh0ioFKNZJIJEhMTMS6desQERGBkJAQ1TJHR0esWbMGgYGBnJ0SUavHQKVaSSQSBAcHIzAwkM/yJSKqBgOV6kwikWD48OG6LoOISC/xoiQiIiIRMFCJiIhE0OoC9eHDh1i5ciXGjRsHGxsbGBgYIDo6uk7rDh8+HAYGBlX+4blEIqLWrdWdQ71z5w5CQ0MhlUrRt29fnDx5ss7rrlixAn5+fmptjx49wptvvokxY8aIXCkRETUnrS5QHR0dkZOTAwcHB/zyyy8YMGBAndd9+eWXNdr27NkDAJg1a5ZoNRIRUfPT6g75Ghsbw8HBQbTx9u7di3bt2mHSpEmijUlERM1PqwtUMeXn5+Po0aPw9vZGu3btdF0OERHpUKs75CumuLg4lJWV1elwb15eHvLz89Xa5HJ5Y5VGRERNjIGqhb1798LOzq7Kc6t/FRERgdWrVzdBVUREpAsM1AbKzMzEmTNn8NZbb6FNm9p3Y0BAAHx8fNTa5HI5vL29G6lCIiJqSgzUBtq7dy+Aul/da29vD3t7+8YsiYiIdIgXJTXQ3r174erqioEDB+q6FCIi0gMM1Grk5OTgjz/+0HipNgBcuHABly9fxsyZM3VQGRER6aNWech3y5YtKCwsxO3btwEA3333HW7evAkAePvtt2FpaYmgoCDs2rUL169fR5cuXdTWj42NBcCHORAR0f/XKgM1PDwcN27cUH1OSEhAQkICAGD27NmwtLSsdt2Kigr8+9//xgsvvIDu3bs3eq1ERNQ8tMpAzcrKqrVPdHR0lQ/NNzQ0VM1miYiIKrXKQG1NQkNDERcXV2s/hUJR5flibUgkEkil0lr7+fr6IiQkRNRtExE1NV6U1MLFxcXp9ROZ5HJ5nQKfiEjfcYbaCshkMqSlpem6jCq5ubnpugQiIlFwhkpERCQCzlBbuC1D7sCpXRnwhYeuS6nS/jG5yH7Ef4ZE1PxxhkpERCQCTg1auLeSbQEAaRtTdFxJ1Sb93zlU/TzDS0RUdwzUFi49PR1A7Rf/6Oq2GblcDplMJup2iYh0gYd8SadkMhl8fX11XQYRkdY4Q23hBEHQdQlERK0CZ6hEREQiYKASERGJgIFKREQkAgYqERGRCBioREREImCgEhERiYCBSkREJAIGKhERkQgYqERERCJgoBIREYmAgUpERCQCBioREZEI+HB8qjOlUonk5GQUFBTAxsYGQ4YMgUQi0XVZRER6gYFKtVIqlQgLC0NERARyc3NV7Y6Ojli4cCGWLVvGYCWiVo+BSjVSKpXw9vbG4cOHIZVKERoaCldXV2RkZCAyMhIhISFISUlBYmIiQ5WIWjUGKtUoLCwMhw8fxpw5cxAZGakWmsuWLYOfnx9iYmKwbt06BAcH67BSIiLd4kVJVC2lUomIiAhIpVKNMAUAiUSCyMhISKVSREREQKlU6qhSIiLdY6BStZKTk5Gbmws/P79qD+dKJBL4+fkhJycHycnJTVwhEZH+YKBStQoKCgAArq6uNfZzcXFR609E1BoxUKlaNjY2AICMjIwa+2VmZqr1JyJqjRioVK0hQ4bAwcEBkZGR1Z4fVSqViIyMhKOjI4YMGdLEFRIR6Q8GKlVLIpEgICAACoUCfn5+GqGqVCoxf/58KBQKBAQE8LYZImrVeNsM1WjZsmVISUlBTEwMTp48CT8/P7i4uCAjIwM7duyAQqHAxIkTERgYqOtSiYh0ioFKNZJIJEhMTMS6desQERGBkJAQ1TJHR0esWbMGgYGBnJ0SUavHQKVaSSQSBAcHIzAwkM/yJSKqBgOV6kwikWD48OG6LoOISC8xUHWopKQEACCXy3VcCRER/a/Kn8uVP6frgoGqQ9nZ2QAAb29v3RZCRERVys7OxgsvvFCnvgaCIAiNXA9Vo7CwEElJSXBycoKxsbGuy6mT48ePY/Hixdi0aRNGjhyp63JaLO7npsH93DSa434uKSlBdnY2PD09YWVlVad1OEPVISsrK0yaNEnXZdRL5WEQqVQKNzc3HVfTcnE/Nw3u56bRXPdzXWemlfhgByIiIhEwUImIiETAQCUiIhIBA5XqRSaTwdPTEzKZTNeltGjcz02D+7lptJb9zKt8iYiIRMAZKhERkQgYqERERCJgoBIREYmAgUpERCQCBirVKjc3F56enrC1tYWhoSEMDAzg5+en67JanF27dqFPnz4wMTGBgYEB2rRpAycnJ3z//fe6Lq1F2b9/Pzp16gSJRAIDAwMYGhrC0tISH374oa5La9FefvllGBgYwMTERNelNBo+epBqJZfL8dNPP8HIyAiWlpYoLCzUdUkt0ooVK5CTk4NevXqhX79+uHXrFk6cOIFx48YhISEBkydP1nWJLcLFixfx5MkTDB06FE5OTnj48CGOHTuGjz76CNevX8eePXt0XWKLc+7cOfz444+6LqPR8bYZqlVRURGysrLQp08fxMTE4I033sD8+fMRGRmp69JalG3btmHWrFkwNzdXtR09ehRjxoxBly5dcP36dR1W17KVlpbC0tISFRUV9XpdF9WNVCrFw4cPUVFRgSdPnuDJkye6LqlR8JAv1crCwgJ9+vTRdRktnr+/v1qYAk8Pk5mamuLPP//UUVWtQ9u2bWFhYYGysjJdl9LibN68GdnZ2fjyyy91XUqjY6AS6bHKGZOZmZmuS2lx8vLycOXKFRw/fhze3t7Iy8uDk5OTrstqUUpLS/HBBx+gR48emDp1qq7LaXQ8h0qkxxYtWoSKigq8+uqrui6lxRk+fDguX76s+vzss8/ixIkTOqyo5Zk9ezaePHmCffv26bqUJsFAJdJThw8fxtatW2Fubo5t27bpupwWJzw8HKmpqZDL5di/fz8qKirw6NEjXZfVYly7dg3x8fF45ZVX0KNHD12X0yQYqER66Pfff8ekSZNgZGSEpKQktG3bVtcltTgTJkzAhAkTAADbt29H+/btMXDgQBQVFcHQkGfDtOXt7Y02bdrgq6++0nUpTYb/aoj0jEKhwMCBA1FeXo6EhAS88MILui6pVRg/fjwePXrE+35FcPToUaSnp8PLywsXL17E6dOncfr0aZSVlaGiogKnT59GRkaGrssUHWeoRHqksLAQvXv3RnFxMbZt2wYvLy9dl9RqPH78GMDTB5mQdtLS0gAA+/btq/L86UsvvYR+/frhwoULTV1ao2KgEumJ0tJS9OjRA0VFRVi1ahUWLFig65JapLS0NLi5uam1PX78GEeOHAHwdKZK2hk/fnyVt3pt3LgRZWVleP/999G/f38dVNa4+GAHqhMfHx/cu3cPubm5SEtLQ8eOHVUXGuzcuRNSqVTHFTZ/zz//PC5evIgOHTpU+VSkf/3rXzqoquXp2LEjnjx5gr59+0IqleLWrVs4deoUSktL4eXlhf379+u6xBbLysqqRT/YgYFKddKmTRuUl5dXuezUqVMYOnRoE1fU8lhZWeH+/fvVLud/quJYvHgxYmNjUVhYiIqKChgYGMDa2hpvvvkm1q5dq+vyWjQGKhEREdWKV/kSERGJgIFKREQkAgYqERGRCBioREREImCgEhERiYCBSkREJAIGKhERkQgYqERERCJgoBIREYmAgUpERCQCBiqRyE6ePAkDAwPEx8frupQ6+fPPPzFt2jS0b98eBgYG+Oyzz6rsl5WVBQMDA4SHh9c65qpVq2BgYFCn7RsYGGDVqlWijim2Ll26YO7cuTrZNjUfDFRqlqKjo2FgYAATExPcunVLY/nw4cPRq1cvHVTW/Lzzzjv4/vvvERQUhN27d2PcuHG6LomoWWKgUrNWUlKCsLAwXZfRrB0/fhyTJk3Ce++9h9mzZ+O5557Teszg4GAUFxeLUB1R88FApWatX79++PLLL3H79m1dl9LkHj16JMo4eXl5sLKyEmWsSm3atIGJiYmoY1LVysrKUFpaqusyCAxUauaWL1+O8vLyWmeplef/oqOjNZb99Rxe5bm6q1evYvbs2bC0tISdnR0+/PBDCIKA7OxsTJo0CRYWFnBwcMCnn35a5TbLy8uxfPlyODg4oF27dvDy8kJ2drZGv5SUFIwbNw6WlpYwMzODp6cnkpOT1fpU1pSeno6ZM2fC2tq61nfQZmZmwsfHBzY2NjAzM8PAgQNx6NAh1fLKw+aCIOCLL76AgYFBnc9Rbt++Ha6urjA2NsaAAQNw7ty5Kuv9XyUlJXjnnXdgZ2eHZ555Bl5eXrh582aV458+fRoDBgyAiYkJXF1dsW3btmpr2bNnD1588UWYmprCxsYGM2bM0NjPlacA0tPTMWLECJiZmeHZZ5/FP//5zzp9378qKCjAe++9h969e8Pc3BwWFhYYP348fvvtN1Wfhw8fol27dliyZInG+jdv3oSRkRE++eQTVVthYSGWLl0KJycnGBsbQyaTYd26daioqFD1+d/z2J999pnq7yA9PR0AsHnzZri5ucHMzAzW1tbo378/9u7d26DvSPXXRtcFEGnD2dkZc+bMwZdffolly5ahY8eOoo3t6+uLHj16ICwsDIcOHcJHH30EGxsbbNu2DSNHjsS6desQGxuL9957DwMGDMCwYcPU1l+7di0MDAwQGBiIvLw8fPbZZxg9ejQuXrwIU1NTAE8Pt44fPx4vvvgiVq5cCUNDQ0RFRWHkyJE4deoU3N3d1cb08fFB165d8fHHH9f4wvE///wTgwcPxuPHj7F48WK0b98eu3btgpeXF+Lj4zF58mQMGzYMu3fvxuuvv46XX34Zc+bMqdN+2bt3Lx48eAB/f38YGBjgn//8J6ZMmYLMzExIJJJq1/Pz88OePXswc+ZMDB48GMePH8fEiRM1+l26dAljxoyBnZ0dVq1ahbKyMqxcuRIdOnTQ6Lt27Vp8+OGHmD59Ovz8/JCfn4/Nmzdj2LBhuHDhgtrM+969exg3bhymTJmC6dOnIz4+HoGBgejduzfGjx9fp+9eKTMzE99++y18fHzg7OyMP//8E9u2bYOnpyfS09PRsWNHmJubY/LkyYiLi8OGDRtgZGSkWv+rr76CIAiYNWsWAODx48fw9PTErVu34O/vD6lUiv/+978ICgpCTk6OxoViUVFRePLkCRYsWABjY2PY2Njgyy+/xOLFizFt2jQsWbIET548we+//46UlBTMnDmzXt+PGkggaoaioqIEAMK5c+eEjIwMoU2bNsLixYtVyz09PQU3NzfV5+vXrwsAhKioKI2xAAgrV65UfV65cqUAQFiwYIGqraysTOjUqZNgYGAghIWFqdrv3bsnmJqaCm+88Yaq7cSJEwIA4dlnnxWKiopU7V9//bUAQPj8888FQRCEiooKoWvXrsLYsWOFiooKVb/Hjx8Lzs7Owssvv6xR02uvvVan/bN06VIBgHDq1ClV24MHDwRnZ2ehS5cuQnl5udr3X7RoUa1jVu7D9u3bCwUFBar2/fv3CwCE7777TqPeShcvXhQACAEBAWpjzpw5U2P/e3t7CyYmJsKNGzdUbenp6YKRkZHamFlZWYKRkZGwdu1atTEvXboktGnTRq3d09NTACDExMSo2kpKSgQHBwdh6tSptX73zp07q/0dP3nyRG0fCsLT/WNsbCyEhoaq2r7//nsBgPCf//xHrW+fPn0ET09P1ec1a9YI7dq1E65evarWb9myZYKRkZGgUChU2wAgWFhYCHl5eWp9J02apPZvnpoeD/lSs+fi4oLXX38d27dvR05Ojmjj+vn5qf6/kZER+vfvD0EQMH/+fFW7lZUVunfvjszMTI3158yZg2eeeUb1edq0aXB0dMThw4cBABcvXsS1a9cwc+ZM3L17F3fu3MGdO3fw6NEjjBo1Cj/99JPa4T4AePPNN+tU++HDh+Hu7q52WNjc3BwLFixAVlaW6hBhQ/j6+sLa2lr1+aWXXgKAKvfB/9YDAIsXL1ZrX7p0qdrn8vJyfP/99/D29oZUKlW19+jRA2PHjlXrm5CQgIqKCkyfPl217+7cuQMHBwd07doVJ06cUOtvbm6O2bNnqz63bdsW7u7uNdZdHWNjYxgaGqpqvnv3LszNzdG9e3f8+uuvqn6jR49Gx44dERsbq2pLTU3F77//rlbLN998g5deegnW1tZq32X06NEoLy/HTz/9pLb9qVOnws7OTq3NysoKN2/e1Dj8Tk2HgUotQnBwMMrKykS94vd/f6ADgKWlJUxMTGBra6vRfu/ePY31u3btqvbZwMAAMpkMWVlZAIBr164BAN544w3Y2dmp/YmMjERJSQnu37+vNoazs3Odar9x4wa6d++u0d6jRw/V8ob6636pDNeq9sH/1mNoaAhXV1e19r/WmJ+fj+LiYo19V1Xfa9euQRAEdO3aVWP/Xb58GXl5eWr9O3XqpHFe19rausa6q1NRUYGNGzeia9euMDY2hq2tLezs7PD777+r/Z0ZGhpi1qxZ+Pbbb/H48WMAQGxsLExMTODj46P2XY4cOaLxPUaPHg0AGt+lqn8HgYGBMDc3h7u7O7p27YpFixZpnIunxsVzqNQiuLi4YPbs2di+fTuWLVumsby6i23Ky8urHfN/z3nV1AagxvOZ1amcfa5fvx79+vWrso+5ubna58pzr7ok5j7QRkVFBQwMDPCf//ynypr+uu/ErPvjjz/Ghx9+iL/97W9Ys2YNbGxsYGhoiKVLl2ocVZgzZw7Wr1+Pb7/9Fq+99hr27t2LV155BZaWlmrf5eWXX8YHH3xQ5fa6deum9rmqfwc9evTAlStXcPDgQRw5cgT79u1DREQEQkJCsHr16np/R6o/Biq1GMHBwdizZw/WrVunsaxyFlVYWKjWrs1MrTaVM9BKgiBALpejT58+AKCarVlYWKhmImLp3Lkzrly5otH+xx9/qJY3pc6dO6OiogIZGRlqM82/1mhnZwdTU1ONfVdVX1dXVwiCAGdnZ43AaWzx8fEYMWIEduzYodZeWFiocQSjV69eeP755xEbG4tOnTpBoVBg8+bNan1cXV3x8OFDrf8dtGvXDr6+vvD19UVpaSmmTJmCtWvXIigoiLcxNQEe8qUWw9XVFbNnz8a2bduQm5urtszCwgK2trYa56IiIiIarZ6YmBg8ePBA9Tk+Ph45OTmqK0pffPFFuLq6Ijw8HA8fPtRYPz8/v8HbnjBhAs6ePYszZ86o2h49eoTt27ejS5cu6NmzZ4PHbojK77xp0ya19r9evWpkZISxY8fi22+/hUKhULVfvnwZ33//vVrfKVOmwMjICKtXr9aYZQqCgLt374r4DdQZGRlpbPObb76p8qldAPD666/jhx9+wGeffYb27dtrXFU8ffp0nDlzRuM7Ak9DuqysrNaa/vp927Zti549e0IQBCiVylrXJ+1xhkotyooVK7B7925cuXIFbm5uasv8/PwQFhYGPz8/9O/fHz/99BOuXr3aaLXY2Nhg6NChmDdvHv7880989tlnkMlk+Pvf/w7g6fm1yMhIjB8/Hm5ubpg3bx6effZZ3Lp1CydOnICFhQW+++67Bm172bJl+OqrrzB+/HgsXrwYNjY22LVrF65fv459+/apLqhpKv369cNrr72GiIgI3L9/H4MHD8axY8cgl8s1+q5evRpHjhzBSy+9hICAAJSVlanur/z9999V/VxdXfHRRx8hKCgIWVlZ8Pb2xjPPPIPr168jMTERCxYswHvvvdco3+eVV15BaGgo5s2bh8GDB+PSpUuIjY2Fi4tLlf1nzpyJDz74AImJiVi4cKHG7UXvv/8+Dhw4gFdeeQVz587Fiy++iEePHuHSpUuIj49HVlaWxsz3r8aMGQMHBwcMGTIEHTp0wOXLl7FlyxZMnDhR7eI4ajwMVGpRZDIZZs+ejV27dmksCwkJQX5+PuLj4/H1119j/Pjx+M9//gN7e/tGqWX58uX4/fff8cknn+DBgwcYNWoUIiIiYGZmpuozfPhwnDlzBmvWrMGWLVvw8OFDODg4wMPDA/7+/g3edocOHfDf//4XgYGB2Lx5M548eYI+ffrgu+++q/Lez6awc+dO2NnZITY2Ft9++y1GjhyJQ4cOwcnJSa1fnz598P333+Mf//gHQkJC0KlTJ6xevRo5OTlqgQo8/cWhW7du2Lhxo+o8oZOTE8aMGQMvL69G+y7Lly/Ho0ePsHfvXsTFxeGFF17AoUOHqjx/Dzz9+xgzZgwOHz6M119/XWO5mZkZkpKS8PHHH+Obb75BTEwMLCws0K1bN6xevVrtfGt1/P39ERsbiw0bNuDhw4fo1KkTFi9ejODgYK2/L9WNgdDUVxIQEbVCkydPxqVLl6qclVPLwHOoRESNLCcnB4cOHapydkotBw/5EhE1kuvXryM5ORmRkZGQSCRaHcYn/ccZKhFRI0lKSsLrr7+O69evY9euXXBwcNB1SdSIeA6ViIhIBJyhEhERiYCBSkREJAIGKhERkQgYqERERCJgoBIREYmAgUpERCQCBioREZEIGKhEREQiYKASERGJgIFKREQkAgYqERGRCP4fpJUsZnJKHmoAAAAASUVORK5CYII=\n"},"metadata":{}}],"source":["losses = np.zeros((n_repetitions, 4))\n","accurs = np.zeros((n_repetitions, 4))\n","\n","for i, n_layers in enumerate([1,2,3,4]):\n","    sample = df[(df['n_neurons'] == 512) & (df['n_layers'] == n_layers)& (df['dropout'] == 0.25) & (df['optimizer'] == 'adam') & (df['epochs'] == 5)]\n","    loss   = sample[[f'loss {i}' for i in  range(1, n_repetitions+1)]].to_numpy()\n","    accy   = sample[[f'accuracy {i}' for i in  range(1, n_repetitions+1)]].to_numpy()\n","\n","    losses[:, i] = loss\n","    accurs[:, i] = accy\n","\n","fig,ax=plt.subplots(2,1,figsize=(4,4),dpi=120,sharex=True)\n","\n","ax[0].boxplot(accurs, labels=[1,2,3,4])\n","ax[1].boxplot(losses, labels=[1,2,3,4])\n","\n","ax[0].set_ylabel('Accuracy')\n","ax[1].set_ylabel('Loss')\n","ax[1].set_xlabel('Number of hidden layers')\n","fig.align_ylabels()\n","plt.tight_layout()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LhGykygaz5dm","colab":{"base_uri":"https://localhost:8080/","height":484},"executionInfo":{"status":"ok","timestamp":1712251804966,"user_tz":180,"elapsed":19,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"307fcb52-fb6e-46b6-89d0-0cfd76f0e88c"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 480x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["losses = np.zeros((n_repetitions, 3))\n","accurs = np.zeros((n_repetitions, 3))\n","\n","for i, dropout in enumerate([0, 0.25, 0.5]):\n","    sample = df[(df['n_neurons'] == 512) & (df['n_layers'] == 1)& (df['dropout'] == dropout) & (df['optimizer'] == 'adam') & (df['epochs'] == 5)]\n","    loss   = sample[[f'loss {i}' for i in  range(1, n_repetitions+1)]].to_numpy()\n","    accy   = sample[[f'accuracy {i}' for i in  range(1, n_repetitions+1)]].to_numpy()\n","\n","    losses[:, i] = loss\n","    accurs[:, i] = accy\n","\n","fig,ax=plt.subplots(2,1,figsize=(4,4),dpi=120,sharex=True)\n","\n","ax[0].boxplot(accurs, labels=[0,0.25,0.5])\n","ax[1].boxplot(losses, labels=[0,0.25,0.5])\n","\n","ax[0].set_ylabel('Accuracy')\n","ax[1].set_ylabel('Loss')\n","ax[1].set_xlabel('Dropout')\n","fig.align_ylabels()\n","plt.tight_layout()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4OKePqKW0UPU","colab":{"base_uri":"https://localhost:8080/","height":484},"executionInfo":{"status":"ok","timestamp":1712251805540,"user_tz":180,"elapsed":587,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"e27e69f3-cf5b-49cd-e681-6944d2c96b24"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 480x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["losses = np.zeros((n_repetitions, 3))\n","accurs = np.zeros((n_repetitions, 3))\n","\n","for i, optimizer in enumerate(['adam','SGD','RMSprop']):\n","    sample = df[(df['n_neurons'] == 512) & (df['n_layers'] == 1)& (df['dropout'] == 0.25) & (df['optimizer'] == optimizer) & (df['epochs'] == 5)]\n","    loss   = sample[[f'loss {i}' for i in  range(1, n_repetitions+1)]].to_numpy()\n","    accy   = sample[[f'accuracy {i}' for i in  range(1, n_repetitions+1)]].to_numpy()\n","\n","    losses[:, i] = loss\n","    accurs[:, i] = accy\n","\n","fig,ax=plt.subplots(2,1,figsize=(4,4),dpi=120,sharex=True)\n","\n","ax[0].boxplot(accurs, labels=['adam','SGD','RMSprop'])\n","ax[1].boxplot(losses, labels=['adam','SGD','RMSprop'])\n","\n","ax[0].set_ylabel('Accuracy')\n","ax[1].set_ylabel('Loss')\n","ax[1].set_xlabel('Optimizer')\n","fig.align_ylabels()\n","plt.tight_layout()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H7k3KTdi1Iil","colab":{"base_uri":"https://localhost:8080/","height":484},"executionInfo":{"status":"ok","timestamp":1712251805916,"user_tz":180,"elapsed":378,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"bcd8e70f-877e-4edf-e6c1-c2feb4b491bb"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 480x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}],"source":["losses = np.zeros((n_repetitions, 4))\n","accurs = np.zeros((n_repetitions, 4))\n","\n","for i, epochs in enumerate([5,10,20,30]):\n","    sample = df[(df['n_neurons'] == 512) & (df['n_layers'] == 1)& (df['dropout'] == 0.25) & (df['optimizer'] == 'adam') & (df['epochs'] == epochs)]\n","    loss   = sample[[f'loss {i}' for i in  range(1, n_repetitions+1)]].to_numpy()\n","    accy   = sample[[f'accuracy {i}' for i in  range(1, n_repetitions+1)]].to_numpy()\n","\n","    losses[:, i] = loss\n","    accurs[:, i] = accy\n","\n","fig,ax=plt.subplots(2,1,figsize=(4,4),dpi=120,sharex=True)\n","\n","ax[0].boxplot(accurs, labels=[5,10,20,30])\n","ax[1].boxplot(losses, labels=[5,10,20,30])\n","\n","ax[0].set_ylabel('Accuracy')\n","ax[1].set_ylabel('Loss')\n","ax[1].set_xlabel('Epochs')\n","fig.align_ylabels()\n","plt.tight_layout()"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"W-NuUsLIfBYm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1712253129201,"user_tz":180,"elapsed":266564,"user":{"displayName":"Gabriel Toffanetto França da Rocha","userId":"17996844547274708735"}},"outputId":"f4f024af-56da-4e6e-da61-502fcb26f932"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.8284 - accuracy: 0.3487\n","Epoch 2/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.6438 - accuracy: 0.4247\n","Epoch 3/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.5659 - accuracy: 0.4525\n","Epoch 4/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.5133 - accuracy: 0.4704\n","Epoch 5/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.4717 - accuracy: 0.4866\n","Epoch 6/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.4343 - accuracy: 0.4997\n","Epoch 7/30\n","1563/1563 [==============================] - 7s 4ms/step - loss: 1.3990 - accuracy: 0.5162\n","Epoch 8/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.3706 - accuracy: 0.5222\n","Epoch 9/30\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.3442 - accuracy: 0.5317\n","Epoch 10/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.3172 - accuracy: 0.5403\n","Epoch 11/30\n","1563/1563 [==============================] - 8s 5ms/step - loss: 1.2941 - accuracy: 0.5496\n","Epoch 12/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.2724 - accuracy: 0.5613\n","Epoch 13/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.2517 - accuracy: 0.5651\n","Epoch 14/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.2301 - accuracy: 0.5750\n","Epoch 15/30\n","1563/1563 [==============================] - 7s 4ms/step - loss: 1.2107 - accuracy: 0.5813\n","Epoch 16/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.1934 - accuracy: 0.5865\n","Epoch 17/30\n","1563/1563 [==============================] - 7s 4ms/step - loss: 1.1745 - accuracy: 0.5925\n","Epoch 18/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.1574 - accuracy: 0.6008\n","Epoch 19/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.1406 - accuracy: 0.6056\n","Epoch 20/30\n","1563/1563 [==============================] - 7s 4ms/step - loss: 1.1221 - accuracy: 0.6124\n","Epoch 21/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.1037 - accuracy: 0.6189\n","Epoch 22/30\n","1563/1563 [==============================] - 7s 4ms/step - loss: 1.0888 - accuracy: 0.6239\n","Epoch 23/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.0721 - accuracy: 0.6332\n","Epoch 24/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.0551 - accuracy: 0.6388\n","Epoch 25/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.0364 - accuracy: 0.6455\n","Epoch 26/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 1.0206 - accuracy: 0.6498\n","Epoch 27/30\n","1563/1563 [==============================] - 7s 4ms/step - loss: 1.0065 - accuracy: 0.6532\n","Epoch 28/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 0.9910 - accuracy: 0.6606\n","Epoch 29/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 0.9742 - accuracy: 0.6677\n","Epoch 30/30\n","1563/1563 [==============================] - 7s 5ms/step - loss: 0.9596 - accuracy: 0.6728\n","313/313 [==============================] - 1s 3ms/step - loss: 1.4718 - accuracy: 0.4927\n","Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," flatten (Flatten)           (None, 3072)              0         \n","                                                                 \n"," dense (Dense)               (None, 4096)              12587008  \n","                                                                 \n"," dropout (Dropout)           (None, 4096)              0         \n","                                                                 \n"," dense_1 (Dense)             (None, 10)                40970     \n","                                                                 \n","=================================================================\n","Total params: 12627978 (48.17 MB)\n","Trainable params: 12627978 (48.17 MB)\n","Non-trainable params: 0 (0.00 Byte)\n","_________________________________________________________________\n","Evaluate on test data\n","test loss, test acc: 1.4717955589294434 0.4927000105381012\n"]}],"source":["# Please, replace the values obtained by the simplified grid search here and execute the code\n","d = model_and_evaluate(n_neurons=4096,\n","                       n_layers=1,\n","                       dropout=0,\n","                       optimizer='SGD',\n","                       epochs=30)"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.10 (default, Mar 13 2023, 10:26:41) \n[GCC 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