diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..86b5602 --- /dev/null +++ b/.gitignore @@ -0,0 +1,16 @@ +bloco7_autoenconders/data/MNIST/raw/t10k-images-idx3-ubyte +bloco7_autoenconders/data/MNIST/raw/t10k-images-idx3-ubyte.gz +bloco7_autoenconders/data/MNIST/raw/t10k-labels-idx1-ubyte +bloco7_autoenconders/data/MNIST/raw/t10k-labels-idx1-ubyte.gz +bloco7_autoenconders/data/MNIST/raw/train-images-idx3-ubyte +bloco7_autoenconders/data/MNIST/raw/train-images-idx3-ubyte.gz +bloco7_autoenconders/data/MNIST/raw/train-labels-idx1-ubyte +bloco7_autoenconders/data/MNIST/raw/train-labels-idx1-ubyte.gz +bloco6_autoenconders/data/MNIST/raw/t10k-images-idx3-ubyte +bloco6_autoenconders/data/MNIST/raw/t10k-images-idx3-ubyte.gz +bloco6_autoenconders/data/MNIST/raw/t10k-labels-idx1-ubyte +bloco6_autoenconders/data/MNIST/raw/t10k-labels-idx1-ubyte.gz +bloco6_autoenconders/data/MNIST/raw/train-images-idx3-ubyte +bloco6_autoenconders/data/MNIST/raw/train-images-idx3-ubyte.gz +bloco6_autoenconders/data/MNIST/raw/train-labels-idx1-ubyte +bloco6_autoenconders/data/MNIST/raw/train-labels-idx1-ubyte.gz diff --git a/README.md b/README.md index 46b1f44..4785d21 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,26 @@ -# RedesNeurais -Disciplina de Redes Neurais SCC0270 +# Redes Neurais e Aprendizado Profundo / Neural Networks and Deep Learning (2022) + +Compilado de programas, anotações e atividades da disciplina de SCC0270 - Redes Neurais e Aprendizado Profundo oferecida pelo ICMC-USP. Todos os programas desenvolvidos na disciplina foram feitos em linguagem Python com o framework Pytorch e possuem apenas fins educacionais. + +Compiled from programs, notes and activities from the subject SCC0270 - Neural Networks and Deep Learning offered by ICMC-USP. All programs developed in the course were written in Python with the Pytorch framework and are for educational purposes only. + +## A disciplina/ The discipline + +### Objetivos / Goals + +Apresentar ao aluno os conceitos básicos de Redes Neurais Artificiais e os principais modelos existentes. Analisar o comportamento destes modelos, suas capacidades fundamentais e limitações, possibilitando a utilização destas técnicas na resolução de problemas práticos. + +To present to the students the basic concepts of Artificial Neural Networks and the current most important models. To analyze the behavior of these models, their fundamental capabilities and limitations, allowing the use of these techniques to solve practical problems. + +### Programa resumido / Summary program + +Definição de modelos conexionistas. +Aprendizado em modelos conexionistas: aprendizado supervisionado, não-supervisionado, competitivo. +Arquiteturas básicas: Perceptron, Adaline, Perceptron Multi-Camadas, Redes RBF. +Aprendizado profundo: arquiteturas convolucionais (CNN), encoder-decoder, redes adversárias, transfer learning, redes recorrentes e modelos de atenção. +Sistemas de auto-organização: PCA, LDA e rede de Kohonen. Memórias Associativas: Redes de Hopfield. Aplicações. + +Definition of connectionist models. +Learning in connectionist models: supervised, unsupervised, and competitive learning. +Basic architectures: Perceptron, Adaline, Multi-Layer Perceptron, RBF Networks. Deep learning: convolutional architectures (CNN),encoder-decoder, adversarial networks, transfer learning, recurrent networks and attention models. +Self-organization systems: PCA, LDA and Kohonen network. Associative Memories: Hopfield Networks. Applications. diff --git a/Trabalho1/SCC0270-T1-11800910-11800584.ipynb b/Trabalho1/SCC0270-T1-11800910-11800584.ipynb index abd09a3..c3facd9 100644 --- a/Trabalho1/SCC0270-T1-11800910-11800584.ipynb +++ b/Trabalho1/SCC0270-T1-11800910-11800584.ipynb @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 557, + "execution_count": 54, "id": "d6a6358b", "metadata": {}, "outputs": [], @@ -85,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 558, + "execution_count": 55, "id": "7538835c", "metadata": {}, "outputs": [ @@ -102,7 +102,7 @@ "device(type='cpu')" ] }, - "execution_count": 558, + "execution_count": 55, "metadata": {}, "output_type": "execute_result" } @@ -143,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 559, + "execution_count": 56, "id": "a567d63e", "metadata": {}, "outputs": [], @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 560, + "execution_count": 57, "id": "9be98bd0", "metadata": {}, "outputs": [ @@ -187,7 +187,7 @@ "60000" ] }, - "execution_count": 560, + "execution_count": 57, "metadata": {}, "output_type": "execute_result" } @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 561, + "execution_count": 58, "id": "c00177e0", "metadata": {}, "outputs": [ @@ -209,7 +209,7 @@ "torch.Size([100, 1, 28, 28])" ] }, - "execution_count": 561, + "execution_count": 58, "metadata": {}, "output_type": "execute_result" } @@ -222,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 562, + "execution_count": 59, "id": "3a096b21", "metadata": {}, "outputs": [ @@ -253,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 563, + "execution_count": 60, "id": "df314fc6", "metadata": {}, "outputs": [], @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 564, + "execution_count": 61, "id": "644bacf0", "metadata": {}, "outputs": [ @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 565, + "execution_count": 62, "id": "2facaa06", "metadata": {}, "outputs": [], @@ -398,7 +398,6 @@ " total += len(labels)\n", " \n", " ### INÍCIO DO CÓDIGO ### (≈ 1 linha)\n", - " #! não tenho certeza dessa parte não, mas acho que é isso\n", " outputs = model.forward(images) # propagação para frente\n", " \n", " ### FIM DO CÓDIGO ###\n", @@ -449,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 566, + "execution_count": 63, "id": "444c110e", "metadata": {}, "outputs": [], @@ -458,33 +457,40 @@ "\n", " def __init__(self):\n", " \n", - " ### INÍCIO DO CÓDIGO ### (≈ 5 linhas)\n", - " pass\n", - " #self.fc1 = # ...\n", - " # ...\n", - " \n", - " ### FIM DO CÓDIGO ###\n", + " # Inicialização da classe pai\n", + " super(NetworkDense, self).__init__()\n", + "\n", + " self.fc1 = nn.Linear(28*28, 1024) # 28*28 = tamanho da imagem\n", + " self.fc2 = nn.Linear(1024, 512) # 1024 = número de neurônios da camada anterior\n", + " self.fc3 = nn.Linear(512, 256) # 512 = número de neurônios da camada anterior\n", + " self.fc4 = nn.Linear(256, 128) # 256 = número de neurônios da camada anterior\n", + " self.fc5 = nn.Linear(128, 10) # 128 = número de neurônios da camada anterior\n", " \n", " def forward(self, x):\n", " \n", - " ### INÍCIO DO CÓDIGO ### (≈ 5 linhas)\n", - " \n", - " #x = # ...\n", - " # ...\n", + " x = torch.flatten(x,1) # achatando a imagem\n", + " x = F.relu(self.fc1(x)) # camada fully connected 1 + ReLU\n", + " x = F.relu(self.fc2(x)) # camada fully connected 2 + ReLU\n", + " x = F.relu(self.fc3(x)) # camada fully connected 3 + ReLU\n", + " x = F.relu(self.fc4(x)) # camada fully connected 4 + ReLU\n", + " x = F.relu(self.fc5(x)) # camada fully connected 5 + ReLU \n", "\n", - " ### FIM DO CÓDIGO ###\n", - " \n", " return x\n" ] }, { - "cell_type": "code", - "execution_count": 567, + "cell_type": "markdown", "id": "d9816ce8", "metadata": {}, - "outputs": [], "source": [ - "# Justifique a escolha da arquitetura" + "### Justifique a escolha da arquitetura\n", + "\n", + "\n", + "**Resposta:** A rede possui 4 camadas lineares simples, com dimensões que inicialmente crescem (em relação ao tamanho da camada anterior) e posteriormente converge para um número menor de neurônios. \n", + "\n", + "Isso tem como objetivo tentar aumentar inicialmente a de aumentar a quantidade de representações internas (como se fosse um \"upsampling\" de informação útil) e logo depois comprime-se a informação, de forma a possibilitar maior assimilação de padrões. Isso mostrou-se (em algumas pesquisas feitas) como sendo o padrão para redes densas simples. \n", + "\n", + "O uso de funções de ativação ReLU tem como objetivo simplificar o uso dos gradientes (por terem uma derivada simples), apesar de, em casos extremos, poder gerar _gradient vanishing_ (não preocupante nesse caso pela rede pouco profunda)." ] }, { @@ -497,34 +503,72 @@ }, { "cell_type": "code", - "execution_count": 568, + "execution_count": 64, "id": "2eba9050", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "NetworkDense(\n", + " (fc1): Linear(in_features=784, out_features=1024, bias=True)\n", + " (fc2): Linear(in_features=1024, out_features=512, bias=True)\n", + " (fc3): Linear(in_features=512, out_features=256, bias=True)\n", + " (fc4): Linear(in_features=256, out_features=128, bias=True)\n", + " (fc5): Linear(in_features=128, out_features=10, bias=True)\n", + ")\n" + ] + } + ], "source": [ "### INÍCIO DO CÓDIGO ### (≈ 4 linhas)\n", - "#model_dense = # ...\n", - "#criterion = # ...\n", - "#learning_rate = # ...\n", - "#optimizer = # ...\n", + "model_dense = NetworkDense() # instanciando o modelo\n", + "criterion = nn.CrossEntropyLoss() # função de custo\n", + "learning_rate = 0.001 # taxa de aprendizado\n", + "optimizer = torch.optim.Adam(model_dense.parameters(), lr=learning_rate) # otimizador\n", "### FIM DO CÓDIGO ###\n", "\n", - "#print(model_dense)" + "print(model_dense)" ] }, { "cell_type": "code", - "execution_count": 569, + "execution_count": 65, "id": "0d77c184", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/15 .. Train Loss: 1.11607 .. Test Loss: 0.90469 .. Test Accuracy: 64.840%\n", + "Epoch 2/15 .. Train Loss: 0.82012 .. Test Loss: 0.86708 .. Test Accuracy: 66.500%\n", + "Epoch 3/15 .. Train Loss: 0.78215 .. Test Loss: 0.83248 .. Test Accuracy: 67.390%\n", + "Epoch 4/15 .. Train Loss: 0.75789 .. Test Loss: 0.81102 .. Test Accuracy: 68.100%\n", + "Epoch 5/15 .. Train Loss: 0.73733 .. Test Loss: 0.84752 .. Test Accuracy: 67.090%\n", + "Epoch 6/15 .. Train Loss: 0.72379 .. Test Loss: 0.82449 .. Test Accuracy: 67.400%\n", + "Epoch 7/15 .. Train Loss: 0.71129 .. Test Loss: 0.78819 .. Test Accuracy: 68.850%\n", + "Epoch 8/15 .. Train Loss: 0.69998 .. Test Loss: 0.79574 .. Test Accuracy: 69.180%\n", + "Epoch 9/15 .. Train Loss: 0.68863 .. Test Loss: 0.80602 .. Test Accuracy: 68.530%\n", + "Epoch 10/15 .. Train Loss: 0.67874 .. Test Loss: 0.79421 .. Test Accuracy: 69.190%\n", + "Epoch 11/15 .. Train Loss: 0.66780 .. Test Loss: 0.79976 .. Test Accuracy: 69.420%\n", + "Epoch 12/15 .. Train Loss: 0.66252 .. Test Loss: 0.80903 .. Test Accuracy: 69.170%\n", + "Epoch 13/15 .. Train Loss: 0.65285 .. Test Loss: 0.80288 .. Test Accuracy: 69.200%\n", + "Epoch 14/15 .. Train Loss: 0.64323 .. Test Loss: 0.81542 .. Test Accuracy: 69.270%\n", + "Epoch 15/15 .. Train Loss: 0.63782 .. Test Loss: 0.83457 .. Test Accuracy: 69.300%\n", + "{'train_losses': [1.116069377064705, 0.8201207951704661, 0.7821544888615608, 0.7578929339845976, 0.7373255743583044, 0.7237914708753427, 0.7112923920651277, 0.6999761442343394, 0.6886283854643503, 0.6787449027597904, 0.6677950521806876, 0.6625166069467863, 0.6528512297074, 0.6432256892323494, 0.6378171909848849], 'test_losses': [0.9046910208463669, 0.867082913517952, 0.8324845147132873, 0.8110221964120865, 0.8475247311592102, 0.8244914597272873, 0.7881929975748062, 0.7957364851236344, 0.8060213947296142, 0.794206055700779, 0.7997610390186309, 0.8090307354927063, 0.8028797155618668, 0.8154192233085632, 0.8345729041099549], 'accuracy_list': [64.83999633789062, 66.5, 67.38999938964844, 68.0999984741211, 67.08999633789062, 67.4000015258789, 68.8499984741211, 69.18000030517578, 68.52999877929688, 69.19000244140625, 69.41999816894531, 69.16999816894531, 69.19999694824219, 69.2699966430664, 69.30000305175781]}\n" + ] + } + ], "source": [ "### INÍCIO DO CÓDIGO ### (≈ 1 linha)\n", - "#den_results = fit(# ...\n", + "dense_results = fit(model_dense, criterion, optimizer, train_loader, test_loader, num_epochs=15) # treinando o modelo\n", + "print(dense_results)\n", "### FIM DO CÓDIGO ###" ] }, @@ -544,43 +588,9 @@ "- Insira uma célula de texto, ou comentários ao longo do código com a justificativa" ] }, - { - "cell_type": "markdown", - "id": "91e66f05", - "metadata": {}, - "source": [ - "class NetworkCNN(nn.Module):\n", - " \n", - " def __init__(self):\n", - " \n", - " ### INÍCIO DO CÓDIGO ### (≈ 5 linhas)\n", - " \n", - " super(NetworkCNN, self).__init__()\n", - " # 1 input image channel, 32 output channels, 3x3 square convolution kernel\n", - " self.cnn1 = nn.Conv2d(in_channels=1, out_channels= 32, kernel_size=3, stride=1, padding=0)\n", - " self.cnn2 = nn.Conv2d(in_channels=32, out_channels= 64, kernel_size=3, stride=1, padding=0)\n", - " self.fc1 = nn.Linear(64*5*5, 120) # entrada = k*k*p, saída = 10 rotulos\n", - " self.fc2 = nn.Linear(120, 10) # entrada = k*k*p, saída = 10 rotulos\n", - "\n", - " def forward(self, x):\n", - " \n", - " ### INÍCIO DO CÓDIGO ### (≈ 5 linhas)\n", - " \n", - " x = F.relu(self.cnn1(x))\n", - " x = F.max_pool2d(x, kernel_size=2, stride=2) # Max pooling over a (2, 2) window\n", - " x = F.relu(self.cnn2(x))\n", - " x = F.max_pool2d(x, kernel_size=2, stride=2)\n", - " x = torch.flatten(x, 1) # flatten all dimensions except the batch dimension\n", - " print(x.shape)\n", - " x = self.fc1(x)\n", - " x = self.fc2(x)\n", - "\n", - " return x" - ] - }, { "cell_type": "code", - "execution_count": 570, + "execution_count": 66, "id": "336672d4", "metadata": {}, "outputs": [], @@ -590,47 +600,44 @@ " def __init__(self):\n", " \n", " ### INÍCIO DO CÓDIGO ### (≈ 5 linhas)\n", - " \n", + " # herda da classe nn.Module\n", " super(NetworkCNN, self).__init__()\n", - " # 1 input image channel, 32 output channels, 3x3 square convolution kernel\n", + " # 1 entrada imagem, 32 saídas, 3x3 convolução\n", " self.cnn1 = nn.Conv2d(in_channels=1, out_channels= 32, kernel_size=3, stride=1, padding=0)\n", + " # 32 entrada imagem, 64 saídas, 3x3 convolução\n", " self.cnn2 = nn.Conv2d(in_channels=32, out_channels= 64, kernel_size=3, stride=1, padding=0)\n", + " # 64 entrada imagem, 128 saídas, 3x3 convolução\n", " self.cnn3 = nn.Conv2d(in_channels=64, out_channels= 128, kernel_size=3, stride=1, padding=0)\n", - " self.fc1 = nn.Linear(128*5*5, 120) # entrada = p*q*d, saída = 120 rotulos\n", - " self.fc2 = nn.Linear(120, 84) # entrada = 160, saída = 84\n", - " self.fc3 = nn.Linear(120, 10) # entrada = 84, saída = 10\n", + " # camada fully connected (128 * 3 * 3 -> 128)\n", + " self.fc1 = nn.Linear(128 * 5 * 5, 128)\n", + " # camada fully connected (128 -> 10)\n", + " self.fc2 = nn.Linear(128, 10)\n", "\n", " def forward(self, x):\n", " \n", " ### INÍCIO DO CÓDIGO ### (≈ 5 linhas)\n", - " \n", - " x = F.relu(self.cnn1(x))\n", - " print(x.shape)\n", - " x = F.max_pool2d(x, kernel_size=3, stride=2) # Max pooling over a (2, 2) window\n", - " x = F.relu(self.cnn2(x))\n", - " x = F.max_pool2d(x, kernel_size=3, stride=1)\n", - " x = F.relu(self.cnn3(x))\n", - " x = F.max_pool2d(x, kernel_size=2, stride=1)\n", - " #x = F.max_pool2d(x, kernel_size=2, stride=1)\n", - " print(x.shape)\n", - " x = torch.flatten(x, 1) # flatten all dimensions except the batch dimension\n", - " print(x.shape)\n", - " x = self.fc1(x)\n", - " x = self.fc2(x)\n", - " x = self.fc3(x)\n", + " x = F.relu(self.cnn1(x)) # primeira camada conv + ReLU\n", + " x = F.max_pool2d(x, kernel_size=3, stride=2) # Max pooling over a (3, 3) window com stride 2\n", + " x = F.relu(self.cnn2(x)) # segunda camada conv + ReLU\n", + " x = F.max_pool2d(x, kernel_size=3, stride=1) # Max pooling over a (3, 3) window com stride 1\n", + " x = F.relu(self.cnn3(x)) # terceira camada conv + ReLU\n", + " x = F.max_pool2d(x, kernel_size=2, stride=1) # Max pooling over a (2, 2) window com stride 1\n", + " x = torch.flatten(x, 1) # comprime a entrada para um vetor\n", + " x = self.fc1(x) # primeira camada fully connected\n", + " x = self.fc2(x) # segunda camada fully connected\n", "\n", "\n", " return x" ] }, { - "cell_type": "code", - "execution_count": 571, + "cell_type": "markdown", "id": "a6952e4e", "metadata": {}, - "outputs": [], "source": [ - "# Justifique a escolha da arquitetura" + "### Justifique a escolha da arquitetura\n", + "\n", + "**Resposta:** A arquitetura utilizada foi baseada em uma rede convolucional sequencial básica, uma vez que as dimensões do problema eram relativamente baixas e o dataset por si só já apresentava uma robustez interessante. Por isso, optou-se por utilizar 3 camadas convolucionais ativadas com uma função ReLU, cada camada separada por um pooling de tamanhos de kernel e strides variados, uma vez que dessa forma foi possível compatibilizar o tamanho da saída achatada (128 * 5 * 5 = 3200) com o da primeira camada fully connected. Foram feitos testes empíricos com 2 camadas convolucionais apenas e pela acurácia final, ficou evidente que utilizar 3 camadas era mais vantajoso." ] }, { @@ -643,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 572, + "execution_count": 67, "id": "58287f52", "metadata": {}, "outputs": [ @@ -651,43 +658,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch.Size([10, 32, 26, 26])\n", - "torch.Size([10, 128, 5, 5])\n", - "torch.Size([10, 3200])\n" - ] - }, - { - "ename": "RuntimeError", - "evalue": "mat1 and mat2 shapes cannot be multiplied (10x84 and 120x10)", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\felip\\Documents\\RedesNeurais\\Trabalho1\\SCC0270-T1-11800910-11800584.ipynb Célula: 31\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 5\u001b[0m optimizer \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39moptim\u001b[39m.\u001b[39mAdam(model_cnn\u001b[39m.\u001b[39mparameters(), lr\u001b[39m=\u001b[39mlearning_rate)\n\u001b[0;32m 6\u001b[0m \u001b[39m### FIM DO CÓDIGO ###\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m loss \u001b[39m=\u001b[39m criterion(model_cnn(images), labels)\n\u001b[0;32m 9\u001b[0m \u001b[39mprint\u001b[39m(loss)\n\u001b[0;32m 10\u001b[0m \u001b[39mprint\u001b[39m(model_cnn)\n", - "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1129\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", - "\u001b[1;32mc:\\Users\\felip\\Documents\\RedesNeurais\\Trabalho1\\SCC0270-T1-11800910-11800584.ipynb Célula: 31\u001b[0m in \u001b[0;36mNetworkCNN.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 31\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc1(x)\n\u001b[0;32m 32\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc2(x)\n\u001b[1;32m---> 33\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfc3(x)\n\u001b[0;32m 36\u001b[0m \u001b[39mreturn\u001b[39;00m x\n", - "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\torch\\nn\\modules\\module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m 1126\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1127\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1128\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1129\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1130\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39m\u001b[39minput\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1131\u001b[0m \u001b[39m# Do not call functions when jit is used\u001b[39;00m\n\u001b[0;32m 1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[39m=\u001b[39m [], []\n", - "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\torch\\nn\\modules\\linear.py:114\u001b[0m, in \u001b[0;36mLinear.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 113\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[1;32m--> 114\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mlinear(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n", - "\u001b[1;31mRuntimeError\u001b[0m: mat1 and mat2 shapes cannot be multiplied (10x84 and 120x10)" + "tensor(2.3151, grad_fn=)\n", + "NetworkCNN(\n", + " (cnn1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (cnn2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1))\n", + " (cnn3): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1))\n", + " (fc1): Linear(in_features=3200, out_features=128, bias=True)\n", + " (fc2): Linear(in_features=128, out_features=10, bias=True)\n", + ")\n" ] } ], "source": [ "### INÍCIO DO CÓDIGO ### (≈ 4 linhas)\n", - "model_cnn = NetworkCNN()\n", - "criterion = nn.CrossEntropyLoss()\n", - "learning_rate = 0.001\n", - "optimizer = torch.optim.Adam(model_cnn.parameters(), lr=learning_rate)\n", + "model_cnn = NetworkCNN() # cria modelo\n", + "criterion = nn.CrossEntropyLoss() # função de custo\n", + "learning_rate = 0.001 # taxa de aprendizado\n", + "optimizer = torch.optim.Adam(model_cnn.parameters(), lr=learning_rate) # otimizador\n", "### FIM DO CÓDIGO ###\n", "\n", - "loss = criterion(model_cnn(images), labels)\n", - "print(loss)\n", - "print(model_cnn)" + "loss = criterion(model_cnn(images), labels) # calcula a função de custo\n", + "print(loss) # imprime o valor da função de custo\n", + "print(model_cnn) # imprime a arquitetura do modelo" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "id": "0b240e9c", "metadata": {}, "outputs": [ @@ -695,145 +692,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - 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"torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n", - "torch.Size([100, 1600])\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\felip\\Documents\\RedesNeurais\\Trabalho1\\SCC0270-T1-11800910-11800584.ipynb Célula: 32\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[39m### INÍCIO DO CÓDIGO ### (≈ 1 linha)\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m cnn_results \u001b[39m=\u001b[39m fit(model_cnn, criterion, optimizer, train_loader, test_loader, num_epochs\u001b[39m=\u001b[39;49m\u001b[39m20\u001b[39;49m\n\u001b[0;32m 3\u001b[0m )\n\u001b[0;32m 4\u001b[0m \u001b[39mprint\u001b[39m(cnn_results)\n", - "\u001b[1;32mc:\\Users\\felip\\Documents\\RedesNeurais\\Trabalho1\\SCC0270-T1-11800910-11800584.ipynb Célula: 32\u001b[0m in \u001b[0;36mfit\u001b[1;34m(model, criterion, optimizer, train_loader, test_loader, num_epochs)\u001b[0m\n\u001b[0;32m 24\u001b[0m loss \u001b[39m=\u001b[39m criterion(outputs, labels) \u001b[39m# cálculo do erro\u001b[39;00m\n\u001b[0;32m 25\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad() \u001b[39m# inicialização dos gradiente a zero\u001b[39;00m\n\u001b[1;32m---> 26\u001b[0m loss\u001b[39m.\u001b[39;49mbackward() \u001b[39m# propagação para trás\u001b[39;00m\n\u001b[0;32m 27\u001b[0m optimizer\u001b[39m.\u001b[39mstep() \u001b[39m# atualização dos pesos\u001b[39;00m\n\u001b[0;32m 29\u001b[0m \u001b[39m### FIM DO CÓDIGO ###\u001b[39;00m\n", - "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\torch\\_tensor.py:396\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m 387\u001b[0m \u001b[39mif\u001b[39;00m has_torch_function_unary(\u001b[39mself\u001b[39m):\n\u001b[0;32m 388\u001b[0m \u001b[39mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m 389\u001b[0m Tensor\u001b[39m.\u001b[39mbackward,\n\u001b[0;32m 390\u001b[0m (\u001b[39mself\u001b[39m,),\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 394\u001b[0m create_graph\u001b[39m=\u001b[39mcreate_graph,\n\u001b[0;32m 395\u001b[0m inputs\u001b[39m=\u001b[39minputs)\n\u001b[1;32m--> 396\u001b[0m torch\u001b[39m.\u001b[39;49mautograd\u001b[39m.\u001b[39;49mbackward(\u001b[39mself\u001b[39;49m, gradient, retain_graph, create_graph, inputs\u001b[39m=\u001b[39;49minputs)\n", - "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\torch\\autograd\\__init__.py:173\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m 168\u001b[0m retain_graph \u001b[39m=\u001b[39m create_graph\n\u001b[0;32m 170\u001b[0m \u001b[39m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[0;32m 171\u001b[0m \u001b[39m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m 172\u001b[0m \u001b[39m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 173\u001b[0m Variable\u001b[39m.\u001b[39;49m_execution_engine\u001b[39m.\u001b[39;49mrun_backward( \u001b[39m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m 174\u001b[0m tensors, grad_tensors_, retain_graph, create_graph, inputs,\n\u001b[0;32m 175\u001b[0m allow_unreachable\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, accumulate_grad\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m)\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "Epoch 1/15 .. Train Loss: 0.59016 .. Test Loss: 0.45081 .. Test Accuracy: 83.670%\n", + "Epoch 2/15 .. Train Loss: 0.37382 .. Test Loss: 0.37704 .. Test Accuracy: 86.430%\n", + "Epoch 3/15 .. Train Loss: 0.32217 .. Test Loss: 0.35318 .. Test Accuracy: 87.110%\n", + "Epoch 4/15 .. Train Loss: 0.29193 .. Test Loss: 0.33348 .. Test Accuracy: 87.880%\n", + "Epoch 5/15 .. Train Loss: 0.26599 .. Test Loss: 0.31793 .. Test Accuracy: 88.530%\n", + "Epoch 6/15 .. Train Loss: 0.24747 .. Test Loss: 0.30698 .. Test Accuracy: 88.930%\n", + "Epoch 7/15 .. Train Loss: 0.23126 .. Test Loss: 0.29816 .. Test Accuracy: 89.510%\n", + "Epoch 8/15 .. Train Loss: 0.21699 .. Test Loss: 0.30631 .. Test Accuracy: 89.340%\n", + "Epoch 9/15 .. Train Loss: 0.20506 .. Test Loss: 0.31194 .. Test Accuracy: 89.510%\n", + "Epoch 10/15 .. Train Loss: 0.19544 .. Test Loss: 0.33229 .. Test Accuracy: 89.110%\n", + "Epoch 11/15 .. Train Loss: 0.18296 .. Test Loss: 0.34901 .. Test Accuracy: 89.210%\n", + "Epoch 12/15 .. Train Loss: 0.17363 .. Test Loss: 0.36742 .. Test Accuracy: 89.280%\n", + "Epoch 13/15 .. Train Loss: 0.16094 .. Test Loss: 0.38934 .. Test Accuracy: 89.110%\n", + "Epoch 14/15 .. Train Loss: 0.15766 .. Test Loss: 0.39360 .. Test Accuracy: 88.960%\n", + "Epoch 15/15 .. Train Loss: 0.14830 .. Test Loss: 0.42078 .. Test Accuracy: 89.120%\n", + "{'train_losses': [0.5901596974829832, 0.37382099010050296, 0.3221676551798979, 0.291927493562301, 0.26598713057736556, 0.24747193014870086, 0.23126275199155014, 0.21699232185880343, 0.20506147284060716, 0.19544484636435905, 0.18296170733248193, 0.17363256953656672, 0.1609382898422579, 0.15766116745149095, 0.14829858073033392], 'test_losses': [0.45080779522657394, 0.3770428079366684, 0.35317881882190705, 0.33348373234272005, 0.31793396174907684, 0.3069762958586216, 0.29816236078739167, 0.30631334699690344, 0.311940198764205, 0.3322942493855953, 0.3490136744081974, 0.36742441944777965, 0.3893440547585487, 0.393603297919035, 0.4207809980213642], 'accuracy_list': [83.66999816894531, 86.43000030517578, 87.11000061035156, 87.87999725341797, 88.52999877929688, 88.93000030517578, 89.51000213623047, 89.33999633789062, 89.51000213623047, 89.11000061035156, 89.20999908447266, 89.27999877929688, 89.11000061035156, 88.95999908447266, 89.12000274658203]}\n" ] } ], "source": [ "### INÍCIO DO CÓDIGO ### (≈ 1 linha)\n", - "cnn_results = fit(model_cnn, criterion, optimizer, train_loader, test_loader, num_epochs=20\n", - ")\n", + "cnn_results = fit(\n", + " model_cnn, criterion, optimizer, train_loader, test_loader, num_epochs=15) # treina o modelo\n", "print(cnn_results)\n", "### FIM DO CÓDIGO ###" ] @@ -849,11 +730,35 @@ ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 69, "id": "87df2a6a", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Numero de parâmetros treináveis para rede densa = 1494154 \n", + "\n", + "Numero de parâmetros treináveis para rede convolucional = 503690 \n", + "\n" + ] + } + ], "source": [ - "(Escreva sua resposta)" + "# Mapeamento do número de parâmetros\n", + "total_params_dense = sum(\n", + " param.numel() for param in model_dense.parameters() if param.requires_grad\n", + ")\n", + "\n", + "# Mapeamento do número de parâmetros\n", + "total_params_cnn = sum(\n", + " param.numel() for param in model_cnn.parameters() if param.requires_grad\n", + ")\n", + "\n", + "print('Numero de parâmetros treináveis para rede densa = ', total_params_dense,'\\n')\n", + "print('Numero de parâmetros treináveis para rede convolucional = ', total_params_cnn,'\\n')" ] }, { @@ -868,25 +773,39 @@ "Utilize o gráfico para auxiliar na análise. Insira uma célula de texto com a sua resposta" ] }, + { + "cell_type": "markdown", + "id": "9268344a", + "metadata": {}, + "source": [ + "O modelo de redes convolucionais obteve melhor desempenho. Isso se deve principalmente por conta de fatores cruciais no poder representativo de cada arquitetura.\n", + "\n", + "Redes CNN tem como viés indutivo de função o fato de a localidade na entrada (pixeis próximos) corresponderem a informações mais relevantes como um todo (para a tarefa). Isso corresponde bem à classificação em mãos, dado que traços, cores e texturas são _features_ importantes para a detecção da classe, e são melhor capturadas por este tipo de função (convolução).\n", + "\n", + "Redes simples (profundas completamente conexas) possuem não somente mais parâmetros (espaço de busca de funções ótimas é mais extenso, o que leva mais tempo e recursos computacionais) como também são menos próprias para estas tarefas. Como a função de ativação fora a ReLU, tem-se a suspeita que o baixo desempenho do modelo como um todo tenha sido por conta de gradientes muito pequenos. " + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "id": "82a9a2fe", "metadata": {}, "outputs": [ { - "ename": "SyntaxError", - "evalue": "invalid syntax (1610989410.py, line 7)", - "output_type": "error", - "traceback": [ - "\u001b[1;36m Input \u001b[1;32mIn [18]\u001b[1;36m\u001b[0m\n\u001b[1;33m plt.xlabel(\"Epoch\")\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" - ] + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "### INÍCIO DO CÓDIGO ### (≈ 2 linhas)\n", - "plt.plot(cnn_results[#...insira seu codigo...#], label='CNN')\n", - "plt.plot(den_results[#...insira seu codigo...#], label='Dense')\n", + "plt.plot(cnn_results['accuracy_list'], label='CNN') # plotando a acurácia da CNN\n", + "plt.plot(dense_results['accuracy_list'], label='Dense') # plotando a acurácia da rede densa\n", "### FIM DO CÓDIGO ###\n", "\n", "plt.legend(frameon=False)\n", @@ -916,13 +835,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "id": "98b72e6a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "plt.plot(cnn_results['train_losses'], label='Training loss')\n", - "plt.plot(cnn_results['test_losses'], label='Validation loss')\n", + "plt.plot(cnn_results['train_losses'], label='Training loss') # plotando a função de custo da CNN\n", + "plt.plot(cnn_results['test_losses'], label='Validation loss') # plotando a função de custo da CNN\n", "\n", "plt.legend(frameon=False)\n", "plt.xlabel(\"Epoch\")\n", @@ -933,13 +863,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "id": "71da0c86", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "plt.plot(den_results['train_losses'], label='Training loss')\n", - "plt.plot(den_results['test_losses'], label='Validation loss')\n", + "plt.plot(dense_results['train_losses'], label='Training loss') # plotando a função de custo da rede densa\n", + "plt.plot(dense_results['test_losses'], label='Validation loss') # plotando a função de custo da rede densa\n", "\n", "plt.legend(frameon=False)\n", "plt.xlabel(\"Epoch\")\n", @@ -947,14 +888,6 @@ "plt.title(\"Loss\")\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8176bf25", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/Trabalho2/SCC0270-T2-11800910-11800584 (2).zip b/Trabalho2/SCC0270-T2-11800910-11800584 (2).zip new file mode 100644 index 0000000..ed6b3e8 Binary files /dev/null and b/Trabalho2/SCC0270-T2-11800910-11800584 (2).zip differ diff --git a/Trabalho2/SCC0270-T2-11800910-11800584.ipynb b/Trabalho2/SCC0270-T2-11800910-11800584.ipynb new file mode 100644 index 0000000..1db61d1 --- /dev/null +++ b/Trabalho2/SCC0270-T2-11800910-11800584.ipynb @@ -0,0 +1,1037 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "A3crojigg7YT" + }, + "source": [ + "# SCC0270 - Redes Neurais e Aprendizado Profundo\n", + "\n", + "## Trabalho Prático 2 - Previsão do valor de ações utilizando LSTM" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iQ6e4jDlhVQ6" + }, + "source": [ + "NOME: Felipe Andrade Garcia Tommaselli \n", + "\n", + "NUSP: 11800910\n", + "\n", + "---\n", + "\n", + "\n", + "NOME: Diego Fleury Corrêa De Moraes\n", + "\n", + "NUSP: 11800584" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3eYT3dgUhfRh" + }, + "source": [ + "Neste trabalho os alunos deverão implementar redes neurais recorrentes do tipo LSTM para tarefa de previsão do valor de ações. Utilizaremos um histórico de ações da empresa Apple negociada na Bolsa de Valores americana NASDAQ, disponibilizada em um arquivo txt.\n", + "\n", + "Será fornecida toda a etapa de prepação dos dados. Os objetivos dos alunos são:\n", + "1. Complementar as lacunas deste notebook com códigos próprios a fim de se criar um modelo preditivo básico. Este modelo deverá ser treinado no conjunto de treino e sua performance deverá ser avaliada no conjunto de teste utilizando a métrica RSME.\n", + "2. Adaptar o modelo básico com quaisquer técnicas que façam parte do escopo da disciplina visto até aqui (módulos 1 a 8) com intuito de reduzir o RSME obtido com o modelo básico. Você também pode alterar quaisquer parâmetros da rede, inclusive sua arquitetura e a função de perda e o algoritmo de otimização utilizados no treinamento.\n", + "\n", + "Referência sobre como melhorar a performance de redes neurais: https://arxiv.org/abs/2109.02752" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hcAsyqS7opKd" + }, + "source": [ + "Ao implentar com sucesso o modelo básico você deverá ter um resultado semelhante ao da imagem abaixo, com RMSE de 53.39 obtido no conjunto de teste. Vamos denominar esse RMSE como __BASE__.\n", + "\n", + "__RMSE BASE__ = 53.39" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eLLd-PEOPhAR" + }, + "source": [ + 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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "u90qNnj1pMsk" + }, + "source": [ + "Alguns ajustes no modelo podem prozudir resultados bem melhores, como o exibido abaixo. Este modelo ajustado produziu RMSE de 3.83 no conjunto de teste. Vamos chamar esse RMSE de __REFERÊNCIA__.\n", + "\n", + "__RMSE REFERÊNCIA__: 3.83" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LTYHlIjNnSN8" + }, + "source": [ + 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+ ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3yPyXkC5qHHH" + }, + "source": [ + "## Avaliação\n", + "\n", + "A avaliação deste trabalho consiste em três critérios:\n", + "1. Implementação do modelo base: **3 pontos**;\n", + "2. Qualidade do código implementado em todo o notebook (estrutura, comentários, legibilidade e justificativas das escolhas realizadas): **3 pontos**;\n", + "3. Melhora apresentada no RMSE obtido com seu modelo adaptado (objetivo 2) medido no conjunto de dados de teste em comparação com o __RMSE BASE__. Essa melhora será medida em termos percentuais, sendo que o __RMSE REFERÊNCIA__ representa 100%: **4 pontos**;\n", + "\n", + "### Apuração da nota do critério 3:\n", + "\n", + "```\n", + "NOTA = (1 - (RMSE REFERÊNCIA - RMSE aluno)/(RMSE REFERÊNCIA - RMSE BASE)) * 4\n", + "```\n", + "\n", + "\n", + "* Se RMSE aluno < RMSE BASE -> NOTA = 0\n", + "* SE RMSE aluno > RMSE REFERÊNCIA -> NOTA = 4\n", + "\n", + "\n", + "\n", + "Ex.1: Se o RMSE do seu modelo foi de 45.0, então sua nota neste critério será: 0.67\n", + "\n", + "Ex.2: Se o RSME do seu modelo foi de 4.50, então sua nota neste critério será: 3.95" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-AmYffYszdKK" + }, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4sN1QKe6wBFm" + }, + "source": [ + "Essa primeira célula não precisa ser executada se você não estiver trabalhando com o Colab." + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pa9m9mY-jQgk", + "outputId": "66b4e7c3-33b1-4b07-af83-98d73c802a9f" + }, + "outputs": [], + "source": [ + "# mount Google Drive\n", + "# from google.colab import drive\n", + "# drive.mount('/content/gdrive')" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": { + "id": "IOx_kT_kv3CL" + }, + "outputs": [], + "source": [ + "# imports\n", + "\n", + "# pacotes padrão para manipulação, operações e visualização de dados \n", + "import pandas as pd\n", + "import numpy as np\n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import random\n", + "import math\n", + "\n", + "# pacotes para estruturação das redes\n", + "import torch\n", + "import torch.nn as nn\n", + "\n", + "# pacotes complementares\n", + "from sklearn.preprocessing import MinMaxScaler # normalização dos dados\n", + "from sklearn.metrics import mean_squared_error # cálculo do erro quadrático médio" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "d0pXgUZAv5aX" + }, + "source": [ + "## Preparação dos dados\n", + "\n", + "Rode todas as células a seguir executando os ajustes necessários" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": { + "id": "4PT0MCT-zVO4" + }, + "outputs": [], + "source": [ + "# Ajuste essa parte do código para fazer a leitura do arquivo txt fornecido de forma adequada ao IDE que está utilizando\n", + "\n", + "path = './' # seu caminho aqui\n", + "aapl_file = 'aapl.us.txt'\n", + "\n", + "df_aapl = pd.read_csv(path+aapl_file, index_col='Date', parse_dates=True, usecols=['Date', 'Close'], na_values=['nan'])" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 255 + }, + "id": "m-TjMOv-zvqv", + "outputId": "3e390eec-8641-4d4e-c6c7-13c4b86eedb8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(8364, 1)\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Close\n", + "Date \n", + "1984-09-07 0.42388\n", + "1984-09-10 0.42134\n", + "1984-09-11 0.42902\n", + "1984-09-12 0.41618\n", + "1984-09-13 0.43927" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(df_aapl.shape)\n", + "df_aapl.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 390 + }, + "id": "Kqce39V4zwyn", + "outputId": "4ead2bd7-bc52-45fb-a029-f72b0f557751" + }, + "outputs": [ + { + "data": { + "image/png": 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iIiIiIiIiImrUdmEcXkBERERERERERNQEDNaIiIiIiIiIiIiagMEaERERERERERFRE3CPNTvodDpUV1c7exnthqura4Nja4mIiIiIiIiIWisGa40gCAKysrKgVqudvZR2RSqVIiYmBq6urs5eChERERERERGR3RisNYIpVAsKCoKHh0ejpkJQ/fR6PTIzM3Ht2jVERUXx/1MiIiIiIiIianMYrDVAp9OJoZq/v7+zl9OuBAYGIjMzE1qtFnK53NnLISIiIiIiIiKyCze4aoBpTzUPDw8nr6T9MbWA6nQ6J6+EiIiIiIiIiMh+DNYaia2Kjsf/T4mIiIiIiIioLWOwRkRERERERERE1AQM1jo4iUSCjRs3OnsZRERERERERERtDoO1di4rKwuPPfYYOnfuDDc3N0RGRmLatGnYuXOns5dGRERERERERNSmcSpoO3blyhWMGDECKpUK77zzDnr37o3q6mps27YNjz76KC5cuODsJRIRERERERERtVmsWGvHHnnkEUgkEhw5cgQzZ85Et27dEB8fj0WLFuHPP/+0+Z4zZ85g/PjxUCgU8Pf3x4IFC1BaWipe3717NwYPHgxPT0+oVCqMGDECV69eFa///PPPuOGGG+Du7o7OnTtj6dKl0Gq1zf61EhERERERERG1NFas2UkQBFRU65zy2Qq5rNGTNAsKCrB161a88cYb8PT0tLquUqmszpWVlWHy5MkYNmwYjh49ipycHDzwwANYuHAhvvzyS2i1Wtx2222YP38+vvnmG1RVVeHIkSPimvbt24d77rkHK1euxKhRo3Dp0iUsWLAAAPDyyy83/QsnIiIiIiIiImqFGKzZqaJah55Ltjnls8+/Ohkero37liUnJ0MQBMTFxTX6+evWrUNlZSXWrFkjhnEfffQRpk2bhrfeegtyuRxFRUW45ZZbEBsbCwDo0aOH+P6lS5fi+eefx7x58wAAnTt3xmuvvYbnnnuOwRoRERERERERtTsM1topQRDsfk9CQgL69u1rUeE2YsQI6PV6JCYmYvTo0bj33nsxefJkTJo0CRMnTsSdd96J0NBQAMCpU6dw4MABvPHGG+L7dTodKisrUV5eDg8Pj+v/woiIiIiIiIiIWgkGa3ZSyGU4/+pkp312Y3Xt2hUSicThAwq++OILPP7449i6dSu+++47vPjii9ixYweGDh2K0tJSLF26FDNmzLB6n7u7u0PXQURERERERERUW3FlNd7acgG7E3Px0yPDEaRs3jyCwZqdJBJJo9sxncnPzw+TJ0/GqlWr8Pjjj1vts6ZWq632WevRowe+/PJLlJWVifcfOHAAUqkU3bt3F+/r378/+vfvj8WLF2PYsGFYt24dhg4dihtuuAGJiYno0qVLs399RERERERERES1rTucirWHUwEABy/l47b+4c36eZwK2o6tWrUKOp0OgwcPxvr165GUlISEhASsXLkSw4YNs7p/7ty5cHd3x7x583D27Fns2rULjz32GO6++24EBwcjJSUFixcvxqFDh3D16lVs374dSUlJ4j5rS5YswZo1a7B06VKcO3cOCQkJ+Pbbb/Hiiy+29JdORERERERERB3QHwk5AIDb+4fjxvjgZv+81l96RU3WuXNnnDhxAm+88QaefvppXLt2DYGBgRgwYAA+/vhjq/s9PDywbds2PPHEExg0aBA8PDwwc+ZMvP/+++L1Cxcu4KuvvkJ+fj5CQ0Px6KOP4sEHHwQATJ48GZs3b8arr74qDjuIi4vDAw880KJfNxERERERERF1PGkF5Th2tQAAsGhStxbpOJQITdnlvp0pLi6Gj48PioqKoFQqLa5VVlYiJSUFMTEx3CfMwfj/LRERERERERE5yvIdF/HBziSM6hqAr+8f0uTn1JcT1cZWUCIiIiIiIiIiavMu55UBAEZ3DQQAFJZVNftnMlgjIiIiIiIiIqI2L72wHAAQ4avA//68ivHv7caJ1MJm/UynBmt79+7FtGnTEBYWBolEgo0bN1rdk5CQgFtvvRU+Pj7w9PTEoEGDkJqaKl6vrKzEo48+Cn9/f3h5eWHmzJnIzs5uwa+CiIiIiIiIiIha0vGrBcgt0VicSy+sAACEqhTYcCIdheXVWLDmGKq0ejTXTmhODdbKysrQt29frFq1yub1S5cuYeTIkYiLi8Pu3btx+vRpvPTSSxb7cT311FP45Zdf8MMPP2DPnj3IzMzEjBkzWupLICIiIiIiIiKiFnQqTY2ZHx/CxPf3iOcqq3Vi0NbJzwNf3z8ESncX5JVWoduLW/DxnkvNshanTgWdMmUKpkyZUuf1F154ATfffDPefvtt8VxsbKz4uqioCP/3f/+HdevWYfz48QCAL774Aj169MCff/6JoUOHNt/iiYiIiIiIiIioxR1JMUz+LKqoRnZxJYKV7shQG6rVPF1lUHnIIZFIMLFnMDacyAAAvL01EQ+NjoVUKnHoWlrtHmt6vR6//vorunXrhsmTJyMoKAhDhgyxaBc9fvw4qqurMXHiRPFcXFwcoqKicOjQIYevhxyLA2mJiIiIiIiIyF4CavKEPy/nA6hpA43w9YBEYgjPYgO9LN53/lqxw9fi1Iq1+uTk5KC0tBRvvvkmXn/9dbz11lvYunUrZsyYgV27dmHMmDHIysqCq6srVCqVxXuDg4ORlZVV57M1Gg00mpo+3OLiuv+PdXV1hVQqRWZmJgIDA+Hq6ip+g6jpBEFAbm4uJBIJ5HK5s5dDRERERERERK3coUv5eGvrBSjkMvFcfqlh8qf54AITlYdl3pBwrRi9wn0cuqZWG6yZKsSmT5+Op556CgDQr18/HDx4EKtXr8aYMWOa/Oxly5Zh6dKljbpXKpUiJiYG165dQ2ZmZpM/k6xJJBJERERAJpM1fDMRERERERERdWhzPv3T6lxxZTXOpBdh+Y4kAJbBmq+Hq8W9f6WpcVOvEHi7O67Ap9UGawEBAXBxcUHPnj0tzvfo0QP79+8HAISEhKCqqgpqtdqiai07OxshISF1Pnvx4sVYtGiReFxcXIzIyMg673d1dUVUVBS0Wi10Ol0TvyKqTS6XM1QjIiIiIiIioib7K1WNFb8niccRvh7ia5XCMkBbdzgVO85n4/DiCQ7ba63VBmuurq4YNGgQEhMTLc5fvHgRnTp1AgAMGDAAcrkcO3fuxMyZMwEAiYmJSE1NxbBhw+p8tpubG9zc3Oxaj6llkW2LREREREREREQtq1SjtXl+z8Vci2PzVk9VrYo1AMgt0SCruBJhKoXVtaZwarBWWlqK5ORk8TglJQUnT56En58foqKi8Oyzz2LWrFkYPXo0xo0bh61bt+KXX37B7t27AQA+Pj64//77sWjRIvj5+UGpVOKxxx7DsGHDOBGUiIiIiIiIiKidSMouadR9g2P8xNe191gzuZRb6rBgzalTQY8dO4b+/fujf//+AIBFixahf//+WLJkCQDg9ttvx+rVq/H222+jd+/e+Oyzz7B+/XqMHDlSfMby5ctxyy23YObMmRg9ejRCQkKwYcMGp3w9RERERERERETkeCl5ZQ3e89k9AyEza/GsK1hbuO4vCIJg85q9JIKjntSGFRcXw8fHB0VFRVAqlc5eDhERERERERERmXl3WyI+2pVc5/VXpvXEvSNiLM4JgoCYxb/ZvH/XM2MRE+Bp85o9OZFTK9aIiIiIiIiIiIga0lDFmq391CQSCVbO6Y9/3RxndS2/VOOQdbXa4QVEREREREREREQAkF1caXEsk0qg09c0YdbV9nlr3zAAwOAYf3xzOBX7k/OQoa6AurzaIetixRoREREREREREbVqBWVVFsc3RKkQ4FVTpeZro2LNXL9IFd66ow9ig7wAAOoKBmtERERERERERNQBFJRbBmthKgWeu6mmxbOhYM1EpTBUtqlrPa+p2ApKREREREREREStVnmVFkW1KswCvdxwxw0ROHy5AOVVWkT4Khr1LF8PU7BWd8XahWvFjV4bgzUiIiIiIiIiImqVDl3Kx5xP/7Q6H+DtBqlUgvfu7GvX83yMlW3qiror1lb+kdTo57EVlIiIiIiIiIiIWqWlv5yzeT7Ay61Jz6tpBbVdsVZRpcP+pLxGP4/BGhERERERERERtUrme6eZT/7092zcnmq1mZ5Ru7XU5KH/HYfZsNEGMVgjIiIiIiIiIiIr3x9Nw8T39yAxq8Rpawj0rqlMi/b3FF97uTdtdzNTUFdoY3jBixvPYM/FXLuex2CNiIiIiIiIiIisPLf+NJJzSnHLh/sgCLbLuOo67yj+XjWVaXEh3uLrEKV7k57nU8fwAnV5Ff73ZyoAYEQX/0Y/j8MLiIiIiIiIiIjIilQC6AWgWidg06lMTO8XbnG9WqfHrR8dgKtMglAfBWYNjsS47kEOXYN5bndDlC+GdvZHqUaLSD+PJj3PtMdaUa1g7VpRpfj6n1Pi8G0jn8dgjYiIiIiIiIiIrCgVcrGy62xGkVWwdjG7BAnXigEAp9KLsPVcFs6/Ohkero6Lm6p0evH1zAERkEkl1/U8lbEVtESjRbVOj9JKLeQuUkz5YB8AQ1Vc5wCvRj+PwRoREREREREREVmo1ukt2iUrqnVW95RXWZ87frUQo7oGOmwdVVpDsPbcTd2vO1QDAB9FzQCEb4+m4aWNZy2u2zttlHusERERERERERGRhfxSy839K6v1Dd4D2A7brke1sWLNVeaYCEsmlUBpHHxQO1QDgLIqrV3PY7BGREREREREREQW8ko1FsdlGi3e3ZaIHeezcSa9CHq9gPwyjdX7NFrrAO56mCrW3FwcF2GZ2kFtKa6orvOaLWwFJSIiIiIiIiIiCzkllRbHW85mNep9Ghsto9fDFKy5OjBY8/V0RWpBucW5rkFeSMopxb9u7mHXsxisERERERERERGRqLCsCvd9ecyu9wzs5ItjVwsdUrGWVlCOWf89hFmDosThBY4M1iJ9FTiVphaP7xsRgxen9oC6ohp+nq4oLi5u9LPYCkpERERERERERKLXf00QX3cO8Gzw/hen9kC4rwKAY1pBn/vxNDKLKrH894vi81xlsut+rkmUn4f42t/TFUum9YRUKoGfZ90tonVhsEZERERERERERKKzGUXi607+HhbXls3ojd8XjcbL03qK5wZG+4l7oFU6oBX0yJUC8XVztIKaB2sqD3k9dzaMwRoREREREREREYlKNTWTMftH+YqvYwM9MXtQJLoEeWNij2Ao5DJM7BGEfpEquLkYKsqut2KtqLwaOr0gHpuCNblMcl3PNRdlFhb61jPIoDG4xxoREREREREREQEAtDo9MtQVAIClt8ajb6QK7++4CAC4IcoXEokh4Ir088DxlyZCLjPUbJkq1jTa66tY25+cZ3FsqoBrroo1pYIVa0RERERERERE5ADlZq2cswdHQiGv2dvM38vN4l4PV5eaYE1uDNaqr69iLTHLcnBAXqnG8HwHBmuhPgrx9fW2rjJYIyIiIiIiIiIiAEC5xhA0uUglcJVJ4S6viY7869nc391BraBZxZUWx8WVhrZURw4vkElr2kpLKrX13NkwtoISERERERERERGAmv3VPFxlkEgkcDerWKtvaqZYsdbEVlBBEPDe9ov4/li6zeuObAU1Zx6yNQUr1oiIiIiIiIiICABQXmUI1rzcDLVY5sFaffuRicMLmtgKevBSPj7alVzndUcHa+/c0QdB3m5Yemv8dT2HFWtERERERERERAQAKDO2gnqIwVpNoOXpWnc75vUOL7iQVWJxrPKQQ11eLR47Olj728BI/G1g5HU/hxVrREREREREREQEoKZizRSiucpqoqMgpZvN9wDmraBNq1g7m1EkvpZKgGGd/S2um6+jNWHFGhERERERERERAQDKqowVa66GyEgikWDZjN4oKKtClyDvOt93Pa2gVVo9difmADAEarMGRUJdXoUtZ7PEezzdHDe8wJEYrBEREREREREREQCg3Di8wDzImjM4qsH3XU8r6B8XclBYXo1gpRv+98AQyKQS/Hk5X7we5O0mBn2tTeusoyMiIiIiIiIiohZXu2KtsUxDDprSCrr+hGES6G39w8Upnb3Cfaye3RoxWCMiIiIiIiIiIgC2K9Yaw1SxVlFtX8WaIAg4dMlQnTatT5h43jSVFAAKy6rsemZLYrBGREREREREREQAml6x5mkMwsqMwVxj5ZZqUKrRQioBugZ7WVxbPCUOAPDGjN52PbMltc4GVSIiIiIiIiIianElldUALCvGGsN0f0mlfcHa5dwyAECEr4c4AMFkwejOuKVvGMJ83O16ZktixRoREREREREREQEACssNbZd+nq52vc/b3RCsabR6VOsav89aSp4hWIsJ8LS6JpFIEK5SQCKR2LWWlsRgjYiIiIiIiIiIAAAFxv3MfO0M1jzNKtzsaQfNKKwAAET6Kez6vNaCwRoREREREREREeHolQIk55QCAPw87AvW5DKpOMCg1I5gLbdEAwAI9m697Z71YbBGRERERERERNTBHb1SgL+tPoS8UlPFmtzuZ5jaQQ8k5zX6PbmlhmAt0NvN7s9rDRisERERERERERF1cFvPZlkc27vHGgBUVhv2Vvvn+jPYdSEHr2w6h6Lyanx96AqKyqttvsdUsdZWgzVOBSUiIiIiIiIi6uCu5pdZHPva2QoKWLaA/uPLowCALw9eAQBsOZuF/949AN7ulpVwOSWVANpusMaKNSIiIiIiIiKiDi4xu8Ti2F0uc+jzD17KR+9Xtot7uAFAeZUW2cWGirUg7rFGRERERERERERtkbrM0Kp57/BofHbPwGb7nE/2XhJfv7vtIgAgwMsNAV72V8i1BgzWiIiIiIiIiIg6MJ1eQImxjXPh+C6Y2DO4Sc95YkLXRt9bUaXDD8fSAAD/vr0XXGRtM6Jqm6smIiIiIiIiIiKHKKmsGSygdLd/GqjJkxO74qmJ3azOj+oaYHXuyJUClGi0CPNxx8QeTQvyWgMGa0RERERERERE7ZBWp69zGqe5ogrDPQq5DK4uTY+KJBIJJvQIsji39clRkEgkZmsSAABHUvIBAEM6+0MqlaCt4lRQIiIiIiIiIqJ2aMmmc/jxWDp+enQ44sN8bN6zOzEHWUWGyZw+iqZXq5nEhXhbHHcNsjze8FcGtpzNQkW1DgDQP0p13Z/pTAzWiIiIiIiIiIjakYKyKrjIJFh3OBUAsOy3C/jfA0Os7ruQVYx7vzgqHjsiWHORSeEilUCrN1SmyWxUo5lCNQDoHuxtdb0tYbBGRERERERERNROZBVVYsoHe1Fo1gKaVVxp895jVwotjpUKx8REHq4yFFdqxeOHRnfG3ou5Nu+N8vdwyGc6C/dYIyIiIiIiIiJqJz7bd9kiVAOA5JxS7EuyDrbOZRZbHLvLZQ5Zg6ebZUA3vEsA9v9zHL5/cJjVvcHe7g75TGdhsEZERERERERE1E6kFpTbPP/KpnNWgwwuZFkGa73Cbe/DZi8PV+uALsLXA4Nj/HDipUmYEFcz4KAtDy4A2ApKRERERERERNRuFFcawrN/394bQzr7wd/TFWPf3Y1LuWX4aFcSXpjaU7z3Sl4ZAGDNfYMR6uOOmABPh6yhdsWaOT9PV8wf3Rk7L+Tgxp7BDvk8Z3JqxdrevXsxbdo0hIWFQSKRYOPGjXXe+9BDD0EikWDFihUW5wsKCjB37lwolUqoVCrcf//9KC0tbd6FExERERERERG1QkUVhr3Nwn0ViA30gsrDFU9M6AoASLhWIt6XW6IRW0YHdPJF12BvuMgcExMNivar9/rQzv7Y9cxYfHhXf4d8njM5NVgrKytD3759sWrVqnrv++mnn/Dnn38iLCzM6trcuXNx7tw57NixA5s3b8bevXuxYMGC5loyEREREREREVGrVVxhCMvMJ3z2CFUCANILa9pEx7+7W3xdX4VZUyya1A0PjumMTQtH1HlPTIAn3Fwcs6ebMzm1FXTKlCmYMmVKvfdkZGTgsccew7Zt2zB16lSLawkJCdi6dSuOHj2KgQMHAgA+/PBD3HzzzXj33XdtBnFERERERERERO1VkY1gLcJXAQC4kl+OY1cKcCA5HyUaQ2Vb50DHtH+a83RzweIpPRz+3NaoVe+xptfrcffdd+PZZ59FfHy81fVDhw5BpVKJoRoATJw4EVKpFIcPH8btt99u87kajQYajUY8Li4utnkfEREREREREVFrl2YcWKB0l6PUGJgp3WsinxBlzeTNO1Yfsnjv1/cPaYEVtl+tOlh766234OLigscff9zm9aysLAQFBVmcc3FxgZ+fH7Kysup87rJly7B06VKHrpWIiIiIiIiIqKWVabQY9fYuq/NKs4o1F5kU3m4uYpWaiZ+nK8JVimZfY3vm1D3W6nP8+HF88MEH+PLLLyGROHb06uLFi1FUVCT+k5aW5tDnExERERERERG1hBTjZE9znq4yyGsNItj/z/FW95XWCtrIfq02WNu3bx9ycnIQFRUFFxcXuLi44OrVq3j66acRHR0NAAgJCUFOTo7F+7RaLQoKChASElLns93c3KBUKi3+ISIiIiIiIiJqS1Lzy3HLh/utzvt5uVqd8/GQY1rfMLiaBW5VWn2zrq8jaLXB2t13343Tp0/j5MmT4j9hYWF49tlnsW3bNgDAsGHDoFarcfz4cfF9f/zxB/R6PYYMYY8wEREREREREbVfz/xwyub5JyZ0s3n+pak9cOrlG/HFvYMgkQAv3dKzOZfXITh1j7XS0lIkJyeLxykpKTh58iT8/PwQFRUFf39/i/vlcjlCQkLQvXt3AECPHj1w0003Yf78+Vi9ejWqq6uxcOFCzJ49mxNBiYiIiIiIiKhd0OkFnE5Xw81FhtggT7i5yAAAKfnWbaC/LxqNLkHeNp8TZBxiMC4uCOeX3gSFq6z5Ft1BODVYO3bsGMaNGyceL1q0CAAwb948fPnll416xtq1a7Fw4UJMmDABUqkUM2fOxMqVK5tjuURERERERERELe7/9l/Gv3+7IB5veWIUYgI8kVuisbivc6BnnaFabQzVHEMiCILg7EU4W3FxMXx8fFBUVMT91oiIiIiIiIioVen+4hZozPZDU7q7YOaACHxx4AoUchnWzR+CreeyMHdwJ0T5ezhxpe2DPTmRUyvWiIiIiIiIiIiofl2DvXA2o1g8Lq7U4osDVwAAEgnQP8oX/aN8nbS6jq3VDi8gIiIiIiIiIiLA16NmyueknsEW12YPimrp5ZAZVqwREREREREREbVixRXVAIB/394bU3qFYNq1/fB2l+Pr+wfD39O1gXdTc2KwRkRERERERETUiqmNwVq3YC/4erpi1zNjIZVIIJNKnLwyYrBGRERERERERNSKFRmDNR+FHAAgl3Fnr9aC3wkiIiIiIiIiolboQlYxyqu0NcGah9zJK6LaWLFGRERERERERNTKbPwrA09+dxJKdxcIguGcqWKNWg9WrBERERERERERtSJ6vYAnvzsJACiu1AIw7K/m5iJz4qrIFgZrREREREREREStSGZRBSL9FOJxlyAv/PDQcCeuiOrCVlAiIiIiIiIiolYko7ACvz4+Cj8cS0d+qQYPjY2F0p1toK0RgzUiIiIiIiIiolZAEAR8fuAKXtt8Hv6ervjpkRGI8vdw9rKoHmwFJSIiIiIiIiJqBXacz8Zrm88DAEJV7hbtoNQ6MVgjIiIiIiIiImoFvj+WLr5+cWpPSCQSJ66GGoOtoERERERERERErcCZDDUAYP3DwzCgk59zF0ONwoo1IiIiIiIiIiInq9bpkVOiAQBE+Xk6eTXUWAzWiIiIiIiIiIicLKdEA0EA5DIJ/D1dnb0caiQGa0RERERERERETnZNXQEACFa6Qyrl3mptBYM1IiIiIiIiIiInu1ZUCQAI8+Ek0LaEwRoRERERERERkZOlFpQDAMJ9Gay1JQzWiIiIiIiIiIicSKcXsOXsNQBAlyAvJ6+G7MFgjYiIiIiIiIjIib44kIKzGcUAgK4M1toUBmtERERERERERE609nCq+LpbsLcTV0L2YrBGREREREREROQkgiCgsLwKADClVwiiAzydvCKyB4M1IiIiIiIiIiInuZRbCnV5NeQyCVbM7ufs5ZCdGKwRERERERERETnJhhMZAIDRXQPh5iJz8mrIXgzWiIiIiIiIiIic5EhKAQBgap9QJ6+EmoLBGhERERERERGREwiCgOTcUgBAXIjSyauhpmCwRkRERERERETkBAVlVVCXV0MiAToHcmhBW8RgjYiIiIiIiIjICS7llgEAInwVcJdzf7W2iMEaEREREREREZETJOcY2kBjA72cvBJqKgZrREREREREREROcCmXwVpbx2CNiIiIiIiIiMgJGKy1fQzWiIiIiIiInKyyWufsJRBRC9ty5hp2J+YCALoGM1hrqxisERERERFRh3LwUh5e2XSu1YRZOr2Avku3Y/iyncgpqXT2coioBQiCgIfXnhCP+0eqnLcYui4M1oiIiIiIqEO569PD+PLgFby3PbFZP2fTqUx8uvdyg/elFZRDo9Ujr7QKfyTkIK2g3KHryC3R4OPdl5BXqnHoc4mo6ap0evH1/FExcJExnmmr+J0jIiIiIqIO6bczWc32bEEQ8Pg3f+GN3xKQcK243ntNeyxV6fR4fsMZjHp7F4orqx22lmd/PIW3tl7Ao2bVMUTkXFXammDt6Ru7O3EldL0YrBERERERUYeUoa5otmeXaLTi65yS+ivFknNKrc6Z9l1yBNOzDqcUOOyZRHR9qnWC+NqV1WptGr97REREREREDlZQWiW+Lq6ov/rMVLFmLjW/zGFr4Q/tRK2PqWLNRSqBVCpx8mroevBPWCIiIiIi6jB0esHiWF/r2FHyy2qq1BpbsabykIvnzjfQPmoPH7PnmrefEZHzmH4vyhl8t3n8DhIRERERUYdRataiCQBlVdo67rw++WYVaznFdU/6rKjSicGar4ereP63M1nYds4xe8C5mFXDcOooUetgGl7g6sJYpq3jd5CIiIiIiDqM2sHataLmCZryy8yCtToq1s5mFKHHkq0ortRCKgGU7i4W1x/8+jh+P599XesQBAEFZmuprGbFGlFrYKpYY7DW9vE7SEREREREHUZJrWmbNy7fC63O8WFTfmlNmJZdR8XaoUv54uu4EKUY+nm4ysTz/9176brWUV6lg8as/bOyWnddzyMixxAr1tgK2ubxO0hERERERB1GaaV162d2A3ugNcXJNLX4uq6KtSvGAQUxAZ74v3sHipVlPz0yAvcM6wQA+CtVfV1hWO1QT6NlsEbUGlSzFbTd4HeQiIiIiIg6jBIbwdqZ9CKHbupfXqXF3qQ88dhWxVpeqQZrD6cCAB4ZG4tQHwUeGhOL+aNiEOLjjqW3xiNY6QatXsDei7nYlZjTpMq6i9mWE0fZCkrUOoitoKxYa/NcGr6FiIiIiIiofcgtta4ee+h/xzHjhnC8f2c/h3zG+cxiVGn18HSVoaxKh5JKLSqqdFCYtXh+eyRVfB0T4AkAeHBMrMVzhscG4Ke/MrDg6+MAgGl9w/DhnP52rSUxq8TiuKKKFWtErYE4FdRF0sCd1NoxGiUiIiIiog5BpxcsAi1zG05kQKcXHPI5568VAwCGdPaHu9zwI1ePJVux92KueE+GuqaKLT7Mx+ZzhsX6Wxz/cioTxWZ7xAmC0GCl3cUcy2Ctkq2gRK2ChhVr7Qa/g0RERERE1CF8ezQVJ1LVdV4/n1lscfzLqUysP55u9+ckGIO1nqFKBCvdxfP3fH4EgmAI78qrDC2pL9zcw6KSzdzwWsEaAFzNK8fB5DwkZpXgkbUnMHTZTmSqK+pcS3pBucUxW0GJWgfusdZ+sBWUiIiIiIg6hM2nromvv/zHIDz2zV8We65dzC5B7whD9Vh5lRaPffMXAGBIZz9E+Ho0+nOu5BnCrNggT/h6uOJqfk24telUJg4k5yGn2NCS6uMhr/M5Eb4eCFcpkGEWnE37aL/VfZ/svYxXbo23+QxTZVyErwLphRWcCkrUSoitoKxYa/P4HSQiIiIionZPEARcyDJUki0Y3Rljuwfh2IsT8f6dfTGpZzAAw0ABE/Mw7GyGZSVbffR6Acm5hoEB4SoPeLtb1jI88e1JfH8sHYcu5wMAfBR1B2sAMD4uqMHPPJFaaPN8ZbVO/Jo6B3qJ54jI+aqMFWturFhr85z6Hdy7dy+mTZuGsLAwSCQSbNy4UbxWXV2Nf/7zn+jduzc8PT0RFhaGe+65B5mZmRbPKCgowNy5c6FUKqFSqXD//fejtLQUREREREREJpdyy1BYXg03FymeubE7AMDNRYYZN0Sgs3F4QG6JebBWJr4+na62et7pdDW+OngFJ9Msrz39wynxOWEqd6tgrbaGgrXnbuqO8XFB9f7wfTajCEXl1VbnrxUZqtUUchlCjS2pDNaIWge2grYfTv0OlpWVoW/fvli1apXVtfLycpw4cQIvvfQSTpw4gQ0bNiAxMRG33nqrxX1z587FuXPnsGPHDmzevBl79+7FggULWupLICIiIiKiVk6j1eG5H09hap9QvH1HH6sfZAO93QAAn+1PwfvbEwEAV8wq1v6z+xLe2XbB4j3fHk3Dy5vO4bcz1/DGr+ex/VwWAOCnvzLEe4KV7vB2qz84U9XTCgoA3u5yfH7vIOx9blyd9+oF4J7PD+PFjWcshhukFxq+hjCVuzhEgXusEbUObAVtP5y6x9qUKVMwZcoUm9d8fHywY8cOi3MfffQRBg8ejNTUVERFRSEhIQFbt27F0aNHMXDgQADAhx9+iJtvvhnvvvsuwsLCmv1rICIiIiKi1m1/Uh5OpKoRrK7Ah7P7W103BWsAsPKPZPxjRAzSam36v2rXJTwwsjN8PV0BQKxy+2TvZQDAp/tSEOGrsHiPXCa97oo1k2ClO359fBSyiiqxMyEb/9l9CS9O7YETqYX47UwWTqUX4VR6ETacyMCORWMQrlLgUo6hk6dzoBfcjQMSWLFG1DpwKmj70aa+g0VFRZBIJFCpVACAQ4cOQaVSiaEaAEycOBFSqRSHDx920iqJiIiIiKg12XLWUE02pVcopFKJ1fVALzeL4/wyDXLM2kJNNp/OxDvbLiA5pwSdAz2trqcX1gwZeGBkDADAw80xwRoAhKsUGNDJF09O7IZfFo7E/SNjEFBr7eVVOizZeBaCICAxuwQA0DXIC+4uxmBNy2CNyJkqq3XYmZCN384YhqnI2Qra5rWZqaCVlZX45z//iTlz5kCpVAIAsrKyEBRkuZmni4sL/Pz8kJWVVeezNBoNNJqaf1EWFzd+M1IiIiIiImpbThn3QRvTPdDm9c6BXpBJJdDpBQBAfmmVzWDtpZ/PAQA+3ZuC7U+Nrvczn58SBwCwkeOJRncLhEIua2j5VlxdpOL0Un/PmmDN39MVxZXV2HkhB5/uu4xvjqQBALoGeyGryPD1VFSxFZTImRZ8fRx7L+aKx6xYa/vaxHewuroad955JwRBwMcff3zdz1u2bBl8fHzEfyIjIx2wSiIiIiIiam0EQRAryaL9ravMACDExx2bFo4QWzkLyqqQZyNYM6nS6a3aPhcbgzQTF+MPyxJYJ2vxYUp8ce8gfHL3AEgk9SRvjRDg7Sq+nnFDOEZ3NYSHXx64Ip4fEuMv7rG2/kS6uLcTEbW8k7Wm+OaXVTlpJeQorT5YM4VqV69exY4dO8RqNQAICQlBTk6Oxf1arRYFBQUICQmp85mLFy9GUVGR+E9aWlqzrZ+IiIiIiJynoKwKFcZ9xcJU7nXeFx/mgx6hhp818sqqxMmeT03sZvP+lzedE6vN4kK88eCYWNjKyGyd6xbsjXFxQXBvQrVabeatoH0iVOJ+cZnGiaB/HxqFMJUC0QE1oeL283V39xBR86mo0qG4UgsAcDGWs/YOV9b3FmoDWnWwZgrVkpKS8Pvvv8Pf39/i+rBhw6BWq3H8+HHx3B9//AG9Xo8hQ4bU+Vw3NzcolUqLf4iIiIiIqP0xVasFK93g5lJ/kOVvHExwJa8MVTpDVddDYzvj6UnW4draw6lYNKkbHhgZg/fv7AcAUNnYL81Wq+eATr52fQ318Tbbw61/lArKWmuI8PUAAIzrHoSbexuKD/5IsCxOIKKWkVNiCLwVchlOLJmEt+/ogzmDo5y8KrpeTt1jrbS0FMnJyeJxSkoKTp48CT8/P4SGhuKOO+7AiRMnsHnzZuh0OnHfND8/P7i6uqJHjx646aabMH/+fKxevRrV1dVYuHAhZs+ezYmgRERERESEDLUhWAtXKRq4E+LEz4Rrhj2YVR5yuLnIrMIqk2Gx/ugV7iMeqzxcUVhebXHPrMGR+OF4Gib0CMb/7UtBlU4vtms6Qpyxys7b3QXhKgWUtaaQ+nnWtIreNbgTfjuTheO1WtGIqGWY9m4MUrpB6S7HnQO5LVV74NRg7dixYxg3bpx4vGjRIgDAvHnz8Morr2DTpk0AgH79+lm8b9euXRg7diwAYO3atVi4cCEmTJgAqVSKmTNnYuXKlS2yfiIiIiIiat1M+xeZWiTrY6pYO37VEDx1Mu7J5u1u+8emLkFeFsf/vr035nz6Jx4f30U8p3SXY/tTYwAAswZGolSjRZS/h51fRd38PF2x77lx8HJzgUQisZoyGuBVE6x1Mn7utaJK6PWCzQmpRORYSdkl+PLgFUgkwP/+TAVQ82cNtQ9ODdbGjh0LQRDqvF7fNRM/Pz+sW7fOkcsiIiIiIqJ2oqjcEKypFA3/IGuq7tIYN/fvbNyXTOluXbHWI1RptUfasFh/nH7lRov2THPm+5w5UqRfTVBXu7rOz2xqaIiPOyQSoEqrx5azWZjaJ7RZ1kNEwOHL+Xh/x0WM7haItYdTLa51qmOQCrVNTg3WiIiIiIiImpPa2Jqp8rDdzmnOr1YVSUyA7Yq1qX1CsWBUZ5vPsBXCtaTan29eGSOXSWGqXXh03Qn0iRhnEcq1Fp/vT0F6YQVemNoDMlbVURv1wFfHUKLR4nBKAe4bEYPvj6XBXS7D5Phg3DcyxtnLIwdisEZERERERO2WusIQrPk0Iljz97RsF40NNLR6utWqTHvmxu5i6Nba1K5Yqy9Q/OLAFSyZ1rO5l2QXrU6PVzefBwAMjvHFTb1YVUdtz6k0NUo0WvF4dLcAPDu5O1xdpAyL26FWPRWUiIiIiIjoehQZg7XGtIL6etaEUBKJobUTAOJCvBFlVtnl79V690fyUdTUTkglgKerZS3FvcOjxdfbzmU1avudlnStqFJ8veM8p5dS27RsS4LF8fLfk+AuZ6jWXrFijYiIiIiI2q0iYyto7U39bTGvWOsa5CW2hrrLZdjz7FgkXCtBRbXO6e2e9fExCxA9XV2sBhQ8d1N3TO0Tir9/dhgZ6gqk5JWhc6BX7cc4TVpBufh6/Yl0hPsqsGhSNyeuiMg+giAg4VoJAOCb+UOx52IuHh4TC4mEoVp7xYo1IiIiIiJqt9QVxuEFjWgFVbjWtHz2iVBZXJNIJOgZpsSATr4OXZ+j+Zp9nXob1Wgeri4YFO2HuFAlAOD8teIWW1tjpBWWWxyv3JmEvFKNk1ZDZL+80ioUVVRDKgH6R6nw/JS4RrWiU9vFYI2IiIiIiNottR0VawDQyd/Q8nnXkKhmW1NzcpHV/Iin1dfd5tkz1BsAcD6zlQVrBRUADO23JjsTsp21HGrD0grK8cWBFGh1+hb93KQcQ7VapJ+H1eRgap/YCkpERERERO2SIAgoKDNUrPl6Nm5ftG8XDEV+aRV6hfs059JaRH3BWlyIoWLtYnZpSy2nQfuScvHRrmQAwO39w3ExuxTrT6SjoKzaySujtmjU27sAAAq5DLMHN09QrtcLOHApD3sv5uJfN/eARCJBco7h91SXVtRiTc2LwRoREREREbVLxRVaMVzyb2SwFuqjQKiPojmX1WJ09QRrQd6G/eTU5VUttZwG3f1/R8TXEb4e4iCDUg2DNWrY/qQ8/HA8DePjgjA4xk88fzmvzOGfdTajCB/sTELvcB+s2pUMjVaPqX3C0C9SVROsBTNY6ygYrBERERERUbuUV2bYm8vbzYUtWbWY9nxSG6emHr6cj+wSDW7tG+bMZYki/RQ4f83wPSvT6MTzV/PL8PbWRCwc3wU9jPvEEX24Mwnv7bgIAPj5ZCbCVTXhuFzm2KEBJZXVmP3JnyjVaLHjfDa6BXvhYnYpbv/PARz+1wQkZbNiraPhHmtERERERNQu5ZcaqrH8vRpXrdaRmPacKzIGa7M++ROPf/MXko37Q7W04krLqrRIXw94uhnqQEo1WvH89FUH8OuZa5jywT6k5tcMOli1Kxkj3vzDYqoodQw6vSCGaiYZ6grxtWmfRUf5+WSmxa9JUzu1IADLfruAQ5fzAQBdg71tvp/aHwZrRERERETU7giCgAy1IWQJ8HJz8mpa1tt39AEAfDinf533qDwMYWNReTUEs+mhWUUtP4Hzp7/S8cJPZ8XjzgGeUHnI4W0K1ioNIUZ5ldYiJJm6cp/4+p1tichQV+BfP51poVVTa3E1v6bVc1z3QEyOD7a47uhg7bcz1yyOuwZ5YdbASADAT39lADBMIe4VxorKjoKtoERERERE1O58svcylm25AKDjVazdOTASt/QJhYdr3T/uqYwVa1U6vTjgAQBkUse2zTVEEAQ89d0p8TjMxx2bHhsJiUQiVqyVVRmCtav5ltVoJRotFn13Ekqzia/7kvKQU1yJIKV7C6yeWoNT6WoAgLe7Cz686wa4u0jR65VtqKw2TAMtdOA+gj+fzMDBS4aKtM/vHYhLOWW4c2Akjl0twHfH0sT7ZvSPsJjQS+0bgzUiIiIiImp3TKEa0PEq1gDUG6oZrssgl0lQrRPEIQEA0MK5mhh+mIT7KuBlDNRqt4Km2mjz3GCsEKp97qExsY5eKrVCgiDgoz8Mk2T/NiBS/LWz8+mx+O5IKlb+keywijWdXsCy3wx/rswdEoXxccEYH2e41idCBakEiAnwxBu398aATr4O+UxqGxihEhERERFRu1JSa7+uvhEq5yykFZNIJPBRGCr5zPejqtbVPUm0OZTUmvhpPmSiditoY/dPyytp+XZWcryzGUX4bN/leqfbFpRV4VKuoRV04fgu4vlwlQLjexhaQh01+fb9HYnIKjaE0PeNjLG4FujthjOvTMbOp8diaGd/yFmt1qHwu01ERERERM0qOacU+aUai728mtOptCKL42Gx/i3yuW2Nj8IQXGWaBWsara6u25uFKTQz8XCtCdbEVlCNdbDWO9wHL07tYfOZhQ7eU4ucY9Z/D+H1XxOwvNZgAnM5xhDVz9MVfp6WLd/+xuO80ipodXqr99rrdLrhz5UALzd0DvC0um769UodD7/zRERERETUbARBwPw1x5CSZ6gq+fSegZjUM7iBdzXN2YwivLr5PGSSmn7Ge4dHI9LPo1k+r63zdjfsTWaqwgFqWjMFQYBE0vx9oebTFQHLFlYvd8tWUFOI8ur0eNwzLNq4TuCN3xIsnuGoCiVyrrIqQ8j71aEreGZyd5v3mH5NBHlbt3uHqxRQyGWoqNbhSn45ugR5Xdd6cooNn/X+nX1b5PcGtR2sWCMiIiIiomah1wu47T8HxVANAEo1zVdN9Oi6EziSUoBDlw2bi786PR6v3BrfbJ/X1nkbg6vc4prWyVJNNW5asRe3/+cg9PW04DlK7Yo1hVnFmrdZsFZRpRMDNtN5AOhpY/KiIzerJ+crqdTik72XsHDdCavKsxxjKBxoI1iTSiXoFuINAEjMKrmuNZzLLEJituEZwRyMQbUwWCMiIiIiomaRV6rBqTS1xblSTfO1GmaZbcIPABN7NE9lXHvhaawOyzHbk+xcZjEuZJXgZJpanLbYnEpqVawpzPZYC/RyQ7hKAb0AHLyUhxJjCOflVjMFNN5GsHYiVY3U/Mbtx0atl1xWUxX2798uYPPpa+JEThPTr926wq4exmDtbGaRzeuNdcuH+8XXIQzWqBYGa0RERERE1CxyS603kS+rFaQ4kqm1EQAeHhuLMJWi2T6rPTC1WmabtYKeyywWX+9KzG3UczafzsQtH+7Drgs5dq+hvj3WJBIJxscFAQA2nMgQK9a8zPayUnm4ItzG93nxT6ftXgu1HoIgwFbB5I7z2fjb6oO4mF0CQRCw7VwWANutoABwg3E65+HL+TavN349Na+VCu6oRZYYrBERERERUbPItTGdsTmDNaVZi2C0P/dVa4gpoDKvWDubUVPZk5TdcPtcSWU1Fq77C2czivHjiXS711B7gqt5KygAzB0aBQD47ew1XDG2FJu3ggJAj1BD1Zr55vVHrxTavRZqPTRavc1poF//eRVHrxTi5Z/P4URqoThQYEqvUJvPGdbZMLjkRKoaZ9KbVrVWXav9lPurUW0M1oiIiIiIqFnkldbsdWVqnyqpbL5gzcsscPFRuNZzJwE1wVpRRU24pdHWhAipBQ23U543q3DLt1Gh2JDawwvMW0EBIC5EiUBvNwgCoDUGLV61pi+a2kHNh1T0Cfexey3UetT+dVFbhroCh1MKAACTegajd4Tt73eknwcGRRuq1v6791KT1mIePO96ZmyTnkHtG4M1IiIiIiJqFqaKtRn9w3H3sE4AmrdirVpXU+Ey0PjDNNXN063+lrbGBGvmraPJOWXYmZBt19CD2kGrR62KNcC6Qs2r1vGILgEAgC6BXvjqvsEAGg5mqHUz/TkhkQAfzumPSD/Ldt/UgnK8vTURADCwU/2/1+cNjwYAXKu1B2NjmfZuDFcpEBPg2aRnUPvGYI2IiIiIiJrFyTRDO16gt5tYZVRW1XyBh2ni6Kq7bkCAl+09l6hG7YCqtpJKLYrK65/iah6s5ZVqcP9XxzDyrT+w5OezNlv5asup1S6scLVek/neeYB1xdrgGD/sfHoMls3oDR+FXFw7tV2mYDTAyw3T+obh8fFd67z3pl4h9T7LVC1rqzXdXFF5Nap1emw9m2URDpv2IAzx4dACso277hERERERkcOlFZRj+/lsAECvcB9UGVsM0woqkFuiQWAdm41fD9NG+N2CvRz+7PbIy826OgwA7hgQgV0XcpBfVoUMdQV8POQ27wOAczamLWYWVWLNoauIDfQSq4XqkqGuaHCdyloBoJuLdX1IbKDhe24K1szbW6ntKTNODzaFqGO6BaJnqBIxAZ749cw18b5184egk3/9VWSmP2tySzQQBMHmHmlvb72A/+yuaRWdN6wTXrqlJ1xkUlzKKQUAdPLjvo1kGyvWiIiIiIjI4RKzSiAIhvapqb1DxbbDMxlFmLR8DwrLqhp4gn0EQRCrlBqqxCIDhbzm/6fR3QLF1y/c3AP+XoY96grq+T5ptDokG0MHWw5eymtwDdeKLIO12iEaYN0KWt/m8aZgrVSjhbbWpvPUdpTVmgAbpHTHb0+Mwnt39hXvWTGrH4bHBjT4LFP1akW1DmVVOpv3fHMk1eL4q0NX8c52Q6vphSzDEI+4UG87vwrqKPhvHCIiIiIicqj0wnI8sOYYAKBXuBJSqcSifU9dXo11R1Lx6LguDvtMjVZf5+b21LBI35o9rFQecvh7ugEoRX5Z3e1zl3LKoNUL8FHIbVaI5ZfWH55WVOmQVmAI1j6c0x/Xiiowqmug1X32fD/Ng7niSq3FpFBqO0ytoJ61qird5TJ8u2AoJACGGCd+NsR8L8F3tl7AtaJKpBdW4JN7BiDC1wM6vYBis9ZhhVyGimoddpzLxuIpPXAhy9Du3D1EeZ1fFbVXrFgjIiIiIiKH+nTvZfF1uMrQPlX7B+SvD11FtQMrisw3q/e0sU8XWRsXF4hb+oRi2YzeUJsFYxKJBH7GirX6wrH0QsNwg07+tlvk8hqYEvrCT2fE11N6hWDB6FjIpNbVaFKzCrVP7h5Q7zNdZFIxiFOXO7YqklpO7Yo1c0M7+zc6VDMxBa5fHbqK7eezcf5aMVb8ngTA0CJq2g/w9v7hWDmnPwDgcl4Z8ks1SMkrAwDEhbBijWxjsEZERERERA5lvme9XjAchKssp/plFVfivNnG99erstrQ4uXmIoXURjhD1txcZPjorhswZ3AUOteadhjgadkKunzHRTz2zV/i/89Azf5o4SoFpvcLs3p+QxVrV/INgUWfCB+4yOr+0dS8fe/G+Po3qgeACGP13flrjvv15Qw6vYCNf2Xgp7/Snb2UFldTseaYkPzjv1sHsqY25Myiml/Hy2f1w6SewQgzDir4+WQm9IKhijOoGfaFpPaBwRoRERERETmMXi9gf3LN3lpjjHt3BSndERtoGd6Ypu3V9Rx7VFYbqt/c5bY35Kf6PTgmFg+PjcUvC0cCAPw8DSFCflkVTqQW4oOdSfjlVCa2ncsS35NRaAgkwlQKvH5bL8y4IRwAMGtgJACgRKO1COJqyzVWtL08Lb7etZVr7JvwOaqrYd+tPYm5dr2vtfnprww8+d1JPPXdKZtDItoz0/ACRwVrI7oEiGGZSXGFFknZJbj38yMAgDBVzfX4cB8AwKubzwMwVKvVt7cfdWwM1oiIiIiIyGHWn0gXW6f+MSIa4+KCaq49PByvTY/HoGhfAMCCr48j0bgxuLn8Ug1GvPUHlv2WgIo6NhuvTaOtqVgj+3m5ueCfN8Whd4QhUPAXW0E1FgHVhhMZ0OsFCIIgVoSFqxTwdpfj/Tv74cqbU/HmzN5wNVag5dcx/EAQBOQUG4K1hiqBTCFHYw2K9gNQs+l8W3XBrOIuvbDh6antSVlV3a2gTdW9VitncWU1vjuaJu6vNtD46wYA4sMs91OL4/5qVA/+W4eIiIiIiBzm+NVC8fWC0Z0trqk8XHH3sGiE+tS0hS7fcdHqGb+duYZrRZX4797L+PXMtUZ9rkZrqFhzk/NHHEdQGqdrllRqLQYT7LmYi/u+Oor+r+3AwUv5ACwrfQDDHm3mwZzJtaIK/HzSEMwVV2rF71lgA8HaQ2M648mJXbHliVGNWrvp11dOSd0Vka1RTkklFm84ja1nr0Gj1SHNuIcd0HBbbXsjtoI6cL/E2sMHiiuqUVhu+LU9PNYfz97YXbxWez+1Gzr5Omwd1P5wV08iIiIiInIY075b942IsQjQzDU0qXH24Chs+CsDf6WqkZxT2qjPvaY2hChuLmwFdQRTxVmVTo/iSsuJn7sTcxHl5wG1MZTw97IOxpTuclwrqkRxRU0b53M/nsa+pDw888Mp9Aw1hBze7i4Ntu96uLrgyYndGr32IKVhPdnFGuy9mIvR3awnjbZGL/x0FjvOZ+ObI2mY2CMYmeqaKrX8BgZBtHYHkvOQnFOK2/qHI79Ug86BXvXeX1bHVNDr0S3Y8jOLzULjW/qEWezNOD4uGF2DvJBk/PNnAIM1qgf/OoeIiIiIiBzmar6hymZyfHCd95hXlcnNWjcTs0qwcN0JpBWUY1qfMOPzyhr8zE/2XsKj604AANxZseYQpu9RlVaPkkrrPc48XGsCD1tBqbdxCmOJMZSrrNZhX5Jh771qnYBT6YY9wxqqVmsKf7P13GPcP6st2HuxpuX294RspBWYVazV0VLbFuj0Ah78+jhe3nQOfZdux/j39mBnQna976lvKmhTdfK33ONRpxfE8NLHWKFp4uoixdYnR2NKrxDcOTDCavgKkTlWrBERERERkUNodXqxYq32D7HmKs32TdMYN7f/4Vganv3xNABDqPCfuTcAgLhfW53Pqtbh379dEI9ZseYYbsaKNY1Wh+KKaqvr5kMJ/OsN1rTYl5SLeXUEXD1CHb93Ve0Jo3q90CYmxZpaY01KzIY2tMVgTacX8Pn+FPh7uYqtnSb3f3UMvy8agy5BtivXTGGuo4YXAMANUSrcPzIG4SqFOJTAtE9g7WANAGRSic1pokS18a9ziIiIiIjIITLVldDpBbi6SOvdkN689c80GdIUqgGGCZ+mKZ+pZlU7QM2QApMDZhNIAQ4vcBRXl5qKtWIbFWvme34p3a1DCW/jueLKavzzx9Ooa8jrwBZosSuyEQy2NkXl9a+xLbaCfnEgBW/8loBF358CYAhgzScDv/Hreav3pBeWo7iyulmGF0gkErx0S0/cNzLG6pqtYI2osZr8b52qqiokJiZCq7Vv9DEREREREbVPphAsys+j3gqhB0bVDDXIKqqEIFinLqZJlOVVOpRptBAEAY9/8xd6vLQVm05lWn2mCYM1xzBV/hlaQa1DH/NqKlvfa1PF2p6Lucgssj1EIMjbDXMGRzliuVb+eVOc+Lq1V3uVVFaj76vb672noJV/DbZ8ezTN4nhQtB9+eGi4eHzocr7F9YRrxRj51i489PVxlGkMAbojK9bqw2CNrofd/9YpLy/H/fffDw8PD8THxyM1NRUA8Nhjj+HNN990+AKJiIiIiKhtuFpgaNvs5OdR732B3m44uWQS3FykuFZUid8TcqzuqdTqxH28cks0yFBXYNOpTOgF4EhKzQ/k6gYqfahpTBVrGq3eZitoQ0wVa6Z91Wz5bN7ABgcXNNXDY2MRE2Cojmrt1V5njPvNAUBArUEQ3YMN0ynz2thU0PIqrVUbd69wJfw8XbHx0READJWpV4z3rD18FVNX7gMAHLyUXzMV1IHDC8w9c6PlMAwGa3Q97A7WFi9ejFOnTmH37t1wd68Zqzxx4kR89913Dl0cERERERG1HabqscgGgjUAUHm44m8DIwAAhy/niz/YPju5Ow48Px4fzO4vbmyfW6qx2EDfPEyr3eanravnkOxiHqyVaKy7lEwDC3qH+9h8v6lizZaeoUqE+rijmzE0ai6mNbb2ai/ziroJcUHo5F/z++cGY6tsQZkG+jb0a/tMehF0xvV6usrQK1yJf4wwtGD2i1SJk1q/OZoKQRDw9tZEi3ZhU2usrTZjR3hoTKzFcX2/XokaYnewtnHjRnz00UcYOXIkJJKakt/4+HhcunTJoYsjIiIiIqK2I9U4EdQ8GKhPbKBh4/L0wgqx3fBvZhP4TPu05ZZoUF5lO1hTl1uGJlpd2wkfWjNTS22pRgsbnboIUbpj08IRWDd/iM33K2pVopmCFAD4eeEI7HpmbLNVq5mYgrXW3gqaXVzTKvuvm3tA5VEzDGLBaEPbtF4A1G1grziTBONQgEk9g3HmlcnY9OhIi7bOOYMiAQC/nr6G9MIKq4C8SmfYY9Hfy3owhiPUHnDRFoZbUOtld7CWm5uLoKAgq/NlZWUWQRsREREREXUs5nusNYYpQLuYUyJWq5hXqJgq1nKKK8U9lwBAXVETlBSW165Ys5ysSE3jWmuvuto/6iXnlKJnqFJs+azNvErs9v7hWDTJ0Ho3oos/5DJps4dqQM200twSDVbtSsbpdDX+8cURzPnkT7GaqjXIMu5BN39UDHw85LjjhnBE+3vgwzn9ERPgKVZzFpS17pZWc+mFxunAxv0WawdXY7oHws1FivTCCry48SwAQyWj+bACd7kUHq7NX0kmlzHHoOtj96/SgQMH4tdff8Vjjz0GAGKY9tlnn2HYsGGOXR0REREREbUJgiDYXbEWZgzWLuca9llydbEMXAK9alpBg5U129CczSjG14eu4O5h0VZVPGwFdYzawZqbi1Sc1AoAEX4K5JVWIcTHvfZbAQA9w5Ti6+Wz+gEA9jw71uL72NxMgdRXh65AXV6Nd7Yliteu5JeJFZPOlmWsWDP9f3P3sGjcPSxavO7v5YqiimrklVahi3WNS6uUoTYEa+G+CpvXPVxdML1fGL4/lo49Fw2DSuLDlCiurBb3V/PzaJ5qtdp8FC3zOdR+2R2s/fvf/8aUKVNw/vx5aLVafPDBBzh//jwOHjyIPXv2NMcaiYiIiIiolVOXV4t7cUX42lexZqKstc9RnwgVborXoEuQF8qqdBbXXvr5HG7tF44itoI2i9rTVd1cZGKwNrV3KFbNvaHe998UH4J3/9YX/aNU4rlO/p4OX2d9fDwMwZqtARcZhRWtJljLKTFUogXVEToGeLrhcm6ZeF9bIAZrKtvBGgC8dlsvfH8sXTwe1S0QF7JKxGo3v2ZqA60tWOnW8E1E9bC7FXTkyJE4efIktFotevfuje3btyMoKAiHDh3CgAEDmmONRERERETUyl3OKwVg2HursW1+Kg+5xV5ctTcqnzkgAk9N6oZSjQ6/ns60ev+pNLVVK2i1jq2gjuBaaw8qd3nNcWNa56RSCe4YEOHU8Kq+SY9X88vqvNbSCo1tswGetoOkLsGG/w8f/+Yv7EzIbrF1XY+Mwvor1gBDWDs4xk88Hts9ECqPmu+Zn2fzBl4vT+sJH4Ucb83s06yfQ+1fkxqWY2Nj8emnnzp6LURERERE1IZodXpkl2gQqnTHW1sNbXa96pgSaYtEIkGYyh2XjK2g3jaCkEOX8vDKL+dtvv9CVjGKKqohk0rEPbNa095ZbZlEIoGrTCpuIu/mIrO41hbUDtY6B3qKbcdXjG3LznL8aiFW7UrGklt6otBYdelbR7B2S59QrDucCgBYfyIdE3oEt9g67XUwOQ9vbUtEflkVXKQSRDdQpbhoUjd8svcy7h7WCUp3OXzN2j/9PJpnIqjJP0bE4N7h0W3m1zO1XnYHa7/99htkMhkmT55scX7btm3Q6/WYMmWKwxZHRERERESt10e7krHi9yT0ClfibIZhCuANnVR2PSNMpRCDNV8bP0j71PPD9c6EHABAj1BvdAn0wsaTmXh0XBe7Pp/q5uZSE6y5y6Xw9ZCjsLwaE3q0jY2+agdr8WE+YrBmqqhylvu/Ogp1eTUy1RVi1aVfHcHa0Bh/dAnyQnJOKTLVlTbvaS3u+uyw+HpQtJ/FJFBbhnb2x9DO/uKxbwtWrAFtJySm1s3uVtDnn38eOp3O6rwgCHj++ecdsigiIiIiImr9VvyeBABiqAYAE+Lsq6Yx34PJ18Zm5bXbQx8dF4tZAyMBAIdTCgAAAzv54b07+2HPs2NxW/9wuz6f6mY+wMDNRYbtT43BV/cNxtTeoU5cVeOpzDalH9jJFyqzoC2nxDkB1e7EHDzx7V/ivm8XskrEKktVHSGyVCrBx8Y97ZKySyAIbaMqc1isf8M31aIyr1jzbN6KNSJHsbtiLSkpCT179rQ6HxcXh+TkZIcsioiIiIiI2pZJPYPx3p19rYKwhoQ1EKzVrjrycHWBj4chWBjYyRfDY/0xLDYAMqmkxTfHb+/MgzV3uRSB3m4Y4x3oxBXZx/zXzmMTuiK1oBw+CjmKKqqRW+qcQQDP/HAaeTY+28vNxaLdtrboAE+4y6Uoq9Jh06lMTO/X+gPkpuyv19IVa0SOYHfFmo+PDy5fvmx1Pjk5GZ6e/BcZEREREVFHUGqcAAoYfhh+e2Yfu0M1AIj0Mw/WbLSC1grWPF1l4rnOgZ5YdGP3JlXGUMPcalWstTXmv3ai/T1w99BO+GXhSABATrGmxSu/isqrxVCtX6TKYmJqXW2gJnKZFAtGxwIAPt+f0mxrvB5lZn8mAIbfn/Yy32eOFWvUVtgdrE2fPh1PPvkkLl26JJ5LTk7G008/jVtvvdWhiyMiIiIiotbpSp5hryovNxccWjyhzo3XGxLlV/PDt8rGM5S1gjVfT1fxnLrWRFByrNoVa22NUuGCqb1DcVN8CKL8PAAAQUpDFZRGq8fBS/niRM6WcMU4iTTI2w0bHx2BT+8ZiH6RKgB1Dy4wd/fQTpBIgFPpRchQO3ePOFu+PHjF4rihwQW2WLaCsmKN2ga7/3R8++234enpibi4OMTExCAmJgY9evSAv78/3n333eZYIxERERERtSKCIODj3Ya/aO8VroS7vOnVTJ38PcTXnq7Wz6ldsRbp5yGeK6pgsNacLPZYu47vsbNIJBKsmnsDVt89QNyk3l0ug7e7YUekuZ8dxsFL+S22HlOwFh1gCJwCvNzwz5viMKKLP/pFNDxNN9DbDQOifAEAexJzm2+hTfTOtkTx9cwbIqCw8fu5IZatoE0L64laWpNaQQ8ePIhff/0VjzzyCJ5++mns3LkTf/zxB1QqlV3P2rt3L6ZNm4awsDBIJBJs3LjR4rogCFiyZAlCQ0OhUCgwceJEJCUlWdxTUFCAuXPnQqlUQqVS4f7770dpaam9XxYRERERETXSplOZ+PXMNQCGSYvXw9/sh+cqrd7qeu3QLsrPQ9yEvrC85aqNOiJP15otuc3bQtu6QO+aSqiW/DWUml8OAOjkVxMmD4v1x9oHhmLp9F6NesboboY97vYlta5grbK6ZsDha9Pj8d6dfZv0HF+LijUGa9Q2NOlPR4lEghtvvBHPPvssFi5ciNGjRzfpw8vKytC3b1+sWrXK5vW3334bK1euxOrVq3H48GF4enpi8uTJqKysmeAyd+5cnDt3Djt27MDmzZuxd+9eLFiwoEnrISIiIiKihv3vz6vi627B9m9Qbk4ikeC2fmHw93TF5PiQBu/393RFbJDhMy/llqG8StvAO6ipzEOO66lKbG1W3XUDhsT4AUCLtoLmlBj2VwvxcW/yM0yto5dzyxyxJIdJLzSEhl5uLvj70E5Nfk6gtxt8FHIEGf+XqC1o1FTQlStXYsGCBXB3d8fKlSvrvffxxx9v9IdPmTIFU6ZMsXlNEASsWLECL774IqZPnw4AWLNmDYKDg7Fx40bMnj0bCQkJ2Lp1K44ePYqBAwcCAD788EPcfPPNePfddxEWFtbotRARERERUcMEQcDZjGIAQFyIN27te/3TCZfP6getXoBcZvvv/bsGeSEppxQxAZ6QSCQIVykQ5uOOzKJK/JWqxoguAde9BrLma7Z5fHuqWOsRqkS/KBUOpxSgsAX36cs1BmvmFXP2MrVXVumsqzud6UqesRrP30Nsu20Kd7kMvz0xCi5SCWTSpj+HqCU1Klhbvnw55s6dC3d3dyxfvrzO+yQSiV3BWn1SUlKQlZWFiRMniud8fHwwZMgQHDp0CLNnz8ahQ4egUqnEUA0AJk6cCKlUisOHD+P222+3+WyNRgONpmbEcXFxsUPWTERERETU3uWXVaGiWgeJBPh54QiHTIuUSCSQy+r+Ifqju27AzgvZuLFnTUXboBg//HwyE0evFDBYaybttWINqPna1M3cCioIghg05RonggZ6NT1YMwWcGrPWy9Ygs8gwTCHCV9HAnQ0LV13/M4haUqOCtZSUFJuvm1NWVhYAIDg42OJ8cHCweC0rKwtBQUEW111cXODn5yfeY8uyZcuwdOlSB6+YiIiIiKj9Sy80/AAd7O3ukFCtMbqHeKN7iLfFuXuHR+O2/uEY0Mm3RdbQEZkHa+2pYg2o2SS/OfdYO5ich0fXncDS6b1wa98wh1SsmX7PaWzsR+hMpj3WzPflI+oo7PrTsbq6GrGxsUhISGiu9bSIxYsXo6ioSPwnLS3N2UsiIiIiImoT9hs3TXdEZcr16B/li3Hdg6B05z5MzUVlNqGxvVWsqYyhYXO2gi76/hQKy6vx+Dd/QRAE5JQY9gq/nmDNNKnV1qAPZ9JUG9bjJm9fASxRY9j1q14ul1sMDmhOISGGMu/s7GyL89nZ2eK1kJAQ5OTkWFzXarUoKCgQ77HFzc0NSqXS4h8iIiIiIqpfan453t1+EQAQ7uRgjZqfyqxirWvQ9Q2paG1aohW0pLImtNudmIvKaj1kUsl1VqwZW0FbW7BmXE9LVbEStSZ2x8mPPvoo3nrrLWi1zTt9JyYmBiEhIdi5c6d4rri4GIcPH8awYcMAAMOGDYNarcbx48fFe/744w/o9XoMGTKkWddHRERERNTRfH6gZluY8XFB9dxJ7YG3e01bX3vbx87UClrQjFNBTYMGAODLg1cAAHcOjITHdbRLmoK1Kp0eer1wXetzJI3W0Ara3lqGiRrD7t/RR48exc6dO7F9+3b07t0bnp6eFtc3bNjQ6GeVlpYiOTlZPE5JScHJkyfh5+eHqKgoPPnkk3j99dfRtWtXxMTE4KWXXkJYWBhuu+02AECPHj1w0003Yf78+Vi9ejWqq6uxcOFCzJ49mxNBiYiIiIgcLDGrBADw7t/6Ynq/658GSq3bkBg/PDouFvFhPu2uFdRUNVZcqUVltc7hX58gCKioqhkwsOeioYV6Wp/Q63quq1lwVaXTw13aOr4vNRVrDNao47E7WFOpVJg5c6ZDPvzYsWMYN26ceLxo0SIAwLx58/Dll1/iueeeQ1lZGRYsWAC1Wo2RI0di69atcHd3F9+zdu1aLFy4EBMmTIBUKsXMmTOxcuVKh6yPiIiIiIhqZBUbtoVx9v5q1DIkEgmenRzn7GU0Cx+FHK4uUlRp9cgt0SDSz8Ohz79WVImyKuvJnXGh17cNkXmrpUarb1QgKAgCyqp08HJrvsECNXustY6gj6gl2f0764svvnDYh48dOxaCUHf5qkQiwauvvopXX321znv8/Pywbt06h62JiIiIiIisCYKAa0WGiaChPu4N3E3UukkkEgR6uSFDXYEcG8HaidRCrD+ejucmx8HHw/4BGX9ezrc6F65SwM/T1cbdjSeXSSCRAIJgar9seG3/2X0J72xLROdAT/zv/iEIUzk+GGcrKHVkTY6sc3JykJiYCADo3r07goK4xwIRERERUWtx16d/IlNdgc/mDUIXB2w8X1RRjUpjVUqwksEatX1BSkOwlltiPaBvxn8OAgAqq/V4786+dj1XpxfwxYErAIDb+4fjcm4pAr3d8fDYzte9ZolEAleZFBqtvtGTQX8+mQEAuJxbhlW7krFkWs/rHjKg0erww7F07E7Mwc29Q9kKSh2a3cFacXExHn30UXz77bfQ6QyptEwmw6xZs7Bq1Sr4+Pg4fJFERERERNR4giAgrbAcaQUVKDabTHg9MtSGajV/T9d2t98WdUzB3oaAOKdEU+c9f6UW2v3csxlFOJNRBAB4cmJXdPL3bOAd9nFzMQRrjZkMWlBWhYvZpeLx2sOp+MeIGHQJ8sK72xJxMbsEH97V3+6g7cfj6Xhx41kAwJGUAvSO8DGujX82UMdjd5w8f/58HD58GJs3b4ZarYZarcbmzZtx7NgxPPjgg82xRiIiIiIissPrvyYgrcAQhJVrrPd5AoD8Ug0mvr8Hy3dcbNQz/0jIAQDEhXo7ZpFEThbgbWjLzKsnWGtKMK2uMLwnLsTb4aEaULOPmWlfs/okXCsGAMQEeKKncX+31IIyqMur8NGuZGw/n43dibl2r+Fkqlp8XVypxYHkfOPaWLFGHY/dv+o3b96Mzz//HJMnT4ZSqYRSqcTkyZPx6aef4pdffmmONRIRERERUSOt3nMJ/7c/RTwuq9LavO/bo2lIzinFBzuToNfXve+xye8J2QCA2zgNlNoJLzfD/mSlGh1+O3MNqfnlVvfklVahqNy+cK1MozU+v3mGBbjKDD/GV+kaDtbSCw1fU5SfBzr5G/aRS7hWgn6v7hDvOZ2utuvzNVodfjieDgAIqdUWzlZQ6ojs/lXv7+9vs93Tx8cHvr6+DlkUERERERHZLym7BG9uuWBxrsJsMmFOSSWKKqqx43w2fjmVKZ6/nFfW4LNzjVU93YJZsUbtg6erofJr/Yl0PLL2BEa/s8vmvmUrdjauqtOk1BSsuTdPsGaqCtNU265GNZdRaKhcjfBViNVz72xLtLjnL7Pqs8b4ePcl8fX9I2Ms18ZWUOqA7A7WXnzxRSxatAhZWVniuaysLDz77LN46aWXHLo4IiIiIiJqnKv5ZZi0fC8AINrfA5N6BgOoqVjbdi4Lw5b9gb5Lt2P+mmO4kFUivvescT+o+hQaq3Z8Pa5vqiFRa+FhrCgrqqipSLv1o/1W9x1JKbDruaaKNc9mqlgzhVeN2WMt3RishfsqEO1vOfm0uzEkv2qjUq8+F7MNf3YEeLliev+wWmtjxRp1PHb/Tv/444+RnJyMqKgoREVFAQBSU1Ph5uaG3Nxc/Pe//xXvPXHihONWSkREREREdbrr08Pi6yEx/mKbWLlGh4oqHRZvOANdHS2f6vIqi2NBEHA2oxjdQrzg5iJDZbUOFcbqGJWnvJm+AqKWZapYM3chqwQ6vQC5TIJqneH3S58I+wb0ia2grs3UCmoMrxozFTTdOHQkXKVA9xDLatM3Z/bG7f85iGtFFajW6SGXNS4Uyy42VK++Nr0XAr3c4O3mghLj18w91qgjsvt3+m233dYMyyAiIiIioqa6ml8mTu0EAHe5FC4yCQBDxdqWs9dQUFZV19vxyi/nkaGuwJzBUegc6IVlWy7gk72XMbCTL75dMBRqY7Wai1QC72aqwiFqaQobwRoAFJZXQQIJAEOwVqWtew/CMo0WH+xMwi19QtEnQgXAsGcb0JwVa8ZW0EYEazWtoB7oHuwNmVQCnV5AoLcb+kaoxADx3i+O4Kb4ENw9LLre55VqtDh+1TApNUjpDolEgphAT5xOLzKuja2g1PHY/Tv95Zdfbo51EBERERFRE/x8MgNPfHtSPHaXS3H3sGh8dzQVAFBepRMnhN4UH4IjVwpQrdMj0tcD540TAwHg030p2JWYi+1PjsYney8DAI5dLcT+5DwEGzcoV3nIIZFIWugrI2pennVUlOWWaCwGA2i0de9ltnzHRXy2PwWf7L2MK29OBWA+vKB5QqaaYK3+Pda0Oj2yiisBGPZYk0gkeOXWePxyKhP/vr03pFIJgrzdkaGuwIHkfBxIzseMGyLqDQQfW1fTlRasdANgmDhaE6yxYo06Hv51ExERERFRG7bpZM0Qgg9m98O0PmGQSiXwMIYGZRottMaWtugAT7z9tz6QS6VQuMrw/o6LWLkzSXx/ck4pcoxDCkxOpxdhYLThh2UV91ejdsSjjuDLVOVlUl/L5ZEr1vuvNfcea+5yw7rLq+oP1rKKK6HTC3CVSRHoZQjB7h7aCXcP7STeM2tQJN7fUTOcIa9UU+e6T6ersSsxVzwO9DY8s1eYD342/jnEijXqiBgnExERERG1YZdySwEAax8Ygun9wiGVGirKPI2hQUWVTtyc3Uchh9JdLrbA2aqoGbpsp8Xx6fQisRXU14P7q1H7UVfFmnlbNVB/y6V56CYIhgC7wLhnYXMFa0p3w+/D4srqeu8zDS4IU7mLfy7U9viErtj33DjxOK+07pbx1zafF19H+CrEEG1cXKB4Xu7CilbqeBisERERERG1URqtDqkFhol+XYO8LK6JFWtVWotgzVxjfvA/k6FGvnF/NlasUXviWVfFWq1gbc/FXMxfcwzlVVpkG1srTcyDtZ5LtuG5H09ht7Gqy6uZgjXT7+PiCm2996UZ/2wI91XUe1+knwf6RaoAGCrW/rycj8vGwF6r06O8Sov8Ug2OXjHsrfbZPQPx7YKh4vtjA71wc+8QDInxQ7C3e5O+JqK2jK2gRERERERt1NX8cugFwNvdRWzLMjGFBtvOZYtTBGsHa/X94O/t5oKyKi2yizU4n2nYPymo1mcQtWWKOirWLueWWZ3bcT4bw9/8A+ryarxxey/MHWJopywwm6hbUa3D98fSxWNfz+YJok2/j02BeV2ScgzhWJdAr3rvA4AAY6vog18fNx674tiLk/DfvZfx/bE0jO1mqEqLC/HGxJ7BFu+VSCT4z9wB9n0RRO2I3RVrZ8+erfPaxo0br2ctRERERERkprxKK1ad2JJs/ME5NtDLaqiAeZubqapGVauV0/yem3uHoG+Ej3hcotGii7EKbmdCDgAgiNUo1I54mk0FndE/HI+MjQUA/J6QbfN+U0v00l8MLZFVWj36Gyu9ant8fBcMj/V34Gpr+CgMv2+LGwjWLmSVAAC6hygbfGaAl2UImFdahdT8cnzwexKu5pfjq0NXAQBjuwc1ZclE7ZrdwdrkyZORkpJidX79+vWYO3euQxZFRERERETAsz+exqi3d+H41UKb1y+ZBWu19Qyz/mG6vlbQJbfE4+eFI7Hklp4AgDdu74X4MEPQZhpoULsqjqgt8zALlm+MD8FzN8Xh5Wk9G3xflJ8HAMDVRYov/jEY80fFWFz/+9AoLLqxO+Sy5tl5SdnIirXELMPU3+4hja9YM7cjIRtVOj28jX9OSCXA3CFR9i6XqN2z+3f6Aw88gIkTJyIrK0s899133+Gee+7Bl19+6ci1ERERERF1aL+evgYAWPZbgs3rpsEFsUGeVtcifD2sztUO1syL3EzX/jEiGocWj8ddg6PEijUTtoJSe+LqIsXDY2MxZ3AkJhnbG+cNi8bEHoaqrLqCZL9aLZ7PT+lhUZ3WNci7mVZsIO6xVlktDkyoTV1ehexiQyDeLbjh9YyLs65EO5pimHhaYpxyOqlnMCL9rP9cIero7A7Wli5diptvvhkTJ05EQUEB1q1bh3/84x9Ys2YN/va3vzXHGomIiIiIOhzzapRjVwtxJc9y36dqnR6HLucDMOx7ZMtLt/SEu9zwn/zebi5WVSnme6yZ7pNIJAj1UUAikVgNRGDFGrU3/7wpDstm9IHMODVTKpXgs3mDsOuZsfhoTn+b7zmSUoDvj6WJoZZMKsGj47qI16P8mzd8MgVrp9OL0HfpdvzffsuOMp1ewKTlewEA4SoFvN0bnuY7oJMvvvjHILw2PV5sCz121RCsxYcpEa5S4B8jYup7BFGH1aThBR9++CHmzp2LoUOHIiMjA9988w2mT5/u6LUREREREXVYKbWCtK3nsvDQmFjx+KuDV5BdrEGAlxtGdgm0+Yz7R8bg/pExuJJXBq1egMLVcgpifJgSj43vgnCVwmqPNgBWFWshPtxjjTqGmABP6PT6Oq8/9+NpBCvdMca4qb+/2R5l0f7WFaSOZF55WlypxWubz+P+kTWh18XsEuQa27d1etsVbbaMM+6f9tWhq8grrUJeqWEww6vT49E3QiWGj0RkqVHB2qZNm6zOzZgxA/v27cOcOXMgkUjEe2699VbHrpCIiIiIqAM6Z5zEaVJQVjN9UBAE/Gf3JQDAwnGx4tTPukQH2P5BXyKR4Okbu9f5vqhabV9sBaWOxM1FVu/13Yk5YrAWbDbYI1ylaNZ1BdsIuKu0eri6SFFRpcOUD/aJ5xeM7mz381VmwZ1cJkF8mA9cmmm/OKL2oFHB2m233Vbntc8//xyff/45AMO/mHU6nUMWRkRERETUUe1MyMYLP50FYGjRrKzWWwRreaVVKCirgkQCzB7cfJuJ1/5h2lZVG1F71VBg/cWBK+gW7I3ZgyLh6+mKNfcNhrtc1uD7rpfSXY5wlQIZ6grx3MFLeUjOKbVo144L8ca84dF2P1/lUVN91ydCBXd5/QEjUUfXqGBNX08JLBEREREROda+pDzx9V2DO+HzAykoNAvWTEMLInwV/KGXqJm4NSIgW7zhDAZ08kW3YG+M7ma7Jbs5xIV4WwRr935xFADgahaGPz6ha5PaN309airWeof7XMcqiToG1nMSERE5iF4v4NfT1/CD2YbGRESNcexKAca8swsHkg2BWmG5IUQbEuOHIZ39AAD5xmBNpxcw+5M/AQBdAr1sPM2xnp7UDQDw+PguDdxJ1L6Yt4IGK93w1MRu+G7BUKx9YAhu7RsGAHh4bGyjpm46Wl0hXpXOUBSjdHfBlF4hTXq2+eRPTgElapjdwwsef/xxdOnSBY8//rjF+Y8++gjJyclYsWKFo9ZGRETUpqzYmYSVO5MAAC9uPIt9/xyHIG9u9E1Etv2VWohvj6Thhk4qvLY5AaUaLeZ+dhhX3pwqtn3+bWAk/DwNbVkn09T44VgaBnTyFZ8xPi6o2df58NhYjOkeiJ6hymb/LKLWxLyl8+beoXhiYlfxuFe4D2YOiMCoLgHOWBruHtoJnm4ukAD43+Gr+CtVbXF9bPegJrdudzKbahrh27z7xRG1B3ZXrK1fvx4jRoywOj98+HD8+OOPDlkUERFRW6PTC/i/fZfFY41Wjx+PpztxRUTU2p1MU+O7Y2n44Vg6SjVai2v5xml8/p6uYrAGAM/+eBoXs2vaQO8eFt3s63SRSdEnQsXNy6nDMW+jnNQz2OKaj0KOMd0CIXXSpEypVII7BkRg5oAIvHNHHywcZ1lR6u1udw2NKMZs2AmDNaKG2f1vx/z8fPj4WPdZK5VK5OXl2XgHERFR+5daUI6yKssBPhezSpy0GiJqC24ytmkdu1pocV6j1YkVa36erlaTOL86eAUALCrXiKh5vDWzN/55UxyGdfZ39lLq1CXIG89M7m4RiHm7y+t5R/06+ZsHa2wFJWqI3cFaly5dsHXrVqvzW7ZsQefO9o/yJSIiag8Ss4oBAL3ClVj99xsAAJfzypy5JCJq5UJ9FBjV1bqN7OClfItgzdtdjq/uG4zuxn2cDl3OBwCn7OtE1NHMGhSFh8fGtomJuAFeNdWt11Ox5qOQY8Wsfnj/zr7wUTQ9oCPqKOz+3bZo0SIsXLgQubm5GD9+PABg586deO+997i/GhERdVgXjNVp3YOV6BJk2Ez8Uk4pBEFoE/8xTkTO8cndA7Ho+5PYeSEH1To9BAFYseOiuAG5v/EH5THdAtE5wBO3rTqA/LIq+Hm64o4BEc5cOhG1MgFeNdWt1xOsAcBt/cOvdzlEHYbdv9vuu+8+aDQavPHGG3jttdcAANHR0fj4449xzz33OHyBREREbUGiMViLC/EW2ybKqnQo0WihvI52DCJqW8qrtHj91wSM6RaIyfF1T+QrKKtCTkkl4kKU+PjvA1Cl1WPPxVzMX3MMp9KLABimEHq41vzneqSfB7Y/NRoHLuWjX4QKwUoORyGiGo4M1oio8Zq0A+nDDz+M9PR0ZGdno7i4GJcvX2aoRkREHZopWOse4g13uQxebob/oDVtQE5EHcPXh65i3eFUPPj1cbGd05b1x9Nx04p9ePaHUwAM0wfDVJZBWYiN4Mzfyw239g1DlD/3PSIiSxbBmhv/Uo+opTR5tE9ubi4SExNx8uRJDi0gIqIOSxAEVFbrcCXfsJ9aXIhhzyPTFL/vj6Uhr1TjtPURUcvacjZLfP37+Wyb95xJL8IbvyUAAPpFqcTzESrLsGzh+K6OXyARtVuhZuF8r3DrgYNE1Dzsrg8tKyvDY489hjVr1kCvN+z9IJPJcM899+DDDz+Ehwf/9oyIiDqGsxlF+Pv/HcaUXqHQC4CXmwsCjdP7/L1ckVpQjo93X8JvZ65hz7PjnLxaImpugiCI1auAYVqwLS9uPCO+vqV3mPhaqaj5T/Nhnf0xqWdwM6ySiNqrqb1DkV5Qjgk9ghHiw1ZxopZid8XaokWLsGfPHvzyyy9Qq9VQq9X4+eefsWfPHjz99NPNsUYiIiKnEwQBV/PLoNcL4vEtH+6Hurwa3xxJBQCE+riLgwr8PWvaMa7m2/7hmojal9wSDSqqdeJxWqH17/20gnJxD7UPZveDj0dNu5b5oJOb+4Q240qJqD3ydHPBohu7o2+kytlLIepQ7K5YW79+PX788UeMHTtWPHfzzTdDoVDgzjvvxMcff+zI9REREbUKX/95FUt+PoeBnXzx1KRuWLzhjNU9oSqF+Np85D0RdQwv/XzW4jjNRsXa4ZQCAMCgaF9M72c9dW/d/CHYn5SHOYMim2eRRERE5FB2B2vl5eUIDrYuSw8KCkJ5Of9GnoiI2qeTqWoAwLGrhfjfn1dttniFmm007l8rWNt8OhOebi4Y1z2oWddJRM5x8FIetp2z3FPtRKoavV/ZhhuifLFiVj/4erribIahWq1PhMrmc4bHBmB4bEBzL5eIiIgcxO5W0GHDhuHll19GZWWleK6iogJLly7FsGHDHLo4IiIiZxMEAd8eScWOhJofmC/nltm813w/k5gAL4trC9f9hX98cRQ5JZW130ZE7cCGExni60WTuomvSyq12HMxF29vSwQAnDEGa725sTgREVG7YHewtmLFChw4cAARERGYMGECJkyYgMjISBw8eBAffPBBc6yRiIjIaX49cw3PbziDkkqteC4xuwQfzO5n1e4Z4VvTCmqaDlrbjjqmBBJR23YhqxgA8OGc/nh8gvU0z23nsqDV6XE+03AfJ/YRERG1D3YHa71790ZSUhKWLVuGfv36oV+/fnjzzTeRlJSE+Pj45lgjERGRUxy9UoCF6/6yeW3Jz+ew65mx8FHUbDzeJcjL5mtzJ66qHbpGInI+nV5AUnYpgJpKtGClYYBJtL8H5DIJCsqqsCsxFxXVOni4yhAT4Om09RIREZHj2L3H2t69ezF8+HDMnz/f4rxWq8XevXsxevRohy2OiIjImd7acsHqXM9QJc5fK8agaF+4y2WQy2r+jirWLExzl8tw15AorDucavH+7GK2ghK1J5/tu4zvjqZBo9VDIZchys8DAPD1/UPw/vaLWHxzHB7/5i+cSi/C/DXHAADxYUrIpJL6HktERERthN0Va+PGjUNBQYHV+aKiIowbN84hiyIiImoNFK4yi+N/3RyHlXP6Y+Wc/vjk7oGQy6QYFusvXle6yy3u//ftvbFiVj+LcwzWiNqX139NQFKOoVqtW7AXpMbArFuwN1bfPQCd/D1x/6jOFu8Z2SWwxddJREREzcPuijVBECCRWP8NW35+Pjw9WdJORETth7d7zb8mn58ShwWjYwFYtnm+OaM3VAo5hnT2s/mMIZ394C6XolonQKcXGKwRtSMlldUWx7GBtlvAb+0bhh3ns/HLqUwAwMwB4c2+NiIiImoZjQ7WZsyYAQCQSCS499574ebmJl7T6XQ4ffo0hg8f7vgVEhEROYn5wIIeoUqb93i6ueC123rV+YxQHwUO/HM89AIw6I3fUVypRUWVzqoajojantoTgpUKeR13Ai9O7YFwlQITewQhwtejuZdGRERELaTRwZqPj2EjVkEQ4O3tDYWiZvKZq6srhg4darXvGhERUVuWV1oFwFBtMrprQJOf4+/lBkEQoJDLUFGtQ3ZxJaIDPCEIAqp0eri5MGQjamsKyqowfdUB8bhbsBcWjO5c5/3BSnc8PyWuJZZGRERELajRwdoXX3wBAIiOjsYzzzzDtk8iImr38ks1AIAFozvb3AbBHhKJBCE+7kjJKxODtflrjuFkWhG2PDEKgd5uDT+EiFoN8zbQ4bH+WDd/qBNXQ0RERM5i9/CC5557zuKHi6tXr2LFihXYvn27QxdGRETkTDq9gPwyQ8VagJdjQq8gY3iWVVwJQRDwe0IO8ko1+OJAikOeT0Qtp5O/J35+dASGx/rjkbFdnL0cIiIichK7hxdMnz4dM2bMwEMPPQS1Wo3BgwfD1dUVeXl5eP/99/Hwww83xzqJiIhaVHZxJXR6AS5SicOqyYKV7gCAnGINis32b0u4VuyQ5xNRy+obqWKlGhERUQdnd8XaiRMnMGrUKADAjz/+iJCQEFy9ehVr1qzBypUrHb5AIiIiZ0gvrAAAhKkUkEmvrw3UJMTHEKy98VsC1h1OFc+nGT+LiNoGQRCw+XQmruSVQRAEZy+HiIiInMjuYK28vBze3t4AgO3bt2PGjBmQSqUYOnQorl696vAFEhEROUN6YTkAINJP0cCdjRdkVvn21tYLFp/FH86J2o60ggosXPcXbly+F9U6/t4lIiLqyOwO1rp06YKNGzciLS0N27Ztw4033ggAyMnJgVKpdPgCiYiInCGtwFBFFqHycNgzo/xsP6uyWi/u50ZErd+pdDUAoEeoN1xd7P7PaSIiImpH7P4vgSVLluCZZ55BdHQ0hgwZgmHDhgEwVK/179/f4QskIqLWTavT474vj2LIv3+Hurz9hEPHrhYAADoHOm4K9tjuQega5GXz2sDXf8eRlAKHfRYRNZ+jVwy/V/tEqJy7ECIiInI6u4O1O+64A6mpqTh27Bi2bt0qnp8wYQKWL1/u0MXpdDq89NJLiImJgUKhQGxsLF577TWLdhlBELBkyRKEhoZCoVBg4sSJSEpKcug6iIiobgu+Po4/LuQgu1iD85ltfxN+QRDwxLd/YV9SHgBgRJcAhz3b1UWK7x8cVuf1h/533GGfRUSOV6XV4+Pdl7DmkGH7k36RKucuiIiIiJyuSbXrISEh6N+/P6TSmrcPHjwYcXFxDlsYALz11lv4+OOP8dFHHyEhIQFvvfUW3n77bXz44YfiPW+//TZWrlyJ1atX4/Dhw/D09MTkyZNRWVnp0LUQEZG13BIN/riQIx6fbwfTLQ9dzsfPJzMBGPZX6xnq2G0OfD1dLZ7ZxayCraSy2qGfRUSO9d3RVHF/xLHdAzGtb5iTV0RERETO1qo3hTh48CCmT5+OqVOnIjo6GnfccQduvPFGHDlyBIChqmDFihV48cUXMX36dPTp0wdr1qxBZmYmNm7c6NzFExG1c+ryKgx643eLc6fTi5y0Gsc5Y/waugV7Yf1DwyF10ERQcyO7GqrgRncLRJ8IH/G8nnugE7VqJ1LVAIAALzd8cvdA7q9GRERErTtYGz58OHbu3ImLFy8CAE6dOoX9+/djypQpAICUlBRkZWVh4sSJ4nt8fHwwZMgQHDp0yClrJiLqKP68nG91buvZLFzOLXXCahznQlYJAODWvmEIUro3y2c8PqErvrpvMP5v3kAo3eXieR2TNaJW4+eTGTiVprY4l2Csyn1zRm+GakRERAQAcHH2Aurz/PPPo7i4GHFxcZDJZNDpdHjjjTcwd+5cAEBWVhYAIDg42OJ9wcHB4jVbNBoNNBqNeFxc3PZbl4iIWlp2cc2foyO6+EMqkWBfUh7+b38K3ri9txNX1jRbzlxDSaUWP/2VAQDoHtJ8k6693FwwplsgAMBHIW/gbiJqaceuFOCJb08CAFKW3QyJRAKNVodLxr84iAv1duLqiIiIqDVp1cHa999/j7Vr12LdunWIj4/HyZMn8eSTTyIsLAzz5s1r8nOXLVuGpUuXOnClREQdz5X8MvH10lt7Ibu4EvuS8rDtXHabC9ZS8srw8NoT4nHnAE+MdODQgvooawVrldU6uMtlLfLZRGTbObNBLFfyy/Hu9kT8evoaAMDbzQXhKoWzlkZEREStTKuuYX/22Wfx/PPPY/bs2ejduzfuvvtuPPXUU1i2bBkAwxAFAMjOzrZ4X3Z2tnjNlsWLF6OoqEj8Jy0trfm+CCKidqaovBrLd1zEoUuGVtDXb+uFLkFe6Bps2IQ/v0zT5loa/0ottDhePqsfFK4tE255u1n+HdeK3znZmshZ3tp6AW9tvYCUvJq/OFjy81kxVAOAAG83SCSO33uRiIiI2qZWXbFWXl5uMXkUAGQyGfR6PQAgJiYGISEh2LlzJ/r16wfA0NZ5+PBhPPzww3U+183NDW5ubs22biKi9uy+r47i+NWaICpMZdiHzM/DFQAgCEBheRUCvJz756wgCKio1sHDteZfdVfzy6AXgJgAT4t7z2QYBhZ4ublg6a3x6BuparF11h6OsHrPJTw/xbFTtomoYfmlGny8+5LV+X1JeRbHzNSIiIjIXKuuWJs2bRreeOMN/Prrr7hy5Qp++uknvP/++7j99tsBABKJBE8++SRef/11bNq0CWfOnME999yDsLAw3Hbbbc5dPBFRO1Sq0VqEagDg52kI0FxkUvh6GNoaC8qqWnxttX158Ap6LtmGXRdyABhaLMe8sxvj3t2Niiqdxb2mSaCvTo/HzAERLbpOWz+jV1brbJwlouakrqi2OHaX1/yZBgCDon0hlQCvT+/V0ksjIiKiVqxVV6x9+OGHeOmll/DII48gJycHYWFhePDBB7FkyRLxnueeew5lZWVYsGAB1Go1Ro4cia1bt8LdvXkmuRERdWRnjZVd5kyVagDg5+mKwvJqvLMtEUtu6YlIPw/8fDIDJZVazB0S1SLtU3q9gHVHUrH0l/MADBV2KcumIjmnZlppSl4ZeoYpkZxTggVrjuOyse2rd7hPs6+vtu4hNZuge7u7oKRSi3OZxRjQybfF10LUkRWbBWsjuvjj37f3hqebCwa+/jskEuCdO/qik78H20CJiIjIQqsO1ry9vbFixQqsWLGiznskEgleffVVvPrqqy23MCKiDkivF2wGa76eNRUd/l5uuJRbhh3ns7HjfDZOvXyjOFnPw1WGGTc0fzWYVCrBusOp4rEgAFlFlXjmh1PiuSv5hmBt5c5kMVRTyGXoHOjV7OurrVe4D1b/fQAifBVYtSsZW85m4WByHoM1ohZWZAzWeoYqsfaBoeL5P54eg9wSDaJrtZATERERAa28FZSIiFqHeZ8fwci3/sD+5Dyra15mm+8HeLlaXDMfCvD5gZTmW2At4+OCLI6HLtuJC1kl4vGexFwAgFxW86/BQTF+kEmdU4lyU68Q9Ar3wQjjJNJ9Nv5/JqLmVVypBQD41JrU2znQC0M6+ztjSURERNQGMFgjIqJ6Xckrw56LucgsqsRuYyBlzrwtytfDMljLLq4UX5/NKEZeqab5Fmrm/pEx6BJUd/XZd8fScC6zCGUaww/S3u4uWH5n3xZZW31GdglAtL8HeoYqIQhta7IqUVtnqlhTKlp1QwcRERG1MvwvByIiqteuxByrc56uMpRVWW+w7+1uWemRXWwZpF3MLmn2aaHvbU+EVCLBl/8YhNSCctz16WEAwGPju+CeYdGY9ckhXM4tw8e7LyHXGPS9PbMP/J08xRQAogM8sfvZcQCAk2lq6PR6DOjk5+RVEXUMpj3WlLX+HCMiIiKqDyvWiIioXlfzy63OPTQmFgCg8rD8AdTb3fLva3JKKi2O1eWWU/ccTRAEfHXwCj7YmQR1ebXFPmV9I1QI9HbDS7f0BABczi1DbokhWAv0dn6oZiIIAj76Iwm3rTqAuz49jPIqrbOXRNQhFFca/nyq3QpKREREVB9WrBERUb1M4VOknwJpBRXwUcjxyLguCPZxx9AYy32HlLWCtdoVawVlVc261vTCChRXaiGXSdAt2BuuLlIsn9UXF7JKxH3XTNUoJZpq5JUY1tOagjWJRIK8UsO6NFo9cks06ORv+1/XWp0er24+j84BnriUW4aHx8YiTKVoyeUStRtixRqDNSIiIrIDgzUiIqqXKVh7bHxX7E8yTKuUSSW4c2Ck1b21W0FzSiyDtcJmDtbOZRYDALoGGUI1ALi9v+UkUlP4l1ZQIZ4L8nZv1nXZ68WpPfDlwSsAgMLyanSqY9/0LWezsObQVfH46z+v4sgLE1rd10PUFpxKM0w9ZjhNRERE9mArKBER2aTXC3h983kcuVIAAOjk54GVc/pj3vDoOt9Te9Pv7CLLVtCC8uYN1s5nGn4wjg9T1nmPV62qulAfdyhcZc26Lnu5yKTi11BfGGmrTfT1zQnNti6i2o5eKcAzP5yyGFTSFqXml+P8tWK4SCWYUGuqMBEREVF9GKwREZFNf6bk47P9KeJxkLLhKiir4QW19lg7fLkAmeoKOML3R9MwfdUBHEkpEM+dNVas9Qr3afQaYwI8HbIeRzNNWP3Hl0dRVMfedKYphubOGsNFIkfQ6vT48Xg6ZvznAL4/loaKWkNLXtl0Dj8eT8fUlfuctELHyDIGg1F+HvD1dG3gbiIiIqIaDNaIiMim0krLaqjG7ENWe3iBIFheP3+tGHd8fBDVOv11rS01vxzPrT+NU2lqLPj6GLTG551rRMWah1wGiaTmOLqVBms+ZoMhnv7hpM178s2q2UZ2CQAAZKoroNcLNu8nstfLm87hmR9O4USqGs/9eBo3fbAXarPK0/RCQ1CeV1qF97cnOmuZ162y2hAYuslbV/UqERERtX4M1oiIyCbzaqhZAyPh5dbwtpy1q8FsySyqxP6kPLvXIwiCWLl1/lpNVZa6vBp3rD4EdXmVOCwhLrTuYE0qlVh8LZ1babBWrqkJNi/lltm8p8A45ODZyd3xxT8GQSaVoLJab1UpSNRUuxNzLY6v5pdjw4kM8dg8cF/5RzJ+P5993cG5M2i0hjW7ufA/jYmIiMg+/K8HIiKyyRSs3do3DG/d0adR7/FytR2+DY7xw/DYmh34D5u1b9blRGohnl9/GmkF5QCAFb8noe+r27EvKddi8AAAnExTY9u5LACAj0LeYAioNAsAW2sr6DWz/enKNNZ7qQFAobFyyNfDFXKZFBG+hk3XU/PLm3+B1GG9ufWC+GtMbQy7PY37FD6w5hiG/ntnm6uaNFWsucv5n8ZERERkH/7XAxER2WQK1lQeDVehmSgVLhjVNQCDY/zgKqv5V4yHqwxr7huM2YMMk0TzSjV1PUL03vZEfHs0DTet2AsA+GBnEgDg7v87gjMZ1vuInTRO9AtqRMuqp1tNu1drbQW9Z1i0+NrWXmpATSuon3FPKB+F4XtVZmOoAZG9Kqt1yKi1J6JEAlRp9Rj9zi6k5JWh2Phr8x8jYsR7ugV7/3979x0eVZm2AfyelkmbTHovJCEQei+hV0FFQcDuCljYVbCAu6742XVFV11x1w6KZUUUhZWiINJLaKEFQgIpkN57mWTK+f6YmZNMCqROJsn9uy6ua+Y97TnjIWYenvd9IJVKLI77/WI21h1KhmCaH15SqRVf24LaijVOBSUiIqKWYWKNiIgaZa5EcXVofmJNIpHg24fH4IelY+Gjrk1wKeVSyGVSDA92A3DjxJreIOBIYgEAoKJG36D75dZzmQCMU1TNvj+RCgDwaUaThbrf54PcHG+4f2e4e1QQ1i8ZBcD4pd9cUVNXoSmx5uFsTKzZm9aHqqrpelPxyLZU6/SIfHGn+P6PlZPwx8pJeG3uQHHs7s+iUWOa9nn7UH9xvK+vymI6qCAIWPptDN7YcQlbz2Vi+/lMDHntd/z32DUr3EnzsGKNiIiIWou/PRARUaPMVVIuLUismUkkEvi5OIjvzVUgnipjAii/vBolVVqcTSu2OK5ap8fRxHxczimzGH931+VGr7NwZCCenNbbYqx+A4XG3DkyEFIJcOtgP9jZ6JpKMqkEU/p4QWaq/Cmu0xn08JV8xGWW4oVb++P1eQPFdeIczIm1RpJwRC2hlMvg41KbHO/trUJvbxXGhrqLY7llxgS5XCpBhLezOP7V0asY/Y8/xPf55bXNDracycDyDWcAAC/+crHD4m8pVqwRERFRa9nmtwkiIup0xeJUULtWHX/nyEDxtcI0LdTT2fhF/UJGKYa8+jvmfXQEv5ytXQh97cFk3LfuOBZ9eQLyOlPJvjyS0uD8UWEeGBni1mAqZ0p+4wv917V0Ujguv3EzPrpveMtuysokEolYMWhOdOaWafDAF8dxy78PYViwK/40NgQeps/V0Y6JNWo/kyK8GoxF+KjwzwWDxWcNME5Blkgk2P7EBPx9diQAoKhSK64NmFpYu+bfyXrrK9b9+19fYUUNtpxJx0ZTNWpHYsUaERERtRZ/eyAiokYVmxbGV7eiYg0A5g0LEF+XaoxJIXNira5vomung72321iZlltWjQuvzrLoOAgA/1wwGD/9JQobl47F2kUjIZFIcPNAPzw2JVxcuH95vQq2pshlXeN/gebP3/zfo6BO9c/IN/6wmHJnrljT1DCxRm2TWlCJFTP7YGKEJ1bPH2Sx7a5RQfhnnYYmatM6jAMD1HhsSjhUpuYhWSXG9dnS6iTWKuo9m09tPNvo9X84mYrb/nMYK344h3d/b7xi9Xre3hmPWe8fbNZ6jgAr1oiIiKj1bjxfhoiIeiRzAsfTuXUVa4o6iSvzlDGPOucKcHVARnEVTqcWoapGDwc7GXp7OeNKbjkAILO4CoMC1Ngbnysec9eo2jXVzBzsZPj77EisnNkHCdllGODv0qp4bZU5aWGuIDQnAMxOXyvCmDBjx1V7UxVRJRNr1AaCIGDm+wdQrTPg4N+mItij4TqEo3rVTgnt7eVssS3AzQHx2WU4dbUI4V7OFhVrAKBSylHWRKdb8/Xf2H5J3Ce/vBrFlTXNrp7V6g34ZH8SAODro1fhq7bH+7uvYNnUcIsmC3VVs2KNiIiIWom/PRARUQOCICDPlAxrrMqspfJKNQCM1SD/d0s/LJsajsN/nwp3JzsIApCYW45jyQViUg0APtqXhGsFtdM6o0zJo6YoZFIMDFBDIpFcd7+uRpwKalpjrapO0mz/X6eISTWAa6xR+yiq1IoJ3LpNSOqq2yRkRn8fi23mLrXPbY5F6KpfEZ1UYLF9y7Lx4ppsIY0k7bJKNA0Sb3V/NtzIpaxS8XV0UgH+b8sF5JdX491dCajRNd7YgxVrRERE1FpMrBERUQOlGp3Y7a/+dMyWMFd/hNdZ2PzRSWH426xISCQS9PVRAQDis0vx8FcnLY5VKqS4u06F2l9n9Wl1HF2ZuUrHvMaaeS2oQQHqBuvLiVNBmVijNsgsNk7h9HRWXjfR9PNjUXhpTn8sHB5oMa4zCBbvo5MtE2uFFTX4xx3G6aWyRhLhv8ZmAQB6eztjch/jOm/1G5qYlWm0+GR/EjadShPHTtRZx+3UtSLxdUWNHsdTLGMx4xprRERE1FqcCkpERA2Yq9VUSjnsFa2v4Ni2fAK+PHK1yXXPIv1UiE4uwNm0You1l448Nw0Brg7QaPUI9TR+ubbV7p0dTVxjrco4NdecAHBo5L+Lg7l5QQumglbr9JBLpWL3UaKsEmOFqb+r/XX3GxHijhEh7g3Gn5oegfvXHW8wbieXokZnQHapBiHuxkq1+lObq2r0eGPHJQBAHx9nBLk74sDlPMSmlwBjGsaw5o8r+OKwsbnJmFAPBHs44tTVooY7mlzMLMXERpoysGKNiIiIWouJNSIiasC84HdbqtUAYwfB+guf1zWhtyfWH7mK/Ql5iPA2rq/m6axEgKuxEYG9QoaZ9aaZ9TTmxFpWsTHZYZ7mqWykssacBE3Ma960uQe/PIHopHxseXw8Bgao2yNc6gbMTQf81NdPrDVlfG9PpKy+Ba9vv2TR0feHpWPh7+oAT2clruQaK9DqV1fGZZWIr/8yORy5pdX4DMk4ebW2Ck2j1eOVrReRlFeOk3WSaD/FpEEhk2LnxWwAwI9/joJBEJBVUoWk3Ap8uC8RcZm100QBQKc34HBiPracMXYnZcUaERERtRQTa0RE1EBuO66vdj3jwj1hJ5cio7gK5hlhH98/vEOv2dW4mpoXbD6TgamR3mJirdGKNdNYzLUi7E/IxZS+3tc9tyAI0OoFnEsvZmKNRBnF5sSaQ6vPIZFIsGJmhEVibWiQq7gGor2pMqx+xdr5dGNibUY/bwwOdEVRhbFSMymvAgXl1XB3ssMXh1Ow8WQa6vv33kSL90OC1GIF2j5TE5St5zLx24UsrLl7GG4d7IcyjQ6L19dOQ2fFGhEREbUU/1mOiIgaMFd1hHk53WDPtnGwk6G/n7GLp2BalsnRjl9s6zJXrAHAE9+fQaJpEXeHRj6nup/dmj+u3PDcQ4NcAQDn0orbFiTZrGqdXqxAba40UxfPQLfWJ9YAQGWvwEOmLpzv3z3EorGIueKyWmdZsXYhw/izx5zodXOyQ2/TGo0j3vgDg1/5He/sShD391PbY3SvhtNRZw/wtUiS9a/TLVirF7Bsw2kxjlG93MRtchmnRBMREVHLMLFGREQAgH0JufjqSAr0BgFnUo3Tq4YFu3b4dc3JHTMnJYup6zJXrJmtP3IVQOMVa3WTAmfTinH/umMor9ddsa4hga4AgHNpJU3uQ13b85svYOQbf+Dfe65Ap2/YEVOj1eOuz6Lx/JZYcSw5z9iNN9zLucH+Lb7+LZE49OxU3DHMssGBOeml1QvQ12l2kGSaxhzhrRLHeteJw9wtNNjdEQlvzEb0qulYu2ikxbmXjO+Fl2/vbzHmrVKK3UrNtHoDRv9jD4pNHXcBIL2oqsX3SERERD0bE2tERIRvoq9iyfqTeGVbHHbEZuGMqYJpeLDb9Q9sB5G+Kov3TqxYs+Bo13iisbGmEvlllpVJRxIL8G6d6p76zBWJ5ql/1L0IgoCfT6cDAP61+zI2xaTDYBBQXFkj7nMipRAnUgqx4XgqzqcXw2AQcLXAmFgL9Wx7xapcJkWQqVFBXXXXMjNXrQmCgGRTYi3cu/baj0wMbXD8d4+MEZNzagcFxoa5QyaV4JuHRuPl2wY0mMZatwux2e0fHkF5tQ45pRoMMSX4Zw3wbcVdEhERUU/GxBoRUQ+nNwh4Z2dt8uXdXQmo0RkwJMhVnILVkXxcLBdIZ8WaJY86VTZ1kxGNTQWd2UhS4ExaMQRBaDAOAN6mz768WofKmqYr26hrulpQafF+1eZYhD3/K4a9vhsbjqcCAC5l1S7mv+1cJi7nlkGjNUAulbR5Kuj12MnqJNa0xkq6gooalGp0kEiAXh61ibWRvdzxw9Kx4vt7Rwc1SNatfXAk9j0zBZP6NOz4afbk9AhEhXmI7833Psp0/kPPTkXfeol+IiIiohvhtxcioh7uSm6ZOL0KAFJN6yvNiPS2WBOpo9RvkNDYFMeeLMJHhbfmD0KAmwPe+/0yzpqqCRv7nAJcHbDh0TG4b+1xcexydhnmfXwUHk52+GLRSPG/6c4LWZDW+e+bW1qNXp78taA7OX2tqNFxQQCe3xJrMf0TANYeSsHaQ8ZmA0ODXCGXddy/v8plUsilEugMAjSmirXL2cZOoUFujg0qMoeH1FbPmhNxdansFVDZKxqM1xUV7oGocA/8GpuFx787LY6PDfOAvULWaGUdERER0Y2wYo2IqIc738T6WsEe1vmS6aWqTazZK6SQSrl4eH33jA7GxAgviy/+TSUgB/hZdves0upxLq0Ye+NzxW6vgLF6aem3MeL73LKWLXBPtu1oUj6e2XQOAKCyb3nCdOGIwBvv1EZKuamBgSlRFpth/Fk0MMClwb4KmRT3jAqCRAIsGterTde9ZZAfvlg0EhHezpg9wBf3jw1u0/mIiIioZ2NijYioh4szTYcKrletUf99R/Fwrp3qqNU3PmWRjILda6fm2TexFp3aUdGgCtDsgilxIQgCSjXGKsUg0znzmFjrVn6NzRJfPzkt4rr77l4xCX7q2inZYZ5OuHNkUIfFZmauSqvWGRNr58XEmrrR/V+bOxDHn58urofWFtP7+WD3ysn49E8jmlzHkIiIiKg5mFgjIurhzF345gz2sxi31rQoRZ3pZnW7A1JDAa61/03cHJue9rbtifFYMr4XAlwt18gyVwRV1OjFz9rcfXHZhtPis0BdX0G5sUGBu5Md5gyp/bu9ZHwvvLNwsPh+8+PjEOGjwtHnpkHtYHymPrp/OGRWqBw1V6xptHrklVXjj7gcAMDoXu6N7m8nl8JbZd/oNiIiIqLOwsQaEVEPl5xn7AA4LdIbtw4yfgEP83KyWDS/o40wrZ80NqzxL9RkNNRUqRPs7ojpkT5N7uendsDLtw3AAH/LKXX7E/IgCAJKqrQAAIVMgtkDaxseTH/vAL48nNL+gZPVZZZoAACr5w+Cr4s9burvg1sG+eKlOf0xZ7C/uF8fU6dMiUSCHU9OwObHx6GfX8OpmB1BWadibVNMGqp1BgwJVIs/D4iIiIi6Ata+ExH1YOXVOmQUVwEAwryc8e6dQ3D/2GAMCXS1SuMCs4/vH45zacUY19vTatfsivr7u+DXJyci2MOx0a6g9bk5WiZHz6YVY8DLu/Dtw2MAAGoHBeYNDcCzP50X93ltexzuGBYANysmVqn9ZZr+Xge4OkAikeDzB0eK2xzsZNj59EQIAuBcpwtvoJsjAt2st4C/uMaaTo//nckAANw/JsSqP3uIiIiI2ooVa0REPdiW0+kAgF4ejnB3soODnQzjwj3hpLTuv7v4uNjjpgG+Fl/yqXH9/V2a/Tm5OjWcLlpZo8cn+xMBAC72CtjJpTj07FSLfaKTC9oeKDXKYBDwxeEUcb27jmCeWgnAYu20uiJ9XaxWmdYUc8VaVokGl3OM05Bn9m+6EpOIiIjIFjGxRkTUg207b1zg/IGxIZ0cCXWEuhVrL9zaD7cP8cf/3dIPRZXGqaAupjW16q/Fll9e28ggu0SDimqdFaLtGb49dg2vb4/DnP8cbnKfDcdTMeWdfUjMbd2ad9FJxsSot0oJdxuuPOzt5QwA+P5EKgAg0M2BlZJERETU5TCxRkTUQxVW1OBsWjEAYGqkd+cGQx3C1aG2Ym1ChCf+fe8wqOzliLlWBKC2WYRUKsH6xaPEffNNC9+nFVZi7Oo9mPvREStG3b3tic8VXy/85Cj+9MXxBtVrz2+JxdWCSrz3e0KrrrH1XCYAYPZAX5ueVmleU/FMajEAYKB/491AiYiIiGwZE2tERD1QbHoJhr++GzU6AzydlQjzdOrskKgDaOt0WQ31dIIgCPjxVJo4diGzNqEzNdIbT06PAAAUVhgr1radNyZoWls5RZYMBgGXs8vE96euFeHQlXz8a/dlcaxuZ9yKGn2Lr5FbqsEOUyXqHcMC2hBtx5vS1xsu9sZpzS72cjw6KayTIyIiIiJqOS5mQ0TUA+1PqK2auX2Iv01XtVDrhddJmCrlxvWsNjw6FpEv7gQATO1rWano6WychpdXVo1qnR7X8iutFGn3JwgC5n50BNmlmgbbLpoSnJdzynDT+wfFcVkr/lq++3sCavQGDA92xbBg2+6u6aVSYtsTE7AvPhdzhvjD01nZ2SERERERtRgTa0REPUSNzoA3dsRhQm9PLJvaG+lFVYhOLsAjE0M7OzTqIFHhHnjvziEWi9TbK2Q4/eJM/PfYNdw5MtBifw8nY2Jj18UczPn3YTjb89eE9pJXVo1Y05TP+cMC8MrcAYjLLMU9nx+DQmacQPD5wWSLY7JKGibhrietsBI/nzZ21/y/W/u3Q9QdL8TDCYvH82cQERERdV38jZmIqIf49tg1fBNt/HP1rVvx9sLBnR0SdTCJRIIFIwIbjLs72YnTPuvycK5dOP4Kp3+2q7rTaV+dOwAqe4XYNCKjuArZJRrU6AwWx2QUVbXoGp8dTILeIGBihCdGhNh2tRoRERFRd8E11oiIeojTpgXrAeNaT0T1eVynI6Oez0ybJOYZE2sz+nlDZW9sKuHtYqwQFARg7Oo9YtOByX28AABl1TqLz/3zg0lYtTkWVaa119KLKnEpqxSAcW21H0+lAwCWTe1thTsiIiIiIoAVa0REPUbdtZ36vbQTT06P4BdwsuBxnTWutHoDZFKZFaPpXpLzKgAA4d7O4ph53bv6npoRgQOX8wAAFTU6uNgrIAgC3vw1HgBQrdPjscnhuP3DI9AZDFg8rhdOpBSiRmfAiBA3jAl17+C7ISIiIiIzVqwREfUAgiDgSk5tN8JqnQHl1bpOjIhskauDwuL9F4tGiq9r9Ib6u1MLFFbUAAC8VfYW47cO9oPaQYEB/rXr4PXxUUFh6lxQrjH+Pa3S1nYI3Xw6AzPfP4gqrR5avYC1h1JwLt24ftvyqb3ZjISIiIjIilixRkTUA+SVV6NUY5lIu290cCdFQ7ZKKq1NyPi4KC26hmp1TKy1RUmVFgDgUq8hxIf3DkO1zgB7hQwllVqkFVXCWSmHs1KOokotKkwJ8NKqGyfCZVIJpvT1av/giYiIiKhJTKwREfUA289lia/7+bngo/uGIcjdsRMjIltnEIyJNrlUAp1BgFbPNdbawpxYU9erCpRIJLBXGKeEqh0VUDuqAQDO9sbEWpk5sabR3vAaHk52rFYjIiIisjJOBSUi6uaMazNdAgBMj/TGb09NRJiX8w2Oop7O3OBCITP+qqDlVNA2MSfGXOol1priZGf8t0/zVNCyJhJrW5ePx8z+PpBIgH/dNbTtgRIRERFRi7BijYiomyut0kFnSpKsvKlPJ0dDXYW5olEhk6BKyzXW2qq0iYq1pqhMU0brTwUdFKDG1uXj8eHeRLg52WFwoCs+e2AEyk1NDoiIiIjIulixRkTUzeWVVwMwflEf4K/u5GjI1m1cOhYTIzzxr7uGAADs5E1XrK07lIxxq/cgtaDSqjF2NYIg1K6x1szEmrPSmFhbdzgFAPB7XDYA499jiUSCJ6ZH4IGxIQCMU3aZVCMiIiLqHKxYIyLq5vJNiTUvZ2UnR0JdwdgwD4wN8xDfi1NBdZZrrBkMAt7YYZxi/MXhZLw6d6D1guxiNFqDuEZdcyvWzEvaxVwrwvHkAnx/Ig1A7X8PIiIiIrINTKwREXVz5sSaJxNr1ApymXExfK3BsmLtYmap+JoL5jdOEAToDbXVajKpBE52smYdeya1SHz98+l08XXdz52IiIiIOh//2ZOIqJvLLzMl1lR2nRwJdUW1FWuWibUTVwvF17llGqvG1BXo9Abc/MEh3PzBIfHzcTFN42yOu0YGia+3nssUX0+K8GzfQImIiIioTWw+sZaRkYEHHngAHh4ecHBwwKBBg3Dq1ClxuyAIeOmll+Dn5wcHBwfMmDEDV65c6cSIiYhsS355DQBWrFHr2IldQS2ngp5NKxZfpxVWWTOkLiE+uwzx2WW4kluO57fEAgB8XOybffyKmX0wNMgVgHEqKWBcX+2FOf3bPVYiIiIiaj2bTqwVFRVh/PjxUCgU+O233xAXF4f33nsPbm5u4j7//Oc/8e9//xuffvopjh8/DicnJ8yaNQsaDf/1nIgIAFLyKwAAAa4OnRwJdUVixVq95gUXMkrE17EZJXjxfxcgCJbJt57sdJ2pnBcyjNM3/Vvwd9BZKcdfJodbjL2zcDDcnVh5SkRERGRLbDqx9vbbbyMoKAjr16/H6NGjERoaiptuugnh4cZfNAVBwJo1a/DCCy9g7ty5GDx4ML755htkZmbif//7X+cGT0RkI+KyjF/q+/u7dHIk1BUpTGus1dRJrAmCgIxiY5Vafz/jc/XtsWtIyitv8fmzSzR47/cEZBa3repNEAT8Z88Vi2mTnSnmWlGDMV918yvWAMDbxbLKNMJH1aaYiIiIiKj92XRibevWrRg5ciTuvPNOeHt7Y9iwYVi7dq24PSUlBdnZ2ZgxY4Y4plarMWbMGERHRzd53urqapSWllr8ISLqjsqrdbhaYKxYMydAiFqisYq1okotakxrrm1ZNg5hXk4AgJ9iMhCf3fz/p+oNAhZ9eQL/2ZuIpzaeaXXFmyAIeH7LBby3+zKe/P4MiitrWnWe9mROrK19cKQ41tIWD96q2sSanUyKEHfH9giNiIiIiNqRTSfWkpOT8cknnyAiIgK7du3CY489hieffBJff/01ACA7OxsA4OPjY3Gcj4+PuK0xq1evhlqtFv8EBQU1uS8RUVd26HIeBAEIdneEB9dYo1awkzdMrGWVGKvLPJ3toJTLMGuALwDg0wNJeOl/F5t97qS8ciTklAEATl4talXFGwAcTszH9ydSxfdv/RYPjVbfqnO1h+wSDdKLqiCVAFHhHuK4bwvWWAMArzqJNS+VEnKZTf/aRkRERNQj2fRvaAaDAcOHD8ebb76JYcOGYenSpXj00Ufx6aeftum8q1atQklJifgnLS2tnSImIrItv10w/iPD7IG+nRwJdVXmirWLGaXYHZcDQRBw0bRmmHlq44jg2rVPUwsrb3jOGp0BG0+kYs+lXIvxxNzWJdYuZlpWyW08mYb3/7jc5P4duRZcVY0eY1fvAQAMCXKFs1KO/z48BveMCsLi8b1adC6lXCa+jvBxbs8wiYiIiKid2HRizc/PD/37W3a/6tevH1JTjf8q7etr/KKYk5NjsU9OTo64rTFKpRIuLi4Wf4iIuptqnR57442JCybWqLXMa6ytO5yCR785hXd/T8CzP58HAPiojIm1aZHeiHnBuCxDdqnmhtViv8Zm4bnNsXh7Z7zFeGsTa+lFxmSe2kEhju04n9VgP0EQcP+6Ywhd9SuWfXcapRptq653PT+fThdf3zbYHwAwIcITby0YDJW9oqnDmuRvSl7ePyakfQIkIiIionZl04m18ePHIyEhwWLs8uXLCAkx/nIZGhoKX19f7NmzR9xeWlqK48ePIyoqyqqxEhHZkopqHX6OyUB5tQ4+LkoMDXTt7JCoi1LUm3740b4k8fXtQ42JI6lUAncnOzjZGSusXvzfBZRUNZ20qp9AU9nLAQBJeRWtivFagTGx9n+39MOzs/sCAAyGhlVp6UVVOJJYAADYEZuFlT+ca9X1rufU1ULx9X1jgtt8vh/+HIVvHx6Nmf19brwzEREREVmdTSfWVqxYgWPHjuHNN99EYmIiNmzYgM8//xzLli0DAEgkEjz99NN44403sHXrVsTGxuLBBx+Ev78/5s2b17nBExF1kmqdHjd/cAjPb4kFAMwe4AuptKXLphMZ2dVJrEnqPEaeznaYOzSgzjYJgkyL62+KSceQV3/HvnjLqZ5m5gozABgSqMbKmX0AtK5iTRAEJJsSckHujrhzhHHd1KxSDap1xsq5Gp0BT3x/Bh/uTbQ4dk98DnJLNS2+5vWcvGpsWvDfh8fAXiG7wd43FuTuiIkRXm0+DxERERF1DHlnB3A9o0aNwpYtW7Bq1Sq89tprCA0NxZo1a3D//feL+zz77LOoqKjA0qVLUVxcjAkTJmDnzp2wt2/ZAsFERN3FjvNZFutczRni34nRUFdnbl4AAEMCXXE2rRiA5fpfZhE+KsRnl4nvl3x1ElffurXBfulFxuYHH943DHMG+4sJtaS8cgiCAImk+YngyznlyCiugp1MioEBLnBWyuFoJ0NljR6nrhbhWHIB7BUybDuXKR6zeFwvnE0rxtm0Ymw8mYYnp0c0+3rXk1lchYziKsikEgwNdm2XcxIRERGRbbPpxBoAzJkzB3PmzGlyu0QiwWuvvYbXXnvNilEREdmuXReNDQvmDPbDHcMCMKqXeydHRF2ZuUEBAAwMcBETa42to7Z0YphFAgsAErLL0NdXZTGWZqpYC3IzVriFeDhCLpWgskaPrBIN/F0dmhVbebUO/2eqzJwY4SmuYebv6oDE3HI89t8YlGp0DY6bPdAXzko5zqYV41+7L0NlL8eS8aHNumZTEnPLcM/nxwEA/f2MCT4iIiIi6v5seiooERG1zJHEfOy6aGzo8sjEMEzvx3WZqG16eTiJr3t71XambCyxNihQjR+WjrUYW38kxeJ9tU6PnNJqAECgmzGBppBJEeJhTLIl5TV/Ouh/9l7BqWvGqZcTIjzFcW+VEgAskmoKmQQKmQR9fJwxJtQdQ4NcxW2vbotr0XUb8+89icgvr24QCxERERF1b/znVCKibuJcWjHuX2esmHG0k2GgPzseU9sFmxJeANDbu7byrKmqsjFhHlhz91DsiM3C7rgcHE7MF7ftuZQDqWmap4NCBncnO3HbhN6eCPV0atG6ZJ8dSBZf163MNCfWzEaGuOH7pWOh1RuQUVQFiUSCUb3cobKXo8yUfJv+3gG8NncAHozq1ezr15VQZwrsoxPDWnUOIiIiIup6mFgjIuomrhbUdlT8y+RwyGUsSqa2q1uxFurlhO8fHYv3fk/Aa3MHNnnMvGEBiAr3wO64HGQWV6FGZ8ChK3l4+OtT4j6Bbg4Wa6m92sj5EnPLsCkmHYvH9YKfumEiL8zTCcn5xuc+ss50U696ibUvl4yCQiaFQiZFhI9xP7WjAgf+NhVxmaV44AtjQvqlXy5iSKArBgeqm73Om0arR1WNHk5KY0LwrfmDLBKGRERERNS9MbFGRNRNVFTXTs17fEp4J0ZC3Ym7kx2WTgqDTi8gwNUBAa4O+OmxcTc8zlulhL1CCo3WgIziKuyIzbLYbu4g2piC8mq4O9lhzR9XsP18Fr44lIL412c3SBbnlRmnXv6xcrLFNg9ny8Sai2nttcbubVy4h8XY3I+O4JXb+mNxM9Zc23giFe/sSkCVVi82eahb4UdERERE3R/LGYiIuokyjRYAMH9YAKvVqF09f0s/vHRb/xYdI5FIEGxKnk19dz82n86w2G5eX62+dYeSMeKNP/Dlkas4kVIIANAZBJy8WmSxX5lGi7Jq4zROf1fLTuBanUF8feL/pl83TqlUgrUPjrQYO266blOySqrw6raLeG5zLAoqalBZo0dxpfHvn6sDq9WIiIiIehJ+8yIi6ibKTUkGZ3sWI5NtCHZ3anJbVJhlpZggCFi1ORZv7LgEAHh9e5zYDAAwrs9WV1aJBgCgdlDA0c7ymb97VBACXB3wxLTe8FZZJt0aM7O/D/b9dQom9DY2HbicU3bd/Vf+cA7rj1xtdJvasfHqOCIiIiLqnphYIyLqJsyLsDsrmVgj2xDSxLRIdyc7TI30thiTSCSwk1mua2YQal+vO5yC70+kiu8ziqsANN5EwdvFHkeem4Znburb7FhDPZ3wzp2DAQBXCypRU6fqDQAKK2oAAKeuFiI6uQAA0MfHGS/OsazkUzswsUZERETUk/DbFxFRFyYIAr47ngpvlVJMrKmaWE+KyNrqJ9b+WDkZV/Mr0MdH1Wj3z5Uz++K2If54e2e8OPUzKsxDTGSt2hyLe0cHAwCyio0Va/7qG1ekNZeviz1c7OUo1egQn12KwYGuAIxJtRn/OoApfbyw+UztlNZdT0+CVi/g9e1x4piTXfO7mhIRERFR18eKNSKiLux0ahFe+N8FLP02Bj+fTgfAqaBkO4LcahNrn/9pBHp7O2NGf58mF/hXOyowspc7lpgaB4wJdccH9w5tdN+sEmPFmp9r+yXWJBIJhoe4AQBO1VnT7b3fE1BYUWORVLtjWICxyk4uxUt1qtaa202UiIiIiLoHJtaIiLqYGp0BJ1IKUa3TY9u5rAbbXZhYIxsR5F47TXNGP59mH3fzQF9seHQMPv/TSHjV6/BplmmqWPNTN94EobVG9XIHABxOzBfH9ifkNdjvrQWDxNf3jA7CsGBX/HlyWLvGQkRERES2j9++iIhuQG8Q8NTGM9h+Pgu3DPLFR/cN77SqlM8OJGH1b/EAjF0V04uqGuxTfyF3os7S21uF1+cOgJdKCam0+X9nJBIJxoV7iu9X3RyJ1b/Fw6HO9NGM4koADTuCttWsAT54Z1cC9iXkIq2wEl4qJTJN1XER3s64kluOJeN7QSmvjcXRTo4tj49v1ziIiIiIqGtgxRoR0Q1cyirF9vPGyrBfY7ORV1Z9gyM6Ro3OgI/2JYrv04uqYK+Q4t7RQRb76Q2G+ocSdZo/RfXC7IF+bTrHXSONz3iVVg+t3vh8J+ZWAADCPJ3bFmA9vb1VGBLkCkEAzqUXI7WwEoIAqOzl+GX5eLy9YBCemh7RrtckIiIioq6LiTUiohvIKtFYvC/VaDsljqNJ+Sg1NSjYunw8BgWoIZVIcPuQAHFB9wm9PTGlr/f1TkPU5ajqTG8u0+hQVFGD/HJjgjvcu30TawAQ7ukEAEgtrERynjGBF+rpBEc7Oe4eFQxXR7t2vyYRERERdU2cL0REPVapRotfzmZieqQ3/F2bXqcpu8RyumVJla6jQ2vUr7HGqrkHxgZjcKArvn5oNNIKKzEkyBVR4R5YPX/QDc5A1DXJZVI42clQUaNHaZUWOaXGZHeAqwOcle3/q0ygu7G5QlphJSQwTmENNSXbiIiIiIjqYmKNiHokQRDw+H9P43BiPj50UWLvM1Pg1MQXdFuoWLtWUCE2KrhlkHFanbuTHdydWDlDPYOLgwIVNXocuJyHl7deBAAMDXLtkGsFmxJr359Iw8z+xqYLTKwRERERUWM4FZSIeqTcsmqx619OaTXOpRU3uW92ab3EWpX1EmuCIECj1eNfuy+jSqvH2DB3jA31sNr1iWyFi70CAPD10avi2NJJHdOFM9JXJb7eHZcDgIk1IiIiImocE2tE1COlFlZavL9aUNnEnsDV/AqL92fTirHuUDLKNFpcyChBgWmtp1KNFs9vib1ukq4ldsflYOb7B7H02xjMHeqPWwf54Z2FQ1rUXZGou3B1NCbWkk1/H/8yORxDOqhibWCAGrcN8bcYa+8mCURERETUPXAqKBHZtMoaHT47kIy5Q/0R5tV+X2xT6yXSnt8SCz+1PaZGWi78r9HqcSGjFAAwJtQdx1MKsf7IVQDAGzsuAQDs5FK8OKc/PtmXiMwSDX44mYakN29pU3wx14rwxPenodEakJhbjoOX87BiRh8EmaaoEfU0Qe6OOJ5SKL5fMDygQ6/35h0Dse1cpvg+woeJNSIiIiJqiBVrRGTTnvs5Fh/suYJHvznVruetX7EGAI9/dxoard5i7Pe4HNToDfBWKTEwQN3ouWp0Brz4vwvINK3FpjcIbY7vt9gsaLQGizE/V/s2n5eoqwquk1RWyqUdPjVTZa+A2sFYJadSymGvkHXo9YiIiIioa2JijYhs2lZTxUhSXsUN9myZqwXG8905IhB2MuOPwiqtXrye2b/3XAEAzB3qL37Jbg6d3nDjnZpgMAjYFZcNAFg4IhB3jgjE2Zdm4q6RQa0+J1FXF+JRm1jr66uCXNbxv8KsXzIK/fxcsHbRyA6/FhERERF1TUysEZHNyjetXWZ28weHsLSdKtcuZhqnd94yyA+XXp+NZ2f3BQD8c2c8anTGpJhWb0CKaT2nJeND4aduWDH25yYWT88s1jQ6fiNavQGPfHMKaYVVcLST4dXbB+CdO4fA1ZHdP6lnq7vGWW9v60zLHB7sht+emoixYWwYQkRERESNY2KNiGzW0aQCi/eXskrxe1wO9iXktum8lTU6JOWVAzAuUi6TSvDIhDC42MuRX16Di5klAICsYg30BgF2cil8Xexx8yA/i/N8fP9w3Ds6GEq5FBHezoh5YQYiTF/4zRVxTckvr8Y7u+IRn12KR74+hTV/XIYgCDifXoy98cb7e3FOfzgpuRQmEQAMDHDBrAE+AIBp9dZCJCIiIiLqLPzGRkQ268iV/EbHl6w/ibMvzbxuFVdhRQ12XsjGwAAXDA50tdh2NLEAggD4utjDS6UEYGxAMKqXO/bE5yLmWhGGBbshrci4DlugmwOkUgmclXI8ND4UXx5JweBANW4xJdpiXpwJR4UMUqkEIR5OuJJbjmsFFQC8mozvHzsuYcuZDHy0LwkA8MelHIR6OkEqMXb8DPN0wr2jg5v1ORH1BBKJBJ8+MAIZxVUIcHXo7HCIiIiIiACwYo2IbFSNzoCfTqcDAO4dHSyug2aWVlh13ePf2BGH57fE4u7PjqGyRmexbePJVADA7UP9LcaHh7gBAM6ll5iuYUysBbnVru30/C2RWD1/EP65cLA45qyUQyo1JsRCPY37fnc8FeNW78GBy3kNYjt1tRBbzmQ0GP9gzxVkmxogNNUogagnk0gkCHRzhMSUgCYiIiIi6mxMrBGRTVq1ORZ6gwCFTIIX5/TDvr9NwYx+PuL23LKm1zATBAFHE43TSKu0ekSbppTmlmpQUF6Ng5eNlXALRwRaHNfHRwUASMwtN13DuMZb3bXV5DIp7h0djEhfl0avHeJh7FQYn12GzBINFn15osE+/9mbCMBYMRfk7oD7xgRDKgGS8ypwNr24wTWJiIiIiIjINnEqKBHZpPOmBNPtQwLgaCeHo50c6xaNxENfncTe+FzkmZJe6UWVeOL7M1g8rhfmDg0AAGQUVyG7tDbxtutiNi7nlOPtnfHiWJink7gempl5QfTkvHLoDQIKK2oAAO5OzW8cUP+cAJBdooGvKVFWozPgREohgNqOgwBwPLkASXkV2GdaX83HhYk1IiIiIiIiW8fEGhHZHL1BwDXTNMynZ0RYbPM2rYlmriZbezAZZ1KLcSb1LABg7tAAXMoqszhm27ksyGWWU8duGuDbYDpZkJsD7GRSVOsMyCiqQlFlyxNro3q5Y2KEJw7VWR/uXHoxfNW+AIALmSWo0urh7mSHvqYKOQDo66tCUl4FKmv0AIAAN64hRUREREREZOs4FZSIbE5mcRVqdAbYyaTwr7dIeW1izViRdsU0bRMA/rX7MgDgco4xsTZnsB/81fao0upRprFcZ232QN8G15XLpIjwMVacTXpnH345mwkAcLtOk4T6pFIJ/jI53GLsbFqxWGGXkG2MbYC/i7guGwCLBgujerlhal92PSQiIiIiIrJ1TKwRkc05Z5oGGuzhCJnUsqrMV21MtF3IKEVsegnOpBaL27JKNDialI93diUAAMK8nDHM1JCgrghvZwwJbLw5wPDghvu3pGINAKLCPPDQ+FD08jA2MvhkfxJG/eMPfH30KlZtjjXFoLI45sGoEMwb6o+oMA+sfXAk7OT88UxERERERGTr+M2NiGzO10evAgBu6u/TYNu0SG9IJcYqsNs+PIwqrR7B7sYEVo3OgGXfnRb3DXZ3xLAgVwDArAE+iHttFv5xx0Bs+ktUk10FRzSSiHNrYWJNKpXgpdv6Y9sTE+DprBTHX956UXwd4uFocYyjnRxr7hmG75eOhWsLKuSIiIiIiIio8zCxRkQ2Rac34Fx6CQDgrpFBDbb7qu3R39+yI+ddIwPh6qgAABRVagEAagcFpkV6Y0iQK/zU9vB0VsLRTo77x4RcN3F10wAf+NZrHODeykSXyl6BSX08G902pa9Xq85JREREREREtoPNC4jIpqQWVqJGZ4C9QipWotVXf82z3t7O8HWxR7EpqSaRADEvzIBcJoWboxuiV01v9vUd7eTY9sQEHE7Mw4ofzgEAPJxbX0E2KECNzaczLMaevyUSIR5OrT4nERERERER2QYm1ojIplzOMTYj6O3tbLG4f131K85CPZ3h7WKPeFNjAHdHO8hlxoLcpqZ8Xo+XSok7hgXCX+0AnUGAk7L1PyrvGxOMA5fzsD8hTxwL93Ju9fmIiIiIiIjIdnAqKJENe317HMat3oOM4qrODsVq4rNLAQB96i3uX5ebadqnWYiHIwLdaruHtqXCrK4xYR4Y37vxqZzNpZTL8OWiUVgwPFAcq9/plIiIiIiIiLomJtaIbIwgCKjRGfD7xWx8cTgFmSUabDh+zWKfrJIqLPvuNFb+eBY1OkMnRdp+yqt1eHXbRRy8nIdzacUAgEFNdO0EjNM1ze4fEwx7hQxhnrVTKz2clI0d1mmkUglWzx+ESF8VVPZyJtaIiIiIiIi6CU4FJbIxa/64gg/2XLEYO5JYgL/Nqn3/9dFr2BGbBQAYG+qBu0Y1XOS/K/m/LbH45Wwm1h+5Ko4NDnRtcv+6sztfnzsQABBaN7HWThVr7clOLsXOpyd1dhhERERERETUjlixRmRD8surLZJq5u6UFzNLxMq0kiotTl0tFPd59ufziLlWiJhrhdBo9W26/tX8ClzNr2jTOVpKqzfgl7OZDcYH1Ov8WVfdVdPM67DVTax5q+xBRERERERE1NFYsUZkQ76qU7HloJDhxz9HYc5/DqFUo8OV3DKoHRSY9f5BVNRYJtAWfBINAHh8SjienR0JANDpDfgm+hoUMglmDfCFt0vTyabcMg3e3ZWAH0+lIyrMAxseHdOqRf9b40xqcYOxUy/MgL1C1uQxKntFg7FQTycsHtcLWSVVWDQupD1DJCIiIiIiImoUE2tEVrLpVBoC3BwwLrzxxfAFQcBPMekAgOVTe2PeMH8Eeziiv78LjiUX4mJmKfLLqy2SatufmIA5/zksvv89LkdMrO26mIPXtscBAFb/Fo+vlozG6FD3Rq+9PyEPP54yXjs6uQChq35FgKsDhoe4YUY/b2QWa1BYUY3nbu4HWROdOmPTS+Dvag8P5+avbyYIAj4/mATA2AV0aJArxoV7wPMG53hgbDD2XMrB7IG+4phEIsErtw9o9rWJiIiIiIiI2oqJNSIrKCivxitbL6KiRo/vHx2LqHCPBvsk5pYju1QDpVyK5dN6ixVbA/zVSMguQ1WNHtFJBeL+T06PaDBdMrtEA0EQIJFIEJ2cL45X1ujxwBfHcfjvUxudJhmfVQYAkEkl0BsEAEBGcRUyiquw7VztNM3s0mq8d+cQ2MktZ5GvP5KCV7fFwUEhw/YnJyDcy7lZn8vFzFL8cSkXCpkEn9w/HBE+TXcCrUtlr8BPj41r1r5EREREREREHYVrrBF1MK3egGUbTqOiRo9BAWqMDTNWjVXW6PDZgSTsi8/FHR8fwcz3DwIAxoR5WEyDfHZ2X5x+cSbmDw/AoSvGZNlzN0fiqekRkEgkuHWwn7hvebUO6UVVAICTKUXi8QBQozNgk6kqrb747FIAwOo7BuFPY5ueRrntXCYOXM5rMD4s2A0SCVCl1WPhJ0dRXq274ediMAg4dbUQz87ui5fm9G92Uo2IiIiIiIjIVrBijagD5ZRqMObNPQCMa6Y9MjEUXx65iocnhOIfOy7hu+OpDY6pn9hSymUQBAFzPzwCAAhyd8CfJ4WJa6Ctnj8Icwb54a2d8bhWUImM4iqcTi1CQo6xCu3ukUHwUdnjmU3nsOF4Kv4yOdxiOqcgCIjPNu4b6afC3GH+GNnLDUm55ZgQ4YXE3HL09XXGR/uSsDc+FzHXijA4UA2DIMBP7QAAGBrkilU3R+LNX+NRVKnFplNpWDI+9LqfTUpBBV7ZFgd7hRQXXpl13X2JiIiIiIiIbBETa0Qd6LfYLPH1Y1PC8crWiyiq1EJvMGDjybQG+z87uy+mR3o3GI/NKEGyqVvnnyeFWzQWcLFX4OZBflh/9CquFVTix5Np2HwmAwAgl0rg4azErYP98Nr2OGQUV+GNHXF4dGIY/F2NSbELGaUorKiBg0KGPj4qKOUyzB0aIJ7fvC7b7AEV2Bufi08PJGHtoWQ4KmTY89fJ8HRSIia1CEsnhQMA3vw1Hoev5N8wsXYhowQA0M/PBXIZi2eJiIiIiIio62FijagDnbxmnI45vrcHnpjWG1VaPT7Zn4Q3f41vsO8H9wy1SGjVtfm0MVF2+xB/PNDEVE0vlXHBf3NSDQAWj+sFALBXyPD0jAi8ui0O649cxa+xWXjvzqG4WlCBrBLj1NEpfb2u24lzeIir+FpvEFBWrcOr2+Kw47wxeXjPqCAxtuMphdDpDU0mzCprdDhsmtY60F/d5DWJiIiIiIiIbBkTa0Qd5O2d8WLS6clpxvXQHp8Sjs8OJMHUHwDje3vgSKKxIUHdDpf13T7UHxqtHrcP8W9yH2+VZSfNJ6dH4LHJ4eL7RVG9oDcIeGPHJeSUVuOBL45b7D+1kUq5usI8GzYkMN8fAPTxUaGfnwvUDgqUVGlxLr0Yw4PdoNEa4GBXm7DTGwTc/dkxxJoq1gYGuDQ4LxEREREREVFXwMQaUQc4eDkPn+xPAgCMCHHDqF7G6ZQqewUGBqhxPt2YVLpvdAjmDwvEgAAXKOXXqRYLdsPwYLfrXtPN0c7i/f1jgi0SWlKpBI9MDINUIsFr2+MaHB8V1rBTaV1SqQQjQtwQc60IAwNccC2/EmWmJgUjQtzwYFQIZFIJosI8sPNiNhZ8Eo1x4R44mlSAr5aMwpS+3kjJr8CiL08gtbASABDs7ojZA/2ud1kiIiIiIiIim8WFjYjaWXm1Dit/PAsA8Ffb4/M/jYC0TrOA0aYkGwCEeDhiwYhARPq2vWqrbkMCoGEFm9mcIX4N9vVwskOgm8MNr/H2gsF4anoEvn90LLY+MQF+anvcNTIQPz82Tpz2ecfw2umsR5OM1XgrfzwHAHjsvzFiUm1kiBt2PDkBagdFM++QiIiIiIiIyLawYo2ojSqqdUjKK0ewuyN+j8vBtYIK5JfXwEulxB/PTIajneVfs4EBtWuKhXg4tlsc948JxsHLeaio0eFvsyItGhzU5a2yx0f3DUeZRou//XQeADAmzL3J/evq7e2MFTP7ADBW3x19blqD42YN8MWR56Zh/Ft7xbHCihqkFlSK3UcB4InpEVDZM6lGREREREREXRcTa0RtkFuqwT2fHxM7dtY1PtyjQVINAKb184af2h7+rg7tmlhydbTDD3+Oata+5vXc7BUybDieihfn9G/VNZtKxgW4OojTRs0mvbPPYp9Rva4/tZWIiIiIiIjI1kkEQRA6O4jmeuutt7Bq1So89dRTWLNmDQBAo9HgmWeewcaNG1FdXY1Zs2bh448/ho+PT7PPW1paCrVajZKSEri4cCF1aqiiWoctZzJw6EoezqWVQC6TwN/VASdSCps85uXb+mPJ+NBGt2m0esilkia7ZnYHaYWViLlWhBq9Ac+aKuPMjj43Df6uN556SkRERERERGRtLckTdZmKtZMnT+Kzzz7D4MGDLcZXrFiBHTt2YNOmTVCr1Vi+fDnmz5+PI0eOdFKkZAvSCiux62I2Fo3rBUW95JXeIOAfOy5h67kMhHk5Y+GIQCwcHmixDppZtU6P8+kluPPT6Abb0ouqAAB+anvMGeyHESFuuKm/LzaeTMOhK3mYOzSgwTFm9oqmGxV0F0Hujghyd4RGq7dIrL1waz8m1YiIiIiIiKhb6BKJtfLyctx///1Yu3Yt3njjDXG8pKQEX3zxBTZs2IBp06YBANavX49+/frh2LFjGDt2bGeFTJ1IEARMemcfBAFQyqX4U1Qvi+17LuXgyyMpAID88kKcSClEVrEGT82IAGBMvG04fg0JOWX477HUBuf3VikxNMgVzvZyeKmU+NPYEAS61a6Vdt+YYNw3JrjjbrCLsVfI4K+2R2aJBgDwyMSwTo6IiIiIiIiIqH10icTasmXLcOutt2LGjBkWibWYmBhotVrMmDFDHIuMjERwcDCio6OZWOuhzqeXwDzB+cTVogaJtUNX8hscs+tiNp6aEYEyjRZ3fXYMl7JKG+wzb6g//hQVghEh7g220fXdMzoY/9p9Ge5Odp0dChEREREREVG7sfnE2saNG3H69GmcPHmywbbs7GzY2dnB1dXVYtzHxwfZ2dlNnrO6uhrV1dXi+9LShkkU6roSc8vF19vOZeKRCaEYEuQKANDqDdgdlwMA+OxPI6DVG7B8wxkk5pVDpzfgo31JjSbVXrmtPxY3sV4a3diyqb3hYi/HuN6enR0KERERERERUbux6cRaWloannrqKezevRv29vbtdt7Vq1fj1VdfbbfzkW3JKK6yeD/3I+N6e49ODEU/Pxdkl2rg6WyHKX29oJBK4aA4jyqtHv89dg2fHkgSj1s8rhfOpRfjan4Fbh3sb9V76G5kUgkTk0RERERERNTt2HRiLSYmBrm5uRg+fLg4ptfrcfDgQXz44YfYtWsXampqUFxcbFG1lpOTA19f3ybPu2rVKqxcuVJ8X1paiqCgoA65B7K+zHqJNbO1h1LE13OHBkApNzYQGBbsiqNJBXhlW5zF/g9GhcDfNRJavQEqe0XHBUxEREREREREXZJNJ9amT5+O2NhYi7ElS5YgMjISf//73xEUFASFQoE9e/ZgwYIFAICEhASkpqYiKiqqyfMqlUoolcoOjZ06j7li7Z2Fg3HTAF98eTgFibnl2BGbJe4zta+3+Prl2wZg1pqDFueIe20WHO2Mfz16QgdPIiIiIiIiImo5m06sqVQqDBw40GLMyckJHh4e4vjDDz+MlStXwt3dHS4uLnjiiScQFRXFxgU9VE6pBqevFQEAgtwdoXZQYMXMPgCAX1ftEJsajA6tbUDQ11eFwYFqnE8vAQD8vmKSmFQjIiIiIiIiImqKtLMDaKv3338fc+bMwYIFCzBp0iT4+vpi8+bNnR0WdQKDQcCj35xCRY0e/f1cMCLEzWL7uwuHAABWzx8EO7nlo//oxDBIJICnsx3CPJ2sFjMRERERERERdV0SQTDX8PRcpaWlUKvVKCkpgYuLS2eHQ63046k0PPvTeaiUcmx/cgJCPBomyCprdE1WoyXnlUMhkyLI3bGjQyUiIiIiIiIiG9WSPFGXr1ijnudESiG+OJwCjVYvjuWUavDClgsAgCUTQhtNqgG47hTPMC9nJtWIiIiIiIiIqNm4kBR1KWUaLR7+6iTKqnWIuVaIj+8fgeLKGjz703nU6A3o66PCE9N6d3aYRERERERERNQDMLFGXYYgCPjbpvMoq9YBAH6NzcYXh1Pwa2wWYkwNC+YNC4BCxkJMIiIiIiIiIup4TKxRl3EipRA7L2ZbjL2+PU58HRXmgftGB1s7LCIiIiIiIiLqoVjaQ11CSaUWi9afAADMGeyHnU9PtNgul0rw30fGQO2o6IzwiIiIiIiIiKgHYmKNuoTTqUXQaA0AgL/e1BeRvi74YelYcbvOIEAmlXRWeERERERERETUAzGxRjbPYBDw4i/Gjp+3D/FHL09jx88xYR4Y4G9se+ulUnZafERERERERETUM3GNNWpgb3wOruSUo6+vClP6end2OPg9LgfpRVUAICbSzL5YNAr/+PUSFo8L6YzQiIiIiIiIiKgHY2Kti8orq4ZGq0eQu2O7nvfk1UI89NUp8f36xaMwNbJzk2vHkgvE19PqxeKrtsd/7h1m7ZCIiIiIiIiIiDgVtCvS6Q2Y99ERTHl3P36OScfuuBwYDEK7nPvzg8kW71/85QJOpBRCpze0y/lbSm8QcOhKHgDgg3uGIsJH1SlxEBERERERERHVx8RaFxSdXICM4iroDQKe2XQOj35zCpPe2Yeiipo2nbeyRoeDl/MsxtKLqnDXZ9HYeDKtTeduDUEQsPLHs0jKq4CTnQwTentaPQYiIiIiIiIioqYwsdYF7Y3PbTCWXlSFVZtjm32O/Qm5+GhfIkqqtOLY2bRiVOsM8FfbI2X1LVi/ZJS47URKYbPOm1lchZd+uYC0wkoAximrJZXaGxzVUI3OgE8OJOGXs5kAgNULBsPDmQ0KiIiIiIiIiMh2cI21Lig2vaTR8Z0XswEAFzJK8Nzm83j+5n4Y10iVV355Nf78bQyqdQbEZZbio/uHAwAuZpQCAAYHukIikWBqX288GBWCb6KvobCZ1XAv/O8C9sbn4tfYLAwNcsPe+BwYBKCvjwovzOmHiRFezTrPM5vOYds5Y1Jt4YhA3D7Ev1nHERERERERERFZCyvWuhid3oALmcbEWohHw8YFeoOA5zafx4WMUty37niD7RqtHp8fTEa1zrhm2qEreRAE4/pssRnG8w4MqO28efNAPwBARrGxK2dBeTWe+P4MJv5zLzadqp0eWqMz4JGvT4nVdPnlNfjjkjGpBgAJOWV45OtTyC+vvu79RScV4NFvTolJtREhbnh6RsQNPhUiIiIiIiIiIutjYq2LySzWQKM1QCmX4renJmL9klF4cU5/cfsfl3JwObtcfB+fXWpx/PcnUi0aFJRqdLhWUAlBEHA8xdh9c2iQm7g90M0BgDGxZjAI+GR/Erady0RaYRX+9tN5ZJoSbr/GZuGPSzkN4p0Y4YkZ/XwAANU6A44k5l/3/o4k5mN3nPE8fmp7/PSXKAS6tW/nUyIiIiIiIiKi9sDEWheTU6YBAPiq7eFoJ8fUvt54eEIo/jI5HADw529jUFOng+fsNYfEajODQcC30dcAAK/PHYChQa4AgFPXirD5dAZySquhlEsxsldtYs1XbQ+FTIIanQHJ+RVIyCmziOfj/YkQBAG7TNNQbx7oi11PT8KwYFd4q5RYPX8Q1i0aiXtHBwEAEnPLodMbcOBynsX6bmYx14rE11P6ekMikbTp8yIiIiIiIiIi6ihcY62LySk1JtZ8VPYW49MivfHpgSTxfainE1LyKwAAK344ix//HIW4rFIk5xs7bN4xPBC5ZdU4m1aMn2LSxHXbZg/0hb1CJp5HIZMiKtwTBy/nYdfFbCTnGc85pa8X9ifk4b/HUhGfVYZTpoTYsqm90ddXhS2Pj7eIL9zLGQDwn72J+OVsJlILKzEoQI2ty8eLybOC8mrEpBrPs+vpSQjzcmqfD42IiIiIiIiIqAOwYq2LyCyuwkNfncTH+4zJM28Xyw6ZoZ6WSaiVM/vg9bkDABg7el7JKcNlU7XZwAA1nJVyTDdN0TyWXIiKGj3CvJywev6gBte+dZAvAODLwyli9dvyqb3F7eak2s0DfTEwQN1o/L29ncXXqaaOobEZJVi8/qQ4/p+9iajRGRDm5YTe3s5QyPh4EhEREREREZHtYuaii/jX7svYG5+LuCzjmmne9SrWPJ3t4KysLUD0cLLDn6J6YVqkNwBg5vsHsfLHcwBqk1xDAtWYPcBXPGbhiEA42jUsYpw7NADeKiUKTJ1BHRQyjAhxw4VXZ+HZ2X3F/ab29W4y/qhwD4wNc28wfuByHhJzjWvCnUgpBACsmNEHMimngBIRERERERGRbWNirQuIuVaIn2LSLcYc7WQW7yUSiUXVmruzHQA0WkFmTqxJJBLcOTJQHB8c4Nro9e0VMqy5Z6j4/q+z+kIikcBZKcd9o4PF8VGhDRNnZkq5DBuXRuHnx6IwPdIbWx4fJyb9lnx1AmmFlWJF3YgQtybPQ0RERERERERkK7jGmg3bl5CL5d+dRkWNHgAwqpcb5g0LwOcHkzFvWECD/YM9HBGbYVwrzd3RmFjr5WHZUVMulSAq3EN8PzKkNhk2MMClyVjGhXti8+PjMMDfBUp5bVLP1dEO65eMQmmVtsF01MaMCHHHF4uN15wa6Y298blIK6zCxH/uA2DsBOqntr/eKYiIiIiIiIiIbAITazaoolqHF/53AVvOZIhjAwNc8H+39sfQIFfcPyak0ePMyTQAcHMyvg7xqE12vTinPxaOCITaQSGOqR0V2Lh0LATBmCS7nuHBjVeSXW8K6PVEhXlYvO/t7YzV8wexEygRERERERERdQlMrNmQyhodXt9+Cd+fSBXHgt0d8fiUcNxTZ8plU+omzMwL/9etIrtlkK/FPmZj6yW4rCXcywlPTOuNfQm5uH2IPxaN62VRDUdEREREREREZMuYWLMh30Zfs0iqvTV/ULMSamazBvjiw32JcHeqrTxzd7LDB/cMhUImhZ/aoV3jbSuJRIJnbuqLZ27qe+OdiYiIiIiIiIhsDBNrNsBgEHAsuQAf7k0EAEglwO8rJotNBpprUKAavywbDz9XyzXK5g5tuB4bERERERERERG1DRNrnaBap4cEEtjJpajRGbDoyxOITi4AAAwOVGPTX6JaPSVySJBrO0ZKRERERERERERNYWLNivQGAesOJeODPVcgl0rwz4WDcfBKvphUA4C/3tSX64wREREREREREXUBTKxZ0Tu7EvDpgSTx/V/+e9pi++wBvhgX3jmNBIiIiIiIiIiIqGWYWLOS9KJKManWz88F6YWVKKvWAQCc7GQ49PdpFk0HiIiIiIiIiIjItjGxZgU6vQEz/3UQgDGp9ttTEwEAGq0enx5IQlSYB5NqRERERERERERdDBNrdRgMgvi6pEqLtQeTcSKlEBuXjoVUKmn1eVPyK1Cl1QMA/npTH3HcXiHD0zP6NHUYERERERERERHZMGlnB2BL/v7zeexLyAUAyKQSfHX0Kk5cLcSRpPw2nfdSdhkAYHiwK6b382lznERERERERERE1PlYsVbHbxeysetKKQDgjmEBuG2IH74/kYavj17FxAivVp3z1NVC/BSTDgCI9HNpt1iJiIiIiIiIiKhzsWKtCVvOZECrN04N3ROfix9PpuH2Dw8jOqmg2efYn5CLhZ9G4+DlPADAtL7eHRIrERERERERERFZHxNrdex7ZjIGB6rF90cT8zE4UA1BAJ79+TzOp5fgo32JjR6r1Ruw80IWBKF2nbafT2eIr9fcPRQz+nMaKBERERERERFRd8GpoHV4udjj+0fHIqtEg7s/i4a7sx1c7BUW+6QWVjY4ThAEvPi/C9h4Mg0AcPfIICwe3wu7LmQDALY8Pg7Dgt06/gaIiIiIiIiIiMhqWLFWj5NSjt7ezvj1qYnYtnwCosI9LLanFVUiJb8C6w4lQ2Pq9PnBnitiUg0AfjiVhps/OIQavQETIzwxNMjVmrdARERERERERERWwIq1Jvi42AMAXB1rK9bs5FLU6AyY+u5+AEB5tQ7OSjnW/HEFAPD32ZFwtJPh5a0XxWPevXMIJBKJ9QInIiIiIiIiIiKrYGLtBm7q74t/7kzAlL5eKKrUio0IAIgJNQCYM9gPj00JBwCcTy/Bz6fT8dzNkWKCjoiIiIiIiIiIuhcm1m7AS6XE2ZdmQhCAF3+50OR+y6b2Fl+/MW8gbhvih0kRXtYIkYiIiIiIiIiIOgHXWGsGiUQCqVSCWQN8AQCDAtRwsTfmJP+5YDAS3piNfn4u4v4OdjJM6esNqZRTQImIiIiIiIiIuitWrLXApD5e+GXZePT1VSGvrBoyqQT+rg6dHRYREREREREREXUCJtZaaIipw2eQu2PnBkJERERERERERJ2KU0GJiIiIiIiIiIhagYk1IiIiIiIiIiKiVmBijYiIiIiIiIiIqBWYWCMiIiIiIiIiImoFJtaIiIiIiIiIiIhagYk1IiIiIiIiIiKiVmBijYiIiIiIiIiIqBVsPrG2evVqjBo1CiqVCt7e3pg3bx4SEhIs9tFoNFi2bBk8PDzg7OyMBQsWICcnp5MiJiIiIiIiIiKinsDmE2sHDhzAsmXLcOzYMezevRtarRY33XQTKioqxH1WrFiBbdu2YdOmTThw4AAyMzMxf/78ToyaiIiIiIiIiIi6O4kgCEJnB9ESeXl58Pb2xoEDBzBp0iSUlJTAy8sLGzZswMKFCwEA8fHx6NevH6KjozF27NgbnrO0tBRqtRolJSVwcXHp6FsgIiIiIiIiIiIb1ZI8kc1XrNVXUlICAHB3dwcAxMTEQKvVYsaMGeI+kZGRCA4ORnR0dKPnqK6uRmlpqcUfIiIiIiIiIiKiluhSiTWDwYCnn34a48ePx8CBAwEA2dnZsLOzg6urq8W+Pj4+yM7ObvQ8q1evhlqtFv8EBQV1dOhERERERERERNTNdKnE2rJly3DhwgVs3LixTedZtWoVSkpKxD9paWntFCEREREREREREfUU8s4OoLmWL1+O7du34+DBgwgMDBTHfX19UVNTg+LiYouqtZycHPj6+jZ6LqVSCaVS2dEhExERERERERFRN2bzFWuCIGD58uXYsmUL9u7di9DQUIvtI0aMgEKhwJ49e8SxhIQEpKamIioqytrhEhERERERERFRD2HzFWvLli3Dhg0b8Msvv0ClUonrpqnVajg4OECtVuPhhx/GypUr4e7uDhcXFzzxxBOIiopqVkdQIiIiIiIiIiKi1pAIgiB0dhDXI5FIGh1fv349Fi9eDADQaDR45pln8P3336O6uhqzZs3Cxx9/3ORU0Ppa0kaViIiIiIiIiIi6r5bkiWw+sWYNJSUlcHV1RVpaGhNrREREREREREQ9WGlpKYKCglBcXAy1Wn3dfW1+Kqg1FBQUAACCgoI6ORIiIiIiIiIiIrIFBQUFTKw1h7u7OwAgNTX1hh+YtYwaNQonT57s7DAs2GJMgG3GZc5u21oVpC1+VrYYE2B7cfGZahlbjMsWY7LF58oWPyeAcTWXLT5TgO19ToBtxgTYZlx8rprPFmMCbDMuPlfNZ4sxAbYXl60+U4DtfVaA7cdUUlKC4OBgMV90PUysAZBKjc1R1Wq1zfwFkMlkNhOLmS3GBNhuXADg4uJiU7HZ4mdlizEBthsXn6nmscW4bDEmM1t6rmz1c2JcLWNLzxRgm5+TLcYE2G5cAJ+r5rDFmADbjQvgc9UcthgTYLtx2dozBdjmZ9VVYjLni67nxntQp1i2bFlnh9CALcYE2G5ctsgWPytbjAmw3bhsja1+TrYYly3GZIts9XNiXF2bLX5OthgTYLtx2SJb/KxsMSbAduOyRbb4WdliTIDtxmWLbPGz6k4xsXkB2BWU2h+fKWpvfKaoI/C5ovbGZ4o6Ap8r6gh8rqi98ZnqXlry35MVawCUSiVefvllKJXKzg6Fugk+U9Te+ExRR+BzRe2NzxR1BD5X1BH4XFF74zPVvbTkvycr1oiIiIiIiIiIiFqBFWtEREREREREREStwMQaERERERERERFRKzCxRkRERERERERE1ApMrBEREREREREREbUCE2tEjVi9ejVGjRoFlUoFb29vzJs3DwkJCRb7aDQaLFu2DB4eHnB2dsaCBQuQk5Njsc+TTz6JESNGQKlUYujQoQ2uo9FosHjxYgwaNAhyuRzz5s3rwLuizmStZ2r//v2YO3cu/Pz84OTkhKFDh+K7777ryFujTmSt5yohIQFTp06Fj48P7O3tERYWhhdeeAFarbYjb486ibWeq7oSExOhUqng6urazndDtsBaz9TVq1chkUga/Dl27FhH3h51Emv+rBIEAe+++y769OkDpVKJgIAA/OMf/+ioW6NOZK3n6pVXXmn055WTk1NH3h51ECbWiBpx4MABLFu2DMeOHcPu3buh1Wpx0003oaKiQtxnxYoV2LZtGzZt2oQDBw4gMzMT8+fPb3Cuhx56CHfffXej19Hr9XBwcMCTTz6JGTNmdNj9UOez1jN19OhRDB48GD///DPOnz+PJUuW4MEHH8T27ds77N6o81jruVIoFHjwwQfx+++/IyEhAWvWrMHatWvx8ssvd9i9Ueex1nNlptVqce+992LixIntfi9kG6z9TP3xxx/IysoS/4wYMaLd74k6nzWfq6eeegrr1q3Du+++i/j4eGzduhWjR4/ukPuizmWt5+qvf/2rxc+prKws9O/fH3feeWeH3Rt1IIGIbig3N1cAIBw4cEAQBEEoLi4WFAqFsGnTJnGfS5cuCQCE6OjoBse//PLLwpAhQ657jUWLFglz585tz7DJhlnjmTK75ZZbhCVLlrRL3GTbrPlcrVixQpgwYUK7xE22raOfq2effVZ44IEHhPXr1wtqtbq9wycb1FHPVEpKigBAOHPmTEeFTjaso56ruLg4QS6XC/Hx8R0WO9kua/1udfbsWQGAcPDgwXaLnayHFWtEzVBSUgIAcHd3BwDExMRAq9VaVJlFRkYiODgY0dHRnRIjdS3WfKZKSkrE61D3Zq3nKjExETt37sTkyZPbFjB1CR35XO3duxebNm3CRx991H4Bk83r6J9Vt99+O7y9vTFhwgRs3bq1fYImm9dRz9W2bdsQFhaG7du3IzQ0FL169cIjjzyCwsLC9r0BsknW+t1q3bp16NOnD6u3uygm1ohuwGAw4Omnn8b48eMxcOBAAEB2djbs7OwarAXj4+OD7OzsToiSuhJrPlM//vgjTp48iSVLlrQlZOoCrPFcjRs3Dvb29oiIiMDEiRPx2muvtUfoZMM68rkqKCjA4sWL8dVXX8HFxaU9wyYb1pHPlLOzM9577z1s2rQJO3bswIQJEzBv3jwm13qAjnyukpOTce3aNWzatAnffPMNvvrqK8TExGDhwoXteQtkg6z1O7tGo8F3332Hhx9+uK0hUyeRd3YARLZu2bJluHDhAg4fPtzZoVA3Ya1nat++fViyZAnWrl2LAQMGdOi1qPNZ47n64YcfUFZWhnPnzuFvf/sb3n33XTz77LMddj3qfB35XD366KO47777MGnSpHY/N9mujnymPD09sXLlSvH9qFGjkJmZiXfeeQe33357u1+PbEdHPlcGgwHV1dX45ptv0KdPHwDAF198gREjRiAhIQF9+/Zt92uSbbDW7+xbtmxBWVkZFi1a1KHXoY7DijWi61i+fDm2b9+Offv2ITAwUBz39fVFTU0NiouLLfbPycmBr6+vlaOkrsRaz9SBAwdw22234f3338eDDz7Y1rDJxlnruQoKCkL//v1x77334q233sIrr7wCvV7f1vDJRnX0c7V37168++67kMvlkMvlePjhh1FSUgK5XI4vv/yyvW6DbEhn/F41ZswYJCYmtukcZNs6+rny8/ODXC4Xk2oA0K9fPwBAampq24Inm2XNn1fr1q3DnDlz4OPj05aQqRMxsUbUCEEQsHz5cmzZsgV79+5FaGioxfYRI0ZAoVBgz5494lhCQgJSU1MRFRVl7XCpC7DmM7V//37ceuutePvtt7F06dJ2iZ9sU2f+rDIYDNBqtTAYDG06D9keaz1X0dHROHv2rPjntddeg0qlwtmzZ3HHHXe02/1Q5+vMn1Vnz56Fn59fm85Btslaz9X48eOh0+mQlJQkjl2+fBkAEBIS0sa7IFtj7Z9XKSkp2LdvH6eBdnGcCkrUiGXLlmHDhg345ZdfoFKpxPnyarUaDg4OUKvVePjhh7Fy5Uq4u7vDxcUFTzzxBKKiojB27FjxPImJiSgvL0d2djaqqqpw9uxZAED//v1hZ2cHAIiLi0NNTQ0KCwtRVlYm7jN06FBr3jJ1MGs9U/v27cOcOXPw1FNPYcGCBeJ17Ozs2MCgG7LWc/Xdd99BoVBg0KBBUCqVOHXqFFatWoW7774bCoWiM26dOpC1nitzxYfZqVOnIJVKxXVsqPuw1jP19ddfw87ODsOGDQMAbN68GV9++SXWrVtn9Xumjmet52rGjBkYPnw4HnroIaxZswYGgwHLli3DzJkzLarYqHuw5vdAAPjyyy/h5+eHm2++2ar3Se2sEzuSEtksAI3+Wb9+vbhPVVWV8Pjjjwtubm6Co6OjcMcddwhZWVkW55k8eXKj50lJSRH3CQkJaXQf6l6s9UwtWrSo0e2TJ0+23s2S1Vjrudq4caMwfPhwwdnZWXBychL69+8vvPnmm0JVVZUV75asxZr/D6xr/fr1glqt7rgbo05jrWfqq6++Evr16yc4OjoKLi4uwujRo4VNmzZZ8U7Jmqz5syojI0OYP3++4OzsLPj4+AiLFy8WCgoKrHSnZE3WfK70er0QGBgoPP/881a6O+ooEkEQhBbk4YiIiIiIiIiIiAhcY42IiIiIiIiIiKhVmFgjIiIiIiIiIiJqBSbWiIiIiIiIiIiIWoGJNSIiIiIiIiIiolZgYo2IiIiIiIiIiKgVmFgjIiIiIiIiIiJqBSbWiIiIiIiIiIiIWoGJNSIiIiIiIiIiolZgYo2IiIiIiIiIiKgVmFgjIiIiIiIiIiJqBSbWiIiIiIiIiIiIWoGJNSIiIiIiIiIiolb4f388Xdx+HDyJAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# esse será o range de datas que iremos utilizar\n", + "dates = pd.date_range('2010-01-02','2017-10-11', freq='B')\n", + "df = pd.DataFrame(index=dates)\n", + "df_aapl = df.join(df_aapl)\n", + "\n", + "# visualização\n", + "df_aapl[['Close']].plot(figsize=(15, 6))\n", + "plt.ylabel(\"stock price\")\n", + "plt.title(\"Apple Stock Price\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "zO6Bo5bE07hx", + "outputId": "490b5ce2-87b4-452b-f974-a99f631fac01" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "DatetimeIndex: 2028 entries, 2010-01-04 to 2017-10-11\n", + "Freq: B\n", + "Data columns (total 1 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Close 1958 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 96.2 KB\n" + ] + } + ], + "source": [ + "df_aapl=df_aapl[['Close']]\n", + "df_aapl.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Xacw3iug1SPL", + "outputId": "f78cdb5f-65c7-4f42-a2f0-491d945fa227" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2028, 1)\n" + ] + } + ], + "source": [ + "# vamos normalizar/escalar os dados\n", + "scaler = MinMaxScaler(feature_range=(-1, 1))\n", + "\n", + "df_aapl=df_aapl.fillna(method='ffill') # ffill: propaga a última observação válida\n", + "df_aapl['Close'] = scaler.fit_transform(df_aapl['Close'].values.reshape(-1,1))\n", + "print(df_aapl.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": { + "id": "j0la5XZN2Da4" + }, + "outputs": [], + "source": [ + "def load_data(df, look_back, test_size):\n", + " '''\n", + " Função que cria os conjuntos de treino e teste e define o comprimento da sequência de análise.\n", + " O comprimento da sequência é o período de tempo (em dias) da previsão.\n", + " Parâmetros:\n", + " df: Pandas DataFrame\n", + " look_back: comprimento da sequência (horizonte de previsão)\n", + " test_size: percentual do conjunto de dados total destinado ao conjunto de teste (0 < test_size < 1).\n", + " '''\n", + "\n", + " data_raw = df.values # extrai os valores somente\n", + " data = [] # lista para acrescentar dados\n", + "\n", + " # cria todas as sequências possíveis de comprimento (horizonte de previsão)\n", + " for index in range(len(data_raw) - look_back): \n", + " data.append(data_raw[index: index + look_back])\n", + "\n", + " data = np.array(data); # converte para numpy array\n", + " \n", + " # criação dos conjuntos de teste e treino\n", + " test_set_size = int(np.round(test_size*data.shape[0]));\n", + " train_set_size = data.shape[0] - (test_set_size);\n", + "\n", + " x_train = data[:train_set_size,:-1,:] # define um comprimento de 59 preços de ações\n", + " y_train = data[:train_set_size,-1,:] # e o 60º é a varível resposta\n", + "\n", + " x_test = data[train_set_size:,:-1]\n", + " y_test = data[train_set_size:,-1,:]\n", + "\n", + " # converte para pytorch tensors\n", + " x_train = torch.from_numpy(x_train).type(torch.Tensor)\n", + " x_test = torch.from_numpy(x_test).type(torch.Tensor)\n", + " y_train = torch.from_numpy(y_train).type(torch.Tensor)\n", + " y_test = torch.from_numpy(y_test).type(torch.Tensor)\n", + "\n", + " return [x_train, y_train, x_test, y_test]" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "id": "oYp2B8b041Sz" + }, + "outputs": [], + "source": [ + "look_back = 60 # horizonte de previsão\n", + "x_train, y_train, x_test, y_test = load_data(df_aapl, look_back, 0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 157, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZGieQFz-5EZR", + "outputId": "8ca324bf-8f9b-4e67-bb6d-3ed1c1d1163d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([1574, 1])\n", + "torch.Size([1574, 59, 1])\n", + "torch.Size([394, 1])\n", + "torch.Size([394, 59, 1])\n" + ] + } + ], + "source": [ + "print(y_train.size())\n", + "print(x_train.size())\n", + "print(y_test.size())\n", + "print(x_test.size())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I1cen-e_5Ipd" + }, + "source": [ + "## Modelagem" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": { + "id": "lu5hb_qvYgp2" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 158, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# defina a seed para garantir a reproducibilidade\n", + "\n", + "torch.manual_seed(0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qoGq4smz5Qb0" + }, + "source": [ + "Estrutura do modelo LSTM Base" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": { + "id": "fWpuqmyV5GUx" + }, + "outputs": [], + "source": [ + "class LSTM_base(nn.Module):\n", + " \n", + " def __init__(self, input_dim, hidden_dim, num_layers, output_dim):\n", + " super(LSTM_base, self).__init__()\n", + " self.hidden_dim = hidden_dim # dimensões ocultas\n", + " self.num_layers = num_layers # número de camadas ocultas\n", + " self.lstm = nn.LSTM(input_dim, hidden_dim, num_layers, batch_first=True) # batch_first=True faz com que os tensores tenham shape = (batch_dim, seq_dim, feature_dim)\n", + " self.fc = nn.Linear(hidden_dim, output_dim) # camada Fully Connected\n", + "\n", + " def forward(self, x):\n", + " # inicialização com zeros\n", + "\n", + " h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n", + " c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n", + "\n", + " # Precisamos fazer o detach, já que realizamos o backpropagation through time (BPTT) truncado.\n", + " # Se não fizermos isso, o backpropagation vai até o início da série\n", + " out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n", + "\n", + " # Só precisamos ficar com o hidden state do último passo:\n", + " out = self.fc(out[:, -1, :])\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 160, + "metadata": { + "id": "4kzhkufnLKzt" + }, + "outputs": [], + "source": [ + "# Parametros\n", + "\n", + "input_dim = 1\n", + "hidden_dim = 32\n", + "num_layers = 2\n", + "output_dim = 1\n", + "lr = 0.1\n", + "num_epochs = 5" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": { + "id": "We2C-0GiLdgM" + }, + "outputs": [], + "source": [ + "# Instancia o modelo\n", + "model = LSTM_base(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)" + ] + }, + { + "cell_type": "code", + "execution_count": 162, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_4Wt5EoyLkI1", + "outputId": "21a3e61b-9d8e-42c3-9c39-0c8976068527" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Modelo:\n", + " LSTM_base(\n", + " (lstm): LSTM(1, 32, num_layers=2, batch_first=True)\n", + " (fc): Linear(in_features=32, out_features=1, bias=True)\n", + ")\n", + "Quantidade de parâmetros: 10\n", + "torch.Size([128, 1])\n", + "torch.Size([128, 32])\n", + "torch.Size([128])\n", + "torch.Size([128])\n", + "torch.Size([128, 32])\n", + "torch.Size([128, 32])\n", + "torch.Size([128])\n", + "torch.Size([128])\n", + "torch.Size([1, 32])\n", + "torch.Size([1])\n" + ] + } + ], + "source": [ + "print('Modelo:\\n', model)\n", + "\n", + "print('Quantidade de parâmetros: ', len(list(model.parameters())))\n", + "for i in range(len(list(model.parameters()))):\n", + " print(list(model.parameters())[i].size())" + ] + }, + { + "cell_type": "code", + "execution_count": 163, + "metadata": { + "id": "t45Vhz2ALogo" + }, + "outputs": [], + "source": [ + "# Define função de perda\n", + "# Lembrando que minimizar a MSE é equivalente a minimizar RMSE\n", + "loss_fn = torch.nn.MSELoss()\n", + "\n", + "# Define otimizador\n", + "optimizer = torch.optim.SGD(model.parameters(), lr=lr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I-j72HzOMyi5" + }, + "source": [ + "### **Agora complemente a célula abaixo para implementar uma função que realize o treinamento e faça as previsões**" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "metadata": { + "id": "GN5DHd_9MCEV" + }, + "outputs": [], + "source": [ + "def train_model(model, num_epochs, x_train, x_test, loss_fn, optimizer):\n", + " \n", + " # vetor para registrar as perdas ao longo das épocas\n", + " hist_train = np.zeros(num_epochs)\n", + " hist_test = np.zeros(num_epochs)\n", + "\n", + " for t in range(num_epochs):\n", + "\n", + " # implemente o passo forward\n", + " y_train_pred = model(x_train)\n", + "\n", + " # compute a perda\n", + " loss = loss_fn(y_train_pred, y_train)\n", + "\n", + " # print de algumas épocas\n", + " if t % 10 == 0 and t !=0:\n", + " print(\"Epoch \", t, \"MSE: \", loss.item())\n", + "\n", + " # armazena a perda de treinamento \n", + " hist_train[t] = loss.item()\n", + "\n", + " # limpe o gradiente\n", + " optimizer.zero_grad()\n", + "\n", + " # implemente o passo backward\n", + " loss.backward()\n", + "\n", + " # atualize os parâmetros\n", + " optimizer.step()\n", + "\n", + " # faça as previsões\n", + " y_test_pred = model(x_test)\n", + "\n", + " # armazena a perda de teste\n", + " hist_test[t] = loss_fn(y_test_pred, y_test).item() \n", + "\n", + " # plot da perda durante o treinamento\n", + " plt.plot(hist_train,label=\"Training loss\")\n", + " plt.plot(hist_test,label=\"Test loss\")\n", + " plt.legend()\n", + " plt.show()\n", + "\n", + " return y_train_pred, y_test_pred, hist_train" + ] + }, + { + "cell_type": "code", + "execution_count": 165, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 334 + }, + "id": "8vpYfUwuNJrt", + "outputId": "2cd41a8f-ef97-42a1-c568-582e4ba8eaf0" + }, + "outputs": [ + { + "data": { + "image/png": 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nT+eRRx5h3rx5zJ07l6eeeor58+fTvn37MqtJROSyFB6Drd+aNx3ctYgSc6Y0uw3i7oaabTRnyhXidGCZO3cuiYmJTJkyhfj4eCZOnEjPnj3Ztm0b1atXP+s2QUFBJa5wOX3swosvvsjrr7/OzJkziYmJ4emnn6Znz55s3rwZHx8fZ0u8MIul3F7vHh4eTlRUFLt27eKuu+4653pBQUH069ePfv360bdvX3r16sWRI0cIDQ3F09MTu93u1Os2btyYzz77DMMwit+/pUuXEhgYSM2aNQHzfe3UqROdOnVizJgx1K5dmy+++ILExEQAWrZsScuWLRk9ejQdOnTgo48+UmAREfdiGHBgnXnKZ9OnkH/6nCn3nJgz5fL++BTnOR1YXnnlFYYMGVJ81GTKlCl8++23vPfee4waNeqs25y8wuVsDMNg4sSJPPXUU9x8880AvP/++4SHh/Pll1/Sv39/Z0us8MaNG8cjjzxCcHAwvXr1Ij8/nzVr1nD06FESExN55ZVXiIyMpGXLllitVj755BMiIiIICQkBzCuFkpKS6NSpE97e3lSpcuG7fT744INMnDiRhx9+mOHDh7Nt2zbGjh1LYmIiVquVlStXkpSURI8ePahevTorV67k8OHDNG7cmN27d/P222/zt7/9jaioKLZt28b27dsZMGBAGf9LiYhcpJzD5pwpGz6EQ5tPtQdHn5ozpUodl5UnTgaWgoIC1q5dy+jRo4vbrFYrCQkJLF++/Jzb5eTkULt2bRwOB61ateL//u//uOoqc+T07t27SUlJISEhoXj94OBg4uPjWb58+VkDS35+Pvn5+cXLWVlZznSj3Lv//vvx8/PjpZde4vHHH8ff359mzZoxYsQIAAIDA3nxxRfZvn07NpuNtm3b8t1332G1mlexv/zyyyQmJjJt2jRq1KjBnj17LviaNWrU4LvvvuPxxx+nRYsWhIaGct999/HUU08B5hGdxYsXM3HiRLKysqhduzYvv/wy119/PampqWzdupWZM2eSnp5OZGQkDz30EA888EBZ/ROJiFzYueZM8fAxj6LEnZwzRTOAuAOLYRjGxa584MABatSowbJly+jQoUNx+xNPPMGiRYtYuXLlGdssX76c7du307x5czIzM5kwYQKLFy/m999/p2bNmixbtoxOnTpx4MCBEoMwb7/9diwWC3Pnzj1jn8888wzjxo07oz0zM5OgoKASbcePH2f37t3ExMSUzeklKdf0/0OkEjq0FTZ8AL/OhdxDp9qjWpkDaJveBr4hLiuvMsnKyiI4OPis39+nK/OrhDp06FAi3HTs2JHGjRszdepUnnvuuUva5+jRo4vHRYDZ4ejo6MuuVUREKqjjmfDb5+bRlD/XnGr3CzPvihx3F4Q3cV19ckFOBZawsDBsNhupqakl2lNTUy96Tg9PT09atmzJjh07AIq3S01NLXGEJTU19Zw33fP29sbb29uZ0kVEpLIpnjPlA9j8Vck5Uxr0NI+mxPYAm6dr65SL4tSJOS8vL1q3bk1SUlJxm8PhICkpqcRRlPOx2+1s2rSpOJzExMQQERFRYp9ZWVmsXLnyovcpIiJSLCMZFj4Pr7eAmTeZg2mLjkFYQ+j+HCRugTtmQ6PeCivliNOnhBITExk4cCBt2rShXbt2TJw4kdzc3OKrhgYMGECNGjWK713z7LPP0r59e+rXr09GRgYvvfQSe/fu5f777wfMK4hGjBjBf/7zH2JjY4sva46KitLdhEVE5OIUHoMt35hzpuxeTPGcKd5B5piUlndDjdaaM6Ucczqw9OvXj8OHDzNmzBhSUlKIi4tj3rx5hIebM/wlJycXX40CcPToUYYMGUJKSgpVqlShdevWLFu2jCZNTp0rfOKJJ8jNzWXo0KFkZGTQuXNn5s2bp0GQIiJyboYBf64zB9Bu+qzknCkxXc05UxrdqDlTKginrhJyV+cbZXzyKpA6derg6+vrogrFXR07dow9e/boKiGR8iTnkHmaZ/2HcHjLqfbgWifmTLlDc6aUE251lZCreXqa5yfz8vIUWOQMBQUFABd912oRcRF7IWw/MWfK9h9OmzPlb+ZNB+t01ZwpFViFDyw2m42QkBAOHTKvtffz8zvj1gBSOTkcDg4fPoyfnx8eHhX+oyBSPhXPmTIHcg+faq/R2hyXctWtmjOlkqgUv6VPXjp9MrSInGS1WqlVq5ZCrIg7OZ4Jv312Ys6Utafa/atB835mUKne2HX1iUtUisBisViIjIykevXqFBYWuroccSNeXl4lBomLiIs4HLBnsTkuZctXUHTcbLd6QGxP85SP5kyp1CpFYDnJZrNprIKIiDs5uhc2fGQ+MpNPtVdrZB5Jad4PAqq7rj5xG5UqsIiIiBsoyIOtf50z5QTvIGjWF+LuhhqtNGeKlKDAIiIiZc8wzPEo6z8wx6fkZ516LqabOWdK4xvBU1dzytkpsIiISNkpPG4eSVn9Dhzeeqo9pJZ5w8EWd0CV2q6rT8oNBRYRESl9hcdg7QxYMhFyUsw2Dx9ocrMZVOp00Zwp4hQFFhERKT0FubBmOix9DXJPTCURVBM6PQot+oFPsGvrk3JLgUVERC5ffg6seReWvg55aWZbcC3okmhOl+/h7dr6pNxTYBERkUuXnw2rpsHySZCXbraF1IauI6F5f/Dwcm19UmEosIiIiPOOZ8Kqt2H5ZDh21GwLrQtdRkLz2zXBm5Q6BRYREbl4xzJg5VRYMdkMLQBV60PXx6FpX7Dpa0XKhv5niYjIheUdgRVvwcopp+ZQCWsAXZ+ApreCVbOIS9lSYBERkXPLO2KOT1n5NhRkm23VGkO3J8xLlBVU5ApRYBERkTPlpsGyN8wJ3wpyzLbwpmZQaXST5lCRK06BRURETsk5BMteh9XvQmGe2RbRHLo9CQ1vUFARl1FgERERyE4x51BZ8x4UHTPbolqaQaVBL92IUFxOgUVEpDLLOmDOSrt2BhQdN9tqtIZuoyC2u4KKuA0FFhGRyihzPyx5Fda9D/YCs61mO7j6Sah3nYKKuB0FFhGRyiQj+URQmQWOQrOtVgfz1E/dqxVUxG0psIiIVAZH98AvL8OGj8BRZLbV6WJe9VOni4KKuD0FFhGRiuzILlj8Mvw6Gwy72RbTzTyiUqeTa2sTcYICi4hIRZS2A36ZABs/PhVU6l1rBpVa7V1bm8glUGAREalIDm+DxRPgt0/BcJht9bubQSW6rWtrE7kMCiwiIhXBoS2w+CX47XPAMNsa9DLHqNRo7dLSREqDAouISHmW+jssehE2/4/ioNKwtxlUouJcWZlIqVJgEREpjw5uhMUvwpavT7U1/ht0fRwim7uuLpEyosAiIlKeHFhvHlHZ9t2JBgtc1ccMKuFXubIykTKlwCIiUh7sXwuLXoDtP5xosEDT26DrSKje2KWliVwJCiwiIu5s3yozqOxYYC5brNDs79BlJFRr4NraRK4gBRYREXe0d7kZVHb9bC5bbNC8n3lEpWo919Ym4gIKLCIi7mTPEjOo7F5sLls9oEV/6PIYhNZ1bW0iLqTAIiLiaoZhBpRFL8LeJWab1RPi7oQuiVCljkvLE3EH1kvZaPLkydSpUwcfHx/i4+NZtWrVRW03Z84cLBYLffr0KdE+aNAgLBZLiUevXr0upTQRkfLDMGDnTzD9enj/b2ZYsXlBm/vgkXXwt9cVVkROcPoIy9y5c0lMTGTKlCnEx8czceJEevbsybZt26hevfo5t9uzZw8jR46kS5cuZ32+V69eTJ8+vXjZ29vb2dJERMoHw4AdSeapn/0n/uCzeUGrgdB5BATXdGl5Iu7I6SMsr7zyCkOGDGHw4ME0adKEKVOm4Ofnx3vvvXfObex2O3fddRfjxo2jbt2zn4P19vYmIiKi+FGlShVnSxMRcW+GAX/8AO9cBx/eZoYVDx+IHwaP/gq9JyisiJyDU4GloKCAtWvXkpCQcGoHVisJCQksX778nNs9++yzVK9enfvuu++c6yxcuJDq1avTsGFDhg0bRnp6+jnXzc/PJysrq8RDRMRtGQZs/Q7evho+uh3+XAsevtBhuBlUrn8egqJcXaWIW3PqlFBaWhp2u53w8PAS7eHh4WzduvWs2yxZsoR3332XDRs2nHO/vXr14tZbbyUmJoadO3fyr3/9i+uvv57ly5djs9nOWH/8+PGMGzfOmdJFRK48hwO2fWue+knZZLZ5+kHb+6HjwxBw7tPoIlJSmV4llJ2dzT333MO0adMICws753r9+/cv/rlZs2Y0b96cevXqsXDhQq677roz1h89ejSJiYnFy1lZWURHR5du8SIil8rhgC1fmXdPTv3NbPMKgHZDzKMq/uf+fSgiZ+dUYAkLC8Nms5GamlqiPTU1lYiIiDPW37lzJ3v27OGmm24qbnM4HOYLe3iwbds26tU7cwKkunXrEhYWxo4dO84aWLy9vTUoV0Tcj8MOv39hBpXDJ446ewVC/APQ4SHwC3VtfSLlmFOBxcvLi9atW5OUlFR8abLD4SApKYnhw4efsX6jRo3YtGlTibannnqK7OxsXnvttXMeFdm/fz/p6elERkY6U56IiGs47PDbZ2ZQSfvDbPMOhvb/gPbDwFcXEYhcLqdPCSUmJjJw4EDatGlDu3btmDhxIrm5uQwePBiAAQMGUKNGDcaPH4+Pjw9NmzYtsX1ISAhAcXtOTg7jxo3jtttuIyIigp07d/LEE09Qv359evbseZndExEpQ/Yi2PQJ/DIB0neYbT4h5tGUdkPBN8SV1YlUKE4Hln79+nH48GHGjBlDSkoKcXFxzJs3r3ggbnJyMlbrxV98ZLPZ2LhxIzNnziQjI4OoqCh69OjBc889p9M+IuKe7IWwcS4sngBHd5ttvlXM8SnthoJPkGvrE6mALIZhGK4u4nJlZWURHBxMZmYmQUH6RSEiZaSoAH6dDb+8DBl7zTa/quYVP23vB+9A19YnUs448/2tewmJiFxIUT5s+BB+eRUyk802/2rQ8RFocy94B7i2PpFKQIFFRORcCo/D+lmwZCJk7TfbAsKh06PQejB4+bm0PJHKRIFFROR0hcdg3fuw5FXIPmi2BUZCpxHQeiB4+rq0PJHKSIFFROSkgjxYOwOWToScE/NNBdWAzv+ElveAp48rqxOp1BRYREQKcmH1u7Dsdcg9bLYFR58IKneDh65YFHE1BRYRqbzyc2D1NFj2BuSduOFqSC3o8hi0uBM8vFxbn4gUU2ARkcrneBasehuWT4ZjR8y2KjHQdSQ07wc2T9fWJyJnUGARkcrjWMapoHI8w2wLrQddH4dmfwebfiWKuCt9OkWk4jt2FFZMgRVvQX6m2VY1Fro9AVfdqqAiUg7oUyoiFVfeEVjxJqycCvlZZlu1RuYRlatuAavNtfWJyEVTYBGRiic3HZZPMk//FOSYbdWbmEdUGt8MTtzvTETcgwKLiFQcOYfNS5NXvwuFuWZbeDMzqDS6UUFFpBxTYBGR8i879VRQKTpmtkW2gG5PQsMbwGJxbX0ictkUWESk/Mo6CEtfg7XToei42RbVCq4eBbE9FFREKhAFFhEpfzL/NKfPXzsT7PlmW8220G0U1L9OQUWkAlJgEZHyI2OfeUPC9bPAXmC2RbeHq5+EutcoqIhUYAosIuL+ju6FJa/A+g/BUWi21e5kjlGJ6aqgIlIJKLCIiPvKz4afx8OqqeAoMtvqdDHHqNTp7NraROSKUmAREfdjGLDlK/h+FGQfMNvqXm0eUand0aWliYhrKLCIiHs5shu+exx2zDeXq9SBG16G2ASXliUirqXAIiLuoSjfnEtl8QTzEmWrJ3T+J3RJBE9fV1cnIi6mwCIirrd7MXyTCOnbzeWYrtD7FQiLdW1dIuI2FFhExHVyDsGPT8HGueayfzXoOR6a9dWVPyJSggKLiFx5Drs5O23Ss3A8E7BA2/vg2qfBN8TV1YmIG1JgEZEr6+Cv8M0/4c+15nJkC7jxVajR2rV1iYhbU2ARkSvjeBb8/F9Y9TYYDvAKhOuehrb3g9Xm6upExM0psIhI2TIM+P0LmDcaclLMtqtuhZ7/B0GRrq1NRMoNBRYRKTtHdsG3I2FnkrkcWhdumGDeoFBExAkKLCJS+oryYclE+OVl827KNi/onGjOq+Lp4+rqRKQcUmARkdK1ayF8+xik7zCX615tzlQbVt+VVYlIOafAIiKlIzsVfvw3bPrEXA4IN8epNL1Nc6qIyGVTYBGRy+Oww5r3IOk5yD8xp0q7IXDtU+AT7OrqRKSCUGARkUt3YL05p8qB9eZyZNyJOVVaubQsEal4FFhExHnHM+Gn/8Dqd8w5VbyD4Lox0OZezakiImVCgUVELp5hwG+fwQ//gpxUs61pX+j5XwiMcG1tIlKhWS9lo8mTJ1OnTh18fHyIj49n1apVF7XdnDlzsFgs9OnTp0S7YRiMGTOGyMhIfH19SUhIYPv27ZdSmoiUlfSdMKsPfHafGVZC68E9X0LfdxVWRKTMOR1Y5s6dS2JiImPHjmXdunW0aNGCnj17cujQofNut2fPHkaOHEmXLl3OeO7FF1/k9ddfZ8qUKaxcuRJ/f3969uzJ8ePHnS1PREpb4XH4eTy82cG8ZNnmDVf/C4Ytg3rXuLo6EakkLIZhGM5sEB8fT9u2bZk0aRIADoeD6OhoHn74YUaNGnXWbex2O127duXee+/ll19+ISMjgy+//BIwj65ERUXx2GOPMXLkSAAyMzMJDw9nxowZ9O/f/4I1ZWVlERwcTGZmJkFBQc50R0TOZ0cSfDfSnLEWoN615ky1Veu5ti4RqRCc+f526ghLQUEBa9euJSEh4dQOrFYSEhJYvnz5Obd79tlnqV69Ovfdd98Zz+3evZuUlJQS+wwODiY+Pv6c+8zPzycrK6vEQ0RKUdZB+GQwfHCrGVYCIqDvdLj7c4UVEXEJpwbdpqWlYbfbCQ8PL9EeHh7O1q1bz7rNkiVLePfdd9mwYcNZn09JSSnex+n7PPnc6caPH8+4ceOcKV1ELobDbl75k/QcFGSDxQrtHoBr/gU+OnopIq5zSYNuL1Z2djb33HMP06ZNIywsrNT2O3r0aDIzM4sf+/btK7V9i1Raf66DadfC90+YYaVGaxjyM1z/vMKKiLicU0dYwsLCsNlspKamlmhPTU0lIuLMqwR27tzJnj17uOmmm4rbHA6H+cIeHmzbtq14u9TUVCIjT91qPjU1lbi4uLPW4e3tjbe3tzOlX7JvNx6kyOHg5rgaV+T1RK64Yxnw03Ow+l3AAO9gSBgLrQdpThURcRtOBRYvLy9at25NUlJS8aXJDoeDpKQkhg8ffsb6jRo1YtOmTSXannrqKbKzs3nttdeIjo7G09OTiIgIkpKSigNKVlYWK1euZNiwYZfWq1Ly674MHpmzHsMwsDsMbm1V06X1iJQqw4BNn5pzquSeuMqveT/o8R8IqO7a2kRETuP0xHGJiYkMHDiQNm3a0K5dOyZOnEhubi6DBw8GYMCAAdSoUYPx48fj4+ND06ZNS2wfEhICUKJ9xIgR/Oc//yE2NpaYmBiefvppoqKizpiv5UprViOY29tEM3tVMo998itFDoPb20S7tCaRUpG23byj8u5F5nLVWOj9MtTt5tq6RETOwenA0q9fPw4fPsyYMWNISUkhLi6OefPmFQ+aTU5Oxmp1bmjME088QW5uLkOHDiUjI4POnTszb948fHx8nC2vVFmtFv7bpykeVguzVuzliU83YncY3NGulkvrErlkhcfgl1dg6USwF4CHD3QdCR0fAY8rc5pVRORSOD0Pizsq63lYDMNg3NebmbFsDwDP3XwV93SoU+qvI1Kmti8w51Q5uttcrt8dbngJQmNcW5eIVFrOfH/rXkIXwWKxMPamJnhYLbyzZDdP/+93ihwGgzvpF72UA1kHYN5o2PyluRwYCde/AI3/BhaLS0sTEblYCiwXyWKx8O/ejfGwWZmyaCfjvt6M3WFwf5e6ri5N5OzsRbB6Gvz031NzqsQPg2tGg3egq6sTEXGKAosTLBYLT/ZqiKfNwhs/7eA/326hyGHwj26a+VPczP418M0ISDlxlV6NNnDjqxDZ3KVliYhcKgUWJ1ksFh7r0RCb1cLEBdt5/vutFNkdDL821tWlicCxo5D0LKyZDhjgEwwJ46DVQHByMLyIiDtRYLlEIxIa4GG1MOHHP5jw4x8UOQxGJDRwdVlSWRkGbPwYfvw35B4221rcAd2fg4Bqrq1NRKQUKLBchuHXxmKzWnlh3lYmLtiO3WGQ2L0BFg1klCvp8B/wbSLs+cVcDmtozqkS08W1dYmIlCIFlss07Op6eNos/OfbLbzx0w4K7QZP9mqo0CJlr/AYLJ4AS18DR6E5p0q3J6DDw+Dh5erqRERKlQJLKbi/S11sVgvjvt7MlEU7sTsc/OuGxgotUnb++NGcUyVjr7kc2xNueBGq1HFpWSIiZUWBpZQM7hSDh9XC0//7nWm/7KbIYTDmxiYKLVK6Mv+EeaNgy1fmclANc06VRjdqThURqdAUWErRPR3qYLNa+dcXm5i+dA9FdoNxf7sKq1VfJHKZ7EWwair8/H9QkAMWG7QfBlePBu8AV1cnIlLmFFhK2Z3xtfCwWnjy843MWrGXIofBf/s0VWiRS7dvFXyTCKkn5lSp2c6cUyWi6fm3ExGpQBRYysDtbaOxWS08/umvzF6VjN3hYPytzbEptIgz8o5A0jhYO8Nc9gmB7s9Cy3s0p4qIVDoKLGXkttY18bBZ+OfcDXy8Zj9FDoOX+rZQaJELMwz4dTb8+DTkpZltcXeZYcU/zLW1iYi4iAJLGbo5rgY2q4VH52zg83V/YncYvPz3FnjY9NexnMOhrfDtY7B3iblcrRH0fgXqdHJtXSIiLqbAUsZubB6FzWLh4dnr+d+GAxQ5DCb2i8NToUX+qiAPFr8Iy94ARxF4+MLVT0L7hzSniogICixXxPXNInnTauGhj9bx7caDOBwGr/VviZeHQosA2+bB949DRrK53OB681LlKrVdW5eIiBvRN+YV0uOqCKbc3Rovm5Xvf0vhoY/WUVDkcHVZ4koZ+2DOXTC7nxlWgmpC/4/gzjkKKyIip1FguYKuaxzO2wNa4+VhZf7mVIZ9sJb8Irury5IrzV4IS1+HyfGw9RuwekDHR+ChldCot6urExFxSwosV9jVDavz7sA2eHtYSdp6iKHvr+V4oUJLpZG8AqZ2g/lPQ2Eu1OoAD/wCPZ7TBHAiIuehwOICXWKrMX1QW3w9bSz64zBD3l/DsQKFlgot7wj8bzi81xMO/Q6+ofC3STDoOwhv4urqRETcngKLi3SsH8aMwW3x87Lxy/Y07p2xmryCIleXJaXNMGD9B/BGa1g/y2xreQ8MXwOtNAGciMjF0m9LF4qvW5X3721HgLcHy3elM2j6anLzFVoqjNTNMP16+N9DcOwIVG8C9/4AN08C/6qurk5EpFxRYHGxNnVCef++dgR6e7Bq9xEGvreK7OOFri5LLkdBLswfA1O7QPJy8PQzZ6l9YDHUau/q6kREyiUFFjfQqlYVPrg/niAfD9bsPcqA91aRpdBSPm39zrz6Z+lr5gRwjW6Eh1ZBp0fB5unq6kREyi0FFjfRIjqEj4a0J9jXk/XJGdzzzkoy8xRayo2MZJh9B8y5AzL3QXAtuGMO9P8QQqJdXZ2ISLmnwOJGmtYIZvaQ9lTx8+TX/Znc9e4KjuYWuLosOR97ISyZaB5V2fadOadKpxHw0ApoeL2rqxMRqTAUWNxMk6ggZg9tT1V/L377M4s731nJEYUW97R3GUzpAgvGQmEe1OoI/1gC3ceBl7+rqxMRqVAUWNxQo4gg5gxtT1iAN1sOZnHntBWk5eS7uiw5KTcdvnzIvALo8Bbwqwo3vwmDv4PqjV1dnYhIhaTA4qZiwwOZM7Q91QO92ZqSzR1vr+BQ9nFXl1W5ORyw7n2Y1Bo2fGC2tRpozqnS8i6wWFxbn4hIBabA4sbqVw9g7gMdiAjyYfuhHPq/vYLULIUWl0j9Hab3gq8ehmNHIbwp3Psj/O118At1dXUiIhWeAoubiwnzZ+4D7akR4suuw7n0f3sFBzOPubqsyiM/B358yhyrsm8lePpDj//C0EVQK97V1YmIVBoKLOVA7ar+zBnanppVfNmdlku/qSv4M0OhpUwZBmz52rz6Z9kbYNih8U0wfBV0HA42D1dXKCJSqSiwlBPRoX7MfaADtUL9SD6SR7+py9l3JM/VZVVMR/fC7P4w927I2g8hteDOj6HfBxBc09XViYhUSgos5UiNEF/mPtCemDB/9h89Rv+3V7A3PdfVZVUcRQXwyyvmUZU/5oHVE7o8Bg+uhAY9XV2diEilpsBSzkQG+zJnaHvqVvPnz4xj9Ju6gt1pCi2Xbc8S894/SeOg6BjU7gzDlsJ1Y8DLz9XViYhUepcUWCZPnkydOnXw8fEhPj6eVatWnXPdzz//nDZt2hASEoK/vz9xcXHMmjWrxDqDBg3CYrGUePTq1etSSqsUwoN8mDO0PbHVA0jJOk6/qcvZeTjH1WWVT7lp8MUwmNEbDm8FvzC4ZSoM+gaqNXR1dSIicoLTgWXu3LkkJiYyduxY1q1bR4sWLejZsyeHDh066/qhoaH8+9//Zvny5WzcuJHBgwczePBgfvjhhxLr9erVi4MHDxY/Zs+efWk9qiSqB/owe2h7GoYHcig7n35TV7A9NdvVZZUfDgesmQ5vtIZfPwIs0HowDF8NLfprThURETdjMQzDcGaD+Ph42rZty6RJkwBwOBxER0fz8MMPM2rUqIvaR6tWrejduzfPPfccYB5hycjI4Msvv3Su+hOysrIIDg4mMzOToKCgS9pHeXUkt4C73lnJloNZVPX34sMh8TSKqFz/Bk5L2QTfJML+E0cGw5vBja9CdFvX1iUiUsk48/3t1BGWgoIC1q5dS0JCwqkdWK0kJCSwfPnyC25vGAZJSUls27aNrl27lnhu4cKFVK9enYYNGzJs2DDS09PPuZ/8/HyysrJKPCqrUH8vZg+Jp2mNINJzC7jj7RVsPlB5/z3OKz8b5v0LpnYzw4pXAPQcD0MXKqyIiLg5pwJLWloadrud8PDwEu3h4eGkpKScc7vMzEwCAgLw8vKid+/evPHGG3Tv3r34+V69evH++++TlJTECy+8wKJFi7j++uux2+1n3d/48eMJDg4ufkRHRzvTjQonxM+LD+9rT4uawRzNK+TOd1bw25+Zri7LfRgGbP4fTGoHKyabc6o0udk8/dPhQc2pIiJSDlyR39SBgYFs2LCBnJwckpKSSExMpG7dulx99dUA9O/fv3jdZs2a0bx5c+rVq8fChQu57rrrztjf6NGjSUxMLF7Oysqq9KEl2M+TWffHM/C9VaxPzuDOaSuYdV88LaJDXF2aa2UdgK9HwPYTY6aq1IEbJkBs9/NtJSIibsapIyxhYWHYbDZSU1NLtKemphIREXHuF7FaqV+/PnFxcTz22GP07duX8ePHn3P9unXrEhYWxo4dO876vLe3N0FBQSUeAkE+nrx/bzva1K5C1vEi7n5nJeuSj7q6LNfZ8g281dEMK1ZP6Po4PLhCYUVEpBxyKrB4eXnRunVrkpKSitscDgdJSUl06NDhovfjcDjIz88/5/P79+8nPT2dyMhIZ8oTINDHk5n3tqNdTCjZ+UUMeHcVa/YccXVZV1ZBLnz9KMy9y7xRYWQLc06Va58CT19XVyciIpfA6cuaExMTmTZtGjNnzmTLli0MGzaM3NxcBg8eDMCAAQMYPXp08frjx49n/vz57Nq1iy1btvDyyy8za9Ys7r77bgBycnJ4/PHHWbFiBXv27CEpKYmbb76Z+vXr07OnZhe9FP7eHswY3JaO9aqSk1/EgPdWsXLXuQcxVygHfzUH1a6dYS53fATuW6A5VUREyjmnx7D069ePw4cPM2bMGFJSUoiLi2PevHnFA3GTk5OxWk/loNzcXB588EH279+Pr68vjRo14oMPPqBfv34A2Gw2Nm7cyMyZM8nIyCAqKooePXrw3HPP4e3tXUrdrHz8vDx4d2Bbhs5awy/b0xg0fTXvDmxDx/phri6tbDgc5oDaBePAUQiBkXDLFKh7tasrExGRUuD0PCzuqDLPw3IhxwvtPDBrLYv+OIy3h5V3BrahS2w1V5dVurIOwpfDYNfP5nKjG+Fvb4BfqGvrEhGR8yqzeVik/PHxtPH2gNZc26g6+UUO7pu5hoXbzj4rcbm09TtzYO2un8HDF26caN5VWWFFRKRCUWCpBLw9bEy5uzXdm4RTUORg6Ptr+Wlr6oU3dGcFefDNP2HOHXDsCEQ0gwcWQZvBmlZfRKQCUmCpJLw8rLx5VyuubxpBgd3BA7PW8uPv557sz62lbIK3r4Y175nLHYbD/UkaWCsiUoEpsFQinjYrr9/Rkt7NIym0Gzz44Tq+33TQ1WVdPIcDlk+GaddC2jYIiIB7voCe/wUPDdAWEanINCd5JeNps/Javzg8rBb+t+EAw2ev5zXD4MbmUa4u7fyyU8yBtTt/Mpcb3gB/mwT+VV1bl4iIXBEKLJWQh83KK7fHYbNa+Hzdnzwyez12h8HNcTVcXdrZbZsH/3sQ8tLNgbU9/wtt7tVYFRGRSkSBpZKyWS281LcFHlYLH6/Zzz/nbqDIbnBb65quLu2UwmPw41Ow+h1zObwZ9H1XY1VERCohBZZKzGa18PytzbFZrcxelczIT3/F7jC4va0b3Egy5Tf47D44vNVcbv8QJIzVWBURkUpKgaWSs1ot/LdPUzysFmat2MsTn22kyGFwZ3wt1xRkGLByCswfA/YC8K8Ot7wF9RNcU4+IiLgFBRbBarXw7M1X4WGzMH3pHv71xSbsDgf3dKhzZQvJOWQOrN2xwFxu0Atungz+FfR2AiIictEUWAQAi8XCmBub4GG1MO2X3Tz9v98pchgM7hRzZQr44wf48kHISwMPH+jxH2h7vwbWiogIoMAif2GxWPjXDY3xsFl5a+FOxn29GbvD4P4udcvuRQuPmad/Vr1tLle/yhxYW71x2b2miIiUOwosUoLFYuGJng3xsFp446cd/OfbLRTaDYZdXa/0Xyz1d/jsfji02VyOHwYJz4CnT+m/loiIlGsKLHIGi8XCYz0a4mG18uqCP3hh3lbsDgfDr40tnRcwDFg59cTA2nxzYG2ftyBWA2tFROTsFFjknB5NiMVmhQk//sGEH/+gyGHw6HWxWC5nXEnOIXOsyo755nJsD7j5TQioVjpFi4hIhaTAIuc1/NpYPGxWnv9+KxMXbKfIbvBYjwaXFlq2zzevAso9DDZv6PEctBuqgbUiInJBCixyQf/oVg8Pq4X/fLuFST/voMhh8GSvhhcfWgqPw4Kx5vwqANWbwG3vQPhVZVe0iIhUKAosclHu71IXD6uFZ77ezJRFOymyO/h378YXDi2pm08MrP3dXG73AHQfB56+ZV+0iIhUGAosctEGdYrBZrPy9Je/8c6S3RQ5DMbe1OTsocUwYNU0mP80FB0H/2rmWJUGPa584SIiUu4psIhT7mlfGw+rhdGfb2LGsj3YHQbj/nYVVutfQkvOYfhqOPwxz1yu3x36vAkB1V1TtIiIlHsKLOK0O9rVwma18ORnG5m1Yi9FDoP/9mlqhpYdC+CLYZB7yBxY2/1ZiH9AA2tFROSyKLDIJbm9TTQeVgsjP/mV2auSsRQd4z+Bn2Nd+Za5QrVGcNu7ENHUtYWKiEiFoMAil+zWVjWxWS1M/vgb7vltFFZrsvlE2yHmJcsaWCsiIqVEgUUunWFwc+H33OjzNDZHPulGIJ9Fj+beXsPwsFldXZ2IiFQgCixyaXLT4KuHYdt32IDD4Z25ad9dpOwI5te5G5jYLw5PhRYRESkl+kYR5+38Cd7qCNu+A5sX9BxPtQe+5tm7rsXTZuHbjQd5+KP1FBQ5XF2piIhUEAoscvGK8uGHf8OsWyAn1RxYO+Qn6PAgWK30uCqCqfe0xstmZd7vKTz00Tryi+yurlpERCoABRa5OIe3wTvXwfJJ5nKb+2DIzxDRrMRq1zYK5+0BrfHysDJ/cyrDPljH8UKFFhERuTwKLHJ+hgGr34Wp3SBlE/iGQv/ZcOMr4OV31k2ublid9wa2xcfTyk9bD/HArLUKLSIiclkUWOTcctNhzl3wbSIUHYO618CDy6HRDRfctHNsGO8Naouvp41Ffxzm/plrOFag0CIiIpdGgUXObufPJwbWfgtWT+jxX7j7cwiMuOhddKwXxozBbfHzsrFkRxr3zlhNXkFRGRYtIiIVlQKLlFRUAD8+BbP6QE4KhDWAIUnQcThYnf/vEl+3Ku/f244Abw+W70pn0PTV5OQrtIiIiHMUWOSUw3+YA2uXvWEutx4MQxdBZIvL2m2bOqG8f187Ar09WLX7CIPeW0X28cJSKFhERCoLBRYxB9aumQ5Tu0LKRnNgbb8P4aaJ5xxY66xWtarw4ZB4gnw8WLP3KAPeW0WWQouIiFwkBZbKLu8IzL0bvhlhDqyN6QbDlkHjG0v9pZrXDOGjIe0J8fNkfXIGd7+zksw8hRYREbmwSwoskydPpk6dOvj4+BAfH8+qVavOue7nn39OmzZtCAkJwd/fn7i4OGbNmlViHcMwGDNmDJGRkfj6+pKQkMD27dsvpTRxxq6F5sDard+YA2u7Pwv3fAlBkWX2kk1rBPPR/e0J9fdi4/5M7nxnBUdzC8rs9UREpGJwOrDMnTuXxMRExo4dy7p162jRogU9e/bk0KFDZ10/NDSUf//73yxfvpyNGzcyePBgBg8ezA8//FC8zosvvsjrr7/OlClTWLlyJf7+/vTs2ZPjx49fes/k3IoKYP4YeL8PZB+EqvXh/gXQ6dFLGljrrCZRQcwe0p6q/l78fiCLO99ZSXpOfpm/roiIlF8WwzAMZzaIj4+nbdu2TJpkznjqcDiIjo7m4YcfZtSoURe1j1atWtG7d2+ee+45DMMgKiqKxx57jJEjRwKQmZlJeHg4M2bMoH///hfcX1ZWFsHBwWRmZhIUFORMdyqftB3w2X1wcIO53Gog9BoPXv5XvJTtqdncMW0laTn5NAwP5MMh8YQFeF/xOkRExDWc+f526s/pgoIC1q5dS0JCwqkdWK0kJCSwfPnyC25vGAZJSUls27aNrl27ArB7925SUlJK7DM4OJj4+Phz7jM/P5+srKwSD7kAw4C1M2FqFzOs+FaBfh/A3153SVgBiA0PZO4D7ake6M221GzueHsFh7J1VE1ERM7kVGBJS0vDbrcTHh5eoj08PJyUlJRzbpeZmUlAQABeXl707t2bN954g+7duwMUb+fMPsePH09wcHDxIzo62pluVD55R+DjAfD1I1CYBzFdTwysvcnVlVGvWgBzH+hAZLAP2w/l0P/tFaRmKbSIiEhJV+QqocDAQDZs2MDq1av573//S2JiIgsXLrzk/Y0ePZrMzMzix759+0qv2Ipm92J4qxNs+QqsHpAwDu75HwRFubqyYjFh/swd2oEaIb7sOpxL/7dXcDDzmKvLEhERN+JUYAkLC8Nms5GamlqiPTU1lYiIc0/ZbrVaqV+/PnFxcTz22GP07duX8ePHAxRv58w+vb29CQoKKvGQ0xQVwIJnYObfIPsAhNYzB9Z2HnFFBtY6q1ZVP+YMbU/NKr7sTsul39QV7D+a5+qyRETETTj1zeXl5UXr1q1JSkoqbnM4HCQlJdGhQ4eL3o/D4SA/37wqJCYmhoiIiBL7zMrKYuXKlU7tU/4ifSe81wOWvAoY0PIeeGAxRLV0dWXnFR3qx9wHOlC7qh/JR/LoN3UF+44otIiIyCWcEkpMTGTatGnMnDmTLVu2MGzYMHJzcxk8eDAAAwYMYPTo0cXrjx8/nvnz57Nr1y62bNnCyy+/zKxZs7j77rsBsFgsjBgxgv/85z989dVXbNq0iQEDBhAVFUWfPn1Kp5eVhWHAulkwpQscWA8+IXD7+3DzJPAOcHV1F6VGiC9zhrYnJsyfPzOO0W/qcvam57q6LBERcTEPZzfo168fhw8fZsyYMaSkpBAXF8e8efOKB80mJydj/csph9zcXB588EH279+Pr68vjRo14oMPPqBfv37F6zzxxBPk5uYydOhQMjIy6Ny5M/PmzcPHx6cUulhJHDsKXz8Km/9nLtfpArdMgeCarq3rEkQGm6Hlzmkr2HnYPD00+0SIERGRysnpeVjcUaWfh2XPEvj8Acjabw6svebfJyaBs7m6sstyODufO6etYPuhHKoHevPRkPbUr14+jhSJiMiFldk8LOJm7IWQ9CzMuNEMK6F14b4foUtiuQ8rANUCvZk9tD2NIgI5lJ1P/7dXsD0129VliYiICyiwlFfpO+G9nvDLy5gDa++GB36BGq1dXVmpCgswj6w0iQwiLccMLVtTNFGgiEhlo8BS3hgGrP8QpnaFP9eCTzD8fQbcPLncDKx1Vqi/Fx8NiadpjSDScwu44+0V/H4g09VliYjIFaTAUp4cOwqfDob/PQgFOVC7E/xjKVx1i6srK3Mhfl58eF97WtQM5mheIXdOW8lvfyq0iIhUFgos5cWepfBWZ/j9C7DY4NqnYeDXEFJ5bksQ7OfJrPvjaVkrhMxjhdw5bQW/7stwdVkiInIFKLC4O3sh/PQfmHliYG2VGLhvPnQdWSEG1joryMeT9+9tR5vaVcg6XsTd76xkXfJRV5clIiJlTIHFnR3ZBe/1gsUvgeGAuLvgH79AzYo1sNZZgT6ezLy3HfExoWTnFzHg3VWs2XPE1WWJiEgZUmBxR4YBG2abM9b+uQa8g6Hve9DnTfAOdHV1bsHf24Ppg9vSsV5VcvKLGPDeKlbsSnd1WSIiUkYUWNzNsQz47D748h/mwNpaHWDYEmh6m6srczt+Xh68O7AtXWLDyCuwM2j6KpbtSHN1WSIiUgYUWNzJ3uXmUZXfPjMH1l7zFAz6FkJquboyt+XrZWPagDZ0a1CN44UOBs9YzS/bD7u6LBERKWUKLO7AXgQ//Rdm3ACZyRBSG+79Abo9XikH1jrLx9PG2wNac12j6uQXObhv5hoWbjvk6rJERKQUKbC42pHdML0XLH7RHFjbvD/8YwlEt3V1ZeWKt4eNt+5uTY8m4RQUORj6/lqStqS6uiwRESklCiyu9Otc8xTQ/tXgHQS3vQu3TgWfSngDx1Lg5WFl8l2tuL5pBAV2B//4YC0//p7i6rJERKQUKLC4wvFM+Ox++GIoFGRDdHvzqEqzvq6urNzztFl5/Y6W3Ng8kkK7wYMfruP7TQddXZaIiFwmBZYrLXklTOkMmz4xB9Ze/S9zYG2V2q6urMLwtFmZ2C+OPnFRFDkMhs9ez8xleyi0O1xdmoiIXCKLYRiGq4u4XFlZWQQHB5OZmUlQkJueTrEXmRPAnRyrElLLPAUU3c7VlVVYdofBE59u5LN1+wGIDvXl4WtjubVlDTxsyuoiIq7mzPe3AsuVcHQvfD4E9q00l5vdDr0nmHdaljLlcBjMWLaHNxfuIC2nAIDaVf145NpYbo6LUnAREXEhBRZ3svET+DYR8rPAKxBufAWa3+7qqiqdvIIiPlixl6mLdpGeawaXmDB/Hr0ulptaRGGzWlxcoYhI5aPA4g6OZ8F3I2HjXHO5Zju4bRpUqePSsiq73Pwi3l++l7cX7+RoXiEA9ar582hCA3o3i1RwERG5ghRYXG3fKvMqoIy9YLFC1yeg6+Ng83B1ZXJCTn4RM5ft4e3Fu8g8ZgaX2OoBjEhowPVNI7AquIiIlDkFFldx2OGXl2Hh82DYIbiWeVSlVnvX1STnlX28kOlL9/DOL7vIOl4EQKOIQEYkxNKjiYKLiEhZUmBxhYxk+HwoJC83l5v2NceraGBtuZB5rJD3luzmvSW7yc43g0vjyCD+mRBL9ybhWCwKLiIipU2B5Urb9Cl8kwj5mebA2t4ToHk/0JdcuZOZV8g7S3Yxfekeck4El6Y1gvhnQgOubVRdwUVEpBQpsFwp+dnw3ePw62xzuWZbuHUahMZcuRqkTBzNLWDaL7uYsWwPeQV2AFrUDGZE9wZc3aCagouISClQYLkS9q+Bz+6Do3vMgbVdRkK3J8DmeWVeX66I9Jx83v5lF+8v28uxQjO4xEWHkNi9AV1iwxRcREQugwJLWXLY4ZdXYOH4EwNro+HWt6F2x7J9XXGptJx8pi7ayawVezleaE7x37p2FRK7N6BjvaoKLiIil0CBpaxk7DsxsHaZudz0Nuj9CviGlN1rils5lH2cKQt38eHKveQXmcGlXUwo/0xoQId6VV1cnYhI+aLAUhZ++xy+GWHeadkrAG6YAC36a2BtJZWadZy3Fu7ko5XJFJy4qWKHulX5Z/cGtIsJdXF1IiLlgwJLacrPhu+fhA0fmss1WsNt70Bo3dJ9HSmXDmYe482fdzJndTKFdvOj1Ll+GP/sHkvr2gouIiLno8BSWtK2w4d/h6O7AQt0eQyuHqWBtXKGPzOOMfnnHXy8eh9FDvMj1bVBNf6ZEEvLWlVcXJ2IiHtSYCktx7NgahewF5kDa+t0Kr19S4W070gek3/ewSdr92M/EVyuaViNf3ZvQPOaIa4tTkTEzSiwlKZDWyEwHHz1V7JcvOT0PN74aTufr/+zOLgkNK7OiIQGNK2h2Y9FRECBxdXliBTbk5bL6z9t58v1f3Iit9CjSTgjEhrQJEr/V0WkclNgEXEzOw/n8EbSdv736wFOfuKubxrBowmxNIrQ/1kRqZwUWETc1I5D2byWtINvNp4KLr2bRzLiulhiwwNdW5yIyBXmzPe39VJeYPLkydSpUwcfHx/i4+NZtWrVOdedNm0aXbp0oUqVKlSpUoWEhIQz1h80aBAWi6XEo1evXpdSmohbq189kDfuaMkPI7rSu1kkAN9uPEiPiYt5ZPZ6dhzKcXGFIiLuyenAMnfuXBITExk7dizr1q2jRYsW9OzZk0OHDp11/YULF3LHHXfw888/s3z5cqKjo+nRowd//vlnifV69erFwYMHix+zZ8++tB6JlAMNwgOZfFcrvn+0C72uisAw4KtfD9Dj1UX8c+4GdqflurpEERG34vQpofj4eNq2bcukSZMAcDgcREdH8/DDDzNq1KgLbm+326lSpQqTJk1iwIABgHmEJSMjgy+//NL5HqBTQlL+/X4gk4kLtjN/cyoAVgvc0rImj1xXn9pV/V1cnYhI2SizU0IFBQWsXbuWhISEUzuwWklISGD58uUXtY+8vDwKCwsJDS05C+jChQupXr06DRs2ZNiwYaSnp59zH/n5+WRlZZV4iJRnV0UFM21AG74e3pnrGlXHYcBn6/Zz7cuLeOLTX9l3JM/VJYqIuJRTgSUtLQ273U54eHiJ9vDwcFJSUi5qH08++SRRUVElQk+vXr14//33SUpK4oUXXmDRokVcf/312O32s+5j/PjxBAcHFz+io6Od6YaI22pWM5h3B7Xly4c6cXXDatgdBh+v2c81ExYy+vON7D+q4CIilZNTp4QOHDhAjRo1WLZsGR06dChuf+KJJ1i0aBErV6487/bPP/88L774IgsXLqR58+bnXG/Xrl3Uq1ePBQsWcN11153xfH5+Pvn5+cXLWVlZREdH65SQVDhr9x5l4oI/+GV7GgCeNgv92kbz0DX1iQz2dXF1IiKXp8xOCYWFhWGz2UhNTS3RnpqaSkRExHm3nTBhAs8//zw//vjjecMKQN26dQkLC2PHjh1nfd7b25ugoKASD5GKqHXtKsy6L55P/9GBTvWrUmg3+GBFMt1eXMjY//1GatZxV5coInJFOBVYvLy8aN26NUlJScVtDoeDpKSkEkdcTvfiiy/y3HPPMW/ePNq0aXPB19m/fz/p6elERkY6U55IhdWmTigf3t+euUPbEx8TSoHdwczle+ny4s+M+/p3Dim4iEgF5/RVQnPnzmXgwIFMnTqVdu3aMXHiRD7++GO2bt1KeHg4AwYMoEaNGowfPx6AF154gTFjxvDRRx/RqdOpmwcGBAQQEBBATk4O48aN47bbbiMiIoKdO3fyxBNPkJ2dzaZNm/D29r5gTbpKSCqbZTvTeHX+H6zecxQAbw8r97SvzQPd6lEt8MKfGRERd+DM97eHszvv168fhw8fZsyYMaSkpBAXF8e8efOKB+ImJydjtZ46cPPWW29RUFBA3759S+xn7NixPPPMM9hsNjZu3MjMmTPJyMggKiqKHj168Nxzz11UWBGpjDrWC6ND3aos3ZHOK/O3sS45g3eW7OaDlXsZ2KEOQ7vWpWqAPj8iUnFoan6Rcs4wDBZvT+OV+X/w674MAPy8bAzsWIehXepSxd/LtQWKiJyD7iUkUgkZhsHCbYd5Zf4fbPozEwB/LxuDO8Vwf5cYQvwUXETEvSiwiFRihmGQtOUQry74g98PmJMqBnp7MLhzDPd1jiHY19PFFYqImBRYRATDMPhxcyqvzv+DrSnZAAT6eHB/57oM7lyHIB8FFxFxLQUWESnmcBj88HsKry74gz9SzbtBB/t6MqRLDIM6xRDg7fTYexGRUqHAIiJncDgMvvvtIBMXbGfHITO4hPh5MrRrXQZ2qIO/gouIXGEKLCJyTnaHwTcbD/Ba0nZ2Hc4FINTfiwe61uWeDrXx81JwEZErQ4FFRC7I7jD46tc/eW3BdvakmzdVDAvw4h/d6nFXfG18vWwurlBEKjoFFhG5aEV2B19uOMDrSdtJPmIGl2qB3gzrVo8742vh46ngIiJlQ4FFRJxWaHfwxbo/ef2n7ew/egyA6oHePHRNffq1jVZwEZFSp8AiIpesoMjBZ+v2M+mnHfyZYQaXyGAfHrymPre3qYm3h4KLiJQOBRYRuWz5RXY+WbOfyT/v4GCmeTfoGiG+PHRNffq2romXh1M3excROYMCi4iUmuOFduau3sebC3eQmpUPQM0qvjx8bX1ubVUTT5uCi4hcGgUWESl1xwvtfLQymbcW7eRwthlcaoX68fC19bmlZQ08FFxExEkKLCJSZo4V2Plw5V6mLNpJWk4BAHWq+vHIdbHcHFcDm9Xi4gpFpLxQYBGRMpdXUMSs5XuZungXR3LN4FK3mj+PXhfLjc2jFFxE5IIUWETkisnNL2Lm8j28vXgXGXmFANSvHsCj18XSu1kkVgUXETkHBRYRueKyjxcyc9kepv2ym8xjZnBpGB7Iowmx9LoqQsFFRM6gwCIiLpN1vJDpS/bwzpJdZB8vAqBRRCAjEhrQ86pwLBYFFxExKbCIiMtlHivk3SW7mb5kN9n5ZnC5KiqIEQkNSGhcXcFFRBRYRMR9ZOQV8M4vu5m+dDe5BXYAmtcMZkRCLNc0VHARqcwUWETE7RzJLWDaL7uYuWwPeSeCS4voEP6ZEEu3BtUUXEQqIQUWEXFb6Tn5vL14F+8v38uxQjO4tKoVwj+7N6Bz/TAFF5FKRIFFRNze4ex8pi7ayawVe8kvcgDQtk4V/tm9AR3rhbm4OhG5EhRYRKTcOJR1nLcW7eTDlckUnAgu8TGh/LN7A9rXreri6kSkLCmwiEi5k5J5nLcW7mD2qn0U2M3g0rFeVf7ZvQFt64S6uDoRKQsKLCJSbh3IOMabC3cwd/U+Cu3mr6fO9cP4W4soOsWGUSPE18UVikhpUWARkXJv/9E8Jv+8k0/W7KPIcerXVJ2qfnSsH0bn+mF0qFuVKv5eLqxSRC6HAouIVBj7juTx8Zp9LNmRxsb9mdj/El4sFmgSGUSn+mF0qh9G2zpV8PPycGG1IuIMBRYRqZCyjheyctcRlu5IY9nONP5IzSnxvKfNQstaVehcP4xO9avSvGYInjari6oVkQtRYBGRSuFQ1nGW7Uxn6Y40lu5I40Dm8RLP+3vZiK9b9cQRmKo0DA/UPC8ibkSBRUQqHcMw2JOeV3z0ZdnOdDLyCkusExbgRcd6ZnjpWC+M6FA/F1UrIqDA4upyRMQNOBwGmw9mmUdfdqazanc6xwsdJdapFepXfPSlY70wQjWAV+SKUmARETlNfpGd9ckZLNuRxpIdafx62gBeODmAtyod64fRrk4o/t4awCtSlhRYREQuIPt4Iat2H2HpDnMMzLbU7BLPe9ostIyuQsf6VelcP4wW0RrAK1LaFFhERJx0ODufZTvTTgzgTefPjGMlnvf3stEuJrT4EuqG4YFYrRrAK3I5nPn+vqQ/FyZPnkydOnXw8fEhPj6eVatWnXPdadOm0aVLF6pUqUKVKlVISEg4Y33DMBgzZgyRkZH4+vqSkJDA9u3bL6U0EZFLUi3Qm5vjavBi3xYsefIaFj1+Nf93SzN6N4ukip8nuQV2ft52mP98u4XrX/uFtv9dwPCP1jFnVTL7juS5unyRCs/pIyxz585lwIABTJkyhfj4eCZOnMgnn3zCtm3bqF69+hnr33XXXXTq1ImOHTvi4+PDCy+8wBdffMHvv/9OjRo1AHjhhRcYP348M2fOJCYmhqeffppNmzaxefNmfHx8LliTjrCISFlyOAy2pGQVH31ZtfsIxwrtJdaJDvWlUz3z6EvHelWpGuDtompFyo8yPSUUHx9P27ZtmTRpEgAOh4Po6GgefvhhRo0adcHt7XY7VapUYdKkSQwYMADDMIiKiuKxxx5j5MiRAGRmZhIeHs6MGTPo37//BfepwCIiV1JBkYMN+zJYsiONZTvS2LAvo8TtAwAaRQSemMAujHYxGsArcjbOfH879QkqKChg7dq1jB49urjNarWSkJDA8uXLL2ofeXl5FBYWEhpq3n119+7dpKSkkJCQULxOcHAw8fHxLF++/KyBJT8/n/z8/OLlrKwsZ7ohInJZvDystIsJpV1MKIndG5CTX8Sq3enFA3i3pmQXP95ZshsPq4W46JDi8S9x0SF4eWgAr4gznAosaWlp2O12wsPDS7SHh4ezdevWi9rHk08+SVRUVHFASUlJKd7H6fs8+dzpxo8fz7hx45wpXUSkzAR4e3Bto3CubWT+HkvLyWfZznSW7Uhj6c409h05xpq9R1mz9yivJW3H7+QA3nphdKxflcYRQRrAK3IBV/QY5fPPP8+cOXNYuHDhRY1NOZfRo0eTmJhYvJyVlUV0dHRplCgictnCArz5W4so/tYiCoDk9DyWnrgCafnOdNJzC1i47TALtx0GINTfiw71qp4YA1OVWqF+uoWAyGmcCixhYWHYbDZSU1NLtKemphIREXHebSdMmMDzzz/PggULaN68eXH7ye1SU1OJjIwssc+4uLiz7svb2xtvbw1oE5HyoVZVP2pVrcUd7WrhcBhsTckuvoR65e4jHMkt4NuNB/l240EAalbxLT760rFeGNUC9ftOxKnA4uXlRevWrUlKSqJPnz6AOeg2KSmJ4cOHn3O7F198kf/+97/88MMPtGnTpsRzMTExREREkJSUVBxQsrKyWLlyJcOGDXOuNyIibs5qtdAkKogmUUHc36UuBUUOft2fYd4DaUc665KPsv/oMeau2cfcNfsAcwDvyXsgxdetSoAG8EoldEmXNQ8cOJCpU6fSrl07Jk6cyMcff8zWrVsJDw9nwIAB1KhRg/HjxwPmJctjxozho48+olOnTsX7CQgIICAgoHid559/vsRlzRs3btRlzSJS6eTmF7FqzxFz/MuOdDYfLHlRgYfVQovoEDrVM28h0LJWCN4eNhdVK3J5yuwqIYB+/fpx+PBhxowZQ0pKCnFxccybN6940GxycjJW66nR72+99RYFBQX07du3xH7Gjh3LM888A8ATTzxBbm4uQ4cOJSMjg86dOzNv3rzLGuciIlIe+Xt7cE3D6lzT0JzXKj0nn+W7Tl2BlHwkj7V7j7J271Fe/2kHvp422saE0qleVTrVD6NJpAbwSsWkqflFRMqRfUfyTox/SWfZzjTScgpKPF/Fz5MO9cyxL53rh1G7qgbwivvSvYRERCoBwzDYlppdfPRl5a50cgtKzsBbI8SXjvWq0jk2jA71qlI9UEeuxX0osIiIVEKFdgcb92ewdEc6S3aksT75KIX2kr/iG4QHFB99ia8bSqCPp4uqFVFgcXU5IiJuIa+giNV7jp64B1Iamw9m8dff+DarheY1g+lcP4yO9cJoVVsDeOXKUmAREZEzHM0tYPmu9OJ7IO1JL3mXaR9PK23rhJq3EKgXRpOoIGwawCtlSIFFREQuaP/RPJbtSD8xC286aTn5JZ4P9vWk44nLpzvVq0pMmL8G8EqpUmARERGnGIbB9kM5LNmexrKdaazYdYSc/KIS60QF+5jhpb55G4HqQRrAK5dHgUVERC5Lkd3Br/szi2/guG5vBgV2R4l1YqsH0Kl+GB3rVaV9vaoEaQCvOEmBRUREStWxAjur9xxh6U7zFgK/HcgsMYDXaoHmNUOKj760ql0FH08N4JXzU2AREZEylZFXwPKd6cUBZldabonnvT3MAbwd61elc/0wrooK1gBeOYMCi4iIXFEHMo6ZN3DcaV6FdDi75ABeX08btav6nXj4Uyv0xM+h/kSF+OBhs55jz1KRKbCIiIjLGIbBjkM55vwvO9NZsTOd7NMG8P6Vh9VCjSq+JUJMrRPhplaoH35eujt1RaXAIiIibqPI7iD5SB57j+SRnJ7HnvRcktNPLB/Jo6DIcd7tqwV6UzvUzwwxof5mkKnqR+1QP0L9vXSpdTlWpndrFhERcYaHzUrdagHUrRZwxnMOh0Fq9nH2puedCDG55s9H8tibnkfmsUIOZ+dzODufNXuPnrF9gLdH8ZGZEoEm1I+oEF+Nm6lAdIRFRETcVmZe4Wkh5tTPBzOPn3dbT5uFmlX8TgWaUHP8zMmfdRWT6+kIi4iIVAjBfp409wuhec2QM547Xmhn/1HzSEyJQHMkj/1HjlFgd7A7LZfdp13BdFJ4kPep8TInTzlV9ad2qB8hfp461eRmFFhERKRc8vG0Ub96IPWrB57xnN1hkJJ1nL1/HS/zl1NO2ceLSM3KJzUrn1V7jpyxfaCPR8kBwH8JNJFBPlh1qumK0ykhERGpVAzDICOvkL0njsicHmhSs/LPu72XzUrNUF9qh552iXZVP2pW0akmZ+iUkIiIyDlYLBaq+HtRxd+LuOiQM54/VmBn39E89qTlFg/+NQNNLvuPmqeadh3OZdfhXODwafuGiCCfv4SYknPOBPvp9gWXSkdYRERELlKR3cHBzOMnQsyJozN/CTS5Bfbzbh/s6/mXAcAl55wJD6x8p5o0D4uIiMgVZhgG6bkFJwYA5/7lUm0z1KTlnP9Uk7eHlehQP+pU9aPWafPN1Kzih5dHxZsNWKeERERErjCLxUJYgDdhAd60rl3ljOdz84uKTzElnzbfzJ8Zx8gvcrDjUA47DuWcsa3VApHBvsVjZWr9Zb6Z2lX9CKwEd8rWERYREREXK7Q7OJBxrMTppb8GmmOF5z/VFOrv9ZfTTH7UOjHfTO1QP6oFervtJdo6wiIiIlKOeNqsJya18z/jOcMwOJyTf8Z4mZNXNqXnFnDkxGPDvowztvf1tFEr9NTpJfNUkznfTI0qvniWkxtP6giLiIhIOZZ9vJDkI38dL5NbPJnewcxjOM7zLW+zWogK8Skx38xfTzn5e5ftcQ0dYREREakkAn08uSoqmKuigs94rqDIYc4GfDLQnDZ+Jr/Iwb4jx9h35BjsOHPfYQFexbc0qBXqx7Cr67lsnhkFFhERkQrKy+P8N548lJ1ffDuD5NNON2XkFZKWU0BaTgHrkjPw8rDy6HWxLuiFSYFFRESkErJaLUQE+xAR7EN83apnPJ95rLDE7QzyCopcOk+MAouIiIicIdjXk2Y1g2lW88xTTa5QPoYGi4iISKWmwCIiIiJuT4FFRERE3J4Ci4iIiLg9BRYRERFxewosIiIi4vYUWERERMTtXVJgmTx5MnXq1MHHx4f4+HhWrVp1znV///13brvtNurUqYPFYmHixIlnrPPMM89gsVhKPBo1anQppYmIiEgF5HRgmTt3LomJiYwdO5Z169bRokULevbsyaFDh866fl5eHnXr1uX5558nIiLinPu96qqrOHjwYPFjyZIlzpYmIiIiFZTTgeWVV15hyJAhDB48mCZNmjBlyhT8/Px47733zrp+27Zteemll+jfvz/e3t7n3K+HhwcRERHFj7CwMGdLExERkQrKqcBSUFDA2rVrSUhIOLUDq5WEhASWL19+WYVs376dqKgo6taty1133UVycvI5183PzycrK6vEQ0RERCoupwJLWloadrud8PDwEu3h4eGkpKRcchHx8fHMmDGDefPm8dZbb7F79266dOlCdnb2WdcfP348wcHBxY/o6OhLfm0RERFxf25xldD111/P3//+d5o3b07Pnj357rvvyMjI4OOPPz7r+qNHjyYzM7P4sW/fvitcsYiIiFxJTt2tOSwsDJvNRmpqaon21NTU8w6odVZISAgNGjRgx44dZ33e29u7xHgYwzAAdGpIRESkHDn5vX3ye/x8nAosXl5etG7dmqSkJPr06QOAw+EgKSmJ4cOHO1/pOeTk5LBz507uueeei1r/5KkjnRoSEREpf7KzswkODj7vOk4FFoDExEQGDhxImzZtaNeuHRMnTiQ3N5fBgwcDMGDAAGrUqMH48eMBc6Du5s2bi3/+888/2bBhAwEBAdSvXx+AkSNHctNNN1G7dm0OHDjA2LFjsdls3HHHHRdVU1RUFPv27SMwMBCLxeJsl84rKyuL6Oho9u3bR1BQUKnu2x1U9P5Bxe+j+lf+VfQ+VvT+QcXvY1n1zzAMsrOziYqKuuC6TgeWfv36cfjwYcaMGUNKSgpxcXHMmzeveCBucnIyVuupoTEHDhygZcuWxcsTJkxgwoQJdOvWjYULFwKwf/9+7rjjDtLT06lWrRqdO3dmxYoVVKtW7aJqslqt1KxZ09muOCUoKKhC/ic8qaL3Dyp+H9W/8q+i97Gi9w8qfh/Lon8XOrJyktOBBWD48OHnPAV0MoScVKdOnQuem5ozZ86llCEiIiKVhFtcJSQiIiJyPgosF+Dt7c3YsWPPO0tveVbR+wcVv4/qX/lX0ftY0fsHFb+P7tA/i3Ex1xKJiIiIuJCOsIiIiIjbU2ARERERt6fAIiIiIm5PgUVERETcngILMHnyZOrUqYOPjw/x8fGsWrXqvOt/8sknNGrUCB8fH5o1a8Z33313hSq9NM70b8aMGVgslhIPHx+fK1itcxYvXsxNN91EVFQUFouFL7/88oLbLFy4kFatWuHt7U39+vWZMWNGmdd5OZzt48KFC894Dy0Wy2XdUb0sjR8/nrZt2xIYGEj16tXp06cP27Ztu+B25eVzeCn9K0+fw7feeovmzZsXTyjWoUMHvv/++/NuU17eu5Oc7WN5ev/O5vnnn8disTBixIjzrnel38dKH1jmzp1LYmIiY8eOZd26dbRo0YKePXty6NChs66/bNky7rjjDu677z7Wr19Pnz596NOnD7/99tsVrvziONs/MGcyPHjwYPFj7969V7Bi5+Tm5tKiRQsmT558Uevv3r2b3r17c80117BhwwZGjBjB/fffzw8//FDGlV46Z/t40rZt20q8j9WrVy+jCi/PokWLeOihh1ixYgXz58+nsLCQHj16kJube85tytPn8FL6B+Xnc1izZk2ef/551q5dy5o1a7j22mu5+eab+f3338+6fnl6705yto9Qft6/061evZqpU6fSvHnz867nkvfRqOTatWtnPPTQQ8XLdrvdiIqKMsaPH3/W9W+//Xajd+/eJdri4+ONBx54oEzrvFTO9m/69OlGcHDwFaqudAHGF198cd51nnjiCeOqq64q0davXz+jZ8+eZVhZ6bmYPv78888GYBw9evSK1FTaDh06ZADGokWLzrlOefsc/tXF9K88fw4NwzCqVKlivPPOO2d9rjy/d391vj6W1/cvOzvbiI2NNebPn29069bNePTRR8+5rivex0p9hKWgoIC1a9eSkJBQ3Ga1WklISGD58uVn3Wb58uUl1gfo2bPnOdd3pUvpH5h3y65duzbR0dEX/CuivClP79/liouLIzIyku7du7N06VJXl3PRMjMzAQgNDT3nOuX5fbyY/kH5/Bza7XbmzJlDbm4uHTp0OOs65fm9g4vrI5TP9++hhx6id+/eZ7w/Z+OK97FSB5a0tDTsdnvxjRtPCg8PP+f5/pSUFKfWd6VL6V/Dhg157733+N///scHH3yAw+GgY8eO7N+//0qUXObO9f5lZWVx7NgxF1VVuiIjI5kyZQqfffYZn332GdHR0Vx99dWsW7fO1aVdkMPhYMSIEXTq1ImmTZuec73y9Dn8q4vtX3n7HG7atImAgAC8vb35xz/+wRdffEGTJk3Oum55fe+c6WN5e//AvKffunXrGD9+/EWt74r38ZJufigVV4cOHUr81dCxY0caN27M1KlTee6551xYmVyshg0b0rBhw+Lljh07snPnTl599VVmzZrlwsou7KGHHuK3335jyZIlri6lTFxs/8rb57Bhw4Zs2LCBzMxMPv30UwYOHMiiRYvO+YVeHjnTx/L2/u3bt49HH32U+fPnu/Xg4EodWMLCwrDZbKSmppZoT01NJSIi4qzbREREOLW+K11K/07n6elJy5Yt2bFjR1mUeMWd6/0LCgrC19fXRVWVvXbt2rl9CBg+fDjffPMNixcvpmbNmuddtzx9Dk9ypn+nc/fPoZeXF/Xr1wegdevWrF69mtdee42pU6eesW55fO/AuT6ezt3fv7Vr13Lo0CFatWpV3Ga321m8eDGTJk0iPz8fm81WYhtXvI+V+pSQl5cXrVu3JikpqbjN4XCQlJR0znOTHTp0KLE+wPz58897LtNVLqV/p7Pb7WzatInIyMiyKvOKKk/vX2nasGGD276HhmEwfPhwvvjiC3766SdiYmIuuE15eh8vpX+nK2+fQ4fDQX5+/lmfK0/v3fmcr4+nc/f377rrrmPTpk1s2LCh+NGmTRvuuusuNmzYcEZYARe9j2U2nLecmDNnjuHt7W3MmDHD2Lx5szF06FAjJCTESElJMQzDMO655x5j1KhRxesvXbrU8PDwMCZMmGBs2bLFGDt2rOHp6Wls2rTJVV04L2f7N27cOOOHH34wdu7caaxdu9bo37+/4ePjY/z++++u6sJ5ZWdnG+vXrzfWr19vAMYrr7xirF+/3ti7d69hGIYxatQo45577ilef9euXYafn5/x+OOPG1u2bDEmT55s2Gw2Y968ea7qwgU528dXX33V+PLLL43t27cbmzZtMh599FHDarUaCxYscFUXzmvYsGFGcHCwsXDhQuPgwYPFj7y8vOJ1yvPn8FL6V54+h6NGjTIWLVpk7N6929i4caMxatQow2KxGD/++KNhGOX7vTvJ2T6Wp/fvXE6/Ssgd3sdKH1gMwzDeeOMNo1atWoaXl5fRrl07Y8WKFcXPdevWzRg4cGCJ9T/++GOjQYMGhpeXl3HVVVcZ33777RWu2DnO9G/EiBHF64aHhxs33HCDsW7dOhdUfXFOXsJ7+uNknwYOHGh069btjG3i4uIMLy8vo27dusb06dOveN3OcLaPL7zwglGvXj3Dx8fHCA0NNa6++mrjp59+ck3xF+FsfQNKvC/l+XN4Kf0rT5/De++916hdu7bh5eVlVKtWzbjuuuuKv8gNo3y/dyc528fy9P6dy+mBxR3eR4thGEbZHb8RERERuXyVegyLiIiIlA8KLCIiIuL2FFhERETE7SmwiIiIiNtTYBERERG3p8AiIiIibk+BRURERNyeAouIiIi4PQUWERERcXsKLCIiIuL2FFhERETE7SmwiIiIiNv7f4iSScm4ffbcAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#! mudar num_epochs\n", + "y_train_pred, y_test_pred, hist = train_model(model=model, num_epochs=num_epochs, x_train=x_train, x_test=x_test, loss_fn=loss_fn, optimizer=optimizer)" + ] + }, + { + "cell_type": "code", + "execution_count": 166, + "metadata": { + "id": "l9UmKppqNUN6" + }, + "outputs": [], + "source": [ + "def model_performance(y_train_pred, y_test_pred, mMscaler):\n", + " # mMscaler : MinMaxScaler usado originalmente para deixar o dataset dentro da escala mais simples de trabalhar\n", + " # aplique a transformação reversa para os dados voltarem à sua escala original\n", + "\n", + " ytr = mMscaler.inverse_transform(y_train.detach().numpy())\n", + " ytrp = mMscaler.inverse_transform(y_train_pred.detach().numpy())\n", + " yte = mMscaler.inverse_transform(y_test.detach().numpy())\n", + " ytep = mMscaler.inverse_transform(y_test_pred.detach().numpy())\n", + "\n", + " # calcule o RSME para os conjuntos de treino e teste\n", + " trainScore = np.sqrt(mean_squared_error(ytr, ytrp))\n", + " print('Train Score: %.2f RMSE' % (trainScore))\n", + " testScore = np.sqrt(mean_squared_error(yte, ytep))\n", + " print('Test Score: %.2f RMSE' % (testScore))\n", + "\n", + " # Plot dos resultados\n", + " figure, axes = plt.subplots(figsize=(15, 6))\n", + " axes.xaxis_date()\n", + "\n", + " axes.plot(df_aapl[len(df_aapl)-len(y_test):].index, y_test.detach().numpy(), color = 'red', label = 'Real Apple Stock Price')\n", + " axes.plot(df_aapl[len(df_aapl)-len(y_test):].index, y_test_pred.detach().numpy(), color = 'blue', label = 'Predicted Apple Stock Price')\n", + " plt.title('Apple Stock Price Prediction')\n", + " plt.xlabel('Time')\n", + " plt.ylabel('Apple Stock Price')\n", + " plt.legend()\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 167, + "metadata": { + "id": "raTU7J4AOsNU" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Score: 28.21 RMSE\n", + "Test Score: 50.53 RMSE\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# execute essa célula\n", + "model_performance(y_train_pred, y_test_pred, scaler)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dEGAKKgCRuAP" + }, + "source": [ + "Agora crie uma nova rede LSTM. Você pode alterar a arquitetura original, configurar os parâmetros, defininir novas funções de custo e otimização etc. Deixe os resultados executados nas células abaixo e utilize as funções de treino e avaliação dos modelos definidas acima." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LSTM Otimizada (Felipe e Diego)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Mudanças \n", + "\n", + "**Mudança de otimizador (SGD -> Adam)**\n", + "\n", + "> optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", + "\n", + "O otimizador Adam fora escolhido por ser o preferido para a obtenção de melhores resultados empíricos, tomando o scheduling da learning rate como sendo dependente da loss landscape presente, sendo essa feita de uma maneira mais eficiente que momentum puro. Isso tudo nos dá maiores garantias de uma eficiência da exploração do espaço de busca, junto de uma mais suave convergência.\n", + "\n", + "**Manter sem dropout**\n", + "> dropout=0.0\n", + "\n", + "A princípio, com algumas mudanças na rede, chegamos no problema de claro de Overfitting, aonde o modelo não consegue generalizar bem. Então, para tentar resolver esse problema, adicionamos o Dropout, que é uma técnica de regularização que consiste em desligar aleatoriamente algumas unidades de uma camada durante o treinamento. Isso ajuda a evitar o overfitting, pois o modelo não pode depender de uma única unidade para fazer suas previsões. Porém, empiricamente não obtivemos bons resultados com o Dropout, no geral ele não alterava significantemente o erro (principalmente depois que achamos um learning rate adequado para melhorar o overfitting) e deixa a função de predição final com um cara meio *ruidosa*, devido ao dropout ser aplicado a cada passo da LSTM. Então, optamos por não utilizar o Dropout.\n", + "\n", + "**Tuning do Learning Rate**\n", + "\n", + "> lr = 1e-2\n", + "\n", + "A partir de um processo iterativo, tunamos o learning rate até chegar em 1e-2, que foi o que melhor convergiu para a resposta desejada. O primeiro passo foi diminuir significativamente o learning rate, optamos por 3e-4, depois, fomos aumentando devagar até ver qual solução melhor diminuia o erro RSME. Ao chegarmos no 1e-2 percebemos que a loss encontrava um mínimo local (muito bom) e se aumentavamos esse learning rate, o resultado era pior, por isso optamos por esse learning rate.\n", + "\n", + "Um processo curioso que percebemos, é que para um learning rate um pouco maior, para o nosso caso foi de 3e-2, a rede simplesmente aprendia que a média dos valores temporais era a melhor solução para diminuir o erro (mínimo local), e a função de predição saia de uma hora para outra desse formato visto no output no fim do código e ia para algo como uma linha reta direto no valor da média dos valores temporais. Isso é um problema, pois a rede não está aprendendo nada, e o erro não diminui. Então, percebemos o quão importante é que o learning rate seja adequado para que a rede aprenda e não fique presa em um mínimo local.\n", + "\n", + "**Adição de fator bidirecional**\n", + "\n", + "Explorando a documentação por parâmetros da LSTM padrão do pytorch que pudessem ser customizados, nos deparamos com este:\n", + "\n", + "> bidirectional – If True, becomes a bidirectional LSTM. Default: False\n", + "\n", + "Ao pesquisar sobre o assunto vimos que LSTMs bidirecionais são uma configuração especial de LSTM nas quais as entradas sequenciais são tomadas em duas frentes, uma em que a LSTM toma as entradas na ordem crescente e na outra de forma decrescente. Cada LSTM tem parâmetros de treino diferentes, sendo o equivalente a uma forma de combinação \"em paralelo\" das LSTMs. Ao final, as respostas de ambas sequências é combinada de forma a gerar a saída final.\n", + "\n", + "Não notou-se uma diferença positiva suficientemente grande nos testes para justificar a maior complexidade, que poderia levar a overfitting, sendo o treino e fine tunning mais difícil de convergir também, portanto, apesar de ter sido um experimento interessante, optamos por não utilizar este artifício." + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "metadata": { + "id": "tSw_MuU0RibG" + }, + "outputs": [], + "source": [ + "class LSTM_optim(nn.Module):\n", + "\n", + " def __init__(self, input_dim, hidden_dim, num_layers, output_dim, bidirectional = False):\n", + " super(LSTM_optim, self).__init__()\n", + " self.hidden_dim = hidden_dim # dimensões ocultas\n", + " self.num_layers = num_layers # número de camadas ocultas\n", + " self.lstm = nn.LSTM(input_dim, \n", + " hidden_dim, \n", + " num_layers=num_layers, \n", + " batch_first=True, # batch_first=True faz com que os tensores tenham shape = (batch_dim, seq_dim, feature_dim)\n", + " bidirectional=bidirectional, # bidirecional=False faz com que o LSTM seja unidirecional\n", + " dropout=0.0 # dropout=0.0 desativa o dropout\n", + " )\n", + " #self.dropout = nn.Dropout(0.001) # dropout para evitar overfitting\n", + " if self.lstm.bidirectional:\n", + " self.num_layers *= 2 # atualiza num_layers e hidden_dim para o caso bidirecional\n", + " self.hidden_dim *= 2\n", + "\n", + " self.fc = nn.Linear(hidden_dim, output_dim) # camada Fully Connected\n", + "\n", + " def forward(self, x):\n", + " # inicialização com zeros\n", + "\n", + " h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n", + " c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_dim).requires_grad_()\n", + "\n", + " # Precisamos fazer o detach, já que realizamos o backpropagation through time (BPTT) truncado.\n", + " # Se não fizermos isso, o backpropagation vai até o início da série\n", + " out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach()))\n", + "\n", + " #out = self.dropout(out)\n", + " \n", + " # Só precisamos ficar com o hidden state do último passo:\n", + " out = self.fc(out[:, -1, :])\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "metadata": {}, + "outputs": [], + "source": [ + "# Parametros\n", + "\n", + "input_dim = 1\n", + "hidden_dim = 32\n", + "num_layers = 2 #? empiricamente 2 é o melhor\n", + "output_dim = 1\n", + "lr = 1e-2 #? original: 0.1\n", + "num_epochs = 70" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LSTM_optim(\n", + " (lstm): LSTM(1, 32, num_layers=2, batch_first=True)\n", + " (fc): Linear(in_features=32, out_features=1, bias=True)\n", + ")" + ] + }, + "execution_count": 170, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Instancia o modelo otimizado\n", + "model = LSTM_optim(input_dim=input_dim, hidden_dim=hidden_dim, output_dim=output_dim, num_layers=num_layers)\n", + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 171, + "metadata": {}, + "outputs": [], + "source": [ + "# Define função de perda\n", + "loss_fn = torch.nn.MSELoss()\n", + "\n", + "# Define otimizador\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=lr) #? original: SGD" + ] + }, + { + "cell_type": "code", + "execution_count": 172, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 10 MSE: 0.015655972063541412\n", + "Epoch 20 MSE: 0.006786006037145853\n", + "Epoch 30 MSE: 0.0017614254029467702\n", + "Epoch 40 MSE: 0.0016536235343664885\n", + "Epoch 50 MSE: 0.0010779384756460786\n", + "Epoch 60 MSE: 0.0008718801545910537\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train Score: 2.03 RMSE\n", + "Test Score: 5.49 RMSE\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# treinamento\n", + "y_train_pred, y_test_pred, hist = train_model(model=model, num_epochs=num_epochs, x_train=x_train, x_test=x_test, loss_fn=loss_fn, optimizer=optimizer)\n", + "# avaliacao\n", + "model_performance(y_train_pred, y_test_pred, scaler)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3.9.13 64-bit (microsoft store)", + "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": "3.9.13" + }, + "vscode": { + "interpreter": { + "hash": "a33b474888067a6169f866e52f630d6f3672d35114c8362b477a93e2a003ce7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git 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"cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "MOpkPhvGuaZ5" + }, + "source": [ + "# SCC0270 - Redes Neurais e Aprendizado Profundo\n", + "## Trabalho Prático 3 - Detecção de bullying em tweets" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vaf3nFrXugNb" + }, + "source": [ + "NOME: Felipe Andrade Garcia Tommaselli \n", + "\n", + "NUSP: 11800910\n", + "\n", + "---\n", + "\n", + "\n", + "NOME: Diego Fleury Corrêa De Moraes\n", + "\n", + "NUSP: 11800584" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "t4ZEL_o4lq3S" + }, + "source": [ + "## Ambiente\n", + "\n", + "**Atenção**: recomenda-se o uso de **GPU** para rodar os modelos de redes neurais abaixo. Se não estiver disponível no seu hardware, sugere-se utilizar um IDE que forneça este recurso gratuitamente, como o **Google Colab**.\n", + "\n", + "Se for utilizar o **Colab**, basta que selecione `Ambiente de execução` no menu horizontal superior, depois selecione a opção `Alterar o tipo de ambiente de execução`, então escolha **GPU** na opção `Acelerador de hardware` e clique em **Salvar**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EJy7yGlpnBiR" + }, + "source": [ + "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbkAAAFqCAYAAACH/iSpAAAgAElEQVR4nOzdeVxU9f7H8dcM48CAIm644AYmUsqi5m6LWmaaJmpleq+mplZuZN7SNCutTMvcb7fMNfcNtDLTTAv3nXBBZXPBBRFwBAaGYeb3B785MWyyuY2f5+PhQ5g5y/csnPd8lzNHNWzYMAtCCCGEHVLf7wIIIYQQd4uEnBBCCLslISeEEMJuScgJIYSwWxJyQggh7JaEnBBCCLslISeEEMJuae53AYrLYpHb+oQQ4mGmUqnu2boeyJCTIBNCCPtV2DW+rAPwgQg5CTUhhBCQNw9KG3r3JeQk1IQQQhRFaUPvnoachJsQQojSsOZIUcPurofcgxBsD0IZhBDCHt3LQSQ55byuF1aGuxZydyNYJKyEEOLBUtLrclmGY2G1u7sScqUNIwkzIYSwbwVd50sTfhaLJc/8ZRpypQknCTYhhBBFbYa80/zWecss5EoSUhJsQgghClKawLPW6kodcsUNKgk2IYQQxVWSwLNYLKULueIEVlmHm4SlEEI8XMpqsElxbiMoccjdq4CTMBNCCPtQ1oNN8htokluJQq6owSP9dEIIIe6kNN9qcqdaXbFD7m4FnASiEELYr7IMroLmuSf3yd2NZkwJMyGEeLiVpKmyuINN8gvHYoXcncKmLANObigXQoiHS2nua7vT/EXpf8tv2iKHXFmFUmHTSc1OCCEeXkW5Npem5lacZswyu08ud8FKMs3drtUJIYR4MBR1kElhgVbUsCvyfXJ3o+mwJMEmYSeEEPbBGlBFrb3l936Z3EJQ2lpWzveLu6yShpqEoRBC3B/F6TfLPV9JAu9OQXdXv/GkoICrWLEir7zyCo0bN8bV1bU0RRBCCGHn9Ho9p06dYv369dy6dQuwDbfCgk5d2ILL6ptKci+nb9++tGnTRgJOCCHEHbm6utKmTRv69u1r83pRWgoLDbnCFHUgSX5NkL6+viVdrRBCiEeUr69vod1a+eVSiUKuNAEHoNPpSrJaIYQQjzBrdhQn6AoMudJ+zVZBhZBBIUIIIUqqoCwpKH+KXZMrKKQk4IQQQtwLxQm6fEOuLL9cWQJOCCFEWStKtlgslpIPPLnTigsqhISdEEKIkipuxhQr5O70LSR3CjOLxSIhJ4QQosQKypEi98mV9EkCd/pZAk4IIURZyJknd8qeItfkSvKEgPwKYTabi7pKIYQQwkbODCnz5sqcitI0mft3qckJIYQorfzypKAcKvXAk6J0AkqTpRBCiLJQWKbkl0F3dXRlfq9LyAkhhCipO2VJ7tc1hb1Z0OtFDSxryub+J4QQQpREQTlSoqcQFHfFhf1e0GtCCCFEcRR1HAiUcXNlUWp8EnRCCCFK6k59cLl/v2eP2jGbzRJwQgghSs1isSi3EtypElUmX9Bc2GtFacYUQgghiqI4Y0SKdAtBWXxZs9xCIIQQoiwU9G0nOd/P6a4MPJFBJ0IIIe6mon5/ZZnfJ1ecQgghhBDFUdxcURdloqKuUG4EF0IIcbcV54bwEtXkSjqYRPrkhBBClEZxcqRMvrvyTgWRWpwQQoiylntAY0EZc8++u1IIIYS428p0dKUEmhBCiAdZsUKupA9OlTAUQghR1gp61E5OZfo8OSGEEOJBclfvkxNCCCHup3v2qB1ptoSrV6/y6aef0rJlS7y8vAgICOCtt97iyJEj97tod1VaWhpr1qxh5cqVJCYmArBr1y7mzZtHSkrKfS7dPw4ePIiXlxeBgYFKOe2ZwWAgKCgILy8v1q1b90CUY/r06fetHOLhkV+eFPS7zUNTy2LFZTFNWUpLS2P16tVs3LiRiIgINBoNTz75JP369aNz585otdp7Uo4LFy4wbNgwzp8/r7ym1+vZvn07PXr0uCdluB+MRiOff/45q1evBmDOnDk8//zz7Nu3Dx8fnwIfdCjubPr06Xz33XcMHz6cDz744H4XR4h7wmKx3PG6kXOaQkOuqI/TeVCdP3+ekSNH2gSLyWTiwIEDHDhwgFdffZWPPvoIFxeXu16WP/74g/Pnz+Pp6cm0adMICAjAaDRy+fJl3Nzc7vr675fExEROnDjBtGnT6NmzJ1u2bOGrr76iRo0ajB079p7seyHEo6tMa3K53c8gTEhI4JNPPuH8+fN06NCBiRMnUr9+fcxmM3/99Rcff/wx69ato169erz11lt3vUYRHx8PwHPPPUeLFi1QqVRotVp8fHzu6nrvtxo1avDLL78ov7/yyiu88sor97FEQgh7U1jt7p4OPLmXobd//372799Pw4YNmTx5Ml5eXqjVajQaDR07dmTYsGEA/P777zb9L+np6axevZquXbsq/WYTJkzg4sWLNsvP2X8TERHBpEmTCAgIwMfHh9GjRxMXF2cz3XfffQfAwoULadCgAUFBQRgMBqZPn55vX4TRaGTDhg106tQJLy8vevTowV9//cWXX35p04dSUD9SYmIigYGBeHl5cfDgwTyv7dy5k2nTpuHj48M333yjrHPLli307t0bb29vfHx8GDJkCGFhYXmOndlsJjQ0lH79+inTvvPOO5w8eTLPtHq9nvnz5/P000/j5eVFy5Yt+eabb0hISMhz3Kxl6NGjhzLthx9+qOxPK4vFwt69e5X1e3t7069fP0JDQ5WHKRYmLi6O0aNH4+PjQ0BAAFOnTuXmzZsFTn/x4kUmTJhAQEAA3t7e9O7dm59++gmTyVTgPDn7mtauXcuvv/6qbFenTp3YtGlTnvktFguHDh1iyJAh+Pj45Ltduc+p7777Ls85ZLFYOHnyJO+88w4+Pj74+PgwYMAA9u7dm+/fodFoZMWKFbRr187mfMuvn+NO5cupOMc+t3Pnzinn/zfffIPRaLzjPOLRUJwsuas1ufvFaDSyb98+ANq0aYOHh0eeaVq1akXr1q1RqVQkJSVRpUoVUlNTmTx5MsHBwcp0er2etWvXsmfPHubMmUOzZs1slhMTE8PQoUNtLsI///wzycnJzJ07F0dHR/z9/YmLiyMhIYGqVavi4eFB1apVUavz/4xhMplYsGAB8+bNU147efIkb775JhpN2RyyL7/8kqioKAAyMzMxGAxMmTKFtWvXotFoaNCgAampqezatYu9e/fy7bff0qFDB6V8//3vf5k/f77NRXrbtm3s27ePBQsW0K5dOyC7Bjt69GgOHTqkTJeQkMD8+fMJDQ1l1qxZ1K9fH4DU1FSmTp1qMwgiISGBNWvWcPDgQb799lu8vb2xWCxs2LCBiRMnAtCgQQMyMzM5cOAAR44cYdy4cQwdOrTAT3a5+0eNRiNLlixhyZIl+U6/d+9e3n33XZsL8/Hjxzl+/DiRkZGMGjXqjsdl4cKFXLx4UdlfMTExjB8/HicnJ7p27Qpgs10596t1uz744AMGDhxY4Dllbfa2WCwEBwfz4Ycf2gTDnj17OHDgAJ9//jl9+vSxKd/06dNJTU1Vfreeb7Nnzy52+az7ojjHPrf4+HgmT55MTEwMgYGBDB8+/J71nwv7Ype3EKSlpREZGQnAE088ke8FyNvbm1WrVrFy5Uoee+wx5Q84ODgYT09Ptm7dSnR0NGFhYbz++uvExcUxe/ZskpOTbZaj1+vp2bMnp0+fJioqisWLF+Pi4sLx48eJjY0lICCA4OBgevfuDUDv3r0JDg5m0qRJODo65lv+sLAwFi9ejEaj4YsvvuDcuXNERkYyb968MvtDv3XrFh999BE7d+7k9ddf5+TJk2zcuBEPDw9CQkL49ddf2b17N5MmTcJoNLJu3TrlIhgeHs7ixYtxdnZm8eLFREZGEhERwciRI9Hr9Xz33Xfo9XpMJhP/+9//OHToEC1btmTPnj1ER0ezf/9+nn32WcLCwli4cKFyIf7ll19Yt26dzbRhYWH07NmTmJgY1q9fj8lkIjExkTVr1gCwYMECtm7dyo4dO5SazPr167l8+XK+220ymVixYgXnz5/H39+fnTt3Eh0dzaFDh3jppZfyTH/jxg3mzZtHcnIy06ZNIzIykqioKJYuXYqrqytr165VzrXCWIMnKiqKsLAwunXrhslkIjQ0VNn+U6dOMXPmTABlXZGRkcycORNHR0fmzZtHeHh4gefU8OHDAYiKimLWrFk4OTmxdOlSoqKiiIyMZNq0aQCsWLFCaT63cnd356effiIqKoqTJ0/yxhtvYDKZWLlypXLOF7V81v1cnGOfU2pqKtOnT1fm/eCDD6TvVpTYPQm5e903ZzabC21Gys/t27fZuXMnAEFBQUpfWYUKFRg2bBienp4cOHCAs2fP2szn6+vLwIEDcXJyQqVS4efnx2OPPUZqamqJh6EfOHCA1NRUOnXqRPfu3dFoNKjVap566imeeuqpEi0zt7FjxzJo0CA8PT2pW7cuLVq04Ny5c4SGhvLEE08AoFarady4MQDXrl0jIyMDi8XC77//roR7+/btUavVaLVa+vXrx+OPP05MTAzx8fFcvXqV0NBQXFxc+M9//kOtWrUAqF69OiNGjMDFxYW//vqLixcvkpKSwu+//w7AsGHDlGkrVKjA66+/jkaj4ciRI+j1etLS0tDr9Tg6OlK5cmVUKhUqlYouXbqwfPlypk6dWuBgnlu3bnHs2DEABg8ejKenJwBVq1alf//+eaYPDw/n0KFDPPvss3Tr1g21Wo1KpaJVq1Z06tSJ+Ph4/v777zvu76FDh9K4cWNUKhUVKlRQjuPNmzeVC/2ff/5JfHw8gYGBBAYGolarUavVdOvWja5du6LX6/n999/v+PcUGhpKXFwcPXv2pG3btqhUKtRqNS+88AL+/v6Eh4cTGxtbYPmcnZ3p168fHh4e/P3338q0xSlfcY59TpmZmXz//ffKh80pU6bg7u5+x/0rHk1FyZZit309DKMqSyI+Pp6YmBhq1qxJo0aNbN5zd3fH19eXmJgYYmJiaNWqlfKe9Q89P0Xpd8gtIyOD6OhoAJo0aWLzCValUuHg4FDsZeYnvzKnpaWxYcMGNm/eTHh4eL4fFNLT05WmWR8fH5tacu5BJgcPHiQqKgpfX1/q1atnsxxPT08ee+wxwsLCuH79Oo6OjsTExADw5ptv5lvmhIQEEhMTqVu3Li1btmT16tUMGTKEzp0706VLF5o1a0br1q0L3e6kpCTi4+OpUqUKXl5ehU4LcObMGSC779bPzy/faazlLkxB58iNGzcwGo0YDAal+bRp06Y2NXatVkvz5s1Zv349Fy9exGAw4OzsnO/yjEYj586dA2D58uUsX7483+kuXLhgsz25y1e1alVq1KhBXFwcMTExNGrUqFjlu3LlSpGPfc7uhFWrVinn3fjx4/H29s63/EIUlV32yVkHmJR03oIuSJB9cbibzGYzWVlZAFSrVu2uriun5ORkxo0bxx9//EGjRo0YMmQIWq2WK1eusHHjxnznKWw/5Z6usGnj4uKoXr066enpSn+gk5NTnulcXV3RaDRotVomTpxIrVq1WLhwIRs2bGDDhg1oNBqee+45Jk6cmG8/bE5OTk4FNhfnZL1Z3drvlZ+yvgWksH11+fJl0tPTCwy5rKwsDAYDALVr16ZKlSr5Tle+fPlCy+Do6IiHhwdHjx4lMzOz2OXLOe2djn3Ofu6c827fvp327duj0+kKLasQhbHLkNPpdNStW5ejR49y+vRpTCZTntA7d+4cn3zyCWazmY8//phy5coB2SFT2Og8a/PW3aJWq5XaWs6BAHfbvn37+OOPP2jevDnz58+nevXqQHZtrKCQK8ooRut0hU2b85O+yWRi0qRJysCVgjg7OzNixAiGDh1KREQEe/fuZcWKFWzbto1bt24xZ84cqlatWuIy5daqVSumT59eYLiUpTvtq6Je9F988UXGjx9f4AAcaxjmJzMzU2lut/5tlKR8xTn2gNLsvWPHDjZv3kzLli3p06ePfGmAKDG7HHji6OioNCnu378/z/BzyL54HzhwAIPBQOXKlZXmmatXr+bpd4uPjyc8PByNRpPnj/JulN3ajBYWFmZzIbJYLEotLzej0ViqIdbWAQMeHh64uroqr+dusnRyclJqNBERETbvJyYmMnLkSAYOHEhsbCzu7u7UrVuX6OjoPDXgmJgYIiMj8fDwoEqVKri7uysfII4fP56nWTwzM1N57fDhw8yaNUsZhenn58fbb7/NqlWr8PT05PDhwwU2IRZ2nPPj6+sLwOnTp7lx44bNeyXp+y2I9YMZZG9/zmNpNBo5evQokH188qvl5lxOw4YNgewRkrdu3bJ5PysrK99zKPcHKmvzvYuLC56ensUuX3GOfU4DBw5k0qRJjBw5Uhm8kvPLHIQoLrsMOYCOHTvSpk0bzp8/z5QpU4iJicFisWA0Gtm5cyfff/89AF26dMHd3Z2KFSvSsWNHIPu+I+sFMC0tjVWrVhETE0Pr1q3z9NfdDW3btsXV1ZWtW7eyefNmTCYTJpOJ3377jdDQUJtpq1SpgoeHB+fPn+fIkSPKNv7888+cOnWqyOu0BszZs2e5du0aAFeuXGHZsmU206lUKp577jlcXV0JCQlh3759Nuvcvn07JpMJNzc3PDw8aNeuHampqSxYsIDr168D2X1ry5YtIzU1laeffpq6devi6urK888/D8APP/zA9u3bMZvNWCwWoqOjGTRoED///DMWi4Vy5cqxePFivvnmG06cOKGULTk5mdTUVKpXr06lSpXy3c6KFSvyzDPPANl9VtYwTEhIYOXKlXmmb968udIfO3PmTKWfNT09nR9//JERI0aUqO81Px06dFCO+y+//KLUgnbt2sVvv/2Gq6srzz33nFKrsdawoqOjuX37trKcTp064e7uzv79+/n2229JS0sDsgdXzZgxg88++yxPqK1YsYJjx45hsViUcz4uLg4/Pz9lmH9xylecY5+TtXnzpZde4qWXXiImJobvvvvunrZqCPtil82VkP2J/eOPP+btt99m165d7Nq1K880r776KgMGDFAuGgMGDCAyMpLg4GBefPFF3N3dSU5Oxmg04uHhQVBQ0D35Ci7riM158+bx4Ycf8uGHHwKg0WjyNLvWrVuXp59+mtWrVzN69GhGjx5tM21RaxqtWrWiYcOGnD17lk6dOimvOzk5odFoSElJITU1lcqVK+Pr68vgwYOZP38+b7zxBm5ubpjNZvR6Pa6urrzzzjvKfhozZgxRUVHs3r2bNm3aULNmTW7cuIHJZMLf35+hQ4cqgxh69erFiRMnCA4O5u2338bV1RW1Wk1ycjIajYbu3btjsVho0qQJgwcPZt68efzrX/+iQYMGODg4EBkZidFoZNSoUQXef6VSqXj11VfZs2cPhw4dyndbc6pevTrvv/8+I0aM4Oeff+bnn3+22YaWLVuW2U3KAQEBTJw4kYkTJ/Lee+/x6aef2mz/Bx98oNQsITuANRoNO3bswN/fX/kOy0aNGvHee+8xceJEFi5cyJIlS6hWrRpXr14FoGfPnpjNZpu+svj4+Dz3zmk0Gvr3768cy+KUT6vVFvnY59ds6uLiwogRIzh+/Dg//fQTrVu3lmZLUSJ2W5OD7Hvh1q5dy9ixY5WailarpX379ixcuJApU6bYjF50cXHh888/Z9KkSfj4+BAfH4+TkxOvvfYaK1euzHMj+N2i0WgYMWIEM2bMUMrdpEkT5s6dy3PPPWczrVarZdy4cQwaNEgZmNG+fXuWL1+eZ9rC1KtXj8WLF/PSSy+h1WpxdXVl0KBBrFixgnr16nHhwgWuXLmilG/kyJEsWrSI1q1bk5KSQnp6Oi+99BLr16+36U9zd3fn22+/ZeTIkdSuXZurV6/i5ubGyJEjWbhwoU0Yubi4MG3aNGbPnk3Tpk1JS0sjLS2NLl26sGrVKl599VVlUNGYMWNYtmwZTz75JFFRUZw7d47GjRsze/ZsRowYUejAI3d3d2bOnJlnW5cuXar0RebUrl07tmzZQt++falatSpXr16lTp06jB8/nu+//14ZHl9aKpWKPn36sGTJEtq3b096ejopKSm0bt2aRYsWMWjQIJvtateuHePHj1eOu/Vcti5n06ZNdO3aFWdnZ65evUqTJk2YMWMGX3zxBRUqVLBZ94wZM5gyZYrN+fbDDz/w4osvlrh8xTn2+fH29ubNN9/EZDIxd+5cZdSoEMWhGjZsmAXyvzWgsMfl5Pd/7p+t/6zNTllZWcpN16JwZrNZuf/LKiEhgSFDhhAeHs4PP/ygNK/eTydOnODf//43vXv3ZvTo0VSuXPl+F0kIYcesNXoHBwflHlDrtTLnNdP6s13X5B5WJpOJefPm8b///U8ZUm391pHw8HDlOwMfBI0aNeKHH35g586dTJkypdARe0IIca/ZbZ/cw+zGjRvs27dPGUVYt25dLl++jNFoRKvV8uabb97xPrB7JS0tjVOnTtkMfBBCiAeF1OQeQDVr1mTJkiVMnDiRxx57jOjoaOWLfDds2EBgYOAD0wEfGRnJ3LlzeeaZZ/jPf/4jN+4KIR4o0icnhBDioSF9ckIIIcT/k5ATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpArAYPBQFBQEE899RSRkZF2sb5169bh5eXFunXrijVfYmIigYGBBAYGkpiYeFfKdi/WIYSwT3Yfclu3bsXLy4ugoCAMBsP9Lo4QQoh7yK5Dzmg08ueffwJw4MABLly4cJ9LJIQQ4l6y65C7ePEi+/bto3bt2qSmprJz506b900mE5s2baJTp054eXnRv39/wsPDlffj4uJ455138Pb2pmXLlvzvf/8jPT0933UlJSUxZcoUfHx8CAgIYOrUqSQlJQEwffp0nnrqKX744QdatmzJunXrSEpKYurUqQQEBODt7c2AAQNs1h0WFkaPHj3w8fFh6tSpeZrpLl68WGDZDh48iJeXF7NmzWLAgAH51mKTkpJ4//338fb2pn///pw6dSrffdOuXbt8y5eb2Wxmy5YttGvXjoCAAFavXo3JZFL24+jRo/Hx8cHHx4fRo0cTFxdXYFnT0tLYuXOnsqzvvvtO2baoqCjatWvHtGnTsFgsnDt3jnbt2inNrNaa+9atW/Mc306dOrFp0yZMJpPSBPzyyy/zv//9Dx8fHw4ePFjs7RZCPNjsOuT279/P9evXeffdd/Hz82Pfvn0kJycr72/fvp3x48fj6urKv//9b86fP8+4ceO4cOECCQkJvP/++xw5coT+/fvj4+PDjBkzWLt2LRaLxWY9BoOBL774glWrVtGrVy+eeuopfvzxR+bNm2dzof/iiy9ISEjAZDIxa9YsfvzxR/71r38xefJkYmJi+Pzzz7lx4wbXr19n8uTJnDt3jl69enH58mX27NmjrO/KlSu888477Nu3j379+ill+/7775X1AcybN89mPiuTycR///tfNmzYQOvWralXrx5r1661mWbLli2MHz+eBg0a0L9/fyIiIpgwYQJXrlzJd1+Hh4fzxx9/0LlzZ7RaLXPmzCEyMpLbt28zceJE/vrrL95//31GjRrFX3/9xVdffWUTvDnLeu7cOSZOnEhmZiYvv/wyhw4d4uzZswC4u7vToEEDYmJiSE1NJTIykqtXr3L8+HGMRiMxMTFUqVKFevXqsXPnTsaPH0/9+vWZM2cOHh4efPzxxxw8eNCm3DNmzMBoNJZou4UQDzbN/S7A3ZKSkkJoaCi+vr48/fTTXLlyhTlz5nD27FlatWpFamoqP/30E3Xq1GH27NnUq1ePxx9/nA8//JCoqCjS09PZv38/c+bMoXv37iQnJzNixAh27txJly5dbNZ19uxZtm3bxsiRIxkxYgSZmZk4OzsTGhrK1atXlem+/fZbOnfujEqlAmDKlClAduhERkayY8cOkpKSuHz5MuHh4YwdO5YRI0aQmprK2LFjOXPmDAB79uzh9OnTfPrpp/z73/9Gr9czduxYfvrpJwIDA5X19evXj8mTJ6PVam3Km5CQwL59+2jTpg1z587Fzc2Nhg0bMnXqVAD0ej0hISG0aNFCeb9Zs2aMGTOGEydOUKtWrTz729fXl2+++YaqVatSt25dpk6dSnR0ND4+PixdutTmuISFhSkhlV9ZlyxZQnx8vLLvr127xpAhQwDQ6XR4eXmxd+9ebty4waFDh4DsYLxx4wbR0dHUrVuXGjVq0LhxY86dO6esw8XFhT179nDp0iWaNWsGQJUqVfj+++9p2rQper2eBQsWFGu7hRAPNrsNubNnz7J//36GDx9O5cqV6dSpEz/++CO///47zZs3JyMjg2vXruHq6kqFChUA6Nu3L3379gWymxgBxowZw5gxY5Tl+vv729SWIPsCm5qayjfffMM333yjvO7h4UFGRobyu5ubmxJwSUlJzJ8/n40bN6LX65VpkpKSSEhIAMDLywuVSoWDgwPOzs7KNDExMQB4e3sD4OrqSsOGDfnrr7+4du2aMl2FChXyBBxkB41er8fPz09ZrouLi/J+fHw8MTExxMXFKWGQc978qNVq1Gq1zbKs08bFxTF9+nS2b9+u1Jg8PDxsatU5yxofH4+Liwt16tQBQKvVKu9pNBr8/f3ZuHEjUVFRhIeH06tXLw4ePEhERIQSrBUqVMBkMrFlyxYWLFig7DPApm/WyclJOf4l2W4hxIPNLkPOYrGwf//+fINn3759JCQk5Hvxz88HH3xA48aNld8dHR0pX758vtO+8cYbdOzYUfldo9FQvXr1PNMZjUa+/vprfvnlF7744gtat27NvHnzWLZsWVE38Z5o0aIFw4YNw9HRUXmtXr16xVpGcnIyEyZMIF5EfrYAACAASURBVC4ujuXLl1O7dm0+++yzUvVzeXt74+TkxC+//EJ8fDxjx44lPj6e4OBg4uPjee2119BqtWzdupXx48czaNAghgwZQnh4OEOHDr3j8stiu4UQDwa77JOLj49nx44dNGzYkLfffptRo0YxatQounTpwpkzZzh8+DCOjo7UqFEDvV7P7du3gez+mMDAQA4ePIinpyeQ3XTXunVr2rdvT+XKlalSpUqegKxXrx4ajYaEhASaNm1K+/btqVOnDjqdTqkl5JSSksLp06fx9PSkdevWuLq62vTzVa1aFYDo6GgsFgtZWVmkpaUp71vLZm2K0+v1nD9/nnr16lGjRo077p/y5cvj6urKxYsXleXmbDqsVKkS7u7uXLlyhdq1a9O+fXuaN2+O0WikZs2adz4AOSQkJBATE4Ovry++vr64urrmqQnn5u7uTmpqKpcuXQKyPxRYa4AANWrUoG7dumzevBlPT08aN26Mr68vW7duRa/X4+PjA2T3t5lMJjp27Ii7u7tSiy5IWW63EOLBYJc1uYiICM6cOcPQoUMZN26ccnE7deoUhw8fZs+ePbzwwgt0796doKAggoKC8PPz49dff6VSpUrUqFEDT09PWrZsycKFC7l48SJVq1bll19+ITAwkFGjRtmsr0mTJnTv3p3g4GCSk5Np3Lgx27Zto3HjxsyYMSNP+awBu3PnTmbMmIFarWbTpk3K+9aL9vz587ly5QrJycn8/vvveHh4ANC+fXueeOIJZs6cSVRUFNHR0ezZs4egoCBq1qx5x0ESVatWpW3btixatIjRo0fj5eVFSEiI8n7lypXp27cvEydO5K233qJLly6cOnWK8+fPs3TpUho1alTkY1GhQgUqV67M7t27mT59Ojdv3mT37t351nCt2rRpg7u7O1OnTuXYsWOcPXuWM2fO4O/vryzT29ub48eP4+vrS8WKFWnevDmA0h8HKH1oc+bMwd/fn23bthVa1rLcbiHEg8HuanJGo1EZPt68eXObT+916tTh8ccf588//yQ6OprOnTvz5ZdfotfrWb16NT4+Pnz99dfUq1cPd3d3Zs6cSa9evdixYwfBwcF0796dt956CwcHB5t1uri4MGnSJEaMGMHff//NkiVL8PX1ZcKECTZ9aTmn/89//kOLFi3YtGkTKSkp9O7dG8iuUVWvXp3PPvsMf39/Nm3aRLVq1WwGu9SqVYvvvvuOtm3bsmrVKiIiInj//fcZNmwYGs2dP7doNBreeecd/v3vf3PgwAHOnz/PgAEDlPdVKhWBgYHMnDkTJycnvvvuO5KTk5k+fbrSD1hU1atXZ9KkSdSsWZM1a9Yo/aPp6ek2/ZU5NWrUiBkzZuDi4kJwcDB+fn60bt1aeV+r1SqBZz3GDRs2pEGDBvj4+FCpUiUAevXqxdChQzl27Bi7d++mT58+uLi4YDAY8q1NluV2CyEeDKphw4ZZgDzD4vN7zWKxKK/l93/un63/zGaz0uxmsVjYsGHD3dsiIYQQdqtPnz7KgDyVSoVarUalUtn8A5Sf7a4mJ4QQQlhJyAkhhLBbEnJCCCHsloScEEIIuyUhJ4QQwm7Z5X1ykP1t9HPmzCEiIsLma7OEKCpXV1d8fHwYO3YsL7zwwv0ujhCiBOyyJrdt2zb69+/PoUOHJOBEien1eg4dOkTfvn3Zvn17qZe3dKmRQYNSadpUj0qVhEqVRNOmegYNSmXz5swyKLEQIje7rMnNmjULgB49ejBhwgTla56EKI6IiAimTZvGli1bmDlzJp07dy7RckJCMnn33TRiY8153jtxIosTJ7JYutRI/fpqZs1ypmfPcqUtuhDi/9llTc76AFAJOFEaPj4+TJgwAcDmkT3FERSURmBgCrGxZurVyw6x48ddsVgqYbFU4vhxV2bNcqZePTWxsWYCA1N49920Oy9YCFEkdhly1i8bloATpWU9h3I+FqiogoLSmDMn+6vLZs1yJja2IkFBjgQE/PO1cAEBDgQFORIbW5FZs7K/Am727AyCgiTohCgLdhlyBbFYLBw+fJh+/frh7e1NQEAAkyZN4vr168o0Bw8exMvLy+Zfjx492L17N2ZzdnPT9OnTWbdunc2yDQYDQUFBeV4Xj6aQkEwl4I4fdyUoKPuxPdbamqfnLTw9bym1PICgIEeOH3cFYM6cDEJCpJ9OiNJ6pEJu3759fPjhhwwZMoSwsDB2796tfFlyzk/q/v7+HDlyhOjoaCIiIpg0aRKfffYZu3fvvo+lFw8Ta01s1ixnpeYWG2umaVM9ISGZxMaaiY01ExKSSdOmeiXoAgIclBqdNFsKUXqPTMilpqayYsUKBgwYQKdOndDpdLi5ufH222+TlZXFsWPH8p1Pq9XSsmVL+vfvz44dO2yeayYeTrNnz8531K1er2fOnDmlXv7SpUYuXMjug7PW4CA7+JKTLTzzjIaYmIrExFTkmWc0JCdbbJong4IclT46qc0JUTqPTMglJiZy7do1mjVrZvO6m5sbK1eutHmit7Bfs2fPZu7cubz++us2QafX63n99deZM2cOs2fPLtU6du/ODqagIKdcr2c/3mfpUhfq11dTv76apUtdAAgLy7KZ1jpvSIh8qBKiNB6ZkLM+u8zR0fEOU9qyWCxERUWxZcsWmjZtmuep4OLhMnjwYHx8fDhz5owSdNaAO3PmDD4+PgwePLhU6zhxIjuwnn3W9g6d5GQ3LJZK1K+f988uKcn2sVbWeXOHnxCieOzyPrmiiIyMZNCgQcTFxQHw5Zdf8uqrrwIQFhbGk08+qUxbtWpV3nrrLXr27HlfyirKjqurK2vWrKFv375K0AFKwK1ZswZXV9dSrcMaTDlHUeYnOdlCYGAKgE2zZs55rYEphCiZRybkypcvD0BKSvZF5bHHHiM0NBSDwaDcC2Xl7+/PokWLqFy58j0vp7j7cgcdUGYBV1SxsWY6dLhNbKyZgQO1fPKJ7p6sV4hHzSPTXFm5cmW8vb3Zs2dPvk9BL45y5cpx+vRpTCaT8lpGRgY3btygatWqpS2quAesQefj41PmAefvf+daWFBQmhJw1n65nKzzWpclhCiZRybktFotQ4YM4ddff2XdunXo9XoMBgMnT54kOjqaihUrFnlZHTp0YOfOnWzfvh2DwUBycjI//vgjDg4O+Pn53cWtEGXJGnRlXYOzNjVaB5rkx/pdlbNnO+f7vnXeOzV5CiEK98iEHIC3tzfffvste/fupWXLlvj7+/Pll18ydOhQOnXqVOTlBAQEMH/+fNasWYO/vz/PPvss169f56uvvpKa3EPG1dW1zJson302+7sn58xJL3Aa69d6ubmp8n1/9uzseXv2lIFOQpSGatiwYRYg3ya83K9ZLBbltfz+z/2z9Z/ZbMZisZCVlYXFYmHDhg13b4uASpUqAZCUlHRX1yMeDSU5n+rXv8WFC2ZmzXLOM6gEQKXKXpbFUinPe7NnZ/Duu2n/f69c0VsYhHgU9OnTB5VKhYODAyqVCrVajUqlsvkHKD8/UjU5Ie4VazPku++mFWuE5IkTWco3nRTUlCmEKDq7DDk3Nzcg+1EpQpSG9RyynlNF1bNnOcaMya7BNW2qV77H0sraXJnT7NkZNG2afYP6mDGO8sgdIcqAXYact7c3ANOmTSvxI1KEsD5PDv45p4pj9mxnJeiCgtLw9LzFnDkZNjW7EyeymDMnA0/PW0oNbswYR6nFCVFG7LJPbtu2bcpNvkKUhXXr1vH888+XaN6QkEyCgtK4cCHvQ1NzqldPzezZ8tBUIQojfXJAly5dWL16NS1atKBChQr3uzjiIVWxYkVatWrF2rVrSxxwkN10GRtbkeDg8gwcqLW5983f34GBA7UEB5cnNraiBJwQZcwuv/FkyZIldOzYkS1btuDk5HTnGYQogMFg4Pr16yxevLjU32nZs2c5CTEh7jG7DLlBgwbd7yIIO6HT6ahfv36pA04IcX/YZXOlEEIIARJyQggh7JiEnBBCCLslISeEEMJuScgJIYSwWxJyQggh7JaEnBBCCLslISeEEMJuScgJIYSwWxJyQggh7JaEnBBCCLtll99def78+ftdBCGEeCQ1bNjwfhfBhl2GHECdOnXudxGEEOKRcunSpftdhDykuVIIIYTdkpATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpAT4j5LSEjg/PnzWCyW+10UIeyO3T5qpygsFgtnzpxhw4YNXL9+nXLlytGqVSt69uyJTqe738WzSzdu3GDt2rWcO3eOrKwsqlatSq9evfDz80OlUpGUlMTXX39NUlKSMo9Wq7U5LjExMaxcuZIRI0ZQqVIlZbpjx46xceNGxo0bZ/P6g+748eMcPHiQMWPGUKFChTJdttls5o8//uC3334jLS2N6tWrM3DgQOrVq1em6xHiQfVIh9zRo0dZu3Yt3bp1w9/fH71ez/r16/n222956623cHZ2vt9FtCsJCQksWLCABg0a8Mknn+Di4sK+fftYvnw5gwYNokmTJsq0b775Js2aNcNsNnP9+nVWrFjB8uXLGTJkyH3cgrvj+eef5/nnn78ryz59+jQ7d+6kX79+1KtXj82bN7NmzRpGjRol57d4JDyyIZeWlsbOnTvp0KEDzz77LACVKlVi0KBBzJ07l7///pvWrVvnW2tYtGgRAEOGDMFisRAaGsrWrVu5ffs29evX54033qBatWrExMSwYsUKGjZsyL59+3jjjTdo1qyZTTmuXr3KihUriI2NpVy5crRv356ePXui0WiwWCwcOnSI4OBg9Ho97u7u9O3bFx8fH2UbNmzYwJEjRwB46qmnePnll9FqtaSlpbFy5UrCw8MB8PPzo2/fvpQvXx6TyURISAh79uwhMzMTLy8vBgwYQLVq1YB/altnzpzB0dGRZ555hm7duqHR5D1d0tPTCQkJ4cCBA8qyBg0aROXKlfNMGxoaik6no3fv3soF9tlnn+XKlSuEhoby+OOP55lHrVZTs2ZNXn75ZZYtW8b169eLfIzDwsJYu3YtycnJuLm58corrxAQEIBKpbKZbuvWrURFRaHT6Th+/DiOjo707t2btm3bolKpWLRoERqNhri4OLRaLSNGjMBisRS47xctWoRWq+XatWvExsZSoUIF+vbty82bN/nll1/IyMigadOm9OvXD2dnZ7Zu3crp06cZMWIE6enpLFiwgP79++Pp6amUz/r+tWvXWLFiBU888QR79+5VltWlSxdWr16trO/111/H39+fSpUqMWDAALy9vXFwcOCZZ55h5cqVZGRkSMiJR8Ij2yd348YN9Hq9EhhWlStXpk6dOkRERBRpOYcOHWL37t2MHj2amTNn4unpybJlyzAYDAAkJydz69YtJkyYYFNTAbh9+zaLFi3Cx8eHOXPmMGHCBE6ePKlcOI8fP05ISAiDBw9m3rx5dOjQgVWrVnHjxg1MJhNr1qwhKyuLGTNm8NFHHxEdHc3vv/8OwE8//YRer2fq1KlMnTqVW7dusXnzZiwWC3v37uXkyZO8//77zJw5k8qVK7Ny5UqMRiO3b99m8eLFVKlShZkzZzJu3DiOHTvGli1b8u0z2rx5M3FxcXz66afMmjWLKlWqsGHDBrKysmymMxqNXLp0iQYNGthcXFUqFf379+ftt9/GwcGhSPu8KK5cucL69evp3r07c+fOpXv37qxfv57Lly/nO/3Fixd58sknmTlzJj169CAkJISoqCjl/ZMnT9KpUyeGDx+ORqMpdN8DnDt3jt69e/P111/j5+fHokWLiI2N5dNPP2XChAlcunSJgwcPlmjbbt26hUqlYurUqbz33ntERkby3//+l86dOzNz5kzatWvH5s2b0ev1eHh48Pjjjyv7Njo6Gq1Wi6OjY4nWLcTD5pENueTkZFQqVZ6+G5VKhZubW56LdH4MBgOhoaE899xz1KpVCycnJ55++mlSU1OJj48HwNXVlcDAQGrWrIlWq7WZX6fTMWrUKF588UU0Gg3Vq1dXAjYrK4uDBw/SvHlz5VN4mzZtaNWqFWlpaVy6dIlLly7RrVs3nJycqFatGk899RQRERGkp6eTkpJC3bp1cXV1pWLFivTt21dp/ktNTaVatWq4u7vj5OREz5496dChAwDnz58nPT2dF198EScnJ2rVqkXv3r0JCwsjOTnZpvwWi4WuXbvyzjvvULFiRbRaLX5+fly6dInbt2/bTJuVlYXRaCxR7eH27dvs2rWLSpUq5VtDzE9GRgYODg54enqi0Who3rw5r732GuXLl893+kaNGuHv74+TkxNt27bFw8ODEydOKO+3atWKVq1aUaFCBS5fvlzovgdo2bIlXl5e6HQ6WrduTYUKFejQoQMVKlSgdu3a1K1bl+jo6GLvC4CKFSvy7LPP4uLigqenJ4899hheXl74+fnh5OREkyZNyMjI4ObNmzbzHTlyhN9++43nn39eanHikfHINleWhfT0dJKSklixYgUrVqxQXtfpdJjNZgAcHBwoV65cvvNrNBpu377N999/T2xsrFJTat68OUajkdTUVFq0aKFMr9Vq6datG5A9yOL69et88sknNsv08vLCYrHQqlUrli1bxtmzZ3nyySdp0aIFHh4eADRp0oQDBw4wZcoU/Pz8aNeunTLw49q1a7i7u9sMgKhSpQoWi4Xk5GSbDwUqlQqNRkNISAgHDx7EaDQC2c2+dxopaDAYWLBggXKh9/LyYsSIEcr7P/zwg816vLy8GDhwYJEHBNWoUQMPDw+++uornnjiCVq2bEnjxo3zbXK1rsPajKnVamnQoIHyQQWwCYWkpKRC9z1gUyu1Ljt3M2lJ5VxW7p8LEhsby8aNG3nttdfyNJkLYc8e2ZBzc3PDYrGQlJRkc+G2XswLuhjm5uDgwIgRI2jcuHGe92JiYgqd9+bNmyxcuJAnn3ySMWPGKP05RVW9enXeffddXF1d87zXpEkTpk6dyunTpzl8+DDbt2+nW7dudOrUibp16zJ58mTOnTvHkSNH+Prrr2ndujW9evXKdz0mk0kJ7ZyysrJYu3YtN2/eZPLkyVSuXFkZ4ZibVqvFxcVFqQ3qdDrGjRsH/NPnlJN14ElJ6XQ6hg4dyrVr1zh69Cjr16/H2dmZ4cOHF2nkZUZGRqHvF7bvH0SnT5+mZs2a+Pn53e+iCHFPPbLNldWqVcPV1TVP31tiYiKXLl2y6auzWCz51kwcHR1xcXHh3LlzNtPmFwj50ev1ymATrVZrsx5rKMTGxirTm0wmTp06RVJSEq6urhgMBpvahtlsxmKxYDQaCQsLw2Aw0KxZM4YPH07Xrl05fvw4KSkpnDlzhuvXr/PEE08wYMAABg4cSHh4OHq9nho1ahAfH2/T3Hjr1i0cHBxwc3OzKb/RaCQhIYEOHToozYgFbbuDgwMNGzbk3LlzeZo9i0utVpOWlsaNGzdsXk9MTKR8+fJotVri4uKIiIjA3d2dl156ibFjx5Kenn7HDx6QHd43btygYsWK+b5f2L4vCwWdb6XRoUMHhgwZkqfJXAh798iGnLOzM506dWLXrl3s3r2bpKQk4uLiWL16NRUrVlQ+8bq5uWE0Gjl69Cjp6emcOHFCCUZnZ2datGjB/v37CQ8PJysri7///ptZs2bZ3OdVEEdHRwwGA8eOHSM1NZU///yTkydPAtmh0KpVK44eParcU7Z3717Wrl2LyWSibt261K5dm+DgYBITE5VRjmvXriUrK4vQ0FA2btyIwWAgPT2d2NhYdDod5cqV48SJE6xatYrExERMJhMxMTGUK1cOjUZDw4YNcXJy4o8//iA9PZ1r166xZcsW/P3984Scg4MDOp2Ow4cPk5iYSHR0ND///HOB29u2bVtcXV1ZsWIFV69exWg0cuXKFSIjI9HpdEUeeFKzZk1q1arF5s2bleWEh4eze/dumjZtiouLC0lJSSxfvpzTp09jNpu5du0aBoOhwObOiIgIwsLCMJlMHDp0iNjYWJo3b57vtHfa96Xh5OSETqfjwIEDGAwGoqOj2b9/f6mWCZCamsqVK1fkhnPxyHlkmyshu+/L2dmZDRs2sH79elQqFVqtlgEDBih9MJUqVaJPnz6sWLGCkJAQvLy8lKH2kD0E3mw2s3z5clJTU5Vh/pUqVbpjjaVWrVq8+uqrrF69mk2bNuHn54evry9Go5GsrCyaNm1KZmYmixcv5vbt29SpU4fBgwcr63/jjTdYs2YNn3zyCVlZWfj5+fHaa6+h0+n417/+xdq1a/nggw/Iysri8ccfp1+/fjg6OhIYGEhISAhTpkwhMzOTOnXqMHDgQKUfbvDgwSxfvpz33nsPR0dHnn76abp27Zqnz0er1fL666+zaNEiPvroI6pVq0ZAQADHjh0jMzMzz/Y6Ozvz9ttvExISwvTp0zEajeh0Onx9fenZsydarZbU1NQ7HjetVsuQIUNsluPm5kbnzp15+umnAWjcuDE9e/Zk9erVJCcn4+rqSrdu3fKMprWqUaMGBw4c4Pvvv8fZ2Zk+ffpQv379Atdf0L4v7QhRnU7HK6+8wqJFixg3bhzVq1enZs2apKWllWq5oaGhXLx4keHDh+Pk5FSqZQnxMFENGzbMAuT7CS/3azmbUfL7P/fP1n/WppysrCzl/qK76fz589SpU6fY85nNZnbu3Mm2bdvo2rUrHTp0QK1+ZCu7j4ytW7dy9epVu7zRXIh76dKlSzRs2PCurqNPnz6oVCocHBxQqVSo1WqbwV25B2U90jW53NRqNc8//zwNGzbEzc1NAk4IIR5yEnL5KKiZSgghxMNFQk488rp27Xq/iyCEuEukPU4IIYTdkpATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpATQghhtyTkhBBC2C0JOSGEEHZLQk4IIYTdkpATQghhtyTkhBB2Kc1oIcsMFgskpVowlu5Rf+IhJSEn7FJiqoVBy1JZtj8j3/ePXzIR8Jmeubvyf/9ORq9Jo/1Xeq7eKtpT4EvqXq3H3vz0dybNP9fTf3EKB2NNDP4xlbPXHqyUu9M5KsqGfEFzISwW+P1MJl/+ls6FRDPl1PBMo3J81FWHh5vqzguwM7fTLQxZnv1Q00UDXKjg9ODug5QMC5E3sqhXuXQPMRX3xq8nMxm5Jo35fZ15sUm5Ui0rMys75D7qpqOKi4q3V6bR+YlyNKrxYJ0Lco7eGxJyhfg5PJP3N6bRsLoD03rquJhoZtUhIwOXpLB8UHlqPYJB97CoW1lN6DjX+10McR+Uc4Dv/+Ws/P5C49KF5t0i5+i9oTl37hyQ/5PBrfJ7Qnjunwt6Lb9/D4OEFDNzd6XTsLoDSwe6UNklO9DqV3Xgg01pbD9t5I22jhiz4Ic9GSzem0GywYJXVTUfd9PR7jENxy+ZGLQsjQ6NNMTeNBMel0X9ymqmvKzj15OZbDhqRKtRMaajI4PaOpKSkV1TMmZB1fJq/jyXSWUXNeO7ONHTv5zyfrLBgnsFNbE3s1g3tAJqtYVPfk7nz7OZAPRpruWDF5wo75g3hK/pzTbTPtOoHJ+85EQNV7VS3sHtHBndwRHIbi47dsnEJ911jNtg4HZ69vF76uvbLHhdx5ydGXnKczEpiy9+Tef01SwcNfB6Cy3vPeeETqvCYoGQsEy+3JZOQoqZGq5qxnXO3j5VjuLmV2vM+Wm//WMaZV/5VHdgc5jtvrymN/PK9yk0q6Nhbl9njFkwf1c6C0Mz0GpUPN1QQ85TMSXDwuydGaw/aiTVmH0cv+zlTLM62Z+yI65lMWZdGlE3zATUdiD3aZx7v+Y8BikZFqb/ls6Go0ZMZvCv7cC0QB0N3fN+gi/NenK7kGjm458M7I00oVHbTlvYeWvd9+mZ0MBdzbaTmWg1KkZ3dMTNWc2M39JJTDXzQuNyfP6yjoo6FaPXpBEWl0VAHQe2n8qefvjTjrzZ3hGtwz/n0fph5alZ0fZci7yexS8ns7dn5Jo0ujUpx5zXnPk5PJOZO9K5lGSmkrOKt55xYlAbLQ5qyDLDyoMZLPjTSEKKGc8qaj7tnl1+i4VC5y1s23O707JuGSxM3Gzgt1PZf6s9/TWsOZLJl4E65Rwt6BwOqONgc46Korlw4UKeJ4HnfiK4lUqlQu3o6Ehx/mm12nx/zv2aVqtV/uX+/WFwMclCvN5CB2+NEnAAHb01zHlVR8D/X/xWHsxg9s502j+mYVpPHRq1inc3GDh99Z/2/8MxJnr6l+OdZxyJSzYzZFkqt9MtTOmhw8NNzbd/ZXD2+j/Tn7qSRUUdfNEzu7nl058NnLhsUt6PumEm/raZLo21qFQWxgcbOHnZxPguTozq6MRPf2cy/bf0PBdIgzF72gPRJkZ1zJ72QLSJ8cEGDMbCP3w0dHdg01vl8avtgF/t7J99amjylCch1cx769PQqFWsH+bCkHaOrDxoZM0RIwBHLmTx0RYDTzd0YMuI8vjWduCjLQaOXChZf8nJK1mkmwrel1Y//Z3Jf//M4KmG5fj4JR230i2kZPyzzQt2Z7DyYAYjOjiy9s3yWIDxm9KIv23mliF7v91MsTChi46OPuWISvinj8y6Xws6Bgt2ZxB8IpPJL+lYMtCFGykW/rPRwC2D7T4v7XpySky1MGpNGpHXs5jU1Yn+rbRsOGpkwe7s/p+inLfn47Oo4qLi45d0uDmrmP5bOkv2ZjDueUe6+ZZj26lMgo8blenjks3oDdnHwr+OA3N2prP9VOYdj+GUl3V8/rIOgM9f1jHlZR1HLmQxIcSAT83s86RDIw0ztxv463z28jYeM/LZr+m08XLgy0AdWo2K9zelEXvTfMd5i7LtVoUty2KBr3eks/NMJgPbaBn3vCOhkVk255Uoe4VlS365pNHpsk+ugmpY+dXQ8vs5d40uvxqc2Wx+aGpy12+Z96n0JAAAIABJREFUScmw4OBg+wm5kouKbr7ZQZ2UZiH4eCYt62f/sei0Kp6o5cDAJakEHzfS1Te7maR3My0D2zhyO93CgWgTiakWJnRxorqrGl05FWPWpRGTYMbDLXsckJ+HAx+/pMPVSUXtSmreXJ5KaGQWj1XLDtbHaziw7A1nqpRXsz/axOGY7JrWK821WCxw9noWf503EX/bTHXXf8YWnbicxeEYE+8+r+PNdtnboNWomLXDwInLWTgV0qqjdYDyzirK/f/iqpX/Z7/kLE9KhoUNwytQ3hEqOKmo4apm03Ejxy9mMahtdg0502ThhcblaFzTgc96OHEwNsvmg0Rx+Hs4MKVH9r4CmBBi4FKimYoe/9SS0jNhc5iR+pXVTO2Rvd89q6r4+3L2RS3LDG+00TKorZYqLmoc1NDdT8vivRnEJZu5ZYAzV7P4zws6hvz/fou4msWxSyab/VrQMYhLMlOjgoqOjTRUd1Xz39eduZiYdyDJ8UtZpVpPzmP9d1wWZ69l8dnL2dNmZsHFRAtHL5i4lGQu9Lwd3dEJgCdqOjC6oxOuTiriUyws2JVO0HOOPOdTjnYNynE41sSxi1m80TZ7ndUrqPj8ZWdqual4sr6Gfj+ksiMik26+hTcXuulUVNRlH7+KOhVuOhWPVVOzY0x5KjmrcSoH/Vpq2XHGRPgVM2284OeTmfhUz/47qeT8z9/JmatZtPbUFDhvQJ3C/2afqKmzKVth5Xiippm/zpt4qmE5PnhBRzkHKO+oYuSatCKcuaKkHB0ds2toavUda3MqlQqNtWZV0pArKNxy/mwNuIcp5Iriut7MVb2ZVp7l0Gmzd2qtimpqVVRzITF7+DKQJygru6hw/v/pHfIZ3+qgBusc9auoqeSiIjJH7cRZmx1OAGevZZFugvHBBsYHG5RpalZUYc61q62jyxrX/Gel1p8vJ5l5zL1kg21zlsdFq2J/1D+DdZTDXSf7P//aGjyrOvD2yjSa1nGgu385XvbXKiFVXDn3VXnH7OZQU678MGRaSE614FXNgcouebfRQQ1aBxXTtqXzy0kj6f9f+bAOrLlx24xG/X/t3Xl4U1X+x/H3TdI0aWlpWazQQqVIrWVpUUSkgjBqWQRm2ESnDoIKODiyjQI6LoiMgAwiFHAGFEVhFNSKLCIFxpVN6lD2vVBKKYWydKFJ2yy/P/JLTNI0baGlkPm+nqcP4S7nntyb5JNz7rm5rvvNWWXHoFcbPzYfLiNxbhHdojX0b68lMdav3LG/1u0425NtRq9VHMfU+TzVobNmr6/bMnP5fQug1yo0DLR/eFhdupdt02zTQaFRoEKzUIWcy5aratkE6xXW7CjlXz+VcrbA9YBeKraQcd7MXc00hAbYKnFflIb9U+oDti8tFa1b2Xu2zGzbV1Wpx5l8Wyv/zltVLuuI2uXn5+cIOfegA8o9vqqQq27A3YwhF1ZfRT1/BbPZtb6XrljZmlFGeIgKnV/FH8xGkxXzdXiqhjLbh9GM/gHcF/XbO02tgoYePtArcqWGuljST5t4McVA9zs0pDxbj+JSK4MXFTnmNw1R+GJUIJsPmViZVspb3xhZ+H0pHz4ZQEwtjX5Tq2wfXFoNaDzskjIzvPmNgR+PmliUFEinKA0LfrCdswHQ+ylo1Ar+FRzvyo5BrzYq2oYH8VV6GavSS1m37wrdo/2YN0Tv+KCtie3UBKPpty9ndWn9vjKmf2tkRBd/Rj+g4+BZ2zm82lzX/tydA8tbWRqVgkop/yVW1C5vIecp6FRqtZqa+FOpVBU+dv//zaB5qMItwQpbjptczp1sO2Fi7EoD6VlmwoJVNAlWcfCsxXFO60y+hTP5Fu64RYX2Kj+zzRawb/HkBQuXrli5PcxzYXc2UaNgO3/SpL6KJvVVhOgVdH5KuZaCfQj1/pzfPsXsj52HV9uD3WqF6n7enc23Umi0kninH/X1Clar4nK+6JMdJby7uYQHojUsfzqQRU8EUmCw8N1hk8fynPfF1X74atUKgTqFQzlmzhXaCrHyW72MZVZOX7IQ1UhFuwi1bWCD0zeUejoFY5mV/56yNXHc94u3Y1BYYmXaN0a+O1LG89392TQuiCfv8+fXUyaOnXd9QteyHfdj3S5cjaHUyrFzFkdZS7eVMP/7EkL03l+33rqtvbFawWq1fbDkXbGSdclKkxCVY1CM83zn/e9JxgUL/n4K3e/QoPNzXT40QEVUY7XtvVFsm5iRZ+FvXxvYc9rsdd3K3rPuz91bWbcEKQTpFPZlmx2tX09q4jUsflNZvrg/1qjVtg83Ty2sikZVuo+S9NaCUxTF0ZID15EvN7JG9VSM7OLPy18ZGLnsCoPu0jouIbitgYrEWC2hAQo92/jx7mYjL60ykNBSw4dbbSfi+7TzK9eFZJdbaKW41FrhdWYHcszM+4+RO8LUfLi1lECdiodiPF/tcXdzNXdHavh4ewkms5XoMDWf7CglNEDhvT8GOLpFAdo0VRPXTMP874yUmmyVW/RTCXHNNLRpqqbEZKVxkMKq3WU0qa/i2Hkzmw+W0fD/z7+pFFCpbF2b3+4vo1OL8nUK0ilo1fBpWilFJVbW7C3jbMFvOyJUr+KtHcWYrZB0j5b/HDFhsUKrMFW5clqFqUn5bynv/1xCw0CFxT9d3UWzOj/4fZyWl74q5vW1RhLv9OPTX0oc3Wh+atuH1a+nzHyyw3b8lm7/bUBF+2Zq7myiZtFPJSjYQvy7w2WOrjJvx2DekAAuXrGwKt1EPX8VtzdWseuUmQYBCrcEuR7/a9mO+7FuF67mjlttgz+KS61cuGJl8U8lDO2kJSzY++u2IkUlVvIKK06m3EIrb6ca6NrKj1W7S7lcbOEPcToUBe5qruab/WUs+MFIfITtde3cjalR2bo7fzhq4pYgFaEBtl8sWbqtlGPnLC7L6/ygTxs/Xllt4I21tu0t22HrTny6s7/XdSt7z7rzVtYtQSq6ttLwxa+lzNxgcLxf7ar7Gj5z2crYFVdIbK1lxP03xwC9uqBWq7225Mp1V9pbVlUNOefgcp/u/LiiywbsYXczGNRei06jMHujkclfGdCqf7sY3H6N3DP324baL9lSwtq9ZUQ1UjH30QDiImyXEHhitlgrDECAZg1UHD1nYem2UhoEqpjaV8edt6o9ntsI0im8M1jPlLVGlu2wDVHv1ELDK711Lh967ssm/8cI/HYJQZBOIQiFiQ/reGW1kVdWG7j3Ng1tI9ScuWw7ZoH+Cn/qZAv+malGFgwpP+w5oaWGFxN1zP1PCTtPmOgXp6V1EzVnCywUGq30auOH0aRnVmoJH28rITRA4a+JerpHl/+Aee4BHYfPmln4QwlhQSq6tPLj819Lyy1XFX3b+ZGTr+O9H4xsO26iX5wfpy799qH5Zr8Axqy4wjubjNzWQMWgu/z4aFspOflW2jdTmDVQz1+/MDD9WyNtw9U8eIefY0CIt2NQX68wpa+emRuMvPxVMWUWiGqk4u1BAS4DRcA26OJqt+N+rBsEKiQ/FsDrawxM+8aIVgNJ92oZ96AtdLy9bu2XibjzdL7TWcNAFSYL/G1VMVqN7bh2bWU7rv3ba0k7ZWZlWilr9yj0au1H9uXfCuvYQkNCSw1f/LcUiwWm/UFP5gULy3eU8t1hE4Pv1pJvsHL8vBmr1TaYy1hmZcEPpY76z388gKjGKiIa+Htd19tzdzekQ8VlAbzwsI5LxVbHe7VzS7XL6N7qvIaLSixk51vLnfsTrpwDriohp4wYMcIK1Qs553lVOR/nfE7OYrHw5Zdf1tLTtzl69CjNmjWr1W3UhpvpF0WEcOZ+Hdz18t4PJXy4rZS3/qDjoZi6v+i7Jn+55WaUlZVFq1atanUbAwcOrDTk4Lfzcprr0X1ob/3ZNyyEEDXh2a7+5BZYWfRjCffeppEvhv8DPF0m4C3kbo5RIEII4UHy9yWs31/GiPv9JeCER/LblTeQIJ3CypH16roaQlRbXf0s1Zjuv/0E3Y2gVxs/jk+rX9fVEE40ZrN9uHLtjK70dE5OCCGEuBpms9lxKZpKpXKcDquoy1K6K4UQQvgsCTkhhBA+S0JOCCGEz5KQE0II4bMk5IQQQvgsCTkhhBA+S0JOCCGEz5KQE0II4bMk5IQQQvgsCTkhhBA+S0JOCCGEz/LZH2jOysqq6yoIIYSoYz4ZcrV90z4hhBA3B+muFEII4bMk5IQQQvgsCTkhhBA+S0JOCCGEz5KQE0II4bMk5IQQQvgsCTkhhBA+S0JOCCGEz5KQE0II4bMk5IQQQvgsCTkhhBA+S0JOCCGEz5KQE0II4bMk5IQQQvgsCTkhhBA+S0JOCCGEz5KQE0II4bMk5G5AJpOJPXv2UFBQ4HG+0Whk9+7dGI3GWq/LlStXmDx5Mm+88QZXrlwpN3/lypXMnDmzRrZVk2V5cu7cOZKSklixYgUWi8WxzXHjxmEwGKpUv6ouW5kdO3bUWFmeWCwWVqxYwbBhwzh37lyFy128eJGnnnqKY8eOVfq68wXHjh2jf//+HDt2rK6rIq4TCbkasHfvXoYOHUp0dDTx8fHMnj2bS5cuXXV5RUVFzJgxg23btgG2D8T+/ftz8eJFAHJycnj11Vc5cuRIjdTfm23btqHRaHjhhRcIDAys9e3VFrPZzOrVq7n33nsZOHAgKpVvv/T379/Pzz//zNtvv80tt9xSpXXcX3eibri/38W10dR1BW52u3btYsyYMfzlL3/hnXfewWAw8K9//YtXX32V6dOnExQUVO0yQ0JC+Pe//13h/BYtWrB69eprqXaVPfTQQzz00EPXZVu1Sa1W88wzz9R1Na6btm3bkpycXK11KnvdCXEz8u2vs7WstLSUzz77jGeeeYZHH32URo0a0axZM1544QUuXbrETz/9BNi6SJ588kk++ugjEhISiImJYfr06WRmZjJmzBiio6Pp1asXO3fuBGxdSP3792fHjh3MnDmTxx9/nN27d9OhQwd27NhRrstl5syZzJkzh2eeecZjF5jJZOLDDz+kY8eOxMTEMH78eHJzcx3znVuiDz74IOvWrcNqtTrKdu5CzMjIICkpiejoaEaPHs2JEydctuWtLHc1WVZ2djajR48mOjqahIQEUlJSMJlMgK3Lde7cucTHxxMfH88HH3xAaWlpuTKOHTvGU0895fIN2v35u29zzJgxxMTE0LFjRz788EPHNt3l5eU5jnVSUhJpaWku86tax8qeq8ViYf369fTq1Yvo6GhGjRrlsl9NJhNLliwhPj6ehIQEVqxY4ej2dn7dAVitVtatW0dCQoKjh2LkyJGsXLmyStty5+14VresnTt30q9fP6KioujXrx+//PKLo6yqHBer1cr8+fOZNWuWY72ioiJGjRrFDz/8UGl9K3vPge0L8JAhQ4iKiiIpKcml5yUjI8Ol7DVr1mCxWDy+38H2+vnb3/5GTEwMCQkJjuVF5STkrkFubi5HjhyhQ4cOKIrimB4SEkLnzp3ZvXu3402RmZmJoihs2LCB1atX89NPP/Hcc8/xpz/9yfFmSk5OLnc+ZNKkSXz66afExcWRlpbGvffe67Eua9euZejQoUydOhWdTucyb/PmzWzatImUlBTS09OJiIjgnXfeobS0lNOnTzNlyhSeeuopDh06xIIFC3j//fc5fPhwuW1cvnyZqVOnkpiYSHp6OuPHj3f5sK6rsvLy8njppZfo2rUrBw4cYNmyZaxYsYItW7ZgMplYuHAhJpOJ7du3s2HDBtLS0li1apXH/VhVhYWFvPbaa3Tu3Jl9+/axatUqNm3axJYtW8otW1paypw5c2jSpAlpaWnMmjXL5XlUp47enivA999/z/vvv8+cOXPYt28f3bt3Z9y4cZw5cwaA1NRUNmzYQEpKChs2bCAgIMAxz116ejr/+Mc/mDlzJlu3buXOO+/kl19+ccyvbFvOKjue1S1rxowZTJgwgSNHjjBhwgSmTp3KqVOnqnxcFEXh/vvv59dff3V8qTl9+jRFRUXExMRU6fXn7T13+PBh3nrrLV577TUOHTpEUlIS06ZN4/z58xQWFvL3v/+d3r17s2/fPubNm8d7773Hrl27PL7fr1y5wrRp02jbti179uzh448/Zvny5dKtXEUSctegpKQEAH9//3LzGjduTG5uruNbcmRkJI888gj16tWjVatWdOnShYSEBDp06IC/vz/33HMPhYWFXgcJePPEE0/QtWtXgoODXQIXID8/n5YtW3Lrrbei0+kYNmwYgwYNAuCWW27hgw8+oGvXrqhUKiIjI4mMjOTgwYPltnH48GEsFgu///3vCQgIoFWrVgwcONAxv67K2rNnDzqdjj59+qDRaGjRogUTJkzAaDRy8uRJdu/eTVJSEjqdjrCwMAYPHsyOHTscx+9q6PV6Zs+ezaBBg9BoNDRt2pR27drx66+/llv21KlTHDlyhD/+8Y8EBwfTtGlThgwZ4phfnTp6e64Gg4HVq1czbNgwYmJi0Gq1DBgwgJiYGH7++WcMBgOpqak88cQTREVFUa9ePR555BEiIyPL1dlqtbJp0yZ69+5NQkICAQEBdO/enW7dugFUui133o5ndcsyGo34+/sTHR2NRqOhc+fOvP7669SrV69axyUqKorAwEBOnjzp2LexsbE0bNiwSq+/it5zVquV1atX07t3b1q3bo1Wq6Vr167o9XpOnjxJWVkZJSUl3HnnnWi1Wu644w5mzJhR4bnTffv2YTAY6NOnD1qtlpYtW9KjRw9HK09455Pn5FJTU5k7d67HD8SqCA8P59VXXyUxMbHG6qRWqx2DHRRFcXlcE7wNCunYsSNffvklSUlJ9OzZk8TEREfr02q1kpmZyezZs9m+fbujW6dz587lysnMzCQ8PJyAgADHNOcBHH5+fnVSVl5eHi1atHDZB/YW744dO9i6dWu59fr163dN3T0ajYaCggJee+01UlNTHV2Lo0aNKrfshQsX0Ov1hIaGuqzvPL+qdfT2XC9evEheXh5RUVGOeVqtlubNm3PixAkMBgNnz56lWbNmjvkqlQq1Wl2uzkajkezsbDp37uzxNWowGLxuy52341ndssLDw2ndujWPPfYYffr04eGHH6Z9+/aOfVrV4xIcHMxdd93F9u3badu2Lbt27aJnz55oNJoqvS8qes/Z9917773Hm2++6TLvqaeeIjg4mAcffJARI0bQu3dvHnroITp06FCuNWiXmZnJxo0badeunct0T89JlOeTLbk333zzqgMObH36c+fOrXQ5ewvOU4vg/PnzhIWFVfjCvZ5uu+02li9fzssvv0xBQQGPP/44S5YswWQycfz4cSZOnMjw4cM5cOAA+/fvp1+/fle1nRu1rPvvv5/09HQyMjIcf++++y56vf6qygPb8Z04cSL33Xcfe/bsISMj45o+dGqjjnbX0mKtqW1dzfGsqCy9Xs/LL7/Mp59+SmRkJDNmzOAvf/kLFy9erPZxSUhI4ODBgxw/fpy8vDxiYmKuur7u5s+f73I8MzIyuPfee9FoNAwfPpx169Y5zhkmJSU5WpSeDBkyhEOHDrmUNWnSpGrV53+VSq1WU50/+zc/T/93n+c+vaJvjTUtOzsboNwLrKp/QJVCMiwsjOjoaNLS0lwGRBQWFrJnzx7i4uJqrKV2tezXPuXk5BAXF8fYsWOZM2cOGzdupKCggAsXLnDnnXfSqVMnxzdYs9nssazIyEiys7MpLi52THNuadRVWY0aNeLEiRMu1/GdPHmSw4cPU79+fc6fP+9ybsdisVQ4gMVsNlephZefn09AQAA9evRAq9VitVorXK9hw4YYDAaXy0qcB0JUp47enqter6dRo0aO1zDYzgfm5ubSokUL9Ho9t956K1lZWS7b8bRf9Xo9zZs359y5cx7rUdm23Hk7ntUtKysri7179xIWFsaQIUP417/+RWFhIUePHq3WcQFbl2VZWRnr1q0jPDychg0bVlrfyvj5+dG4cWN27drlcpztjy9cuMDOnTupX78+vXv35r333uP22293Od/pLCwsjEOHDrm8fry9hn2dp0zxllM+2ZK7XrRaLY899hjvv/8+K1euJC8vj6ysLGbOnInBYPDYtXY1QkNDUavVXLx4sdrfytVqNVu2bGHWrFnk5eVhMpk4dOgQOp0OPz8/9Ho9Bw8eZO/eveTn55OSksLGjRs9lnXHHXegUqn4+uuvKS4u5uDBg6xYscIxv67KateuHUajkbVr12IymThx4gSTJk3i7NmzREVFcdddd7Fw4ULy8vIwGo188sknLFq0qNyHVmhoKMXFxfz888+UlJTwyy+/kJqa6nGbOp2OS5cusXXrVoqKivj222/56quvPC7bvHlzoqOj+fe//01BQQGnT59m6dKljvnVqaO356rX6+nXrx/Lli0jIyOD0tJS1qxZw7Fjx7j//vvR6/UkJiY65hcVFfH555+TmZnpsd7du3dn/fr17Ny5k+LiYr777ju+//57x/Hxti133o5ndcuyD77Zs2cPFouF06dPk5eXh16vr9ZxAVuXZadOnVi6dCldu3Z1dHlW5/XnTqPR0Lt3b7799ls2b96M2WwmIyODcePGkZmZSVFREW+99RbfffcdFouF3NxcMjMzqV+/PlD+/R4XF0dQUBBLliyhuLiYwsJCZs6cydq1a6tUn/91EnLXqH379ixcuJB169bRuXNn+vbtS2hoKHPnziUkJKRGttGsWTPat29P7969+eabb6q1rqIoDB8+nNjYWHr37k1sbCw//vgjr7/+OkFBQbRt25YxY8Ywfvx4HnjgAS5fvkzfvn09/rpJSEiI41xHfHw8ycnJdOrUyTG/rspq1KgR06dP58cffyQ2NpbRo0czbNgwunTpglarZfLkyTRp0oSHHnqI+Ph4MjMzefTRR8v1KjRs2JCJEycyd+5c4uLi+PTTT4mNjfW4XyMiInj55ZdZsGABHTp0YOfOnQwcOBCDwVBuuLpWq2X8+PHk5OTQoUMHJk2aRFxcnMv8qtbR23MF6NatG0888QQjRoygTZs2pKam8vbbb9O0aVMAEhMT6dGjBwMGDKBHjx4Ajnnu4uPjGT16NOPHj6dz584cOHCAu+++2zG/sm05q+x4Vqes+Ph4Jk2axJQpU7j99tt58cUX+etf/0rbtm2rdVzsOnbsSGxsrMuxrs7rr6J9N3v2bBYsWECrVq147rnnePTRR2nevDmRkZFMnTqV5cuXExMT4zi3+OCDDwLl3+8hISHMmjWLc+fOcdddd9GlSxdCQkJ4+OGHq1SX/3XKyJEjrYDHpq/7NKvV6pjm6V/3x/Y/e9PabDZjtVr54osvau8ZgeMEtnP3x/VcXwhfYH//2gcEFRUVMWHCBJ5++ukKL2URorYNGjQIRVFQq9WOQXyKorj8AY7H0pITQniUlpbGpEmTyM3NxWQysXbtWgoKClxGQQpxo/PJSwiEENeuffv2ZGVl8cc//pGsrCw6derE1KlTady4cV1XTYgqk5ATQnik0WgYMGAAAwYMqOuqCHHVpLtSCCGEz5KQE0II4bMk5G4inn4lvybV9k1LhRDiepOQq0MGg4Fx48Y5bl0ihBCiZknICSGE8FkSctfg4sWLPPXUU6xZs8Zx888xY8aQl5fnWCY3N9flZofLli3DZDJx7NgxEhMTWb16NZMnT3bceNFqtZKamuq4eeSYMWNcbnBqNptZvXq1y81X7b/CYLVa2blzp+NGjUOGDGHXrl2OdSub78xkMjFnzhwmTJhAYWFhLe1BIYSoXRJy1+jSpUts3LiRd955h23btmG1Wh2/wVhYWMiUKVOIiIggPT2dFStWsHbtWpYvX07Lli1JTU2lX79+zJgxw/GL8z/99BPLli1j8eLF7Nmzh/j4eKZPn+6483BGRgY5OTmsX7+e1NRUdu/eTUpKCgD79+9nypQpjB07liNHjvDnP/+ZF154gX379lVpvp3VauWrr74iLS2NyZMnExQUdB33qBBC1ByfvE4uPDyc7Ozsa/plBvstNyoTGBjIqFGjCAsLA6BLly6Om10eOHCAK1eu8Pjjj6PT6YiIiODFF19k1qxZ9OnTx+VeamA7R5eSksKwYcOIiIgAoHfv3nz//fecPXsWsP3k2IgRIwgODiY4OJiRI0fy0Ucf0a9fP9avX0+vXr247777UBSFBx54gH379rF+/XpiY2O9zm/durWjHlu3buWjjz5izpw5Fd7IUQghbgY+2ZJ79dVXqxxSnoSHhzNu3LgqLavVasvdGdx+u5bMzEzHHZjt7KHhfNsMO4PBQGZmJs888wxRUVFERUXRuXNnTpw44fg1euebr4Lth4IVRaGoqIjs7GxiY2NdfrstKiqK7OxsLl++7HW+/Q7mR48eZfr06QwZMoRWrVpVdZcJIcQNySdbcomJiTV6V++aZDKZKCsrq3C+v78/KSkpxMfHl5t37Nixam+vslvzuM8vKiritddeY968eXTq1Ino6Ohqb1MIIW4UPtmSu1FERkY67ttld/HiRfz8/AgNDS23vL+/P6Ghoezdu9cxzf2mj+439Tx9+jRWq5V69eoRHh7OgQMHXO4GkZ2dTXh4OCEhIV7n2+9g3r59ezp27MiAAQNYtGhRlW8tIoQQNyIJuVoUGxtLYGAgX3/9NUajkTNnzrBgwQJ69epFgwYN0Ol0hIWFkZOTg8FgQK/X07dvX5YuXcp///tfzGYzaWlpvPDCC44LwDMyMli8eDEFBQWcOnWKRYsW8fDDD1O/fn169erF5s2bSUtLw2QysXXrVtasWUOvXr1QqVRe57vfwbxHjx6Ulpaydu3a/9k7EAshbn4ScrUoKCiIKVOmkJ6eTnx8PIMHD+Z3v/sdjz/+uONeRz179mTVqlUMHTqUgoICevbsydixY3nxxRdp1aoV7777LsOHD6dBgwaAbeBJkyZN6NWrF4mJiXTs2JHBgwcD0Lp1a1566SXefPNNoqNZzkXaAAAND0lEQVSjmTdvHtOmTXMMKqlsvrPAwEDHoJb9+/dfv50mhBA1yCdvmiqEEMI3yU1ThRBCiP8nISeEEMJnScgJIYTwWRJyQgghfJaEnBBCCJ8lISeEEMJnScgJIYTwWRJyQgghfJaEnBBCCJ8lISeEEMJnScgJIYTwWRJyQgghfJaEnBBCCJ8lISeEEMJnScgJIYTwWRJyQgghfJaEnBBCCJ8lISeEEMJnScgJIYTwWRJyQgghfJaEnBBCCJ8lISeEEMJnScgJIYTwWRJyQgghfJaEnBBCCJ8lISeEEMJnScgJIYTwWRJyQgghfJaEnBBCCJ8lISeEEMJnScgJIYTwWRJyQgghfJaEnBBCCJ8lISeEEMJnScgJIYTwWRJyQgghfJaEnBBCCJ8lISeEEMJnScgJIYTwWRJyQgghfJaEnBDif8qxY8fo0qULK1eurOuq1Jl3Nl4h5pVz/PdUWV1XpdZJyAkhakVubi6vvPIK8fHxREdHM3ToUHbv3o3Vaq3rqt30rFZYtcvI/TPyiHgxlxaTcklafImTF8x1XbUbjoScEKLGnTt3jrFjx7JlyxZef/11kpOT8fPz48knn+SXX36p6+rd9NbtLWH8Z/k0DlLxj8HBvNirHgfOmPjT4ktkX7LUdfVuKBJyQoga9/nnn3Py5EmSk5Pp378/PXr04K233iIyMpJVq1ZRWlqK0WhkyZIlJCQkOFp6R44cAX7rUkxOTmbMmDFER0eTlJRERkaGYxvHjx9n1KhRREdHk5CQQEpKCiaTCQCTyURKSgoPPvgg0dHRjB49muzsbI91vXTpErNnzyY+Pp6YmBjGjx9Pbm4uADt27CAqKorZs2fTvXt3Pvvss1rec1WzbreRkAAVs4fU57GOekZ3C2RKvyAy8sz8dLQEgK3HS+kx5wIRL+bS8qVcpqwuxFBavhW9dk8JES/m8tkvBgCMZfDYokt0mZFHboHFazl//iSfuCnnGbH0MrGvniMt88br/pSQE0LUqOLiYg4dOsQ999xDVFSUY3rDhg158803GTx4MGALwnnz5jFx4kTWrFkDwLRp07h8+bJjnZUrV9KtWzdmzJjBqVOnWLx4MaWlpZw5c4axY8diMpn45z//Sf/+/XnllVfYsmULAKmpqUyfPp2kpCT++c9/UlJSwksvveRSNtjCcOHChXzzzTfMmzePTz75hKNHjzJv3jxKS0sdy61YsYJu3brRsmXLWttv1RHRQM35Igufbjc4Auf37XVk/yOMxzrqOZFnZsy/89GoFFY/34BRDwSydEsxy3cYypV1V3M/moaoHOfnzuSbOZxj4r7btRSXWistJ6/IQnpWGb3b6WhU78aLFE1dV0AI4VuMRiOnT58mMjISRVEc0zUaDe3atQPAYrHQt29f+vbtS3BwMCqVij59+pCcnExeXp5jneeee44BAwZgMpnYs2cP+/fvp7i4mPT0dHJzc5k5cyatW7emY8eOHDlyhO3bt3P33XezatUqHnnkEYYOHYpGoyEwMJDhw4dz9OhRQkNDXer77LPP8vzzzxMUFISiKHTp0oXt27dTVFTkWObvf/87Dz/8cC3vuar7y+8C2ZNVxsLvr7BkyxW63eHPsw8E0uE2PxQFGgepWP18Q4J0CkE6hSb11XyeZuDXk2U808W1rMZBKtpF+LH7VBmXiq0cOGPicrGFnm10VSqnUT0Vn45swO23qK//jqgCCTkhxHWnUqlQq9UsWbKE5cuXO4ItPDy83HJgC0i9Xo/JZMJisbB3714iIiJo0qQJAPXq1WPRokUAZGVlceLECTZt2sTHH3/sUp7F4nq+SqPRYDKZmDNnDmvXrqWgoACAuLg4l+WCg4Nr6JnXjPp6hc9GhZJ2sox//nCF7w+XsGF/CY920DNjUDCBWoUtR0t5c20hJy+YcYz1iSxflp8aElvrmPJ1ASfyTPx4pISIUDWtm2qqVI5WA4H+SvmCbxASckKIGqXT6YiIiMBsNruMpDSZTBw4cACTyURMTAyLFy9m3bp1fPDBB7Ru3ZovvviC5OTka96+yWSirKyMP//5zzz99NMu8+rVq8epU6cc/zcYDEyfPp2zZ8+yevVqmjVrxttvv8327duvuR61TVHgnhZ+3NMiBEOplYlfFPDlrwb6xOkICVAY+1k+D8X6s25sQ4pLrfRLvlBhWffc5keAVuE/B0vYfaqMe1tquSVIxa6ssmqVcyO68TpQhRA3tYCAAGJiYkhPTycrK8sxPT8/nzfeeIPPP/+csrIyTp06Rdu2bbn99ttRqVTlWlnetG3bltOnT5OTkwNAaWkpK1euZMOGDTRs2JDmzZuTkZGBv78/DRo0IDQ0FI1Gg1ardSnHYDCQmZlJ+/btad68OVC+tXejySuy0PXtPP4w/yKFRtuXCL1WoWcbHSYLnDhvIueyhUKjlV5tdNTXK1gs4O3KjYhQNW3C/Vjxi4GsS2a63eGPolDtcm5E0pITQtS4gQMHsmnTJiZMmMDIkSPR6XSsXLmS48ePM3HiRPR6PcHBwXz33XesX78egAULFlS5/Pj4eMLCwnjnnXdISkri4MGDLFiwgPnz5xMcHMwf/vAHXnnlFaZPn05iYiJpaWls3ryZhQsXupTj5+dH/fr12b59O+vXrycnJ4cVK1bQokWLGt0fNalhoIr+7fXM2VjE8A8vM+huHcWlVhb/WExogMLdt/lRYLCiVcMn24spNFpYlW4kJ7/i8PZTQ882/mw8UEJ4qIq7mvsBEKRTqlXOjUhackKIGnfrrbcyf/582rZty6RJk3j++ecpKytj6dKldOzYEa1Wy5gxY2jfvj2TJ0/myy+/ZPjw4eTm5nLhQuXdYU2bNmXu3LloNBqeffZZUlJSmDlzJt26dQOgX79+TJs2jW3btjFs2DB27NjByy+/7Git2QUFBfHaa68REBDA2LFjSU9P58knnyQvL4+LFy/Wyr65VooCYx4MZM6Q+pzNN/PC5wW8/nUhoYEKi54MIb6ZH11aaXn5kSB2ZZbx0pcFNK2vpk1TDTn5Zkfrz13HKFsXZYfbtDSpb4uGqynnRqOMHDnSCnj8FQL3aVar1THN07/uj+1/FosFq9Xq6KP/4osvau8ZCSGE8FmDBg1CURTUajWKoqBSqVAUxeUPcDyWlpwQQgif5TXknK9xEUIIIW420pITQgjhs6465K6mlSctQyGEENeqOlkiLTkhhBA+S0JOCCGEz5KQE0II4bOuS8jJuTghhBA1rSrZcs0hJwEmhBDiRiXdlUIIIXxWjYactOqEEELUNecsUnmaWBsbEkIIIWqDt6ypte5K941K4AkhhKgpVc2Yawo550IreiyEEEJcD55yqNohdy0B5nwbBCGEEKK6qpsjtT66UoJNCCFEbahKvsglBEIIIXxWpSFXU60w57u1CiGEEFejullSrZbctQaUff3i4uJrKkcIIcT/Hnt2XPU5uaquWJ3lPC27a9euKq0vhBBC2HnKjsrOy2lqYsOKomC1Wss9dp7vXJmPPvoIk8lEXFwcISEhNVEFIYQQPury5cvs3r2bZcuWOXKkonBzv4ygRkLOuUBvYWeXn59PcnIyZrMZi8VS4Z/VanX8C5T7111F04UQQlxfFbWu3M+pKYqCSqVy/FvRn1qtdixX2facH1c75Ozh5f6vt2U8zQNQqWy9pVartcLH9uUrCjgJNiGEuLFV9Osk7q0y97BzflxRWe7luauxllxFwWbfqD28LBaLo+Luj+1PxD7d3pKzr+9crqdwk8ATQogbQ1W6Eh2/SvL/geYp4NwD0P7YvQz3bdj/LRdy3roZr4Wnrkz3CjsHnT3UnNerrLuysnlCCCFqn7eBIJ66KytrzXlqqVV1AORVteQ8tdaqGi7ulXVuxSmK4rHlVlF3pQSaEELcHDwFlHuAeQo358fV/QWtGh944v4E7C0xT+zh5umx83k4b+fjKpomhBDixlHZebSK/ioLO08h6c5jyFW1ZXY1XZsVBaBKpSoXbNfagpMAFEKI6+NqrrOuStBVJci8qVJL7mpDz/ncm53zyEp7iNlbcc7rVHVEpQSZEELc2LydS/MUdM7/dz8vV9Gy7mVWOPDEeaHqBIi3gPPUbek8zb0V5xx09mU9redOAk8IIW4MVRl84vzYU8BVFHyeAq6i7VX5nFxlIeZpOU9B6d6qc3+y3kZVuj92n1bdZqwQQoja5e18nPNjb2Hn/Liq5dnVyi+euAcd4HG6fTSl/bGnSwycg9BbK84+XwghRN2rrNFR1aADXC4G97acp1ad15Dz1kqraLnKujkrask5Xzfn6WJy+zLO/3or29tyQgghalZ1B55U1CKrqFVWWbdkRetcU0vOW7hV1JpznudeGfsAFE9hV9GTcOata1QIIcT1U50w8vR/Tz/l5WnwSWXlVRpylbXmKjrvVtXzdc7T7ANQoPKuyYrqal9XCCHE9VfdFp2naZW14CoKNE9l/h9xlV9hJIOBSAAAAABJRU5ErkJggg==)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yojnxVQiuj7P" + }, + "source": [ + "Neste trabalho vamos explorar e aplicar técnicas de Processamento de Linguagem Natural (PLN) para tratar textos (em Inglês) e prepará-los para servir de input em modelos de aprendizado de máquina (ML), com intuito de identificar e classificar tweets com conteúdo de bullying.\n", + "\n", + "As classes são tipos de bullying: *religion*, *age*, *ethnicity*, *gender*, *other_cyberbullying* e *not_cyberbullying*.\n", + "\n", + "Os algoritmos de ML que iremos explorar são: Naive Bayes, LSTM e BERT (Transformers).\n", + "\n", + "A base de dados fornecida contém os textos dos tweets e a classe a eles atribuída." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nhe4n_QXTSPc" + }, + "source": [ + "## Contexto\n", + "\n", + "Você é um cientista de dados que trabalha para uma rede social muito famosa. Você faz parte do departamento de Processamento de Linguagem Natural (PLN) que trata textos em inglês e os prepara para servir de input em modelos de aprendizado de máquina (ML).\n", + "\n", + "Certo dia, um bilionário resolveu comprar a rede social que você trabalha. Mas não contente, Melon Rusk, o novo dono, resolveu dar uma de thanos e demitir metade dos funcionários do seu departamento. Com isso, você e seu colega tiveram que herdar uma tarefa inacabada.\n", + "\n", + "Esses seus colegas, que foram demitidos, estavam trabalhando em um novo sistema de detecção e classificação de mensagens com conteúdo de bullying, onde cada classe representa um tipo de bullying: *religion*, *age*, *ethnicity*, *gender*, *other_cyberbullying* e *not_cyberbullying*.\n", + "\n", + "Porém, seus colegas não prestavam atenção nas aulas e não usaram o git corretamente. O notebook que eles estavam desenvolvendo, que continha os códigos mais atualizados, foram perdidos, restando apenas os resultados e as métricas obtidos.\n", + "\n", + "Para que vocês não sejam demitidos também, Melon Rusk exigiu que vocês continuassem o trabalho de detecção de bullying e espera receber um notebook que funcione, em duas semanas. E mais ainda, ele passou o desafio adicional de fazer melhor do que o seu colega que fora demitido.\n", + "\n", + "Uma boa notícia é que diversos comentários ficaram pelo meio caminho, indicando algumas possíveis maneiras de melhorar os resultados." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PbtMEqsTYocj" + }, + "source": [ + "## Avaliação\n", + "\n", + "Utilize os conhecimentos obtidos nas aulas do Moacir para fazer um trabalho fenomenal e obter modelos que atinjam as melhores métricas possíveis. As métricas que você obter, serão comparadas com duas outras: uma feita pelo estagiário (valor base), e outra feita pelos seus colegas demitidos (valor referência).\n", + "\n", + "A nota final do trabalho, será dada segundo a fórmula:\n", + "\n", + "> Nota_final = (N_naive + N_lstm + N_bert) / 3\n", + ">\n", + "> Nota_final *= 10 # pra ficar no padrão USP de 0 a 10\n", + "\n", + "e cada N_i, onde i = {naive, lstm, bert}, é dada segundo a função:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "B2nLYycaJJjB" + }, + "outputs": [], + "source": [ + "def nota(valor_base, valor_referencia, valor_aluno):\n", + " return np.minimum(1, np.maximum(0, 1 - (valor_referencia - valor_aluno) / (valor_referencia - valor_base)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UblCr775Jx8C" + }, + "source": [ + "Ou seja, estamos avaliando o quanto você consegue fazer cada um dos modelos desempenhar melhor do que o modelo base.\n", + "\n", + "Se para um dado modelo, a sua acurácia ficar menor do que a acurácia base, então sua nota para aquele modelo será 0. E se para um dado modelo, sua acurácia ficar maior do que a acurácia de referência, então sua nota para o modelo será 10.\n", + "\n", + "Nos casos intermediários, sua nota é a porcentagem de melhoria para cada modelo.\n", + "\n", + "A nota final do trabalho é composta pela média aritmética dos três modelos.\n", + "\n", + "No final do notebook, sua nota será calculada automaticamente, mas vale ressaltar que para se tornar oficial, o seu trabalho será corrigido manualmente pelo Professor, PAEs e monitores da disciplina, e a nota final oficial será publicada no moodle." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_A5DTdOuurYu" + }, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 482 + }, + "id": "5YuNfpgsuxqD", + "outputId": "81a8c0be-943d-48f8-c1b2-f20383f79306" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: emoji in /usr/local/lib/python3.7/dist-packages (2.2.0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", + "[nltk_data] Package stopwords is already up-to-date!\n", + "[nltk_data] Downloading package punkt to /root/nltk_data...\n", + "[nltk_data] Package punkt is already up-to-date!\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.24.0)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n", + "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.13.2)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.0)\n", + "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.1)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n", + "Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.11.0)\n", + "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2022.6.2)\n", + "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.13.0)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n", + "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n", + "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n", + "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.10.0)\n", + "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n", + "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n", + "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Libraries for general purpose\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "#Text cleaning\n", + "import re, string\n", + "!pip install emoji\n", + "import emoji\n", + "import nltk\n", + "from nltk.stem import WordNetLemmatizer, PorterStemmer\n", + "from nltk.corpus import stopwords\n", + "nltk.download('stopwords')\n", + "nltk.download('punkt')\n", + "stop_words = set(stopwords.words('english'))\n", + "\n", + "#Data preprocessing\n", + "from sklearn import preprocessing\n", + "from sklearn.model_selection import train_test_split\n", + "from imblearn.over_sampling import RandomOverSampler\n", + "\n", + "#Naive Bayes\n", + "from sklearn.feature_extraction.text import CountVectorizer\n", + "from sklearn.feature_extraction.text import TfidfTransformer\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "\n", + "#PyTorch for NN\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n", + "\n", + "#Tokenization\n", + "from collections import Counter\n", + "from gensim.models import Word2Vec\n", + "\n", + "#Transformers library for BERT\n", + "!pip install transformers\n", + "import transformers\n", + "from transformers import BertModel\n", + "from transformers import BertTokenizer\n", + "from transformers import AdamW, get_linear_schedule_with_warmup\n", + "\n", + "# Performance\n", + "from sklearn.metrics import classification_report, confusion_matrix\n", + "\n", + "#Seed for reproducibility\n", + "import random\n", + "\n", + "seed_value=42\n", + "random.seed(seed_value)\n", + "np.random.seed(seed_value)\n", + "torch.manual_seed(seed_value)\n", + "torch.cuda.manual_seed_all(seed_value)\n", + "\n", + "import time\n", + "\n", + "#set style for plots\n", + "sns.set_style(\"whitegrid\")\n", + "sns.despine()\n", + "plt.style.use(\"seaborn-whitegrid\")\n", + "plt.rc(\"figure\", autolayout=True)\n", + "plt.rc(\"axes\", labelweight=\"bold\", labelsize=\"large\", titleweight=\"bold\", titlepad=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "At6U3gpyu400" + }, + "source": [ + "## Preparação dos Dados\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "j7JwIQJOu58j" + }, + "outputs": [], + "source": [ + "# download and unzip the file from a Google Drive repository\n", + "!gdown --id 1ue-_eeS2imcSeLiEofh0rzBo3fnyuZ7r\n", + "!unzip cyberbullying_tweets.csv.zip" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "r9Ov12TdxZzh" + }, + "outputs": [], + "source": [ + "# Import into a PandasDataframe \n", + "df = pd.read_csv('/content/cyberbullying_tweets.csv')\n", + "print(df.shape)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "m4WIUS36xdcy" + }, + "outputs": [], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FAw2SvyExfhh" + }, + "outputs": [], + "source": [ + "# rename columns\n", + "df = df.rename(columns={'tweet_text': 'text', 'cyberbullying_type': 'sentiment'})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-ixrKAYq1RXf" + }, + "outputs": [], + "source": [ + "# check for duplicated rows and eliminate them\n", + "# ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Vg0lBB0m1e7c" + }, + "outputs": [], + "source": [ + "# Class distribution\n", + "print(df.sentiment.value_counts(normalize=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YXOiJtbg1-XG" + }, + "source": [ + "As classes estão balanceadas?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3vsEcYiO2Cvu" + }, + "source": [ + "## Tratamento dos dados textuais" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Cp4kBZadOsg5" + }, + "source": [ + "Dedique uma boa parte do tempo na limpeza do texto, algumas opções são: stemming, remover emojis, remover espaços duplos, etc..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9ReFcHYF1m4j" + }, + "outputs": [], + "source": [ + "#Clean emojis from text\n", + "def remove_emoji(text):\n", + " emoji_pattern = re.compile(\n", + " '['\n", + " u'\\U0001F600-\\U0001F64F' # emoticons\n", + " u'\\U0001F300-\\U0001F5FF' # symbols & pictographs\n", + " u'\\U0001F680-\\U0001F6FF' # transport & map symbols\n", + " u'\\U0001F1E0-\\U0001F1FF' # flags (iOS)\n", + " u'\\U00002702-\\U000027B0'\n", + " u'\\U000024C2-\\U0001F251'\n", + " ']+',\n", + " flags=re.UNICODE)\n", + " return emoji_pattern.sub(r'', text)\n", + "\n", + "#Remove punctuations, links, stopwords, mentions and \\r\\n new line characters\n", + "def strip_all_entities(text): \n", + " \n", + " return text\n", + "\n", + "#remove contractions\n", + "def decontract(text):\n", + " \n", + " return text\n", + "\n", + "# ... seu código aqui\n", + "\n", + "#Then we apply all the defined functions in the following order\n", + "def deep_clean(text):\n", + " text = remove_emoji(text)\n", + " text = decontract(text)\n", + " text = strip_all_entities(text)\n", + " # ... seu código aqui\n", + " return text" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jR_A1PRC2dbM" + }, + "outputs": [], + "source": [ + "texts_new = []\n", + "for t in df.text:\n", + " texts_new.append(deep_clean(t))\n", + "df['text_clean'] = texts_new" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ggAxl-8E2g46" + }, + "outputs": [], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kA8bLXNr2iqP" + }, + "outputs": [], + "source": [ + "# check for duplicated rows and eliminate them\n", + "# ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nl0rGP_b2xSY" + }, + "outputs": [], + "source": [ + "# Class distribution\n", + "df.sentiment.value_counts(normalize=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zO6ZpHpz3Byu" + }, + "source": [ + "Note que a classe 'other_cyberbullying' acabou com menos elementos do que as demais após os tratamentos até aqui aplicados. Vamos optar por excluir os registros da base de dados com esta classificação." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DT7d32gO3aBW" + }, + "outputs": [], + "source": [ + "#df = # ... seu código aqui\n", + "print(df.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "A246DEEF3dGI" + }, + "outputs": [], + "source": [ + "# class labels\n", + "sentiments = [\"religion\", \"age\", \"ethnicity\", \"gender\", \"not bullying\", \"other cyberbullying\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1V37qzUH3le_" + }, + "source": [ + "## Análise Exploratória dos dados e Engenharia de Variáveis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YSB6TlgM3ujm" + }, + "source": [ + "Vamos definir uma nova variável que contém o comprimento dos tweets em termos de número de palavras." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yDDGbHpb3iav" + }, + "outputs": [], + "source": [ + "text_len = []\n", + "for text in df.text_clean:\n", + " tweet_len = len(text.split())\n", + " text_len.append(tweet_len)\n", + "df['text_len'] = text_len" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wqfKeOtr4QzY" + }, + "outputs": [], + "source": [ + "# Short tweets\n", + "plt.figure(figsize=(7,5))\n", + "ax = sns.countplot(x='text_len', data=df[df['text_len']<10], palette='mako')\n", + "plt.title('Count of tweets with less than 10 words', fontsize=20)\n", + "plt.ylabel('count')\n", + "plt.xlabel('')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nJgpzlPQ4VqQ" + }, + "source": [ + "Vamos remover tweets muito curtos (menos de 4 palavras)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2timBstp4Tko" + }, + "outputs": [], + "source": [ + "#remover tweets curtos\n", + "#df = # ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "m4PgMrqG4ufb" + }, + "outputs": [], + "source": [ + "# Long tweets\n", + "plt.figure(figsize=(16,5))\n", + "ax = sns.countplot(x='text_len', data=df[(df['text_len']<=1000) & (df['text_len']>10)], palette='Blues_r')\n", + "plt.title('Count of tweets with high number of words', fontsize=25)\n", + "plt.ylabel('count')\n", + "plt.xlabel('')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jdW7lQPZ40Mm" + }, + "source": [ + "Também vamos remover tweets muito longos (mais do que 100 palavras)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "22wQ6Pqp4wrJ" + }, + "outputs": [], + "source": [ + "#Remover tweets longos\n", + "#df = # ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RQRvxX6t45_6" + }, + "outputs": [], + "source": [ + "# Extract max length\n", + "max_len = np.max(df['text_len'])\n", + "print(max_len)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kIeUS7LUTu-h" + }, + "source": [ + "É necessário realizar um encoding para a variável target." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "z43d01UR5BhC" + }, + "outputs": [], + "source": [ + "# Encoding the target column: sentiment\n", + "df['sentiment'] = df['sentiment'].replace({'religion':0,'age':1,'ethnicity':2,'gender':3, 'other_cyberbullying':4, 'not_cyberbullying':5}) # está correto a qtd de classes?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8RNaXVSOT15h" + }, + "source": [ + "Separação do conjunto de dados em subconjuntos de treino, validação e teste." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UVnw9Fs-5w7b" + }, + "outputs": [], + "source": [ + "# Splitting data into train and test subsets and then trian into train (again) and validation\n", + "X = df['text_clean']\n", + "y = df['sentiment']\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=seed_value)\n", + "\n", + "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.1, stratify=y_train, random_state=seed_value)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FT77sGX-6Db5" + }, + "outputs": [], + "source": [ + "# target distribution\n", + "(unique, counts) = np.unique(y_train, return_counts=True)\n", + "np.asarray((unique, counts)).T" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8ge4Xq0h6IN9" + }, + "source": [ + "As classes não estão muito balanceadas, então vamos aplicar uma técnica de ______ para equilibrá-las." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "LT53FEhT6Ese" + }, + "outputs": [], + "source": [ + "tecnica = # ... seu código aqui\n", + "X_train, y_train = tecnica.fit_resample(np.array(X_train).reshape(-1, 1), np.array(y_train).reshape(-1, 1))\n", + "train_os = pd.DataFrame(list(zip([x[0] for x in X_train], y_train)), columns = ['text_clean', 'sentiment'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "e53qsk7F614A" + }, + "outputs": [], + "source": [ + "X_train = train_os['text_clean'].values\n", + "y_train = train_os['sentiment'].values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2-auNbAI63qN" + }, + "outputs": [], + "source": [ + "# checking if classes are well balanced\n", + "(unique, counts) = np.unique(y_train, return_counts=True)\n", + "np.asarray((unique, counts)).T" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1QPzqFlj7Bf-" + }, + "source": [ + "## Modeling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-_8UGi-g7-P7" + }, + "outputs": [], + "source": [ + "# function to print and plot confusion matrix\n", + "def conf_matrix(y, y_pred, title, labels):\n", + " fig, ax =plt.subplots(figsize=(7.5,7.5))\n", + " ax=sns.heatmap(confusion_matrix(y, y_pred), annot=True, cmap=\"Purples\", fmt='g', cbar=False, annot_kws={\"size\":30})\n", + " plt.title(title, fontsize=25)\n", + " ax.xaxis.set_ticklabels(labels, fontsize=16) \n", + " ax.yaxis.set_ticklabels(labels, fontsize=14.5)\n", + " ax.set_ylabel('Real', fontsize=25)\n", + " ax.set_xlabel('Predicted', fontsize=25)\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SHEcFqLz7Ds1" + }, + "source": [ + "### Naive Bayes\n", + "Vamos implementar um modelo base utilizando o algoritmo Naive Bayes. Outros modelos também poderiam ser testados aqui, como Random Forest, Support Vector Machines etc." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DcYS7vDq65Gf" + }, + "outputs": [], + "source": [ + "# create a bag of words with CountVectorizer()\n", + "clf = # ... seu código aqui\n", + "X_train_cv = # ... seu código aqui\n", + "X_test_cv = # ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EjFa6M1U7jjV" + }, + "outputs": [], + "source": [ + "# apply TF-IFD transformation to associate weigths to the different words based on their frequency (rarer words will be given more importance).\n", + "tf_transformer = # ... seu código aqui\n", + "X_train_tf = # ... seu código aqui\n", + "X_test_tf = # ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "U0qCsGfN7zj1" + }, + "outputs": [], + "source": [ + "# instantiate Naive Bayes model\n", + "nb_clf = # ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hhL4y5rQ736s" + }, + "outputs": [], + "source": [ + "# train...\n", + "nb_clf.fit(X_train_cv, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Kb_VQ10Z76sS" + }, + "outputs": [], + "source": [ + "# and predict\n", + "nb_pred = nb_clf.predict(X_test_cv)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "75qljJwP8Maa" + }, + "outputs": [], + "source": [ + "report_naivebayes = classification_report(y_test, nb_pred, target_names=sentiments, output_dict=True)\n", + "print('Classification Report for Naive Bayes:\\n', classification_report(y_test, nb_pred, target_names=sentiments))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IEpLqXwM8Qg-" + }, + "outputs": [], + "source": [ + "conf_matrix(y_test,nb_pred,'Naive Bayes Sentiment Analysis\\nConfusion Matrix', sentiments)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0mP-N3re8WB4" + }, + "source": [ + "O modelo criado com o algoritmo Naive Bayes teve xxx desempenho, com acurácia geral de xxxxxx. Apesar do xxxx aplicado, a classe que originalmente apresentava menor representação nos dados foi a que teve xxxxx desempenho." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7genSngM88jI" + }, + "source": [ + "### Bidirectional LSTM\n", + "Da mesma forma que fizemos com Naive Bayes, precisamos pré-processar os dados. As frases serão convertidas em vetores numéricos com preenchimento (*padding*) para o número máximo de palavras em uma frase." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RKwWSYy38TCQ" + }, + "outputs": [], + "source": [ + "def Tokenize(column, seq_len):\n", + " ##Create vocabulary of words from text column\n", + " corpus = [word for text in column for word in text.split()]\n", + " count_words = Counter(corpus)\n", + " sorted_words = count_words.most_common()\n", + " vocab_to_int = {w:i+1 for i, (w,c) in enumerate(sorted_words)}\n", + "\n", + " ##Tokenize the columns text using the vocabulary\n", + " text_int = []\n", + " for text in column:\n", + " r = [vocab_to_int[word] for word in text.split()]\n", + " text_int.append(r)\n", + " \n", + " ##Add padding to tokens\n", + " features = np.zeros((len(text_int), seq_len), dtype = int)\n", + " for i, review in enumerate(text_int):\n", + " if len(review) <= seq_len:\n", + " zeros = list(np.zeros(seq_len - len(review)))\n", + " new = zeros + review\n", + " else:\n", + " new = review[: seq_len]\n", + " features[i, :] = np.array(new)\n", + "\n", + " return sorted_words, features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-wRTfxDj9rny" + }, + "outputs": [], + "source": [ + "vocabulary, tokenized_column = Tokenize(df['text_clean'], max_len)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "m0nuGhdf9vUz" + }, + "outputs": [], + "source": [ + "# example of the vectorized text\n", + "print('Original sentence:\\n')\n", + "print(df['text_clean'].iloc[100])\n", + "print('\\n Vectorized text:\\n')\n", + "tokenized_column[100]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "B94ygxUA91wc" + }, + "outputs": [], + "source": [ + "# check the TOP 20 most common words\n", + "keys = []\n", + "values = []\n", + "for key, value in vocabulary[:20]:\n", + " keys.append(key)\n", + " values.append(value)\n", + "\n", + "plt.figure(figsize=(15, 5))\n", + "ax = sns.barplot(x=keys, y=values, palette='mako')\n", + "plt.title('Top 20 most common words', size=25)\n", + "plt.ylabel(\"Words count\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iO1PBtpL-l6u" + }, + "source": [ + "#### Word Embedding by Word2Vec\n", + "Em seguida vamos utilizar um modelo pré treinado para criar uma matriz de representação numérica dos tweets. Anteriormente utilizamos a técnica Tf-idf. Agora utilizaremos a técnica Word2Vec." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "srA0ejmn-hus" + }, + "outputs": [], + "source": [ + "# text preparation\n", + "word2vec_train_data = list(map(lambda x: x.split(), X_train))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uKIWk1q3_lqR" + }, + "outputs": [], + "source": [ + "# dimension of the vectors created by the method\n", + "EMBEDDING_DIM = # ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5_Yrk0gc_r7O" + }, + "outputs": [], + "source": [ + "word2vec_model = # ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sxXmPN6k_xir" + }, + "outputs": [], + "source": [ + "print(f\"Vocabulary size: {len(vocabulary) + 1}\")\n", + "VOCAB_SIZE = len(vocabulary) + 1 #+1 for the padding" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7AAjxPK_ADeA" + }, + "outputs": [], + "source": [ + "#define empty embedding matrix\n", + "embedding_matrix = # ... seu código aqui\n", + " \n", + "#fill the embedding matrix with the pre trained values from word2vec\n", + "for word, token in vocabulary:\n", + " if word2vec_model.wv.__contains__(word):\n", + " embedding_matrix[token] = word2vec_model.wv.__getitem__(word)\n", + "\n", + "print(\"Embedding Matrix Shape:\", embedding_matrix.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SRrZvUwVAEWu" + }, + "outputs": [], + "source": [ + "# use the tokenized sentences to create a training, validation and test datasets\n", + "X = tokenized_column\n", + "y = df['sentiment'].values\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=seed_value)\n", + "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.1, stratify=y_train, random_state=seed_value)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UreBs19SAUqc" + }, + "outputs": [], + "source": [ + "# check the balance of the target classes\n", + "(unique, counts) = np.unique(y_train, return_counts=True)\n", + "np.asarray((unique, counts)).T" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pRcWMZnwAcoX" + }, + "source": [ + "Dados desbalanceados. Novamente aplicaremos xxxxx." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GFNh01SHAZeE" + }, + "outputs": [], + "source": [ + "# ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "6zI_TaadAkJZ" + }, + "outputs": [], + "source": [ + "# transform to tensor and prepare dataloaders to extract the data in batches for the LSTM training, validation and testing.\n", + "train_data = TensorDataset(torch.from_numpy(X_train), torch.from_numpy(y_train))\n", + "test_data = TensorDataset(torch.from_numpy(X_test), torch.from_numpy(y_test))\n", + "valid_data = TensorDataset(torch.from_numpy(X_valid), torch.from_numpy(y_valid))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9JHTOMmtA0Fh" + }, + "outputs": [], + "source": [ + "BATCH_SIZE = # ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wBg_Ao3xA1I2" + }, + "outputs": [], + "source": [ + "train_loader = DataLoader(train_data, shuffle=False, batch_size=BATCH_SIZE, drop_last=True) \n", + "valid_loader = DataLoader(valid_data, shuffle=False, batch_size=BATCH_SIZE, drop_last=True)\n", + "test_loader = DataLoader(test_data, shuffle=False, batch_size=BATCH_SIZE, drop_last=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZDPpd4iwBHPf" + }, + "source": [ + "#### Modeling" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "obkihUkMA437" + }, + "outputs": [], + "source": [ + "# Hyperparameters\n", + "\n", + "# number of classes\n", + "NUM_CLASSES = # ... seu código aqui\n", + "\n", + "# number of neurons of the internal state (internal neural network in the LSTM)\n", + "HIDDEN_DIM = # ... seu código aqui\n", + "\n", + "#Number of stacked LSTM layers\n", + "LSTM_LAYERS = # ... seu código aqui\n", + "\n", + "# Learning rate\n", + "LR = # ... seu código aqui\n", + "\n", + "#LSTM Dropout\n", + "DROPOUT = # ... seu código aqui\n", + "\n", + "#Boolean value to choose if to use a bidirectional LSTM or not\n", + "BIDIRECTIONAL = # ... seu código aqui\n", + "\n", + "#Number of training epoch\n", + "EPOCHS = # ... seu código aqui\n", + "\n", + "# set GPU if available\n", + "DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "print(DEVICE)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IkB_8Ja0Bt0V" + }, + "outputs": [], + "source": [ + "class BiLSTM_Sentiment_Classifier(nn.Module):\n", + "\n", + " def __init__(self, vocab_size, embedding_dim, hidden_dim, num_classes, lstm_layers, bidirectional,batch_size, dropout):\n", + " super(BiLSTM_Sentiment_Classifier,self).__init__()\n", + " \n", + " # ... seu código aqui\n", + " \n", + " def forward(self, x, hidden):\n", + " self.batch_size = x.size(0)\n", + " ##EMBEDDING LAYER\n", + " # ... seu código aqui\n", + " #LSTM LAYERS\n", + " # ... seu código aqui\n", + " #Extract only the hidden state from the last LSTM cell\n", + " out = out[:,-1,:]\n", + " #FULLY CONNECTED LAYERS\n", + " out = self.fc(out)\n", + " out = self.softmax(out)\n", + "\n", + " return out, hidden\n", + "\n", + " def init_hidden(self, batch_size):\n", + " #Initialization of the LSTM hidden and cell states\n", + " h0 = torch.zeros((self.lstm_layers*self.num_directions, batch_size, self.hidden_dim)).detach().to(DEVICE)\n", + " c0 = torch.zeros((self.lstm_layers*self.num_directions, batch_size, self.hidden_dim)).detach().to(DEVICE)\n", + " hidden = (h0, c0)\n", + " return hidden" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZpIvQs2OCVVh" + }, + "outputs": [], + "source": [ + "model = BiLSTM_Sentiment_Classifier(VOCAB_SIZE, EMBEDDING_DIM, HIDDEN_DIM, NUM_CLASSES, LSTM_LAYERS, BIDIRECTIONAL, BATCH_SIZE, DROPOUT)\n", + "model = model.to(DEVICE)\n", + "\n", + "#Initialize embedding with the previously defined embedding matrix\n", + "model.embedding.weight.data.copy_(torch.from_numpy(embedding_matrix))\n", + "\n", + "#Allow the embedding matrix to be fined tuned to better adapt to out dataset and get higher accuracy\n", + "model.embedding.weight.requires_grad=True\n", + "\n", + "print(model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "blymMR5LCknG" + }, + "outputs": [], + "source": [ + "criterion = nn.NLLLoss()\n", + "optimizer = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay = 5e-6)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bn3_pwSDDMz2" + }, + "source": [ + "Agora vamos definir um loop de treinamento, onde incluímos uma funcionalidade *early stopping* e salvamos apenas os melhores modelos em termos de precisão de validação." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GJLAkx3-Cq56" + }, + "outputs": [], + "source": [ + "total_step = len(train_loader)\n", + "total_step_val = len(valid_loader)\n", + "\n", + "early_stopping_patience = 4\n", + "early_stopping_counter = 0\n", + "\n", + "valid_acc_max = 0 # Initialize best accuracy top 0\n", + "\n", + "for e in range(EPOCHS):\n", + "\n", + " #lists to host the train and validation losses of every batch for each epoch\n", + " train_loss, valid_loss = [], []\n", + "\n", + " #lists to host the train and validation accuracy of every batch for each epoch\n", + " train_acc, valid_acc = [], []\n", + "\n", + " #lists to host the train and validation predictions of every batch for each epoch\n", + " y_train_list, y_val_list = [], []\n", + "\n", + " #initalize number of total and correctly classified texts during training and validation\n", + " correct, correct_val = 0, 0\n", + " total, total_val = 0, 0\n", + " running_loss, running_loss_val = 0, 0\n", + "\n", + " ###################################################################\n", + " ############### TRAINING LOOP ######################\n", + " ###################################################################\n", + "\n", + " model.train()\n", + "\n", + " for inputs, labels in train_loader:\n", + " inputs, labels = inputs.to(DEVICE), labels.to(DEVICE) #load features and targets in device\n", + "\n", + " h = model.init_hidden(labels.size(0))\n", + "\n", + " model.zero_grad() #reset gradients \n", + "\n", + " output, h = model(inputs,h) #get output and hidden states from LSTM network\n", + " \n", + " loss = criterion(output, labels)\n", + " loss.backward()\n", + " \n", + " running_loss += loss.item()\n", + " \n", + " optimizer.step()\n", + "\n", + " #get tensor of predicted values on the training set\n", + " y_pred_train = torch.argmax(output, dim=1)\n", + "\n", + " #transform tensor to list and the values to the list\n", + " y_train_list.extend(y_pred_train.squeeze().tolist()) \n", + " \n", + " #count correctly classified texts per batch\n", + " correct += torch.sum(y_pred_train==labels).item()\n", + " \n", + " #count total texts per batch\n", + " total += labels.size(0) \n", + "\n", + " train_loss.append(running_loss / total_step)\n", + " train_acc.append(100 * correct / total)\n", + "\n", + "\n", + " ###################################################################\n", + " ############## VALIDATION LOOP #####################\n", + " ###################################################################\n", + " \n", + " with torch.no_grad():\n", + " \n", + " model.eval()\n", + " \n", + " for inputs, labels in valid_loader:\n", + " inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)\n", + "\n", + " val_h = model.init_hidden(labels.size(0))\n", + "\n", + " output, val_h = model(inputs, val_h)\n", + "\n", + " val_loss = criterion(output, labels)\n", + " running_loss_val += val_loss.item()\n", + "\n", + " y_pred_val = torch.argmax(output, dim=1)\n", + " y_val_list.extend(y_pred_val.squeeze().tolist())\n", + "\n", + " correct_val += torch.sum(y_pred_val==labels).item()\n", + " total_val += labels.size(0)\n", + "\n", + " valid_loss.append(running_loss_val / total_step_val)\n", + " valid_acc.append(100 * correct_val / total_val)\n", + "\n", + " #Save model if validation accuracy increases\n", + " if np.mean(valid_acc) >= valid_acc_max:\n", + " torch.save(model.state_dict(), './state_dict.pt')\n", + " print(f'Epoch {e+1}:Validation accuracy increased ({valid_acc_max:.6f} --> {np.mean(valid_acc):.6f}). Saving model ...')\n", + " valid_acc_max = np.mean(valid_acc)\n", + " early_stopping_counter=0 #reset counter if validation accuracy increases\n", + " else:\n", + " print(f'Epoch {e+1}:Validation accuracy did not increase')\n", + " early_stopping_counter+=1 #increase counter if validation accuracy does not increase\n", + " \n", + " if early_stopping_counter > early_stopping_patience:\n", + " print('Early stopped at epoch :', e+1)\n", + " break\n", + " \n", + " print(f'\\tTrain_loss : {np.mean(train_loss):.4f} Val_loss : {np.mean(valid_loss):.4f}')\n", + " print(f'\\tTrain_acc : {np.mean(train_acc):.3f}% Val_acc : {np.mean(valid_acc):.3f}%')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-dgHWFnCEGzQ" + }, + "outputs": [], + "source": [ + "# Loading the best model\n", + "model.load_state_dict(torch.load('./state_dict.pt'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QF53Y_0YHOHI" + }, + "outputs": [], + "source": [ + "# LSTM Testing\n", + "\n", + "model.eval()\n", + "y_pred_list = []\n", + "y_test_list = []\n", + "for inputs, labels in test_loader:\n", + " inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)\n", + " test_h = model.init_hidden(labels.size(0))\n", + "\n", + " output, val_h = model(inputs, test_h)\n", + " y_pred_test = torch.argmax(output, dim=1)\n", + " y_pred_list.extend(y_pred_test.squeeze().tolist())\n", + " y_test_list.extend(labels.squeeze().tolist())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i83pVIXcHSkO" + }, + "outputs": [], + "source": [ + "report_bilstm = classification_report(y_test_list, y_pred_list, target_names=sentiments, output_dict=True)\n", + "print('Classification Report for Bi-LSTM :\\n', classification_report(y_test_list, y_pred_list, target_names=sentiments))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "bnJOq7E6HSO_" + }, + "outputs": [], + "source": [ + "conf_matrix(y_test_list,y_pred_list,'PyTorch Bi-LSTM Sentiment Analysis\\nConfusion Matrix', sentiments)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v2Z2IIgjHeZE" + }, + "source": [ + "O modelo LSTM apresentou melhora significativa na performance. A acurácia geral xxxx para xx% e o numero geral de acertos também aumentou, especialmente na classe xxxxx." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L_7QkjQwK1wV" + }, + "source": [ + "### BERT" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ESsvHXLVX8SI" + }, + "source": [ + "#### Train - validation - test split" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Tg-9iOd1X50Q" + }, + "outputs": [], + "source": [ + "X = df['text_clean'].values\n", + "y = df['sentiment'].values\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=seed_value)\n", + "\n", + "X_train, X_valid, y_train, y_valid = train_test_split(X_train, y_train, test_size=0.1, stratify=y_train, random_state=seed_value)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "c-O4D6S9YEJQ" + }, + "outputs": [], + "source": [ + "(unique, counts) = np.unique(y_train, return_counts=True)\n", + "np.asarray((unique, counts)).T" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rjyA4f1yYPdY" + }, + "source": [ + "Vamos aplicar o xx para balancear as classes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dx4WIJToYLzq" + }, + "outputs": [], + "source": [ + "# ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PPUdhbT0YYcY" + }, + "outputs": [], + "source": [ + "# ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xu8v68AVYZQM" + }, + "outputs": [], + "source": [ + "(unique, counts) = np.unique(y_train, return_counts=True)\n", + "np.asarray((unique, counts)).T" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MrVczwQaYcD4" + }, + "source": [ + "#### BERT Tokenization\n", + "\n", + "A implementação do BERT da biblioteca Transformers possui seu próprio método de tokenização, que nos ajuda a extrair \"*input ids*\" e a \"*attention mask*\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "3odRmMQ9Ya4d" + }, + "outputs": [], + "source": [ + "tokenizer = # ... seu código aqui" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xY2H2QGAZ1fk" + }, + "source": [ + "Agora definimos a função para tokenização utilizando o tokenizador carregado acima." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Cy3irUTcZtjd" + }, + "outputs": [], + "source": [ + "def bert_tokenizer(data, MAX_LEN):\n", + "\n", + " input_ids = []\n", + " attention_masks = []\n", + " \n", + " for sentiment in data:\n", + " encoded_sent = tokenizer.encode_plus(\n", + " text=sentiment,\n", + " add_special_tokens = True, # Add `[CLS]` and `[SEP]` special tokens\n", + " max_length = max_len, # Choose max length to truncate/pad\n", + " pad_to_max_length = True, # Pad sentence to max length \n", + " return_attention_mask = True # Return attention mask\n", + " )\n", + " \n", + " input_ids.append(encoded_sent.get('input_ids'))\n", + " attention_masks.append(encoded_sent.get('attention_mask'))\n", + "\n", + " # Convert lists to tensors\n", + " input_ids = torch.tensor(input_ids)\n", + " attention_masks = torch.tensor(attention_masks)\n", + "\n", + " return input_ids, attention_masks" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xpry576gayqP" + }, + "source": [ + "Como precisamos especificar o comprimento da sentença tokenizada mais longa, tokenizamos os tweets do conjunto de treino usando o método \"encode\" do tokenizer BERT original e verificamos a sentença mais longa." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Z1thD7J8aXo_" + }, + "outputs": [], + "source": [ + "# Tokenize train tweets\n", + "encoded_tweets = [tokenizer.encode(sentiment, add_special_tokens=True) for sentiment in X_train]\n", + "\n", + "# Find the longest tokenized tweet\n", + "max_len = max([len(sent) for sent in encoded_tweets])\n", + "print('Max length: ', max_len)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x7W3Oy1eb_t_" + }, + "source": [ + "Vamos \"arredondar\" o max length..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7g51Oq_ia3MN" + }, + "outputs": [], + "source": [ + "MAX_LEN = # ... seu código aqui" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nNtuwUQCsWLt" + }, + "source": [ + "Agora aplicamos o tokenizador definido" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9uONWwmPcJWp" + }, + "outputs": [], + "source": [ + "train_inputs, train_masks = bert_tokenizer(# ... seu código aqui\n", + "val_inputs, val_masks = bert_tokenizer(# ... seu código aqui\n", + "test_inputs, test_masks = bert_tokenizer(# ... seu código aqui" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eBoCWROXspLQ" + }, + "source": [ + "Então criaremos os dataloaders a partir dos arrays convertidos em tensores " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cNR6Y77_sdwG" + }, + "outputs": [], + "source": [ + "# Convert target columns to pytorch tensors format\n", + "train_labels = torch.from_numpy(y_train)\n", + "val_labels = torch.from_numpy(y_valid)\n", + "test_labels = torch.from_numpy(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ZcSBz_Cxs23b" + }, + "outputs": [], + "source": [ + "batch_size = # ... seu código aqui\n", + "\n", + "# Create the DataLoader for our training set\n", + "train_data = TensorDataset(train_inputs, train_masks, train_labels)\n", + "train_sampler = RandomSampler(train_data)\n", + "train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n", + "\n", + "# Create the DataLoader for our validation set\n", + "val_data = TensorDataset(val_inputs, val_masks, val_labels)\n", + "val_sampler = SequentialSampler(val_data)\n", + "val_dataloader = DataLoader(val_data, sampler=val_sampler, batch_size=batch_size)\n", + "\n", + "# Create the DataLoader for our test set\n", + "test_data = TensorDataset(test_inputs, test_masks, test_labels)\n", + "test_sampler = SequentialSampler(test_data)\n", + "test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uTRHV6cFKxq2" + }, + "source": [ + "Agora podemos criar um classificador BERT personalizado, incluindo o modelo original e camadas densas adicionais para executar a tarefa de classificação." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YIYtHBlZUi3U" + }, + "outputs": [], + "source": [ + "class Bert_Classifier(nn.Module):\n", + "\n", + " def __init__(self, n_input, n_hidden, n_output, freeze_bert=False):\n", + " super(Bert_Classifier, self).__init__()\n", + " # Specify hidden size of BERT, hidden size of the classifier, and number of labels\n", + " n_input = n_input\n", + " n_hidden = n_hidden\n", + " n_output = n_output\n", + "\n", + " # Instantiate BERT model\n", + " self.bert = BertModel.from_pretrained('bert-base-uncased')\n", + "\n", + " # Add dense layers to perform the classification\n", + " self.classifier = nn.Sequential(\n", + " nn.Linear(n_input, n_hidden),\n", + " nn.ReLU(),\n", + " nn.Linear(n_hidden, n_output)\n", + " )\n", + "\n", + " # Add possibility to freeze the BERT model\n", + " if freeze_bert:\n", + " for param in self.bert.parameters():\n", + " param.requires_grad = False\n", + " \n", + " def forward(self, input_ids, attention_mask):\n", + " # Feed input data to BERT\n", + " outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n", + " \n", + " # Extract the last hidden state of the token `[CLS]` for classification task\n", + " last_hidden_state_cls = outputs[0][:, 0, :]\n", + "\n", + " # Feed input to classifier to compute logits\n", + " logits = self.classifier(last_hidden_state_cls)\n", + "\n", + " return logits" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FP4NGZkxVvBw" + }, + "source": [ + "Além do classificador também vamos definir uma função para inicializar alguns parâmetros da rede." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Og5UQ7uIVuV4" + }, + "outputs": [], + "source": [ + "def initialize_model(epochs=4, lr=1e-3, eps=1e-6, n_input=768, n_hidden=50, n_output=5):\n", + "\n", + " # Instantiate Bert Classifier\n", + " bert_classifier = Bert_Classifier(freeze_bert=False, n_input=n_input, n_hidden=n_hidden, n_output=n_output)\n", + " \n", + " bert_classifier.to(device)\n", + "\n", + " # Set up optimizer\n", + " optimizer = AdamW(bert_classifier.parameters(),\n", + " lr=lr, # learning rate, set to default value\n", + " eps=eps # decay, set to default value\n", + " )\n", + " \n", + " # Calculate total number of training steps\n", + " total_steps = len(train_dataloader) * epochs\n", + "\n", + " # Set up learning rate scheduler\n", + " scheduler = get_linear_schedule_with_warmup(optimizer,\n", + " num_warmup_steps=0,\n", + " num_training_steps=total_steps)\n", + " return bert_classifier, optimizer, scheduler" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QrK9Y0CXtY9l" + }, + "source": [ + "Utilizamos a GPU se estiver disponível para acelerar o processamento" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Xd9Fu0oOtQ4D" + }, + "outputs": [], + "source": [ + "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", + "print(device)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "-BuEWbSlUrKQ" + }, + "outputs": [], + "source": [ + "# BERT Parameters\n", + "n_input = # ... seu código aqui\n", + "n_hidden = # ... seu código aqui\n", + "n_output = # ... seu código aqui\n", + "EPOCHS = # ... seu código aqui\n", + "lr = # ... seu código aqui\n", + "eps = # ... seu código aqui" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nsN6J_KEtynk" + }, + "outputs": [], + "source": [ + "# intialize the BERT model calling the \"initialize_model\"\n", + "bert_classifier, optimizer, scheduler = initialize_model(epochs=EPOCHS, lr=lr, eps=eps, n_input=n_input, n_hidden=n_hidden, n_output=n_output)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tUIv8QlQu1vE" + }, + "source": [ + "#### Training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hYjM_i8Jt4bj" + }, + "outputs": [], + "source": [ + "# Define Cross entropy Loss function for the multiclass classification task\n", + "loss_fn = nn.CrossEntropyLoss()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "x0yXRbqPu5KN" + }, + "outputs": [], + "source": [ + "def bert_train(model, train_dataloader, val_dataloader=None, epochs=4, evaluation=False):\n", + "\n", + " print(\"Start training...\\n\")\n", + " for epoch_i in range(epochs):\n", + " print(\"-\"*10)\n", + " print(\"Epoch : {}\".format(epoch_i+1))\n", + " print(\"-\"*10)\n", + " print(\"-\"*38)\n", + " print(f\"{'BATCH NO.':^7} | {'TRAIN LOSS':^12} | {'ELAPSED (s)':^9}\")\n", + " print(\"-\"*38)\n", + "\n", + " # Measure the elapsed time of each epoch\n", + " t0_epoch, t0_batch = time.time(), time.time()\n", + "\n", + " # Reset tracking variables at the beginning of each epoch\n", + " total_loss, batch_loss, batch_counts = 0, 0, 0\n", + " \n", + " ###TRAINING###\n", + "\n", + " # Put the model into the training mode\n", + " model.train()\n", + "\n", + " for step, batch in enumerate(train_dataloader):\n", + " batch_counts +=1\n", + " \n", + " b_input_ids, b_attn_mask, b_labels = tuple(t.to(device) for t in batch)\n", + "\n", + " # Zero out any previously calculated gradients\n", + " model.zero_grad()\n", + "\n", + " # Perform a forward pass and get logits.\n", + " logits = model(b_input_ids, b_attn_mask)\n", + "\n", + " # Compute loss and accumulate the loss values\n", + " loss = loss_fn(logits, b_labels)\n", + " batch_loss += loss.item()\n", + " total_loss += loss.item()\n", + "\n", + " # Perform a backward pass to calculate gradients\n", + " loss.backward()\n", + "\n", + " # Clip the norm of the gradients to 1.0 to prevent \"exploding gradients\"\n", + " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", + "\n", + " # update model parameters\n", + " optimizer.step()\n", + " # update learning rate\n", + " scheduler.step()\n", + "\n", + " # Print the loss values and time elapsed for every 100 batches\n", + " if (step % 100 == 0 and step != 0) or (step == len(train_dataloader) - 1):\n", + " # Calculate time elapsed for batches\n", + " time_elapsed = time.time() - t0_batch\n", + " \n", + " print(f\"{step:^9} | {batch_loss / batch_counts:^12.6f} | {time_elapsed:^9.2f}\")\n", + "\n", + " # Reset batch tracking variables\n", + " batch_loss, batch_counts = 0, 0\n", + " t0_batch = time.time()\n", + "\n", + " # Calculate the average loss over the entire training data\n", + " avg_train_loss = total_loss / len(train_dataloader)\n", + "\n", + " ###EVALUATION###\n", + " \n", + " # Put the model into the evaluation mode\n", + " model.eval()\n", + " \n", + " # Define empty lists to host accuracy and validation for each batch\n", + " val_accuracy = []\n", + " val_loss = []\n", + "\n", + " for batch in val_dataloader:\n", + " batch_input_ids, batch_attention_mask, batch_labels = tuple(t.to(device) for t in batch)\n", + " \n", + " # We do not want to update the params during the evaluation,\n", + " # So we specify that we dont want to compute the gradients of the tensors by calling the torch.no_grad() method\n", + " with torch.no_grad():\n", + " logits = model(batch_input_ids, batch_attention_mask)\n", + "\n", + " loss = loss_fn(logits, batch_labels)\n", + "\n", + " val_loss.append(loss.item())\n", + "\n", + " # Get the predictions starting from the logits (get index of highest logit)\n", + " preds = torch.argmax(logits, dim=1).flatten()\n", + "\n", + " # Calculate the validation accuracy \n", + " accuracy = (preds == batch_labels).cpu().numpy().mean() * 100\n", + " val_accuracy.append(accuracy)\n", + "\n", + " # Compute the average accuracy and loss over the validation set\n", + " val_loss = np.mean(val_loss)\n", + " val_accuracy = np.mean(val_accuracy)\n", + " \n", + " # Print performance over the entire training data\n", + " time_elapsed = time.time() - t0_epoch\n", + " print(\"-\"*61)\n", + " print(f\"{'AVG TRAIN LOSS':^12} | {'VAL LOSS':^10} | {'VAL ACCURACY (%)':^9} | {'ELAPSED (s)':^9}\")\n", + " print(\"-\"*61)\n", + " print(f\"{avg_train_loss:^14.6f} | {val_loss:^10.6f} | {val_accuracy:^17.2f} | {time_elapsed:^9.2f}\")\n", + " print(\"-\"*61)\n", + " print(\"\\n\")\n", + " \n", + " print(\"Training complete!\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oc7HbbS1voTi" + }, + "outputs": [], + "source": [ + "bert_train(bert_classifier, train_dataloader, val_dataloader, epochs=EPOCHS)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aazDgE0UnnMC" + }, + "source": [ + "Agora definimos uma função para realizar as previsões com o BERT treinado." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wbrJ-YHJvuLa" + }, + "outputs": [], + "source": [ + "def bert_predict(model, test_dataloader):\n", + " \n", + " # Define empty list to save the predictions\n", + " preds_list = []\n", + " \n", + " # Put the model into evaluation mode\n", + " model.eval()\n", + " \n", + " for batch in test_dataloader:\n", + " batch_input_ids, batch_attention_mask = tuple(t.to(device) for t in batch)[:2]\n", + " \n", + " # Avoid gradient calculation of tensors by using \"no_grad()\" method\n", + " with torch.no_grad():\n", + " logit = model(batch_input_ids, batch_attention_mask)\n", + " \n", + " # Get index of highest logit\n", + " pred = torch.argmax(logit,dim=1).cpu().numpy()\n", + " # Append predicted class to list\n", + " preds_list.extend(pred)\n", + "\n", + " return preds_list" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lAgtNZ31nyD4" + }, + "outputs": [], + "source": [ + "# BERT predictions\n", + "bert_preds = bert_predict(bert_classifier, test_dataloader)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dRKu-SSvEpq-" + }, + "outputs": [], + "source": [ + "report_bert_reference = classification_report(y_test, bert_preds, target_names=sentiments, output_dict=True)\n", + "print('Classification Report for BERT :\\n', classification_report(y_test, bert_preds, target_names=sentiments))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ueywIwTIoJmY" + }, + "outputs": [], + "source": [ + "conf_matrix(y_test, bert_preds,' BERT Sentiment Analysis\\nConfusion Matrix', sentiments)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0QunJFzBoXlb" + }, + "source": [ + "O modelo BERT conseguiu resultados ainda melhores do que a rede Bi-LSTM, com f1-score superior para todos as classes, maior número de acertos geral e também por classes (com excessão da classe 'not bullyng') e melhora de 1 ponto percentual na acurácia." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nNWbnVVBjaaP" + }, + "source": [ + "# Avaliação\n", + "\n", + "Não altere as células a seguir, apenas as execute" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "qyU9qVfaoMzm" + }, + "outputs": [], + "source": [ + "# Valores Base e Referência obtidos anteriormente\n", + "br = 0.95\n", + "lr = 0.93\n", + "nr = 0.86\n", + "\n", + "bb = 0.60\n", + "lb = 0.33\n", + "nb = 0.77" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GjsY-Ml-pnou" + }, + "outputs": [], + "source": [ + "# Valores obtidos pelo aluno\n", + "\n", + "ba = round(report_bert['accuracy'], 2)\n", + "la = round(report_bilstm['accuracy'], 2)\n", + "na = round(report_naivebayes['accuracy'], 2)\n", + "\n", + "ba, la, na" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "c1JRyfk9R3Ym" + }, + "outputs": [], + "source": [ + "# Exemplo de valores obtidos pelo aluno\n", + "#ba, la, na = .8, .8, .8" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 531 + }, + "id": "aq_AMe83nt5b", + "outputId": "4e11cd51-05ca-41f7-8641-116341f5e255" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10 , 7))\n", + "\n", + "# results_ = [base, referencia, aluno]\n", + "results_bert = [bb, br, ba]\n", + "results_lstm = [lb, lr, la]\n", + "results_naiv = [nb, nr, na]\n", + "\n", + "xcol = 1\n", + "\n", + "for rm in [results_naiv, results_lstm, results_bert]:\n", + " rb = rm[0]\n", + " rr = rm[1] \n", + " ra = rm[2]\n", + "\n", + " plt.bar(xcol + 0.5, ra)\n", + " plt.annotate(f'aluno = {ra}', xy=[xcol , ra + 0.01], fontsize=10)\n", + "\n", + " plt.annotate(f'base = {rb}', xy=[xcol + 0.9, rb + 0.01], fontsize=10)\n", + " plt.plot([xcol, xcol+1], [rb, rb], color='black')\n", + "\n", + " plt.annotate(f'referência = {rr}', xy=[xcol + 0.9, rr + 0.01], fontsize=10)\n", + " plt.plot([xcol, xcol+1], [rr, rr], color='black')\n", + "\n", + " xcol += 2\n", + "\n", + "plt.xticks([1.5, 3.5, 5.5], ['Naive Bayes', 'Bi-LSTM', 'BERT'], fontsize=15)\n", + "\n", + "plt.xlabel('Modelo')\n", + "plt.ylabel('Acurácia')\n", + "plt.title('Acurácia de cada modelo')\n", + "\n", + "plt.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 531 + }, + "id": "C7xrBFNzxpsE", + "outputId": "70e3464e-724f-4155-c7a0-edf09e854ba4" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 7))\n", + "\n", + "x = np.linspace(0, 1)\n", + "\n", + "# valores base e referencia\n", + "plt.plot(x, nota(bb, br, x), label='BERT')\n", + "plt.plot(x, nota(lb, lr, x), label='Bi-LSTM')\n", + "plt.plot(x, nota(nb, nr, x), label='Naive')\n", + "\n", + "# nota do aluno\n", + "nota_bert = nota(bb, br, ba)\n", + "nota_lstm = nota(lb, lr, la)\n", + "nota_naive = nota(nb, nr, na)\n", + "\n", + "plt.plot(ba, nota_bert, 'go')\n", + "plt.plot(la, nota_lstm, 'go')\n", + "plt.plot(na, nota_naive, 'go')\n", + "\n", + "nota_final = round((nota_bert + nota_lstm + nota_naive) / 3, 1)\n", + "print(nota_final)\n", + "plt.axhline(nota_final, color='r', label='Nota Final')\n", + "\n", + "plt.legend()\n", + "plt.ylabel('Sua nota')\n", + "plt.xlabel('Acurácia do modelo')\n", + "plt.title(f'Nota Final = {nota_final*10}')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SAutWOJi4aIX" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [], + "toc_visible": true + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.13 (tags/v3.9.13:6de2ca5, May 17 2022, 16:36:42) [MSC v.1929 64 bit (AMD64)]" + }, + "vscode": { + "interpreter": { + "hash": "a33b474888067a6169f866e52f630d6f3672d35114c8362b477a93e2a003ce7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/bloco5_regularizacao/NN_fooled.png b/bloco5_regularizacao/NN_fooled.png new file mode 100644 index 0000000..a4e2168 Binary files /dev/null and b/bloco5_regularizacao/NN_fooled.png differ diff --git a/bloco5_regularizacao/ajuste_fino.png b/bloco5_regularizacao/ajuste_fino.png new file mode 100644 index 0000000..2887d69 Binary files /dev/null and b/bloco5_regularizacao/ajuste_fino.png differ diff --git a/bloco5_regularizacao/aula6_teo.ipynb b/bloco5_regularizacao/aula6_teo.ipynb new file mode 100644 index 0000000..19060fe --- /dev/null +++ b/bloco5_regularizacao/aula6_teo.ipynb @@ -0,0 +1,273 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AULA 6 TEORIA- egularização, Normalização e Transferência de Aprendizado\n", + "\n", + "05/10 - Aula presencial\n", + "\n", + "---\n", + "\n", + "## Regularização, Normalização e Transferência de Aprendizado\n", + "\n", + "- Generalização e complexidade de modelos\n", + "- Overfitting/underfitting\n", + "- Técnicas de regularização: funções de custo regularizadas, dropout\n", + "- Early stopping\n", + "- Data augmentation\n", + "- Normalização por batch e camada\n", + "- Transferência de aprendizado\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Treinamento de redes profundas em cenários reais\n", + "\n", + "### Suposições para convergência e aprendizado\n", + "\n", + "- **suposições Dados de treinamento**:\n", + " - Limpos\n", + " - Representativos e bem definidos\n", + " - Baixa taxa de erro de rótulo\n", + " - Quantidade de dados é suficiente\n", + "\n", + "* E se não for possível?\n", + " * overfitting, baixa generalização, dificuldade de treinamento\n", + "\n", + "- **Complexidade de modelos**:\n", + " - algoritmo ajusta uma f a partir de um espço de funções F\n", + " - *\"muitas\" funções:* mais graus de liberdade, menor garantia de convergência, possível overfitting\n", + " - *\"poucas\" funções:* menos graus de liberdade, maior garantia de convergência, possível underfitting\n", + " - \n", + "\n", + "* ou seja:\n", + " * mais funções -> mais opções para conter a função ótima do problema, porém, mais dificil será achar a melhor função do conjunto escolhido.\n", + " * menos funções -> mais fácil achar a melhor função do conjunto, porém, talvez ela não seja ótima o suficiente\n", + "\n", + "- obs: complexidade de modelos = \"viés\" segundo a Teoria do Aprendizado Estatístico\n", + "\n", + "* exemplo com KNN (K nearst neighboughrsj):\n", + " * ver slide (ta sem explicacao mas era sobre overfitting e underfitting)\n", + "\n", + "- **viés x variância**:\n", + " - complexidade baixa: pouca variancia e muito viés\n", + " - underfitting\n", + " - complexidade alta: muita variancia e pouco viés\n", + " - overfitting\n", + " - complexidade ótima: erro de generalizaçaõ minimo\n", + " - \n", + "\n", + "* **overparametrization**:\n", + " * expansão do conceito anterior -> dependencia dos parametros\n", + " * proposta inteira EMPÍRICA (sem comprovaçaõ matemática ainda)\n", + " * a rede enquanto \"underparametrized\", quanto mais complexidade de funções, mais ela DECORA o dataset, até que chegamos no \"interpolation threshhold\", em que a rede decora tudo que há de dataset e a partir daí aprende a interpolar as respostas com o dataset decorado. \n", + " * a rede volta aprender e reduzir seu regime, voltando a ter uma generalização aceitável \n", + " * \n", + "\n", + "- **Hipótese do bilhete de loteria**:\n", + " - inicializar aleatoriamente uma rede densa com Θo\n", + " - treinar a rede até atingir convergência com parâmetros Θ\n", + " - Podar Θ e criar uma máscara m\n", + " - A configuração de inicialização “winning ticket” é Θ ⊗ m (produto interno)\n", + "\n", + "* **Ataques adversariais**:\n", + " * enganar a rede neural com alguns pixels e coódigos de pixels que traduzem features aprendidas de forma \"artifical\", podendo confundir a rede \n", + " * \n", + " * \n", + " * note que adicionar um ponto em cada imagem era muito influente na rede, de tal forma que o ponto branco no centro deu para a rede a certeza falsa de que era um avião\n", + "\n", + "- Zhang et al (2017): \"... nossos experimentos estabeleceram que redes convolucionais profundas do estado da arte (...) facilmente ajustam rótulos aleatórios nos dados de treinamento.\"\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Estratégias para melhorar a generalização\n", + "\n", + "- Regularização\n", + " - Regularização L2\n", + " - Regularização L1\n", + " - Dropout\n", + " - Early stopping\n", + " - Data augmentation\n", + " - Normalização por batch e camada\n", + " - Transferência de aprendizado\n", + "\n", + "* **[I]: Relgularização L2 (Tikonox)**:\n", + " * \n", + " * Objetivo: limitar a capacidade do modelo de se especializar demais nos dados (decorar o dataset)\n", + " * Regularização: \n", + " * Global -> na função de perda ponderada por um hiperparâmetro λ\n", + " * Definido -> na função de perda de cada camada\n", + "\n", + "- **[II]: Dropout**:\n", + " - \n", + " - Objetivo: limitar a capacidade de certos parâmetros do modelo a memorizarem os dados\n", + " - implementado na forma de \"camada\"\n", + " - em cada iteração desliga aleatoriamente uma porção dos neurônios da camada\n", + "\n", + "* **[III]: Early Stopping**:\n", + " * Objetivo: evitar que o modelo se especialize demais nos dados de treinamento \n", + " * Parar o treinamento quando a função de custo de validação não melhora mais\n", + "\n", + "- **[IV]: Coletar mais dados**:\n", + " - Objetivo: aumentar a quantidade de dados de treinamento e impedir que o treinamento considere apenas um conjunto limitado de exemplos\n", + " - Baseado na lei dos grandes números, quanto maior a amostra,teremos um melhor estimador\n", + "\n", + "* **[V]: Data Augmentation**:\n", + " * Objetivo: aumentar a quantidade de dados de treinamento *> gerar exemplos artificiais na *esperança* de que melhore as propriedades de convergência\n", + " * Gera novos dados a partir de transformações aleatórias nos dados de treinamento\n", + " * Implementado por meio da manipulação de exemplos existentes, ou sua combinação\n", + " * Exemplos:\n", + " * rotação\n", + " * zoom\n", + " * translação\n", + " * espelhamento\n", + " * mudança de brilho\n", + " * mudança de contraste\n", + " * Dropout na camada de entrada: eliminando features caleatoriamente a cada iteração\n", + " * etc\n", + "\n", + "- *obs: Dica para melhoria de performance final*\n", + " - para cada exemplo de teste:\n", + " - 1) Gerar m exemplos com data augmentation\n", + " - 2) Predizer o resultado dos m exemplos\n", + " - 3) Combinar as predições (média, maioria etc)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Normalização de dados\n", + "\n", + "* exemplos de normalização:\n", + " * **Normalizaçaõ z-score:** valores com média 0 e desvio padrão 1\n", + " * **Normalizaçaõ min-max:** valores entre 0 e 1\n", + " * **Normalizaçaõ por batch:** normaliza os dados de cada batch de forma independente\n", + "\n", + "- Objetivo: evitar que os dados de entrada sejam muito grandes ou muito pequenos -> evitar que o gradiente exploda ou desapareça -> otimização \n", + " - suaviza as ativações dos neurônios, reduzindo a varianca do gradiente\n", + " - ataca o problema de \"vanishing gradient\"\n", + "\n", + "* tipos de normalização baseada em camadas!\n", + " * batch \n", + " * camada \n", + " * instância\n", + "\n", + "- Normalização de Dados\n", + " - Normalização por batch\n", + " - Normalização por camada\n", + " - Normalização por instância\n", + " - \n", + "\n", + "* **Batch normalization (BN):** para cada batch\n", + " * média e desvio calculados por canal (total C)\n", + " * normalização por canal (ao longo de N instancias do batch)\n", + " * funciona melhor com batchsize > 32\n", + "\n", + "- **Layer normalization (LN):** para cada camada\n", + " * média e desvio calculados por instancia (total N)\n", + " * normalização por instancia (ao longo de todas as ativações do batch)\n", + " * Independe do tamanho do batch, mais comum em redes recorrentes e adversariais\n", + "\n", + "- **Instance normalization (IN):** para cada instância\n", + " * média e desvio calculados por instancia e canal (total N*C)\n", + " * normalização por instancia (ao longo de cada canal)\n", + " * Independe do tamanho do batch, mais comum em redes recorrentes e adversariais\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transferência de aprendizado\n", + "\n", + "- **Transferência de aprendizado:** Utilizar modelo treinado em uma determinada tarefa ou domínio, aproveitando o aprendizado para uma outra tarefa ou domínio alvo\n", + " - **Transferência de aprendizado entre tarefas:** transferir o conhecimento de uma tarefa para outra\n", + " - **Transferência de aprendizado entre domínios:** transferir o conhecimento de um domínio para outro\n", + "\n", + "* Modos mais comuns:\n", + " * ajuste-fino/ adaptação de parâmetros\n", + " * transferência de aprendizado por *feature extraction*\n", + "\n", + "- **Ajuste-fino/ adaptação de parâmetros:**\n", + " - Transferencia de aprendizado\n", + " - Inicializar o modelo com os pesos pré-treinados\n", + " - treinamento a partir dos pesos pré-treinados\n", + " - Permitir adaptação apenas da últimas camadas, congelando as demais\n", + " - Ajuste fino\n", + " - Inicializar o modelo com os pesos pré-treinados (feito após o anterior)\n", + " - treinamento a partir dos pesos pré-treinados\n", + " - Permitir adaptação de todos os pesos (principalmente das ultimas camadas até o meio, do começo depende da disponibilidade de dados)\n", + " - \n", + "\n", + "* *Dicas*: \n", + " * CNNs com menos parâmetros costumam generalizar melhor para dados muito diferentes do treinamento\n", + " * Exemplos: MobileNet, SqueezeNet, etc. funcionam melhor em imagens médicas do que ResNet e Inception.\n", + " * Ajuste-fino pode não convergir se tivermos poucos dados, ex.menos de 100 instâncias por classe.\n", + "\n", + "- **Transferência de aprendizado por *feature extraction*:**\n", + " - Características para dados não estruturados\n", + " - Carregar rede neural treinada em grande base de dados\n", + " - Passar exemplos de sua base de dados pela rede para predição (não treinamento!)\n", + " - Obter os mapas de ativação de alguma camada\n", + " - \n", + "\n", + "* *Dicas*:\n", + " * Aplicar redução de dimensionalidade baseada em PCA,Product Quantization ou outra\n", + " * Treinar modelo de aprendizado raso com maiores garantias de aprendizado com poucos dados: SVM, árvore de decisão, etc\n", + " * Essas características também são efetivas para recuperação baseada em conteúdo\n", + " * Podem ser usados métodos de projeção para as características aprendidas: tSNE, UMAP, PCA\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusão\n", + "\n", + "- Deep Learning não pode ser tratado como panacéia\n", + "- Há ainda preocupações sobre sua capacidade de generalização\n", + "- Grande utilidade está no aprendizado de representações em particular para dados não estruturados\n", + " - representações que parecem ter excelente cpaacidade de transferẽncia de aprendizado\n", + "\n", + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 64-bit (microsoft store)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a33b474888067a6169f866e52f630d6f3672d35114c8362b477a93e2a003ce7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/bloco5_regularizacao/dropout.png b/bloco5_regularizacao/dropout.png new file mode 100644 index 0000000..f6b16e0 Binary files /dev/null and b/bloco5_regularizacao/dropout.png differ diff --git a/bloco5_regularizacao/espaco_funcoes.png b/bloco5_regularizacao/espaco_funcoes.png new file mode 100644 index 0000000..8c04e1e Binary files /dev/null and b/bloco5_regularizacao/espaco_funcoes.png differ diff --git a/bloco5_regularizacao/feature_extraction.png b/bloco5_regularizacao/feature_extraction.png new file mode 100644 index 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a/bloco5_regularizacao/vies_variancia.png b/bloco5_regularizacao/vies_variancia.png new file mode 100644 index 0000000..1c84208 Binary files /dev/null and b/bloco5_regularizacao/vies_variancia.png differ diff --git a/bloco6_autoenconders/L1_overcompleted.jpg b/bloco6_autoenconders/L1_overcompleted.jpg new file mode 100644 index 0000000..0b80fc2 Binary files /dev/null and b/bloco6_autoenconders/L1_overcompleted.jpg differ diff --git a/bloco6_autoenconders/aula7_dem1.ipynb b/bloco6_autoenconders/aula7_dem1.ipynb new file mode 100644 index 0000000..ec7ade1 --- /dev/null +++ b/bloco6_autoenconders/aula7_dem1.ipynb @@ -0,0 +1,334 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AULA 7 DEMONSTRAÇÃO 1- Autoencders\n", + "\n", + "Aula 7- Aula Assíncrona\n", + "> https://www.youtube.com/watch?v=2EBt0sS7kwI&ab_channel=MoacirAntonelliPonti\n", + "\n", + "---\n", + "\n", + "## Autoencoder em pytorch\n", + "\n", + "- utilizar o minist como exemnplo de dataset\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "from torchvision import datasets, transforms\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Podemos carregar um dataset já existente, utilizando os seguintes parâmetros:\n", + "- `root`: caminho onde os dados serão armazenados localmente\n", + "- `train`: variável binária que define se carregar os dados de treinamento (`True`) o teste (`False`)\n", + "- `download`: se `True` faz download da Internet caso os dados não estejam disponíveis localmente\n", + "- `transform` e `target_transform` especifica transformações para as features e labels." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# tranforma imagens em tensores\n", + "tensor_transform = transforms.ToTensor()\n", + "\n", + "# Download e carregamento do dataset MNIST\n", + "dataset = datasets.MNIST(root='./data', train=True, download=True, transform=tensor_transform)\n", + "\n", + "# Cria um DataLoader para o dataset\n", + "dataloader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "class Autoencoder(nn.Module):\n", + " \n", + " def __init__(self):\n", + " super().__init__()\n", + " # Encoder\n", + " self.encoder = nn.Sequential(\n", + " nn.Linear(28*28, 128), # 28*28 = 784 -> 128\n", + " nn.ReLU(),\n", + " nn.Linear(128, 32), # 128 -> 32\n", + " nn.ReLU(),\n", + " ) # saida do encoder: 32\n", + " # Decoder\n", + " self.decoder = nn.Sequential(\n", + " nn.Linear(32, 128), # 32 -> 128\n", + " nn.ReLU(),\n", + " nn.Linear(128, 28*28), # 128 -> 28*28 = 784\n", + " nn.Sigmoid(), # simoid para normalizar os valores entre 0 e 1\n", + " ) # saida do decoder: 784\n", + "\n", + " def forward(self, x):\n", + " z = self.encoder(x)\n", + " x_hat = self.decoder(z)\n", + " return x_hat\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Autoencoder(\n", + " (encoder): Sequential(\n", + " (0): Linear(in_features=784, out_features=128, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=128, out_features=32, bias=True)\n", + " (3): ReLU()\n", + " )\n", + " (decoder): Sequential(\n", + " (0): Linear(in_features=32, out_features=128, bias=True)\n", + " (1): ReLU()\n", + " (2): Linear(in_features=128, out_features=784, bias=True)\n", + " (3): Sigmoid()\n", + " )\n", + ")\n" + ] + } + ], + "source": [ + "net = Autoencoder()\n", + "print(net)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "# função custo\n", + "loss_function = nn.MSELoss()\n", + "optim = torch.optim.Adam(net.parameters(), lr=2e-3, weight_decay=1e-7)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> com os parametros da rede definidos, vamos fazer o treinamento agora" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 1 2 3 4 5 6 7 8 9 " + ] + } + ], + "source": [ + "epochs = 10\n", + "outputs = []\n", + "losses = []\n", + "\n", + "for epoch in range(epochs):\n", + " print(f'{epoch} ', end='')\n", + " for (image, _) in dataloader:\n", + " # como autoencoder é denso, redimensiona as imagens para 1D\n", + " image = image.reshape(-1, 28*28)\n", + " # saída do autoencoder\n", + " reconstructed = net(image)\n", + " # calcula a função custo\n", + " loss = loss_function(reconstructed, image)\n", + " # zera os gradientes\n", + " optim.zero_grad()\n", + " # backpropagation\n", + " loss.backward()\n", + " # atualiza os pesos\n", + " optim.step()\n", + " losses.append(loss)\n", + " \n", + " outputs.append((epoch, image, reconstructed))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18750\n" + ] + } + ], + "source": [ + "# extraindo os valores de MSE para as epocas\n", + "losses_val = [l.item() for l in losses]\n", + "\n", + "print(len(losses_val))" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.xlabel('iterations')\n", + "plt.ylabel('loss')\n", + "plt.plot(losses_val)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> podemos reconstruir as imagens para saber como fica" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'Line2D' object has no property 'cmap'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\felip\\Documents\\RedesNeurais\\bloco7_autoenconders\\aula7_dem1.ipynb Célula: 13\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m item \u001b[39m=\u001b[39m item\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m28\u001b[39m, \u001b[39m28\u001b[39m)\n\u001b[0;32m 3\u001b[0m plt\u001b[39m.\u001b[39msubplot(\u001b[39m211\u001b[39m)\n\u001b[1;32m----> 4\u001b[0m plt\u001b[39m.\u001b[39;49mplot(item[\u001b[39m0\u001b[39;49m], cmap\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mgray\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[0;32m 6\u001b[0m \u001b[39mfor\u001b[39;00m i, item \u001b[39min\u001b[39;00m \u001b[39menumerate\u001b[39m(reconstructed):\n\u001b[0;32m 7\u001b[0m item \u001b[39m=\u001b[39m item\u001b[39m.\u001b[39mreshape(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m28\u001b[39m, \u001b[39m28\u001b[39m)\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\matplotlib\\pyplot.py:2769\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m 2767\u001b[0m \u001b[39m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[39m.\u001b[39mplot)\n\u001b[0;32m 2768\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mplot\u001b[39m(\u001b[39m*\u001b[39margs, scalex\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, scaley\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m-> 2769\u001b[0m \u001b[39mreturn\u001b[39;00m gca()\u001b[39m.\u001b[39mplot(\n\u001b[0;32m 2770\u001b[0m \u001b[39m*\u001b[39margs, scalex\u001b[39m=\u001b[39mscalex, scaley\u001b[39m=\u001b[39mscaley,\n\u001b[0;32m 2771\u001b[0m \u001b[39m*\u001b[39m\u001b[39m*\u001b[39m({\u001b[39m\"\u001b[39m\u001b[39mdata\u001b[39m\u001b[39m\"\u001b[39m: data} \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m {}), \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\matplotlib\\axes\\_axes.py:1632\u001b[0m, in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\matplotlib\\axes\\_base.py:312\u001b[0m, in \u001b[0;36m__call__\u001b[1;34m(self, data, *args, **kwargs)\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\matplotlib\\axes\\_base.py:538\u001b[0m, in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\matplotlib\\axes\\_base.py:538\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\matplotlib\\axes\\_base.py:531\u001b[0m, in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\matplotlib\\axes\\_base.py:351\u001b[0m, in \u001b[0;36m_makeline\u001b[1;34m(self, x, y, kw, kwargs)\u001b[0m\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\matplotlib\\lines.py:393\u001b[0m, in \u001b[0;36mLine2D.__init__\u001b[1;34m(self, xdata, ydata, linewidth, linestyle, color, marker, markersize, markeredgewidth, markeredgecolor, markerfacecolor, markerfacecoloralt, fillstyle, antialiased, dash_capstyle, solid_capstyle, dash_joinstyle, solid_joinstyle, pickradius, drawstyle, markevery, **kwargs)\u001b[0m\n\u001b[0;32m 389\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mset_markeredgewidth(markeredgewidth)\n\u001b[0;32m 391\u001b[0m \u001b[39m# update kwargs before updating data to give the caller a\u001b[39;00m\n\u001b[0;32m 392\u001b[0m \u001b[39m# chance to init axes (and hence unit support)\u001b[39;00m\n\u001b[1;32m--> 393\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mupdate(kwargs)\n\u001b[0;32m 394\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpickradius \u001b[39m=\u001b[39m pickradius\n\u001b[0;32m 395\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mind_offset \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\matplotlib\\artist.py:1064\u001b[0m, in \u001b[0;36mupdate\u001b[1;34m(self, props)\u001b[0m\n", + "\u001b[1;31mAttributeError\u001b[0m: 'Line2D' object has no property 'cmap'" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i, item in enumerate(image):\n", + " item = item.reshape(-1, 28, 28)\n", + " plt.subplot(211)\n", + " plt.imgshow(item[0], cmap='gray')\n", + "\n", + "for i, item in enumerate(reconstructed):\n", + " item = item.reshape(-1, 28, 28)\n", + " plt.subplot(212)\n", + " plt.imgshow(item[0].detach().numpy(), cmap='gray')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "> podemos também utilizar APENAS o encoder treinado para obter feautures das iamgens" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "features = net.encoder(image)\n", + "\n", + "features[0].detach().numpy()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 64-bit (microsoft store)", + "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": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a33b474888067a6169f866e52f630d6f3672d35114c8362b477a93e2a003ce7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/bloco6_autoenconders/aula7_teo.ipynb b/bloco6_autoenconders/aula7_teo.ipynb new file mode 100644 index 0000000..3fde3d2 --- /dev/null +++ b/bloco6_autoenconders/aula7_teo.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AULA 7 TEORIA- Redes Auto-enconders/auto-associativas\n", + "\n", + "Semana 10-14/10 - Aula assíncrona\n", + "> https://www.youtube.com/watch?v=O6C6wl357-o&ab_channel=MoacirAntonelliPonti\n", + "\n", + "\n", + "Auto-encoder e tarefa de reconstrução\n", + "Denoising Autoencoders\n", + "Variational Autoencoders\n", + "\n", + "---\n", + "\n", + "## Regularização, Normalização e Transferência de Aprendizado\n", + "\n", + "- Autoenconders\n", + "- Undercomplete\n", + "- Overcomplete\n", + "- Denoising Autoencoders\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autoencoders\n", + "\n", + "- Autoencoders são redes neurais que tem como objetivo aprender uma representação compacta dos dados de entrada.\n", + " - redes neurais que buscam aprender SEM RÓTULOS -> **NÃO SUPERVISIONADO**\n", + "\n", + "* autoenconders associam a entrada e a saída (representação compacta)\n", + " * x -> encoder -> x^ (representação compacta)\n", + " * \n", + "\n", + "- **Encoder:** aprende um *códigos* que representa a entrada (também chamado de *representação latente* ou *feature embedding*)\n", + " - h = s(Wx + b) = f(x)\n", + "\n", + "* **Decoder:** aprende a reconstruir a entrada a partir do código\n", + " * x^ = s(W^Th + b^T) = g(h)\n", + "\n", + "- exemplo: digito\n", + " - entrada: 28x28 = 784 pixels\n", + " - saída: 28x28 = 784 pixels\n", + " - representação latente: 10 dimensões\n", + " - código resistringiu em 10 o espaço de representações -> menor resolução\n", + " - \n", + "\n", + "* saída x^ = g(f(x)), com isso minimizamos o erro na recostrução da entrada com:\n", + " * L(x, x^) = L(x, g(f(x)))\n", + " * exemplo: L(x,x^) = ||x - x^||^2 (mean squadred error)\n", + "\n", + "- **tipos de autoenconder:**\n", + " - **undercomplete:** número de neurônios na camada oculta é menor que o número de neurônios na camada de entrada\n", + " - a camada do código é chamada de gargalo ou \"bottleneck\" por ser restrita\n", + " - **overcomplete:** número de neurônios na camada oculta é maior que o número de neurônios na camada de entrada\n", + " - há diferentes versões desse tipo para compensar a falta de restrição do código\n", + "\n", + "* *por que usar um autoenconder?*\n", + " * a fubnção h do código ela aprende as característica de x, enquanto isso ela pode diminuir a dimensionalidade dos dados (restrição)\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Undercomplete\n", + "\n", + "- código é uma compressão cm perdas da entrada\n", + " - camada de código é chamada de bottheneck\n", + " - código produz boa representaçaõ dos dados de treinamento em particular para reconstrução\n", + " - exemplo:\n", + " - \n", + " - note que ela reconstroi ruido tentando montar um padrão de digito, pq foi isso que ela aprendeu a fazer\n", + "\n", + "* **vantagens:**\n", + " * redução de dimensionalidade\n", + " * aprendizado de representações não supervisionado\n", + " * aprendizado de representações não lineares\n", + " * aprendizado de representações invariantes a ruído\n", + "\n", + "* **desvantagens:**\n", + " * possibilidade de decorar features muito específicas se o modelo tiver ma alta capacidade de representação\n", + "\n", + "- Um autoencoder denso com uma única camada encoder/decoder tem relações com o método \"Principal Component Analysis\" (PCA)\n", + "\n", + "* *obs:* Os autoenconders podem ser profundos, isto é, podem adimitir camada não densas (conv, pooling, etc), porém, a camada do CÓDIGO é comumente **densa** (Fully Connected) para permitir a projeção dos dados\n", + "\n", + "- exemplo: UNET\n", + " - \n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Overcomplete\n", + "\n", + "- **Overcomplete:** número de neurônios na camada oculta é maior que o número de neurônios na camada de entrada\n", + " - uma implementação simples permitira a cópia simples (e perfeita) dos dados de forma que x = x^\n", + " - \n", + "\n", + "* NÃO QUEREMOS CÓPIA PERFEITA, para isso, podemos utilizar uma regularização (exemplo L1)\n", + " * no fim, a ideia é que a funçaõ de custo tente manter um baixo numero de ativações por entrada\n", + " * dropout também pode ser usado, nesse caso imediatamente antes da camada do código (para impedir que ele confie na cópia como sendo perfeita)\n", + " * \n", + " * note que os neuorios em preto estão zerados (ou com valores muito baixos) \n", + " \n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Denoising Autoencoders\n", + "\n", + "* regularização **adicionando ruido na entrada** -> x~ = N(x) (ruido)\n", + " * note que a perda é computada usando a função NÃO ruidosa x\n", + " * e o autoenconder tenta reconstruir x a partir de x~\n", + " * o encoder deve aprender a remover o ruido mantendo apenas as informações essenciais n ocódigos, permitindo que o decoder reconstrua a entrada\n", + " * \n", + " * obs: como há o ruido, é possivel usar overcompleted sem perigo de ele copiar a entrada\n", + "\n", + "- Adicioanr ruído:\n", + " - comulmente: Ruído Gaussiano/Normal com média 0 e variância >= 1\n", + " - pode ser usado outros tipos de ruído (ex: salt and pepper/impulsivo)\n", + " - Ruído impulsivo: atribuir zero a uma porcentagem da entrada, com probabilidade p (dropout na entrada)\n", + "\n", + "* técnicas de regularização também podem ser usadas em Denoising Autoencoders\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusão \n", + "\n", + "* AEs podem ser boa escolha com dados não supervisionados para aprendizado de manifolds e agrupamento\n", + " * Representam uma nova tarefa: reconstrução que pode ser acoplada a outras arquiteturas\n", + "\n", + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 64-bit (microsoft store)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a33b474888067a6169f866e52f630d6f3672d35114c8362b477a93e2a003ce7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/bloco6_autoenconders/denoising.jpg b/bloco6_autoenconders/denoising.jpg new file mode 100644 index 0000000..0d82631 Binary files /dev/null and b/bloco6_autoenconders/denoising.jpg differ diff --git a/bloco6_autoenconders/digito.jpg b/bloco6_autoenconders/digito.jpg new file mode 100644 index 0000000..1972608 Binary files /dev/null and b/bloco6_autoenconders/digito.jpg differ diff --git a/bloco6_autoenconders/encoder_decoder.jpg b/bloco6_autoenconders/encoder_decoder.jpg new file mode 100644 index 0000000..81adfa3 Binary files /dev/null and b/bloco6_autoenconders/encoder_decoder.jpg differ diff --git a/bloco6_autoenconders/exemplo_undercomplete.jpg b/bloco6_autoenconders/exemplo_undercomplete.jpg new file mode 100644 index 0000000..8c8e32e Binary files /dev/null and b/bloco6_autoenconders/exemplo_undercomplete.jpg differ diff --git a/bloco6_autoenconders/simplees_overcomplete.jpg b/bloco6_autoenconders/simplees_overcomplete.jpg new file mode 100644 index 0000000..0313b3c Binary files /dev/null and b/bloco6_autoenconders/simplees_overcomplete.jpg differ diff --git a/bloco6_autoenconders/unet.jpg b/bloco6_autoenconders/unet.jpg new file mode 100644 index 0000000..42f7af6 Binary files /dev/null and b/bloco6_autoenconders/unet.jpg differ diff --git a/bloco7_geradoras/ELBO.png b/bloco7_geradoras/ELBO.png new file mode 100644 index 0000000..230f359 Binary files /dev/null and b/bloco7_geradoras/ELBO.png differ diff --git a/bloco7_geradoras/GAN.png b/bloco7_geradoras/GAN.png new file mode 100644 index 0000000..900668b Binary files /dev/null and b/bloco7_geradoras/GAN.png differ diff --git a/bloco7_geradoras/VAE_matematicamente.png b/bloco7_geradoras/VAE_matematicamente.png new file mode 100644 index 0000000..3fe7613 Binary files /dev/null and b/bloco7_geradoras/VAE_matematicamente.png differ diff --git a/bloco7_geradoras/VAExdifusao.png b/bloco7_geradoras/VAExdifusao.png new file mode 100644 index 0000000..3b97169 Binary files /dev/null and b/bloco7_geradoras/VAExdifusao.png differ diff --git a/bloco7_geradoras/aritmetica.png b/bloco7_geradoras/aritmetica.png new file mode 100644 index 0000000..e27b26e Binary files /dev/null and b/bloco7_geradoras/aritmetica.png differ diff --git a/bloco7_geradoras/aula8_teo.ipynb b/bloco7_geradoras/aula8_teo.ipynb new file mode 100644 index 0000000..7ba9bea --- /dev/null +++ b/bloco7_geradoras/aula8_teo.ipynb @@ -0,0 +1,207 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AULA 8 TEORIA- Modelos geradores\n", + "\n", + "19/10 - Aula assíncrona\n", + "> https://www.youtube.com/watch?v=1LLDe8VheUs&ab_channel=MoacirAntonelliPonti\n", + "> https://www.youtube.com/watch?v=1jjHO5_8lQk&ab_channel=MoacirAntonelliPonti\n", + "\n", + "\n", + "* Redes Geradoras\n", + "* Autoencoders Variacionais\n", + "* Redes adversariais: Discriminador e Gerador\n", + "* Redes baseadas em Difusão\n", + "\n", + "---\n", + "\n", + "## Redes Geradoras\n", + "\n", + "- Modelos geradores\n", + "- Autoencoders variacionais (VAEs)\n", + "- Redes adversárias geradoras (GANs)\n", + "- Modelos baseados em difusão\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modelos geradores\n", + "\n", + "* Redes geradoras são modelos que aprendem a gerar novos dados\n", + " * tipo aquelas que geram faces de pessoas a partir de fotos reais\n", + "\n", + "- rede tenta aprender a distribuição que \"gera\" os dados para amostrar novos dados a partir dela\n", + " - aprender como é a distribuição dos dados, no exemplo é tipo uma gaussiana, logo a maior densidade de dados é no pico dela, e a menor nas extremidades\n", + " - \n", + "\n", + "* **tipos de métodos:**\n", + " * **Função de densidade explítica:** buscar a função densidade igual no exemplo acima\n", + " * Fully Visible Belief Networks (FVBNs)\n", + " * Boltzmann Machines (BM)\n", + " * Variational Autoencoders (VAEs) - *mais comum*\n", + " * **Função de densidade implícita:** aprender a gerar dados sem saber a distribuição (intui a forma que os dados são gerados)\n", + " * Monte Carlo\n", + " * Likehood-free inference via classification\n", + " * Generative Adversarial Networks (GANs) - *mais comum*\n", + "\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Autoencoders variacionais (VAEs)\n", + "\n", + "* relembrando: autoenconders tradicionais tentam codificar atributos de forma discreta, e depois decodificar para reconstruir uma estimativa do dado original\n", + " * obs: eles aprendem os atributos da imgem principal, e não a imagem em si (exemplo sorriso, cor da pele...) \n", + " * \n", + " * *disentaglement: separar os atributos da imagem principal* \n", + "\n", + "- VAEs são autoencoders que aprendem a distribuição dos dados (mesma ideia acima)\n", + " - \n", + " - para cada atributo, dado pelo encoder a rede vai tentar aprender a média e desvio padrão da distribuição\n", + " - vai ser amostrado um dado dessa distribuição \n", + " - essa amostra será decodificada pelo decoder\n", + "\n", + "* diferenças: o autoencoder codifica a imagem e tenta reconstruir ela a partir dos proprios dados, decodificando esses dados\n", + " * o VAE codifica a imagem e tenta reconstruir ela a partir de uma amostra da distribuição dos dados que a rede está tentando aprender\n", + " * \n", + "\n", + "- depois que a rede já estiver boa, é possível assumir as distribuições aprendidas são boas e podemos jogar a parte do codificador fora\n", + " - assim, podemos só a partir de amostrar da distribuição, gerar novos dados\n", + "\n", + "* Função custo ELBO (Evidence Lower Bound)\n", + " * L = reconstrução + divergência Kullback-Leibler (KL)\n", + " * L = MSE/Binary_Cross_Entropy + KL\n", + "\n", + "- a divergença KL é uma medida de similaridade entre duas distribuições\n", + " - enquanto a reconstrução tenta olhar pros valores específicos de entrada e saída, a divergencia KL olha pras distribuições inteiras\n", + " - \n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Redes adversárias geradoras (GANs)\n", + "\n", + "- adversárias: componentes que disputam entre si\n", + "\n", + "* Gerador G: redecebe um exemplo z' obtido da distribuição e gera x por meio de uma função:\n", + " * x = G(z')\n", + " * G projeta no espaço x um ponto específico de z (z -G-> x)\n", + "\n", + "- Discriminador D\n", + " - recebe um exemplo x e tenta classificar se ele é da distribuição original ou pela aproximaão do gerador (real ou artificial)\n", + "\n", + "* Paralelo: Casa da moeda e o ladrão\n", + " * casa da moeda e ladrão geram células, cabe a polícia descobrir qual é real e qual é falsa -> punição ou melhoria...\n", + " * \n", + " * \n", + " * duas fontes: gerador (artificial) e dados de treinamento (real) -> discriminador tenta definir qual é qual\n", + "\n", + "- formulação clássica:\n", + " - \n", + " - maximizar D e minimar G (minimax)\n", + " - primeiro termo WEx[ log(D(x))]: \n", + " - referente aos dados reais (x)\n", + " - entropia cruzada: 1 * log(D(X)), onde 1 é o valor esperado de D(x) (se for real)\n", + " - segundo termo: Ez[ log(1 - D(G(z)))]:\n", + " \n", + " - referente aos dados gerados (G(z))\n", + " - entropia cruzada: 1 * log(1 - D(G(z))\n", + " - x^ = G(z) é o dado gerado\n", + "\n", + "* a perda clássica acima causa um comportamento muito ocilatório na função de perda, fazendo com que **seja dificl otimizar GANs**\n", + "\n", + "- aritimetica de esppaço latente\n", + " - \n", + " - sem mulheres com oculos no espaço, é possível gerar esse tipo de imagem -> pegar as features HOMEM e OCULOS, subtrair HOMEM SEM OCULOS e adcionar MULHER SEM OCULOS\n", + " - aritimetica: HOMEM - HOMEM cancela, SEM OCULOS - SEM OCULOS cancela, sobra MULHER COM OCULOS\n", + " - isso tuod com as features extraidas que se comportam como esses atributos\n", + "\n", + "* obs: as imagens \"as vezes\" são realistas, elas chegam a gerar boas imagens, mas também algumas muito ruins\n", + " * as GANs não são boas em entender varios padrões/classes diferentes em uma mesma imagem\n", + "\n", + "- modelos relevantes: SAGAN (2019), StyleGAN (2021), Diffusion-based Model (2021)...\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modelos baseados em difusão\n", + "\n", + "* adicionar ruido em um dado até ele se perder por completo\n", + " * difusão dos features de entrada até virar puro ruido\n", + " * x0 (imagem rea) -> xT (puro ruido)\n", + "\n", + "\n", + "- ideia similar do VAE: codificar e decodificar\n", + " - no caso é a difusão foward e backward (codificar e decodificar)\n", + " - \n", + " - \n", + "\n", + "* diferença da difusão e VAE: \n", + " * VAE: codifica e decodifica a partir de uma amostra da distribuição\n", + " * difusão: codifica e decodifica a partir de um dado específico\n", + " * difusão não precisa aprender a CODIFICAR, só a decodificar, enquanto VAEs precisam aprender os dois\n", + " * \n", + "\n", + "- perda nos modelos de difusão (não tem mais reconstrução e KL):\n", + " - olha basicamente pro processo de remoção de ruido da imagem\n", + " - \n", + "\n", + "* modelo mais importante atualmente: BigGAN\n", + " * \n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LSTMs e GRUs\n", + "\n", + "* pra um tempo curto o sumário consegue segurar bem a memória (curto prazo), pra longo prazo é necessário usar uma LSTM\n", + " * Long Short Termo Unit (LSTM)\n", + " * \n", + "\n", + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 64-bit (microsoft store)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a33b474888067a6169f866e52f630d6f3672d35114c8362b477a93e2a003ce7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/bloco7_geradoras/autoencoder.png b/bloco7_geradoras/autoencoder.png new file mode 100644 index 0000000..9b1e7b6 Binary files /dev/null and b/bloco7_geradoras/autoencoder.png differ diff --git a/bloco7_geradoras/autoencoder2.png b/bloco7_geradoras/autoencoder2.png new file mode 100644 index 0000000..cf26b04 Binary files /dev/null and b/bloco7_geradoras/autoencoder2.png differ diff --git a/bloco7_geradoras/backward_difusao.png b/bloco7_geradoras/backward_difusao.png new file mode 100644 index 0000000..8abf04f Binary files /dev/null and b/bloco7_geradoras/backward_difusao.png differ diff --git a/bloco7_geradoras/bigGAN.png b/bloco7_geradoras/bigGAN.png new file mode 100644 index 0000000..d9432bd Binary files /dev/null and b/bloco7_geradoras/bigGAN.png differ diff --git a/bloco7_geradoras/distribuicao_dados.png b/bloco7_geradoras/distribuicao_dados.png new file mode 100644 index 0000000..4eca583 Binary files /dev/null and b/bloco7_geradoras/distribuicao_dados.png differ diff --git a/bloco7_geradoras/formula.png b/bloco7_geradoras/formula.png new file mode 100644 index 0000000..f116633 Binary files /dev/null and b/bloco7_geradoras/formula.png differ diff --git a/bloco7_geradoras/forward_difusao.png b/bloco7_geradoras/forward_difusao.png new file mode 100644 index 0000000..3e9768b Binary files /dev/null and b/bloco7_geradoras/forward_difusao.png differ diff --git a/bloco7_geradoras/loss_difusao.png b/bloco7_geradoras/loss_difusao.png new file mode 100644 index 0000000..3d7e906 Binary files /dev/null and b/bloco7_geradoras/loss_difusao.png differ diff --git a/bloco7_geradoras/moeda.png b/bloco7_geradoras/moeda.png new file mode 100644 index 0000000..6e33cd1 Binary files /dev/null and b/bloco7_geradoras/moeda.png differ diff --git a/bloco7_geradoras/vae.png b/bloco7_geradoras/vae.png new file mode 100644 index 0000000..2187c0a Binary files /dev/null and b/bloco7_geradoras/vae.png differ diff --git a/bloco8_recorrentes/GRU.png b/bloco8_recorrentes/GRU.png new file mode 100644 index 0000000..d863fa2 Binary files /dev/null and b/bloco8_recorrentes/GRU.png differ diff --git a/bloco8_recorrentes/LSTM.png b/bloco8_recorrentes/LSTM.png new file mode 100644 index 0000000..0368606 Binary files /dev/null and b/bloco8_recorrentes/LSTM.png differ diff --git a/bloco8_recorrentes/RNN.png b/bloco8_recorrentes/RNN.png new file mode 100644 index 0000000..5bcaa69 Binary files /dev/null and b/bloco8_recorrentes/RNN.png differ diff --git a/bloco8_recorrentes/aula9_teo.ipynb b/bloco8_recorrentes/aula9_teo.ipynb new file mode 100644 index 0000000..67d43fd --- /dev/null +++ b/bloco8_recorrentes/aula9_teo.ipynb @@ -0,0 +1,151 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AULA 9 TEORIA- Redes Recorrentes\n", + "\n", + "19/10 - Aula assíncrona\n", + "> https://www.youtube.com/watch?v=OzC_vwAqRTU&ab_channel=MoacirAntonelliPonti\n", + "\n", + "* Redes neurais para dados sequenciais e diferentes tipos de problemas\n", + "* Camada recorrente\n", + "* LSTM\n", + "* GRU\n", + "* \"Lookback\" no treinamento de redes recorrentes\n", + "\n", + "---\n", + "\n", + "## Redes Recorrentes\n", + "\n", + "- Dados sequenciais (recorrência)\n", + "- Camada recorrente básica (RNN)\n", + "- LSTMs e GRUs\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dados sequenciais (recorrência)\n", + "\n", + "* dados recorrentes -> depênn de um contexto anterior (ex: texto, áudio, vídeo, TEMPO...)\n", + "\n", + "- redes neurais convencionais não são capazes de lidar com dados sequenciais\n", + " - cada entrada é independente da anterior no modelo original\n", + " - Camadas densas e convolucionais consideram apenas o exemplo atual para computar a saída\n", + " - para isso -> usamos **redes recorrentes**\n", + "\n", + "* redes recorrentes tem \"memória\" dos dados passados -> a ordem dos elementos de entrada IMPORTA\n", + " * possibilidades de RNNs:\n", + " * 1 entrada, saida sequencial\n", + " * ex: imagem de entrada e saida, sequencial de palavras descevendo a imagem \"casa\", \"com\", \"sol\", \"azul\".\n", + " * entrada sequencial, 1 saida\n", + " * ex: texto com opinião e a saida é classificaçaõ de sentimento (positivo ou negativo)\n", + " * entrada sequencial, saida sequencial\n", + " * ex: tradução de texto de um idioma para outro\n", + "\n", + "- *obs:* em tradução de texto é interssante por um atraso, para que acumulem algumas palavras e ele não faça uma tradução literal palavra pro palavra, mas sim por um contexto local \n", + "\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Camada recorrente básica (RNN)\n", + "\n", + "* \n", + "\n", + "* ut = Wh * ht-1 + Wx * xt + b\n", + " * ht-1 = saída da camada anterior\n", + " * xt = entrada atual\n", + " * Wh = pesos da camada recorrente\n", + " * Wx = pesos da camada de entrada\n", + " * b = bias\n", + "\n", + "- ht = f(ut)\n", + " - f = função de ativação (ex: tanh, sigmoid, relu, etc)\n", + " - ht é chamado de variável de memória ou sumário\n", + "\n", + "* y = Wy * ht + by\n", + " * ht = saída da camada recorrente\n", + " * Wy = pesos da camada de saída\n", + " * by = bias da camada de saída\n", + "\n", + "- **Exemplo:** Predizer próximo caracter \n", + " - definimos uma codificação one-hot para os caracteres\n", + " - h = [1,0,0,0]\n", + " - e = [0,1,0,0]\n", + " - l = [0,0,1,0]\n", + " - o = [0,0,0,1]\n", + " - rede para predizer:\n", + " - \n", + "\n", + "### LSTM (Long Short Term Memory)\n", + "\n", + "> https://colah.github.io/posts/2015-08-Understanding-LSTMs/\n", + "\n", + "- Funcionamento básico\n", + " - \n", + " - **Cell State**: Responsável pela memória longa (contribuiçaõ ao longo da iteração anterior)\n", + " - **Forget State**: Decide qual elementos de C serão esquecidos (zerados) com base no sumário anterior e a entrada atual\n", + " - algo entre 0 (esquecer) e 1 (manter) para cada dimensão de C\n", + " - **Input Gate**: Decide quais elementos de C serão atualizados (o que será adicionado)\n", + " - **Update State**: Atualiza o sumário com base no novo C\n", + " - **Output Gate**: Decide quais elementos de C (qual sumário) serão usados para gerar a saída\n", + " - igual uma RNN (essa parte)\n", + "\n", + "### GRU (Gated Recurrent Unit)\n", + "\n", + "* facilitar a LSTM (menos parametros e menos complexidade)\n", + " * proposta mais recente\n", + "\n", + "- Funcionamento básico\n", + " - \n", + " - não possui cell state\n", + " - **Reset Gate r:** filtra qual parte de ht-1 será utilizada para compor o novo sumário candidato em conjunto com xt\n", + " - **Update Gate z:** pondera partes do sumário anterior de forma complementar ao novo estado candidato\n", + " - h~t é o sumário \"candidato\"\n", + "\n", + "### GRU x LSTM \n", + "\n", + "- não há consenso sobre qual é melhor\n", + " - LSTM é mais complexa e mais lenta\n", + " - GRU é mais simples e mais rápida\n", + " - GRU em muitos casos tem resultados simalar ao LSTM, só que com menos parametros\n", + " - ultimamente não se usa mais as RNNs clássicas\n", + "\n", + "* há uma versão recente, **JANET** que simplificou aidna mais o modelom removendo o \"Reset Gate\"\n", + " * existem também as **Temporal Convolutional Networks**, que utiliam convoliuções 1D para aprender posiconamento local de elementos sequenciais, elas também se mostram eficientes em alguns cenários\n", + "\n", + "\n", + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 64-bit (microsoft store)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a33b474888067a6169f866e52f630d6f3672d35114c8362b477a93e2a003ce7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/bloco8_recorrentes/caracter.png b/bloco8_recorrentes/caracter.png new file mode 100644 index 0000000..84f938e Binary files /dev/null and b/bloco8_recorrentes/caracter.png differ diff --git a/bloco9_seq2seq_e_atencao/BERT_ELMo.jpg b/bloco9_seq2seq_e_atencao/BERT_ELMo.jpg new file mode 100644 index 0000000..aa53711 Binary files /dev/null and b/bloco9_seq2seq_e_atencao/BERT_ELMo.jpg differ diff --git a/bloco9_seq2seq_e_atencao/arq_TransfNEt.jpg b/bloco9_seq2seq_e_atencao/arq_TransfNEt.jpg new file mode 100644 index 0000000..e000918 Binary files /dev/null and b/bloco9_seq2seq_e_atencao/arq_TransfNEt.jpg differ diff --git a/bloco9_seq2seq_e_atencao/arq_atencao.jpg b/bloco9_seq2seq_e_atencao/arq_atencao.jpg new file mode 100644 index 0000000..788a590 Binary files /dev/null and b/bloco9_seq2seq_e_atencao/arq_atencao.jpg differ diff --git a/bloco9_seq2seq_e_atencao/atencao.jpg b/bloco9_seq2seq_e_atencao/atencao.jpg new file mode 100644 index 0000000..0e47bf6 Binary files /dev/null and b/bloco9_seq2seq_e_atencao/atencao.jpg differ diff --git a/bloco9_seq2seq_e_atencao/atencao2.jpg b/bloco9_seq2seq_e_atencao/atencao2.jpg new file mode 100644 index 0000000..030ae29 Binary files /dev/null and b/bloco9_seq2seq_e_atencao/atencao2.jpg differ diff --git a/bloco9_seq2seq_e_atencao/aula10_teo.ipynb b/bloco9_seq2seq_e_atencao/aula10_teo.ipynb new file mode 100644 index 0000000..ebe4952 --- /dev/null +++ b/bloco9_seq2seq_e_atencao/aula10_teo.ipynb @@ -0,0 +1,115 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AULA 10 TEORIA- Sequence-to-Sequence e Mecanismo de Atenção \n", + "\n", + "09/11 - Aula presencial\n", + "\n", + "- Métodos sequence-to-sequence\n", + "- Atenção: cues, pooling e funções de scoring\n", + "- Mecanismo básico de atenção\n", + "- Multi-head attention\n", + "- Self-attention\n", + "\n", + "---\n", + "\n", + "## Word2Vec, Sequence2Sequence e Mecanismo de Atenção\n", + "\n", + "- Word2Vec: representações para texto\n", + "- Sequence to Sequence e Mecanismo de atenção\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Word2Vec\n", + "\n", + "* Representação (embedding) para palavras\n", + " * função custo para aprender essa representação: p((w1, w2, ..., wn) | wt)\n", + " * primeiro todas as palavras do contexto de t e depois a palavra alvo\n", + " * otimiza em função de palavras que devem estar próximas se tiverem o mesmo **contexto**\n", + "\n", + "- **Skip-Grams (SG)**\n", + " - predição de palavras em uma certa janela de proximdiade m de uma palavra t\n", + " - formuçação do problema: p(wt | w1, ..., wm)\n", + " - softmax:\n", + " - \n", + "\n", + "* Skip gram com one-hot de uma plaavra: W * wt = vc\n", + " * \n", + " * W aprende representação (nas colunas) opara cada palavra quando são centrais\n", + " * Uo aprende representações (nas linhas) para cada palavra quando são contexto (ou seja, quando são vizinhas)\n", + "\n", + "- Outros\n", + " - **GloVe**\n", + " > https://nlp.stanford.edu/projects/glove/\n", + " - NILC (ICMC)\n", + " > http://www.nilc.icmc.usp.br/embeddings\n", + "\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Sequence-to-Sequence e Mecanismo de atenção\n", + "\n", + "### Sequence-to-Sequence\n", + "\n", + "- Sequence to sequence: tradução de uma sequência para outra\n", + " - \n", + "\n", + "\n", + "### Mecanismo de atenção\n", + "\n", + "* Encontrar qual parte de uma sequência é mais importante para predizer uma certa saída\n", + " * Em unidades recorrentes, cada entrada perturba a memória prejudicando conhecimento de dados anteriores\n", + "\n", + "- Exemplos\n", + " - imagens\n", + " - \n", + " - rede faz o highlight dos mencanismo de atenção\n", + " - previsão tá horrível\n", + " - texto\n", + " - \n", + " - cada linha é um vetor de atenção -> matriz de atenção\n", + " - entende as relações entre as palavras \n", + "\n", + "* implementação \n", + " * Computar o alinhamento/similaridade entre o sumário atual do decoder, si com sumários anteriores do encoder, hj\n", + " * Usa softmax para obter pesos na forma de probabilidades\n", + " * Atenção produz um vetor de \"contexto\" a ser usado para produzir a saída atual\n", + "\n", + "\n", + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 64-bit (microsoft store)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a33b474888067a6169f866e52f630d6f3672d35114c8362b477a93e2a003ce7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/bloco9_seq2seq_e_atencao/aula11_teo.ipynb b/bloco9_seq2seq_e_atencao/aula11_teo.ipynb new file mode 100644 index 0000000..d7d8e82 --- /dev/null +++ b/bloco9_seq2seq_e_atencao/aula11_teo.ipynb @@ -0,0 +1,139 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# AULA 11 TEORIA- Arquitetura Transformer e BERT\n", + "\n", + "16/11 - Aula assíncrona\n", + "> https://www.youtube.com/watch?v=ksseZzTegWk&ab_channel=MoacirAntonelliPonti\n", + "\n", + "\n", + "- Arquitetura Transformer\n", + "- BERT - Bidirectional Encoder Representations from Transformers\n", + "\n", + "---\n", + "\n", + "## Transformer Networks e BERT\n", + "\n", + "- Arquitetura Transformer\n", + "- BERT - Bidirectional Encoder Representations from Transformers\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transformer Networks\n", + "\n", + "### RNN vs Transforme Nets\n", + "\n", + "* RNNs\n", + " * podem não funcionar com **dependencias longas**\n", + " * recorrencia dificulta **computação paralela** (pontos da sequencia não podem ser porcessadas em paralelo)\n", + " * podem sofrer com exlosão ou desaparecimento de gradiente\n", + "\n", + "- Transformer Networks\n", + " - não tem recorrência (apenas atenção)\n", + " - captura dependencias longas \n", + " - processamento paralelo: atençaõ é invariante a permutação\n", + "\n", + "### Arquitetura\n", + "\n", + "- arquitetura\n", + " - \n", + " - explicação na aula\n", + " - diferencial: **POsitional Encoding** e **Multi-Head Attention**\n", + "\n", + "* Multi-Head Attention (atenção)\n", + " - \n", + " - não só mais a combinação linear de todos elementos de entrada\n", + " - \n", + " - Recuperar um valor vi para uma consulta/query q baseada numa chave/key ki\n", + " - A similaridade entre uma consulta e todas as chaves, ponderadas pelos valores\n", + " - Somar ao longo de todas as chaves/valores, produz uma distribuição de pesos relacionando consulta e todos os valores\n", + " - \n", + "\n", + "- **Transformer Huggingface**\n", + " - https://transformer.huggingface.co/\n", + " - https://huggingface.co/transformers/model_doc/transformer.html\n", + "\n", + "* Após transformer\n", + " * GPT e GPT2\n", + " * BERT\n", + " * Lambda Networks" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### BERT \n", + "\n", + "- Bidirectional Encoder Representations from Transformers\n", + " - https://arxiv.org/pdf/1810.04805.pdf (copilot que sugeriu essa brincadeira aqui)\n", + "\n", + "* Método para **pré-treinar** encoders do tipo Transformer\n", + " * Modelo Base:\n", + " * 12 camadas Transformer\n", + " * Embedding com 768 dimensões\n", + " * 110 milhões de parametros\n", + " * Large:\n", + " * 24 camadas Transformer\n", + " * Embedding com 1024 dimensões\n", + " * 340 milhões de parametros\n", + "\n", + "- Embeddings BERT \n", + " - \n", + " - cada pedaço da frase é separado em 3 embeddings: Token, Segment e Positional\n", + " - OBS: o bert não separa exatamete em palavras, ele separa os geundios (play ##ing) por exemplo\n", + "\n", + "* Exemplo de Embeddings: GloVe\n", + " * vetor fixo por palavra independente do contexto\n", + "\n", + "- BERT utiliza o ELMo -> olha para a setença inteira antes de atribuir o vetor\n", + " - usa LSTM bidirecional para criar o embedding\n", + " - aprende (sem labels) a predizer a prox palavra (e a anterior)\n", + " - BERT usa essa ideia, mas transformer\n", + " - LSTM bidirecional: faz uma predição do comeõ da frase pra frente e do fim da frase pra trás\n", + " - BERT: faz uma predição utiilizando todas as palavras da frase \n", + " - palavra 3: usa a palvra 1, 2, 4... N\n", + " - \n", + "\n", + "* BERT: ULM-FIT\n", + " * https://arxiv.org/pdf/1801.06146.pdf (copilot que sugeriu)\n", + " * metodos afetivos para pré trienamento para alem \n", + " * de words embeddings\n", + " * de words embeddings contextualizado\n", + " * modelo de linguagem + estratégia par aajustar modelo para várias tarefas\n", + " * descongelamento gradual: última camada até a primeira\n", + "\n", + "- \n", + "\n", + "---" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 64-bit (microsoft store)", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a33b474888067a6169f866e52f630d6f3672d35114c8362b477a93e2a003ce7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/bloco9_seq2seq_e_atencao/embeddings_BERT.jpg b/bloco9_seq2seq_e_atencao/embeddings_BERT.jpg new file mode 100644 index 0000000..0846732 Binary files /dev/null and b/bloco9_seq2seq_e_atencao/embeddings_BERT.jpg differ diff --git a/bloco9_seq2seq_e_atencao/matriz_mec_atencao.jpg b/bloco9_seq2seq_e_atencao/matriz_mec_atencao.jpg new file mode 100644 index 0000000..c8f83bc Binary files /dev/null and b/bloco9_seq2seq_e_atencao/matriz_mec_atencao.jpg differ diff --git a/bloco9_seq2seq_e_atencao/mec_atencao.jpg b/bloco9_seq2seq_e_atencao/mec_atencao.jpg new file mode 100644 index 0000000..b72e62f Binary files /dev/null and b/bloco9_seq2seq_e_atencao/mec_atencao.jpg differ diff --git a/bloco9_seq2seq_e_atencao/onehot.jpg b/bloco9_seq2seq_e_atencao/onehot.jpg new file mode 100644 index 0000000..73a52cd Binary files /dev/null and b/bloco9_seq2seq_e_atencao/onehot.jpg differ diff --git a/bloco9_seq2seq_e_atencao/s2s_enc_dec.jpg b/bloco9_seq2seq_e_atencao/s2s_enc_dec.jpg new file mode 100644 index 0000000..8e4815b Binary files /dev/null and b/bloco9_seq2seq_e_atencao/s2s_enc_dec.jpg differ diff --git a/bloco9_seq2seq_e_atencao/softmax_skipgram.jpg b/bloco9_seq2seq_e_atencao/softmax_skipgram.jpg new file mode 100644 index 0000000..365fafc Binary files /dev/null and b/bloco9_seq2seq_e_atencao/softmax_skipgram.jpg differ diff --git a/cuda.ipynb b/cuda.ipynb new file mode 100644 index 0000000..0331ffd --- /dev/null +++ b/cuda.ipynb @@ -0,0 +1,72 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Is CUDA supported by this system? False\n", + "CUDA version: None\n" + ] + }, + { + "ename": "AssertionError", + "evalue": "Torch not compiled with CUDA enabled", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAssertionError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32mc:\\Users\\felip\\Documents\\RedesNeurais\\cuda.ipynb Célula: 1\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCUDA version: \u001b[39m\u001b[39m{\u001b[39;00mtorch\u001b[39m.\u001b[39mversion\u001b[39m.\u001b[39mcuda\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 6\u001b[0m \u001b[39m# Storing ID of current CUDA device\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m cuda_id \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39;49mcuda\u001b[39m.\u001b[39;49mcurrent_device()\n\u001b[0;32m 8\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mID of current CUDA device:\u001b[39m\u001b[39m{\u001b[39;00mtorch\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39mcurrent_device()\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 10\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mName of current CUDA device:\u001b[39m\u001b[39m{\u001b[39;00mtorch\u001b[39m.\u001b[39mcuda\u001b[39m.\u001b[39mget_device_name(cuda_id)\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\torch\\cuda\\__init__.py:482\u001b[0m, in \u001b[0;36mcurrent_device\u001b[1;34m()\u001b[0m\n\u001b[0;32m 480\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcurrent_device\u001b[39m() \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mint\u001b[39m:\n\u001b[0;32m 481\u001b[0m \u001b[39mr\u001b[39m\u001b[39m\"\"\"Returns the index of a currently selected device.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 482\u001b[0m _lazy_init()\n\u001b[0;32m 483\u001b[0m \u001b[39mreturn\u001b[39;00m torch\u001b[39m.\u001b[39m_C\u001b[39m.\u001b[39m_cuda_getDevice()\n", + "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\torch\\cuda\\__init__.py:211\u001b[0m, in \u001b[0;36m_lazy_init\u001b[1;34m()\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n\u001b[0;32m 208\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mCannot re-initialize CUDA in forked subprocess. To use CUDA with \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m 209\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mmultiprocessing, you must use the \u001b[39m\u001b[39m'\u001b[39m\u001b[39mspawn\u001b[39m\u001b[39m'\u001b[39m\u001b[39m start method\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 210\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mhasattr\u001b[39m(torch\u001b[39m.\u001b[39m_C, \u001b[39m'\u001b[39m\u001b[39m_cuda_getDeviceCount\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m--> 211\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mTorch not compiled with CUDA enabled\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m 212\u001b[0m \u001b[39mif\u001b[39;00m _cudart \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 213\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mAssertionError\u001b[39;00m(\n\u001b[0;32m 214\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mlibcudart functions unavailable. It looks like you have a broken build?\u001b[39m\u001b[39m\"\u001b[39m)\n", + "\u001b[1;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled" + ] + } + ], + "source": [ + "\n", + "import torch\n", + "\n", + "print(f\"Is CUDA supported by this system? {torch.cuda.is_available()}\")\n", + "print(f\"CUDA version: {torch.version.cuda}\")\n", + "\n", + "# Storing ID of current CUDA device\n", + "cuda_id = torch.cuda.current_device()\n", + "print(f\"ID of current CUDA device:{torch.cuda.current_device()}\")\n", + " \n", + "print(f\"Name of current CUDA device:{torch.cuda.get_device_name(cuda_id)}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.9.13 64-bit (microsoft store)", + "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": "3.9.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "a33b474888067a6169f866e52f630d6f3672d35114c8362b477a93e2a003ce7e" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}