Final Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Music Genre Classification Using Neural Networks - Shiraz & Oran.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjTqcg1oyMhu"
      },
      "source": [
        "import pandas as pd\n",
        "from google.colab import drive\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils import data\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4n4Dz69wyfEV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a6901590-17a7-4515-8fa1-f59dbfd0a858"
      },
      "source": [
        "torch.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'1.4.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThYfHxTlgp_m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "0475c0a9-b653-4682-93f4-0c6c95f8a9dd"
      },
      "source": [
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iops2UW2g032",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "108a946a-04c8-4788-917f-7565d71d03c4"
      },
      "source": [
        "# Load shuffled audio files converted into MEL-Spectorgrams - TRAIN\n",
        "npzfile = np.load('/content/drive/My Drive/audiodataset/shuffled_train.npz')\n",
        "print(npzfile.files)\n",
        "X_train = npzfile['arr_0']\n",
        "y_train = npzfile['arr_1']\n",
        "print(\"X_train.shape: \", X_train.shape, \"y_train.shape: \", y_train.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['arr_0', 'arr_1']\n",
            "X_train.shape:  (6394, 640, 128) y_train.shape:  (6394, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EhWdo1SAW5Hi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "6e96b244-e204-49c7-d4ef-8569f5d7fb48"
      },
      "source": [
        "# Load shuffled audio files converted into MEL-Spectorgrams - VALID\n",
        "npzfile = np.load('/content/drive/My Drive/audiodataset/shuffled_valid.npz')\n",
        "print(npzfile.files)\n",
        "X_valid = npzfile['arr_0']\n",
        "y_valid = npzfile['arr_1']\n",
        "print(\"X_valid.shape: \", X_valid.shape, \"y_valid.shape: \", y_valid.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['arr_0', 'arr_1']\n",
            "X_valid.shape:  (800, 640, 128) y_valid.shape:  (800, 8)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfyFUtcF7wj7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "f20c8b98-e58a-47de-d904-4eb54cfa3f03"
      },
      "source": [
        "# Load audio files converted into MEL-Spectorgrams - TEST\n",
        "npzfile = np.load('/content/drive/My Drive/audiodataset/test_arr.npz')\n",
        "print(npzfile.files)\n",
        "X_test = npzfile['arr_0']\n",
        "y_test = npzfile['arr_1']\n",
        "y_test -= 1 # Labels are one-indexed, switch to zero-index\n",
        "print(\"X_test.shape: \", X_test.shape, \"y_test.shape: \", y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['arr_0', 'arr_1']\n",
            "X_test.shape:  (800, 640, 128) y_test.shape:  (800,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dREqnkBhbrI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "9d407dd0-835e-4c74-ee7b-8e498fa32378"
      },
      "source": [
        "# Creating a dataloader for trainning data\n",
        "tensor_x_train = torch.Tensor(X_train) # transform to torch tensor\n",
        "tensor_y_train = torch.Tensor(y_train)\n",
        "tensor_x_train = torch.unsqueeze(tensor_x_train, 1)\n",
        "print(\"tensor_x_train.shape: \", tensor_x_train.shape)\n",
        "\n",
        "train_set = data.TensorDataset(tensor_x_train,tensor_y_train)\n",
        "train_dataloader = data.DataLoader(train_set,batch_size=32, shuffle=True)\n",
        "\n",
        "# Creating a dataloader for validation data\n",
        "tensor_x_valid = torch.Tensor(X_valid)\n",
        "tensor_y_valid = torch.Tensor(y_valid)\n",
        "tensor_x_valid = torch.unsqueeze(tensor_x_valid, 1)\n",
        "print(\"tensor_x_valid.shape: \", tensor_x_valid.shape)\n",
        "\n",
        "valid_set = data.TensorDataset(tensor_x_valid, tensor_y_valid)\n",
        "valid_dataloader = data.DataLoader(valid_set, batch_size=32, shuffle=False)\n",
        "\n",
        "# Creating a dataloader for test data\n",
        "tensor_x_test = torch.Tensor(X_test)\n",
        "tensor_y_test = torch.Tensor(y_test)\n",
        "tensor_x_test = torch.unsqueeze(tensor_x_test, 1)\n",
        "print(\"tensor_x_test.shape: \", tensor_x_test.shape)\n",
        "test_set = data.TensorDataset(tensor_x_test, tensor_y_test)\n",
        "test_dataloader = data.DataLoader(test_set, batch_size=32, shuffle=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tensor_x_train.shape:  torch.Size([6394, 1, 640, 128])\n",
            "tensor_x_valid.shape:  torch.Size([800, 1, 640, 128])\n",
            "tensor_x_test.shape:  torch.Size([800, 1, 640, 128])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "raVzub3LlWb0"
      },
      "source": [
        "def plot_accuracies(train_accuracy_per_epoch, valid_accuracy_per_epoch, epochs, model_name):\n",
        "  plt.figure(figsize=(10,6))\n",
        "  plt.plot(range(1, epochs + 1), train_accuracy_per_epoch, 'b', label='Train Accuracy per epoch')\n",
        "  plt.plot(range(1, epochs + 1), valid_accuracy_per_epoch, 'r', label='Validation Accuracy per epoch')\n",
        "  plt.xlabel(\"Number of Epochs\")\n",
        "  plt.ylabel(\"Accuracy\")\n",
        "  plt.legend(loc='best')\n",
        "  plt.title(model_name + \" Accuracy\")\n",
        "  plt.show()\n",
        "\n",
        "def plot_loss(train_loss_per_epoch, valid_loss_per_epoch, epochs, model_name):\n",
        "  plt.figure(figsize=(10,6))\n",
        "  plt.plot(range(1, epochs + 1), train_loss_per_epoch, 'b', label='Train Loss per epoch')\n",
        "  plt.plot(range(1, epochs + 1), valid_loss_per_epoch, 'r', label='Validation Loss per epoch')\n",
        "  plt.xlabel(\"Number of Epochs\")\n",
        "  plt.ylabel(\"Loss\")\n",
        "  plt.legend(loc='best')\n",
        "  plt.title(model_name + \" Loss\")\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGxfabI6hCAY"
      },
      "source": [
        "def calc_validation_accuracy(net):\n",
        "  with torch.no_grad():\n",
        "    correct = 0 \n",
        "    for i, data in enumerate(valid_dataloader, 0):\n",
        "      inputs, labels = data      \n",
        "      inputs = inputs.cuda() # -- For GPU\n",
        "      labels = labels.cuda() # -- For GPU\n",
        "\n",
        "      outputs = net(inputs)\n",
        "      labels = torch.max(labels, 1)[1]\n",
        "      indices_max = torch.max(outputs, 1)[1]\n",
        "      correct += (indices_max == labels).sum().item()\n",
        "\n",
        "    accuracy = 100 * correct / len(valid_dataloader.dataset)\n",
        "  return accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRNlyHtOJZG2"
      },
      "source": [
        "def calc_test_accuracy(net):\n",
        "  with torch.no_grad():\n",
        "    correct = 0 \n",
        "    for i, data in enumerate(test_dataloader, 0):\n",
        "      inputs, labels = data      \n",
        "      inputs = inputs.cuda() # -- For GPU\n",
        "      labels = labels.cuda() # -- For GPU\n",
        "\n",
        "      outputs = net(inputs)\n",
        "      indices_max = torch.max(outputs, 1)[1]\n",
        "      correct += (indices_max == labels).sum().item()\n",
        "\n",
        "    accuracy = 100 * correct / len(test_dataloader.dataset)\n",
        "  return accuracy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zWTtp1rmedAg"
      },
      "source": [
        "def calc_validation_loss(net, criterion):\n",
        "  with torch.no_grad():\n",
        "    loss = 0.0\n",
        "    for inputs, labels in valid_dataloader:\n",
        "      inputs = inputs.cuda() # -- For GPU\n",
        "      labels = labels.cuda() # -- For GPU\n",
        "      outputs = net(inputs)\n",
        "      labels = torch.max(labels, 1)[1]\n",
        "      loss += criterion(outputs, labels).item()\n",
        "    return loss/len(valid_dataloader)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lj5RlY32Ya8k"
      },
      "source": [
        "# training loop\n",
        "\n",
        "def train(net, train_dataloader, epochs, optim):\n",
        "  # define loss function, optimizer\n",
        "  criterion = nn.CrossEntropyLoss()\n",
        "  optimizer = optim\n",
        "  \n",
        "  train_accuracy_per_epoch = []\n",
        "  valid_accuracy_per_epoch = []\n",
        "  train_loss_per_epoch = []\n",
        "  valid_loss_per_epoch = []\n",
        "\n",
        "  for epoch in range(epochs):\n",
        "    correct = 0\n",
        "    running_loss = 0.0\n",
        "    epoch_loss = 0.0\n",
        "\n",
        "    for i, data in enumerate(train_dataloader, 0):\n",
        "      # get the inputs\n",
        "      inputs, labels = data\n",
        "      \n",
        "      inputs = inputs.cuda() # -- For GPU\n",
        "      labels = labels.cuda() # -- For GPU\n",
        "\n",
        "      # zero the parameter gradients\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      # forward + backward + optimize\n",
        "      outputs = net(inputs)\n",
        "      labels = torch.max(labels, 1)[1]\n",
        "      loss = criterion(outputs,  labels)\n",
        "      loss.backward()\n",
        "      optimizer.step()\n",
        "\n",
        "      # accuracy and loss maintaining\n",
        "      running_loss += loss.item()\n",
        "      epoch_loss += loss.item()\n",
        "      indices_max = torch.max(outputs, 1)[1]\n",
        "      correct += (indices_max == labels).sum().item()\n",
        "    \n",
        "\n",
        "    valid_loss = calc_validation_loss(net, criterion)\n",
        "    train_loss = epoch_loss / len(train_dataloader)\n",
        "    valid_loss_per_epoch.append(valid_loss)\n",
        "    train_loss_per_epoch.append(train_loss)\n",
        "    \n",
        "    train_accuracy = 100 * correct / len(X_train)\n",
        "    train_accuracy_per_epoch.append(train_accuracy)\n",
        "    valid_accuracy = calc_validation_accuracy(net)\n",
        "    valid_accuracy_per_epoch.append(valid_accuracy)\n",
        "\n",
        "    print(\"Epoch #\", epoch)\n",
        "    print(\"Train Accuracy = {}\".format(train_accuracy))\n",
        "    print(\"Valid Accuracy = {}\".format(valid_accuracy))\n",
        "    print(\"Train Loss = {}\".format(train_loss))\n",
        "    print(\"Valid Loss = {}\".format(valid_loss))\n",
        "\n",
        "  print('Finished Training')\n",
        "  return train_accuracy_per_epoch, valid_accuracy_per_epoch, train_loss_per_epoch, valid_loss_per_epoch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TzJULD4QQIRH"
      },
      "source": [
        "class CRNN(nn.Module):\n",
        "  def __init__(self):\n",
        "      super(CRNN, self).__init__()\n",
        "      \n",
        "      self.conv1 = nn.Conv1d(in_channels=1, out_channels=32, kernel_size=5)\n",
        "      torch.nn.init.xavier_uniform_(self.conv1.weight)\n",
        "      self.bn1 = nn.BatchNorm1d(32, momentum = 0.9)\n",
        "      self.drop1  = nn.Dropout(0.5)\n",
        "\n",
        "      self.conv2 = nn.Conv1d(in_channels=32, out_channels=40, kernel_size=5)\n",
        "      torch.nn.init.xavier_uniform_(self.conv2.weight)\n",
        "      self.bn2 = nn.BatchNorm1d(40, momentum = 0.9)\n",
        "      self.drop2  = nn.Dropout(0.5)\n",
        "\n",
        "      self.conv3 = nn.Conv1d(in_channels=40, out_channels=52, kernel_size=5)\n",
        "      torch.nn.init.xavier_uniform_(self.conv3.weight)\n",
        "      self.bn3 = nn.BatchNorm1d(52, momentum = 0.9)\n",
        "      self.drop3  = nn.Dropout(0.5)\n",
        "\n",
        "      self.rnn = nn.LSTM(10236, 96, 1)\n",
        "      self.drop4  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc1    = nn.Linear(in_features=4992, out_features=64)\n",
        "      self.drop5  = nn.Dropout(0.5)\n",
        "      self.fc2    = nn.Linear(in_features=64,  out_features=8)\n",
        "\n",
        "  def forward(self, x):\n",
        "      x = x.view(-1,1, self.num_flat_features(x)) # keep channel dimension '1' for conv, flatten images\n",
        "\n",
        "      # 3 CNN\n",
        "      x = F.max_pool1d(F.relu(self.bn1(self.conv1(x))), 2)\n",
        "      x   = self.drop1(x)\n",
        "      x = F.max_pool1d(F.relu(self.bn2(self.conv2(x))), 2)\n",
        "      x   = self.drop2(x)\n",
        "      x = F.max_pool1d(F.relu(self.bn3(self.conv3(x))), 2)\n",
        "      x   = self.drop3(x)\n",
        "\n",
        "      # LSTM\n",
        "      x, (hn,cn) = self.rnn(x)\n",
        "      x = self.drop4(x)\n",
        "\n",
        "      # fully connected\n",
        "      x = x.view(-1,self.num_flat_features(x))\n",
        "      x = self.fc1(x)\n",
        "      x   = self.drop5(x)\n",
        "      x = self.fc2(x)\n",
        "      return x\n",
        "\n",
        "  def num_flat_features(self, x):\n",
        "      size = x.size()[1:]  # all dimensions except the batch dimension\n",
        "      num_features = 1\n",
        "      for s in size:\n",
        "          num_features *= s\n",
        "      return num_features\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KCHCG6hrNM7"
      },
      "source": [
        "class CRNN_stacked(nn.Module):\n",
        "  def __init__(self):\n",
        "      super(CRNN_stacked, self).__init__()\n",
        "      \n",
        "      self.conv1 = nn.Conv1d(in_channels=1, out_channels=32, kernel_size=3, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv1.weight)\n",
        "      self.bn1 = nn.BatchNorm1d(32)\n",
        "      self.drop1  = nn.Dropout(0.3)\n",
        "\n",
        "      self.conv2 = nn.Conv1d(in_channels=32, out_channels=32, kernel_size=3, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv2.weight)\n",
        "      self.bn2 = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv3 = nn.Conv1d(in_channels=32, out_channels=32, kernel_size=3, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv3.weight)\n",
        "      self.bn3 = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv4 = nn.Conv1d(in_channels=32, out_channels=32, kernel_size=3, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv4.weight)\n",
        "      self.bn4 = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv5 = nn.Conv1d(in_channels=32, out_channels=32, kernel_size=3, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv5.weight)\n",
        "      self.bn5 = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.rnn = nn.LSTM(2560, 96, 2)\n",
        "      self.drop2  = nn.Dropout(0.5)\n",
        "      self.fc1    = nn.Linear(in_features=3072, out_features=512)\n",
        "      self.drop3  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc2 = nn.Linear(in_features=512, out_features=64)\n",
        "      self.drop8  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc3    = nn.Linear(in_features=64,  out_features=8)\n",
        "\n",
        "  def forward(self, x):\n",
        "      x = x.view(-1,1, self.num_flat_features(x)) # keep channel dimension '1' for conv, flatten images\n",
        "\n",
        "      # 5 CNN\n",
        "      x = F.max_pool1d(F.relu(self.bn1(self.conv1(x))), 2)\n",
        "      x   = self.drop1(x)\n",
        "      x = F.max_pool1d(F.relu(self.bn2(self.conv2(x))), 2)\n",
        "      x = F.max_pool1d(F.relu(self.bn3(self.conv3(x))), 2)\n",
        "      x = F.max_pool1d(F.relu(self.bn4(self.conv4(x))), 2)\n",
        "      x = F.max_pool1d(F.relu(self.bn5(self.conv5(x))), 2)\n",
        "\n",
        "      # LSTM\n",
        "      x, (hn,cn) = self.rnn(x)\n",
        "      x = self.drop2(x)\n",
        "\n",
        "      # fully connected\n",
        "      x = x.view(-1,self.num_flat_features(x))\n",
        "      x = self.fc1(x)\n",
        "      x   = self.drop3(x)\n",
        "      x = self.fc2(x)\n",
        "      return x\n",
        "\n",
        "  def num_flat_features(self, x):\n",
        "      size = x.size()[1:]  # all dimensions except the batch dimension\n",
        "      num_features = 1\n",
        "      for s in size:\n",
        "          num_features *= s\n",
        "      return num_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30J_7bPJDTnP"
      },
      "source": [
        "class RNN(nn.Module):\n",
        "  def __init__(self):\n",
        "      super(RNN, self).__init__()\n",
        "      self.rnn = nn.LSTM(81920, 96, 1)\n",
        "      self.drop1  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc1    = nn.Linear(in_features=96, out_features=64)\n",
        "      self.drop2  = nn.Dropout(0.5)\n",
        "      self.fc2    = nn.Linear(in_features=64,  out_features=8)\n",
        "\n",
        "  def forward(self, x):\n",
        "      x = x.view(-1,1, self.num_flat_features(x)) # keep channel dimension '1' for lstm, flatten images\n",
        "      # LSTM\n",
        "      x, (hn,cn) = self.rnn(x)\n",
        "      x = self.drop1(x)\n",
        "\n",
        "      # fully connected\n",
        "      x = x.view(-1,self.num_flat_features(x))\n",
        "      x = self.fc1(x)\n",
        "      x   = self.drop2(x)\n",
        "      x = self.fc2(x)\n",
        "      return x\n",
        "\n",
        "  def num_flat_features(self, x):\n",
        "      size = x.size()[1:]  # all dimensions except the batch dimension\n",
        "      num_features = 1\n",
        "      for s in size:\n",
        "          num_features *= s\n",
        "      return num_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rl2qJtp2L27p"
      },
      "source": [
        "class CNN_1D_Shallow(nn.Module):\n",
        "  def __init__(self):\n",
        "      super(CNN_1D_Shallow, self).__init__()\n",
        "\n",
        "      self.conv1  = nn.Conv1d(in_channels=1,     out_channels=8,    kernel_size=3,  stride=2, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv1.weight)\n",
        "      self.bn1    = nn.BatchNorm1d(8)\n",
        "\n",
        "      self.conv2  = nn.Conv1d(in_channels=8, out_channels=16,      kernel_size=3,  stride=2, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv2.weight)\n",
        "      self.bn2    = nn.BatchNorm1d(16)\n",
        "\n",
        "      self.conv3  = nn.Conv1d(in_channels=16, out_channels=64,      kernel_size=3,  stride=2, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv3.weight)\n",
        "      self.bn3    = nn.BatchNorm1d(64)\n",
        "\n",
        "      self.conv4  = nn.Conv1d(in_channels=64, out_channels=128,      kernel_size=3,  stride=2, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv4.weight)\n",
        "      self.bn4    = nn.BatchNorm1d(128)\n",
        "\n",
        "      self.fc1    = nn.Linear(in_features=40960,  out_features=1024)\n",
        "      self.drop1  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc2    = nn.Linear(in_features=1024,  out_features=256)\n",
        "      self.drop2  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc3    = nn.Linear(in_features=256,   out_features=8)\n",
        "\n",
        "  def forward(self, x):\n",
        "      x = x.view(-1,1, self.num_flat_features(x)) # keep channel dimension '1' for conv, flatten images\n",
        "      x = F.max_pool1d(F.relu(self.bn1(self.conv1(x))), 2)\n",
        "      x = F.max_pool1d(F.relu(self.bn2(self.conv2(x))), 2)\n",
        "      x = F.max_pool1d(F.relu(self.bn3(self.conv3(x))), 2)\n",
        "      x = F.max_pool1d(F.relu(self.bn4(self.conv4(x))), 2)\n",
        "      x = x.view(-1, self.num_flat_features(x))\n",
        "      x = F.relu(self.fc1(x))\n",
        "      x   = self.drop1(x)\n",
        "      x = F.relu(self.fc2(x))\n",
        "      x   = self.drop2(x)\n",
        "      x = self.fc3(x)\n",
        "      return x\n",
        "\n",
        "  def num_flat_features(self, x):\n",
        "      size = x.size()[1:]  # all dimensions except the batch dimension\n",
        "      num_features = 1\n",
        "      for s in size:\n",
        "          num_features *= s\n",
        "      return num_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AcGX7d6lcRU"
      },
      "source": [
        "class CNN_1D_Deep(nn.Module):\n",
        "  def __init__(self):\n",
        "      super(CNN_1D_Deep, self).__init__()\n",
        "      \n",
        "      self.conv1  = nn.Conv1d(in_channels=1,     out_channels=32,    kernel_size=3, stride=2, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv1.weight)\n",
        "      self.bn1    = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv2  = nn.Conv1d(in_channels=32, out_channels=32,      kernel_size=3,stride=2, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv2.weight)\n",
        "      self.bn2    = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv3  = nn.Conv1d(in_channels=32, out_channels=32,      kernel_size=3, stride=2,   padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv3.weight)\n",
        "      self.bn3    = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv4  = nn.Conv1d(in_channels=32, out_channels=32,      kernel_size=3, stride=2,  padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv4.weight)\n",
        "      self.bn4    = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv5  = nn.Conv1d(in_channels=32, out_channels=32,      kernel_size=3, stride=2,   padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv5.weight)\n",
        "      self.bn5    = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv6  = nn.Conv1d(in_channels=32, out_channels=32,      kernel_size=3, stride=2,   padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv6.weight)\n",
        "      self.bn6    = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv7  = nn.Conv1d(in_channels=32, out_channels=32,      kernel_size=3,stride=2,    padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv7.weight)\n",
        "      self.bn7    = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv8  = nn.Conv1d(in_channels=32, out_channels=32,      kernel_size=3, stride=2,  padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv8.weight)\n",
        "      self.bn8    = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv9  = nn.Conv1d(in_channels=32, out_channels=32,      kernel_size=3,  stride=2,  padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv9.weight)\n",
        "      self.bn9    = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.conv10  = nn.Conv1d(in_channels=32, out_channels=32,      kernel_size=3, stride=2, padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv10.weight)\n",
        "      self.bn10    = nn.BatchNorm1d(32)\n",
        "\n",
        "      self.fc1    = nn.Linear(in_features=160,  out_features=1024)\n",
        "      self.drop1  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc2    = nn.Linear(in_features=1024,  out_features=256)\n",
        "      self.drop2  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc3    = nn.Linear(in_features=256,   out_features=8)\n",
        "\n",
        "  def forward(self, x):\n",
        "      x = x.view(-1,1, self.num_flat_features(x)) # keep channel dimension '1' for conv, flatten images\n",
        "      x = F.relu(self.bn1(self.conv1(x)))\n",
        "      x = F.relu(self.bn2(self.conv2(x)))\n",
        "      x = F.max_pool1d(F.relu(self.bn3(self.conv3(x))), 2)\n",
        "      x = F.relu(self.bn4(self.conv4(x)))\n",
        "      x = F.relu(self.bn5(self.conv5(x)))\n",
        "      x = F.max_pool1d(F.relu(self.bn6(self.conv6(x))), 2)\n",
        "      x = F.relu(self.bn7(self.conv7(x)))\n",
        "      x = F.relu(self.bn8(self.conv8(x)))\n",
        "      x = F.max_pool1d(F.relu(self.bn9(self.conv9(x))), 2)\n",
        "      x = F.max_pool1d(F.relu(self.bn10(self.conv10(x))), 2)\n",
        "\n",
        "      x = x.view(-1, self.num_flat_features(x))\n",
        "      x = F.relu(self.fc1(x))\n",
        "      x = self.drop1(x)\n",
        "      x = F.relu(self.fc2(x))\n",
        "      x = self.drop2(x)\n",
        "      x = self.fc3(x)\n",
        "      return x\n",
        "\n",
        "  def num_flat_features(self, x):\n",
        "      size = x.size()[1:]  # all dimensions except the batch dimension\n",
        "      num_features = 1\n",
        "      for s in size:\n",
        "          num_features *= s\n",
        "      return num_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGvil6NDDT1t"
      },
      "source": [
        "class CNN_2D_Shallow(nn.Module):\n",
        "  def __init__(self):\n",
        "      super(CNN_2D_Shallow, self).__init__()\n",
        "      \n",
        "      self.conv1  = nn.Conv2d(in_channels=1,     out_channels=8,    kernel_size=3,  stride=[2,1],   padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv1.weight)\n",
        "      self.bn1    = nn.BatchNorm2d(8)\n",
        "\n",
        "      self.conv2  = nn.Conv2d(in_channels=8, out_channels=16,      kernel_size=3,  stride=[2,1],   padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv2.weight)\n",
        "      self.bn2    = nn.BatchNorm2d(16)\n",
        "\n",
        "      self.conv3  = nn.Conv2d(in_channels=16, out_channels=64,      kernel_size=3,  stride=[2,1],   padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv3.weight)\n",
        "      self.bn3    = nn.BatchNorm2d(64)\n",
        "\n",
        "      self.conv4  = nn.Conv2d(in_channels=64, out_channels=128,      kernel_size=3,  stride=[2,1],   padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv4.weight)\n",
        "      self.bn4    = nn.BatchNorm2d(128)\n",
        "\n",
        "      self.fc1    = nn.Linear(in_features=2048,  out_features=1024)\n",
        "      self.drop1  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc2    = nn.Linear(in_features=1024,  out_features=256)\n",
        "      self.drop2  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc3    = nn.Linear(in_features=256,   out_features=8)\n",
        "\n",
        "  def forward(self, x):\n",
        "      x = F.max_pool2d(F.relu(self.bn1(self.conv1(x))), 2)\n",
        "      x = F.max_pool2d(F.relu(self.bn2(self.conv2(x))), 2)\n",
        "      x = F.max_pool2d(F.relu(self.bn3(self.conv3(x))), 2)\n",
        "      x = F.max_pool2d(F.relu(self.bn4(self.conv4(x))), 2)\n",
        "      x = x.view(-1, self.num_flat_features(x))\n",
        "      x = F.relu(self.fc1(x))\n",
        "      x = self.drop1(x)\n",
        "      x = F.relu(self.fc2(x))\n",
        "      x = self.drop2(x)\n",
        "      x = self.fc3(x)\n",
        "      return x\n",
        "\n",
        "  def num_flat_features(self, x):\n",
        "      size = x.size()[1:]  # all dimensions except the batch dimension\n",
        "      num_features = 1\n",
        "      for s in size:\n",
        "          num_features *= s\n",
        "      return num_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UIdH6xlM6WAg"
      },
      "source": [
        "class CNN_2D_Deep(nn.Module):\n",
        "  def __init__(self):\n",
        "      super(CNN_2D_Deep, self).__init__()\n",
        "      \n",
        "      self.conv1  = nn.Conv2d(in_channels=1,     out_channels=32,    kernel_size=3,  stride=[2,1],   padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv1.weight)\n",
        "      self.bn1    = nn.BatchNorm2d(32)\n",
        "\n",
        "      self.conv2  = nn.Conv2d(in_channels=32, out_channels=32,      kernel_size=3, stride=[2,1], padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv2.weight)\n",
        "      self.bn2    = nn.BatchNorm2d(32)\n",
        "\n",
        "      self.conv3  = nn.Conv2d(in_channels=32, out_channels=32,      kernel_size=3,    padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv3.weight)\n",
        "      self.bn3    = nn.BatchNorm2d(32)\n",
        "\n",
        "      self.conv4  = nn.Conv2d(in_channels=32, out_channels=32,      kernel_size=3,   padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv4.weight)\n",
        "      self.bn4    = nn.BatchNorm2d(32)\n",
        "\n",
        "      self.conv5  = nn.Conv2d(in_channels=32, out_channels=32,      kernel_size=3,    padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv5.weight)\n",
        "      self.bn5    = nn.BatchNorm2d(32)\n",
        "\n",
        "      self.conv6  = nn.Conv2d(in_channels=32, out_channels=32,      kernel_size=3,    padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv6.weight)\n",
        "      self.bn6    = nn.BatchNorm2d(32)\n",
        "\n",
        "      self.conv7  = nn.Conv2d(in_channels=32, out_channels=32,      kernel_size=3,    padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv7.weight)\n",
        "      self.bn7    = nn.BatchNorm2d(32)\n",
        "\n",
        "      self.conv8  = nn.Conv2d(in_channels=32, out_channels=32,      kernel_size=3,   padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv8.weight)\n",
        "      self.bn8    = nn.BatchNorm2d(32)\n",
        "\n",
        "      self.conv9  = nn.Conv2d(in_channels=32, out_channels=32,      kernel_size=3,    padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv9.weight)\n",
        "      self.bn9    = nn.BatchNorm2d(32)\n",
        "\n",
        "      self.conv10  = nn.Conv2d(in_channels=32, out_channels=32,      kernel_size=3,  padding=1)\n",
        "      torch.nn.init.xavier_uniform_(self.conv10.weight)\n",
        "      self.bn10    = nn.BatchNorm2d(32)\n",
        "\n",
        "      self.fc1    = nn.Linear(in_features=2560,  out_features=1024)\n",
        "      self.drop1  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc2    = nn.Linear(in_features=1024,  out_features=256)\n",
        "      self.drop2  = nn.Dropout(0.5)\n",
        "\n",
        "      self.fc3    = nn.Linear(in_features=256,   out_features=8)\n",
        "\n",
        "  def forward(self, x):\n",
        "      x = F.relu(self.bn1(self.conv1(x)))\n",
        "      x = F.relu(self.bn2(self.conv2(x)))\n",
        "      x = F.max_pool2d(F.relu(self.bn3(self.conv3(x))), 2)\n",
        "      x = F.relu(self.bn4(self.conv4(x)))\n",
        "      x = F.relu(self.bn5(self.conv5(x)))\n",
        "      x = F.max_pool2d(F.relu(self.bn6(self.conv6(x))), 2)\n",
        "      x = F.relu(self.bn7(self.conv7(x)))\n",
        "      x = F.relu(self.bn8(self.conv8(x)))\n",
        "      x = F.max_pool2d(F.relu(self.bn9(self.conv9(x))), 2)\n",
        "      x = F.max_pool2d(F.relu(self.bn10(self.conv10(x))), 2)\n",
        "      x = x.view(-1, self.num_flat_features(x))\n",
        "      x = F.relu(self.fc1(x))\n",
        "      x = self.drop1(x)\n",
        "      x = F.relu(self.fc2(x))\n",
        "      x = self.drop2(x)\n",
        "      x = self.fc3(x)\n",
        "      return x\n",
        "\n",
        "  def num_flat_features(self, x):\n",
        "      size = x.size()[1:]  # all dimensions except the batch dimension\n",
        "      num_features = 1\n",
        "      for s in size:\n",
        "          num_features *= s\n",
        "      return num_features"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E9Jkc2xsTb3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bfc7c81d-1a51-434b-91cb-563e1ff6bbaa"
      },
      "source": [
        "epochs = 70\n",
        "net_crnn = CRNN().cuda()\n",
        "optimizer = torch.optim.Adam(net_crnn.parameters(),weight_decay=0.01)\n",
        "train_accuracy_crnn, valid_accuracy_crnn, train_loss_crnn, valid_loss_crnn = train(net_crnn,train_dataloader, epochs, optimizer)\n",
        "plot_accuracies(train_accuracy_crnn,valid_accuracy_crnn,epochs,\"CRNN\")\n",
        "plot_loss(train_loss_crnn, valid_loss_crnn, epochs, \"CRNN\")\n",
        "test_accuracy_crnn = calc_test_accuracy(net_crnn)\n",
        "print(\"Test accuracy: \", test_accuracy_crnn)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch # 0\n",
            "Train Accuracy = 21.05098529871755\n",
            "Valid Accuracy = 26.875\n",
            "Train Loss = 1.9952952533960342\n",
            "Valid Loss = 1.8201413440704346\n",
            "Epoch # 1\n",
            "Train Accuracy = 32.71817328745699\n",
            "Valid Accuracy = 39.625\n",
            "Train Loss = 1.7782627195119858\n",
            "Valid Loss = 1.6930090522766112\n",
            "Epoch # 2\n",
            "Train Accuracy = 36.518611197998126\n",
            "Valid Accuracy = 38.125\n",
            "Train Loss = 1.6915639299154281\n",
            "Valid Loss = 1.7543040895462036\n",
            "Epoch # 3\n",
            "Train Accuracy = 38.17641538942759\n",
            "Valid Accuracy = 35.25\n",
            "Train Loss = 1.6583441281318665\n",
            "Valid Loss = 1.7066899156570434\n",
            "Epoch # 4\n",
            "Train Accuracy = 40.897716609321236\n",
            "Valid Accuracy = 39.0\n",
            "Train Loss = 1.612891286611557\n",
            "Valid Loss = 1.6312387371063233\n",
            "Epoch # 5\n",
            "Train Accuracy = 42.05505161088521\n",
            "Valid Accuracy = 46.375\n",
            "Train Loss = 1.5941806799173355\n",
            "Valid Loss = 1.5379016304016113\n",
            "Epoch # 6\n",
            "Train Accuracy = 43.29058492336566\n",
            "Valid Accuracy = 43.375\n",
            "Train Loss = 1.58240338742733\n",
            "Valid Loss = 1.6099552726745605\n",
            "Epoch # 7\n",
            "Train Accuracy = 43.994369721614014\n",
            "Valid Accuracy = 43.875\n",
            "Train Loss = 1.5529770851135254\n",
            "Valid Loss = 1.608086757659912\n",
            "Epoch # 8\n",
            "Train Accuracy = 44.71379418204567\n",
            "Valid Accuracy = 42.125\n",
            "Train Loss = 1.5438250124454498\n",
            "Valid Loss = 1.575740909576416\n",
            "Epoch # 9\n",
            "Train Accuracy = 46.62183296840788\n",
            "Valid Accuracy = 47.375\n",
            "Train Loss = 1.5252251988649368\n",
            "Valid Loss = 1.5484181308746339\n",
            "Epoch # 10\n",
            "Train Accuracy = 46.590553644041286\n",
            "Valid Accuracy = 45.5\n",
            "Train Loss = 1.506907513141632\n",
            "Valid Loss = 1.5511647987365722\n",
            "Epoch # 11\n",
            "Train Accuracy = 46.934626212073816\n",
            "Valid Accuracy = 46.375\n",
            "Train Loss = 1.4972556638717651\n",
            "Valid Loss = 1.5344382524490356\n",
            "Epoch # 12\n",
            "Train Accuracy = 48.67062871441977\n",
            "Valid Accuracy = 46.0\n",
            "Train Loss = 1.4789253336191177\n",
            "Valid Loss = 1.5148374509811402\n",
            "Epoch # 13\n",
            "Train Accuracy = 48.62370972786988\n",
            "Valid Accuracy = 47.75\n",
            "Train Loss = 1.4689847505092621\n",
            "Valid Loss = 1.519109811782837\n",
            "Epoch # 14\n",
            "Train Accuracy = 49.35877385048483\n",
            "Valid Accuracy = 43.25\n",
            "Train Loss = 1.4429041796922684\n",
            "Valid Loss = 1.5764632844924926\n",
            "Epoch # 15\n",
            "Train Accuracy = 49.65592743196747\n",
            "Valid Accuracy = 44.375\n",
            "Train Loss = 1.4312732988595962\n",
            "Valid Loss = 1.512472515106201\n",
            "Epoch # 16\n",
            "Train Accuracy = 50.422270878949014\n",
            "Valid Accuracy = 45.125\n",
            "Train Loss = 1.4099515917897225\n",
            "Valid Loss = 1.5846638250350953\n",
            "Epoch # 17\n",
            "Train Accuracy = 51.72036284016265\n",
            "Valid Accuracy = 45.0\n",
            "Train Loss = 1.3949069914221763\n",
            "Valid Loss = 1.5808401918411255\n",
            "Epoch # 18\n",
            "Train Accuracy = 52.31467000312793\n",
            "Valid Accuracy = 41.625\n",
            "Train Loss = 1.3687727788090707\n",
            "Valid Loss = 1.5977492237091064\n",
            "Epoch # 19\n",
            "Train Accuracy = 54.37910541132312\n",
            "Valid Accuracy = 46.125\n",
            "Train Loss = 1.3245806443691253\n",
            "Valid Loss = 1.589186749458313\n",
            "Epoch # 20\n",
            "Train Accuracy = 54.77009696590554\n",
            "Valid Accuracy = 38.0\n",
            "Train Loss = 1.307649017572403\n",
            "Valid Loss = 1.746433687210083\n",
            "Epoch # 21\n",
            "Train Accuracy = 55.67719737253675\n",
            "Valid Accuracy = 43.875\n",
            "Train Loss = 1.2901424542069435\n",
            "Valid Loss = 1.5664973163604736\n",
            "Epoch # 22\n",
            "Train Accuracy = 56.17766656240225\n",
            "Valid Accuracy = 41.5\n",
            "Train Loss = 1.2569649779796601\n",
            "Valid Loss = 1.6141845273971558\n",
            "Epoch # 23\n",
            "Train Accuracy = 57.491398185799184\n",
            "Valid Accuracy = 42.0\n",
            "Train Loss = 1.2261656022071838\n",
            "Valid Loss = 1.6670816326141358\n",
            "Epoch # 24\n",
            "Train Accuracy = 59.07100406631217\n",
            "Valid Accuracy = 43.375\n",
            "Train Loss = 1.1894285929203035\n",
            "Valid Loss = 1.752926115989685\n",
            "Epoch # 25\n",
            "Train Accuracy = 59.35251798561151\n",
            "Valid Accuracy = 41.25\n",
            "Train Loss = 1.1874193787574767\n",
            "Valid Loss = 1.6877619171142577\n",
            "Epoch # 26\n",
            "Train Accuracy = 60.60369096027526\n",
            "Valid Accuracy = 38.875\n",
            "Train Loss = 1.1501117223501205\n",
            "Valid Loss = 1.728225932121277\n",
            "Epoch # 27\n",
            "Train Accuracy = 60.65060994682515\n",
            "Valid Accuracy = 41.875\n",
            "Train Loss = 1.128150756061077\n",
            "Valid Loss = 1.7208485698699951\n",
            "Epoch # 28\n",
            "Train Accuracy = 62.543009071004064\n",
            "Valid Accuracy = 39.25\n",
            "Train Loss = 1.089769860804081\n",
            "Valid Loss = 1.8203128814697265\n",
            "Epoch # 29\n",
            "Train Accuracy = 62.30841413825461\n",
            "Valid Accuracy = 37.625\n",
            "Train Loss = 1.0747008526325226\n",
            "Valid Loss = 1.9032701587677001\n",
            "Epoch # 30\n",
            "Train Accuracy = 63.80982170785111\n",
            "Valid Accuracy = 35.625\n",
            "Train Loss = 1.0581188467144966\n",
            "Valid Loss = 1.8758982944488525\n",
            "Epoch # 31\n",
            "Train Accuracy = 64.04441664060056\n",
            "Valid Accuracy = 40.375\n",
            "Train Loss = 1.0430393651127816\n",
            "Valid Loss = 1.788298192024231\n",
            "Epoch # 32\n",
            "Train Accuracy = 64.92023772286518\n",
            "Valid Accuracy = 38.0\n",
            "Train Loss = 1.0165717387199402\n",
            "Valid Loss = 1.8420222854614259\n",
            "Epoch # 33\n",
            "Train Accuracy = 66.04629340006255\n",
            "Valid Accuracy = 34.75\n",
            "Train Loss = 0.9933285820484161\n",
            "Valid Loss = 1.822417016029358\n",
            "Epoch # 34\n",
            "Train Accuracy = 66.17141069752893\n",
            "Valid Accuracy = 38.25\n",
            "Train Loss = 0.9899331271648407\n",
            "Valid Loss = 1.9525501775741576\n",
            "Epoch # 35\n",
            "Train Accuracy = 66.71879887394432\n",
            "Valid Accuracy = 36.75\n",
            "Train Loss = 0.9550175303220749\n",
            "Valid Loss = 2.002352657318115\n",
            "Epoch # 36\n",
            "Train Accuracy = 68.17328745699093\n",
            "Valid Accuracy = 39.625\n",
            "Train Loss = 0.9293365201354027\n",
            "Valid Loss = 2.053861904144287\n",
            "Epoch # 37\n",
            "Train Accuracy = 67.90741319987488\n",
            "Valid Accuracy = 38.0\n",
            "Train Loss = 0.9410100319981575\n",
            "Valid Loss = 2.0310201454162597\n",
            "Epoch # 38\n",
            "Train Accuracy = 68.56427901157335\n",
            "Valid Accuracy = 39.375\n",
            "Train Loss = 0.9175920909643174\n",
            "Valid Loss = 1.9598776006698608\n",
            "Epoch # 39\n",
            "Train Accuracy = 69.51829840475446\n",
            "Valid Accuracy = 35.625\n",
            "Train Loss = 0.8984814310073852\n",
            "Valid Loss = 2.13834659576416\n",
            "Epoch # 40\n",
            "Train Accuracy = 69.98748827025337\n",
            "Valid Accuracy = 36.25\n",
            "Train Loss = 0.89255899772048\n",
            "Valid Loss = 2.105247344970703\n",
            "Epoch # 41\n",
            "Train Accuracy = 70.06568658116984\n",
            "Valid Accuracy = 35.375\n",
            "Train Loss = 0.8779066714644432\n",
            "Valid Loss = 2.064577512741089\n",
            "Epoch # 42\n",
            "Train Accuracy = 70.86330935251799\n",
            "Valid Accuracy = 33.375\n",
            "Train Loss = 0.8756786690652371\n",
            "Valid Loss = 2.2494466209411623\n",
            "Epoch # 43\n",
            "Train Accuracy = 71.53581482639974\n",
            "Valid Accuracy = 37.375\n",
            "Train Loss = 0.8422121787071228\n",
            "Valid Loss = 2.0295145177841185\n",
            "Epoch # 44\n",
            "Train Accuracy = 70.73819205505161\n",
            "Valid Accuracy = 35.25\n",
            "Train Loss = 0.8814851060509682\n",
            "Valid Loss = 2.164830837249756\n",
            "Epoch # 45\n",
            "Train Accuracy = 68.43916171410697\n",
            "Valid Accuracy = 36.625\n",
            "Train Loss = 0.9197576037049293\n",
            "Valid Loss = 2.0889987087249757\n",
            "Epoch # 46\n",
            "Train Accuracy = 69.15858617453863\n",
            "Valid Accuracy = 37.0\n",
            "Train Loss = 0.8974609196186065\n",
            "Valid Loss = 2.0705612564086913\n",
            "Epoch # 47\n",
            "Train Accuracy = 70.62871441976853\n",
            "Valid Accuracy = 35.375\n",
            "Train Loss = 0.8645367163419724\n",
            "Valid Loss = 2.1406212139129637\n",
            "Epoch # 48\n",
            "Train Accuracy = 70.58179543321864\n",
            "Valid Accuracy = 35.5\n",
            "Train Loss = 0.8776949286460877\n",
            "Valid Loss = 2.0000143384933473\n",
            "Epoch # 49\n",
            "Train Accuracy = 70.94150766343446\n",
            "Valid Accuracy = 36.75\n",
            "Train Loss = 0.862304298132658\n",
            "Valid Loss = 2.0057827949523928\n",
            "Epoch # 50\n",
            "Train Accuracy = 71.64529246168283\n",
            "Valid Accuracy = 36.125\n",
            "Train Loss = 0.8367040345072746\n",
            "Valid Loss = 2.0829298067092896\n",
            "Epoch # 51\n",
            "Train Accuracy = 70.61307475758524\n",
            "Valid Accuracy = 35.125\n",
            "Train Loss = 0.8439859609305859\n",
            "Valid Loss = 2.1772841215133667\n",
            "Epoch # 52\n",
            "Train Accuracy = 71.81732874569909\n",
            "Valid Accuracy = 38.0\n",
            "Train Loss = 0.8349059872329235\n",
            "Valid Loss = 2.139509472846985\n",
            "Epoch # 53\n",
            "Train Accuracy = 72.72442915233032\n",
            "Valid Accuracy = 32.5\n",
            "Train Loss = 0.812248360812664\n",
            "Valid Loss = 2.451418843269348\n",
            "Epoch # 54\n",
            "Train Accuracy = 72.36471692211448\n",
            "Valid Accuracy = 33.25\n",
            "Train Loss = 0.8125761763751507\n",
            "Valid Loss = 2.128752655982971\n",
            "Epoch # 55\n",
            "Train Accuracy = 73.66280888332813\n",
            "Valid Accuracy = 31.875\n",
            "Train Loss = 0.7917112372815609\n",
            "Valid Loss = 2.118946294784546\n",
            "Epoch # 56\n",
            "Train Accuracy = 71.20738192055052\n",
            "Valid Accuracy = 34.625\n",
            "Train Loss = 0.8540750047564507\n",
            "Valid Loss = 2.186045732498169\n",
            "Epoch # 57\n",
            "Train Accuracy = 71.59837347513293\n",
            "Valid Accuracy = 36.375\n",
            "Train Loss = 0.844064315110445\n",
            "Valid Loss = 2.1475927305221556\n",
            "Epoch # 58\n",
            "Train Accuracy = 74.7888645605255\n",
            "Valid Accuracy = 33.125\n",
            "Train Loss = 0.744103666394949\n",
            "Valid Loss = 2.302093758583069\n",
            "Epoch # 59\n",
            "Train Accuracy = 75.43009071004066\n",
            "Valid Accuracy = 35.875\n",
            "Train Loss = 0.7456221398711205\n",
            "Valid Loss = 2.3722369194030763\n",
            "Epoch # 60\n",
            "Train Accuracy = 75.16421645292462\n",
            "Valid Accuracy = 33.25\n",
            "Train Loss = 0.7513271550834179\n",
            "Valid Loss = 2.317839779853821\n",
            "Epoch # 61\n",
            "Train Accuracy = 76.27463246793869\n",
            "Valid Accuracy = 34.25\n",
            "Train Loss = 0.7085697403550149\n",
            "Valid Loss = 2.372542691230774\n",
            "Epoch # 62\n",
            "Train Accuracy = 75.30497341257428\n",
            "Valid Accuracy = 33.25\n",
            "Train Loss = 0.74075837880373\n",
            "Valid Loss = 2.268666400909424\n",
            "Epoch # 63\n",
            "Train Accuracy = 75.03909915545825\n",
            "Valid Accuracy = 32.375\n",
            "Train Loss = 0.7303367179632186\n",
            "Valid Loss = 2.3610494041442873\n",
            "Epoch # 64\n",
            "Train Accuracy = 76.82202064435408\n",
            "Valid Accuracy = 37.125\n",
            "Train Loss = 0.6963357482850552\n",
            "Valid Loss = 2.3579479026794434\n",
            "Epoch # 65\n",
            "Train Accuracy = 77.16609321238661\n",
            "Valid Accuracy = 31.625\n",
            "Train Loss = 0.6773883109539747\n",
            "Valid Loss = 2.3199504661560058\n",
            "Epoch # 66\n",
            "Train Accuracy = 76.99405692837034\n",
            "Valid Accuracy = 33.0\n",
            "Train Loss = 0.6945672234892846\n",
            "Valid Loss = 2.4893886375427248\n",
            "Epoch # 67\n",
            "Train Accuracy = 77.04097591492024\n",
            "Valid Accuracy = 31.125\n",
            "Train Loss = 0.6830452100932598\n",
            "Valid Loss = 2.437728328704834\n",
            "Epoch # 68\n",
            "Train Accuracy = 77.07225523928683\n",
            "Valid Accuracy = 35.5\n",
            "Train Loss = 0.6883394742012023\n",
            "Valid Loss = 2.2504358434677125\n",
            "Epoch # 69\n",
            "Train Accuracy = 77.74476071316859\n",
            "Valid Accuracy = 34.875\n",
            "Train Loss = 0.6884815869480372\n",
            "Valid Loss = 2.3739148664474485\n",
            "Finished Training\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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Ytg3atLHpvQIFbEucadMs+SpVCq6+Grp1g7Fj4Y47bArwxRfhzTchWzx/mw4Y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\n6drVAoREZUqmTLFWlZtvzv+Ya9bYviefjH4e/3QY8+bl9k1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1rAeY8SIiIiJ3MfACAy8i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\nShAGXkREREQJ8v8bgZiwVAxRfgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AFi2S+VqJMXeqYUPZo/OLL+QzHz0qWagePSQQ8ka1asm2VY5Nzb1ku6bINDhT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\niIiISA5RciYiIiKSQ/4/4l0gHEYh0kQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy:  27.125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7cto-eorapR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bb865cb7-6abb-4e3b-814a-1b6575235fa1"
      },
      "source": [
        "epochs = 70\n",
        "net_crnn_stacked = CRNN_stacked().cuda()\n",
        "optimizer = torch.optim.Adam(net_crnn_stacked.parameters(),weight_decay=0.01)\n",
        "train_accuracy_crnn_stacked, valid_accuracy_crnn_stacked, train_loss_crnn_stacked, valid_loss_crnn_stacked = train(net_crnn_stacked,train_dataloader, epochs, optimizer)\n",
        "plot_accuracies(train_accuracy_crnn_stacked,valid_accuracy_crnn_stacked,epochs,\"CRNN stacked\")\n",
        "plot_loss(train_loss_crnn_stacked, valid_loss_crnn_stacked, epochs, \"CRNN stacked\")\n",
        "test_accuracy_crnn_stacked = calc_test_accuracy(net_crnn_stacked)\n",
        "print(\"Test accuracy: \", test_accuracy_crnn_stacked)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch # 0\n",
            "Train Accuracy = 25.383171723490772\n",
            "Valid Accuracy = 35.125\n",
            "Train Loss = 1.9880328530073166\n",
            "Valid Loss = 1.7935513591766357\n",
            "Epoch # 1\n",
            "Train Accuracy = 38.6143259305599\n",
            "Valid Accuracy = 36.625\n",
            "Train Loss = 1.6871531665325166\n",
            "Valid Loss = 1.787045111656189\n",
            "Epoch # 2\n",
            "Train Accuracy = 40.710040663121674\n",
            "Valid Accuracy = 41.125\n",
            "Train Loss = 1.649209185242653\n",
            "Valid Loss = 1.6122266244888306\n",
            "Epoch # 3\n",
            "Train Accuracy = 42.74319674695027\n",
            "Valid Accuracy = 42.5\n",
            "Train Loss = 1.6112798088788987\n",
            "Valid Loss = 1.5618028688430785\n",
            "Epoch # 4\n",
            "Train Accuracy = 44.27588364091336\n",
            "Valid Accuracy = 42.25\n",
            "Train Loss = 1.5676328653097154\n",
            "Valid Loss = 1.551754403114319\n",
            "Epoch # 5\n",
            "Train Accuracy = 46.449796684391615\n",
            "Valid Accuracy = 48.75\n",
            "Train Loss = 1.5544339889287948\n",
            "Valid Loss = 1.4817856645584107\n",
            "Epoch # 6\n",
            "Train Accuracy = 47.857366280888336\n",
            "Valid Accuracy = 45.625\n",
            "Train Loss = 1.5134899759292602\n",
            "Valid Loss = 1.5405667781829835\n",
            "Epoch # 7\n",
            "Train Accuracy = 48.013762902721304\n",
            "Valid Accuracy = 47.125\n",
            "Train Loss = 1.501678051352501\n",
            "Valid Loss = 1.5367885160446166\n",
            "Epoch # 8\n",
            "Train Accuracy = 48.795746011886145\n",
            "Valid Accuracy = 45.125\n",
            "Train Loss = 1.4795115187764167\n",
            "Valid Loss = 1.5171032285690307\n",
            "Epoch # 9\n",
            "Train Accuracy = 49.67156709415077\n",
            "Valid Accuracy = 47.125\n",
            "Train Loss = 1.4604237413406371\n",
            "Valid Loss = 1.5227906274795533\n",
            "Epoch # 10\n",
            "Train Accuracy = 50.28151391929934\n",
            "Valid Accuracy = 45.625\n",
            "Train Loss = 1.446504333615303\n",
            "Valid Loss = 1.4979818344116211\n",
            "Epoch # 11\n",
            "Train Accuracy = 50.954019393181106\n",
            "Valid Accuracy = 48.25\n",
            "Train Loss = 1.4361333864927293\n",
            "Valid Loss = 1.4933111810684203\n",
            "Epoch # 12\n",
            "Train Accuracy = 51.4701282452299\n",
            "Valid Accuracy = 47.0\n",
            "Train Loss = 1.4280613857507705\n",
            "Valid Loss = 1.5093763065338135\n",
            "Epoch # 13\n",
            "Train Accuracy = 52.29903034094463\n",
            "Valid Accuracy = 47.875\n",
            "Train Loss = 1.4024288800358773\n",
            "Valid Loss = 1.5119763278961182\n",
            "Epoch # 14\n",
            "Train Accuracy = 52.51798561151079\n",
            "Valid Accuracy = 48.625\n",
            "Train Loss = 1.3850122863054275\n",
            "Valid Loss = 1.4939904308319092\n",
            "Epoch # 15\n",
            "Train Accuracy = 53.34688770722553\n",
            "Valid Accuracy = 49.875\n",
            "Train Loss = 1.3594301027059554\n",
            "Valid Loss = 1.4900854802131653\n",
            "Epoch # 16\n",
            "Train Accuracy = 53.37816703159212\n",
            "Valid Accuracy = 43.625\n",
            "Train Loss = 1.3578288036584854\n",
            "Valid Loss = 1.5457365322113037\n",
            "Epoch # 17\n",
            "Train Accuracy = 54.80137629027213\n",
            "Valid Accuracy = 46.5\n",
            "Train Loss = 1.3411237317323685\n",
            "Valid Loss = 1.5441420078277588\n",
            "Epoch # 18\n",
            "Train Accuracy = 54.80137629027213\n",
            "Valid Accuracy = 45.25\n",
            "Train Loss = 1.3229026082158089\n",
            "Valid Loss = 1.5701605367660523\n",
            "Epoch # 19\n",
            "Train Accuracy = 54.41038473568971\n",
            "Valid Accuracy = 45.75\n",
            "Train Loss = 1.3218241050839423\n",
            "Valid Loss = 1.5431416034698486\n",
            "Epoch # 20\n",
            "Train Accuracy = 55.19236784485455\n",
            "Valid Accuracy = 44.0\n",
            "Train Loss = 1.3082422026991845\n",
            "Valid Loss = 1.5196145176887512\n",
            "Epoch # 21\n",
            "Train Accuracy = 55.78667500781983\n",
            "Valid Accuracy = 47.625\n",
            "Train Loss = 1.2873668956756592\n",
            "Valid Loss = 1.5146012282371522\n",
            "Epoch # 22\n",
            "Train Accuracy = 56.255864873318735\n",
            "Valid Accuracy = 51.5\n",
            "Train Loss = 1.2873072949051858\n",
            "Valid Loss = 1.5001171612739563\n",
            "Epoch # 23\n",
            "Train Accuracy = 56.068188927119174\n",
            "Valid Accuracy = 47.25\n",
            "Train Loss = 1.276848708987236\n",
            "Valid Loss = 1.5080623388290406\n",
            "Epoch # 24\n",
            "Train Accuracy = 57.69471379418204\n",
            "Valid Accuracy = 49.25\n",
            "Train Loss = 1.254873812496662\n",
            "Valid Loss = 1.474814760684967\n",
            "Epoch # 25\n",
            "Train Accuracy = 57.35064122614951\n",
            "Valid Accuracy = 50.0\n",
            "Train Loss = 1.2490260928869248\n",
            "Valid Loss = 1.5057939672470093\n",
            "Epoch # 26\n",
            "Train Accuracy = 56.897091022833905\n",
            "Valid Accuracy = 50.875\n",
            "Train Loss = 1.2412015175819398\n",
            "Valid Loss = 1.398635983467102\n",
            "Epoch # 27\n",
            "Train Accuracy = 57.585236158898965\n",
            "Valid Accuracy = 47.5\n",
            "Train Loss = 1.237648260295391\n",
            "Valid Loss = 1.5094593286514282\n",
            "Epoch # 28\n",
            "Train Accuracy = 58.601814200813266\n",
            "Valid Accuracy = 49.25\n",
            "Train Loss = 1.2261738276481629\n",
            "Valid Loss = 1.442180302143097\n",
            "Epoch # 29\n",
            "Train Accuracy = 59.03972474194558\n",
            "Valid Accuracy = 49.375\n",
            "Train Loss = 1.2190750631690026\n",
            "Valid Loss = 1.4748604106903076\n",
            "Epoch # 30\n",
            "Train Accuracy = 58.77385048482953\n",
            "Valid Accuracy = 49.375\n",
            "Train Loss = 1.2037120231986045\n",
            "Valid Loss = 1.5058929586410523\n",
            "Epoch # 31\n",
            "Train Accuracy = 58.16390365968095\n",
            "Valid Accuracy = 48.875\n",
            "Train Loss = 1.210767843425274\n",
            "Valid Loss = 1.5111237621307374\n",
            "Epoch # 32\n",
            "Train Accuracy = 59.13356271504536\n",
            "Valid Accuracy = 49.75\n",
            "Train Loss = 1.1878086996078492\n",
            "Valid Loss = 1.5279661512374878\n",
            "Epoch # 33\n",
            "Train Accuracy = 60.16578041914295\n",
            "Valid Accuracy = 45.625\n",
            "Train Loss = 1.1905156168341637\n",
            "Valid Loss = 1.5634951066970826\n",
            "Epoch # 34\n",
            "Train Accuracy = 58.601814200813266\n",
            "Valid Accuracy = 49.625\n",
            "Train Loss = 1.2035022547841072\n",
            "Valid Loss = 1.6070887541770935\n",
            "Epoch # 35\n",
            "Train Accuracy = 60.00938379730998\n",
            "Valid Accuracy = 47.375\n",
            "Train Loss = 1.169192180633545\n",
            "Valid Loss = 1.5257726621627807\n",
            "Epoch # 36\n",
            "Train Accuracy = 60.60369096027526\n",
            "Valid Accuracy = 47.375\n",
            "Train Loss = 1.147373623251915\n",
            "Valid Loss = 1.5331332874298096\n",
            "Epoch # 37\n",
            "Train Accuracy = 60.4003753518924\n",
            "Valid Accuracy = 51.875\n",
            "Train Loss = 1.166036185324192\n",
            "Valid Loss = 1.5011694383621217\n",
            "Epoch # 38\n",
            "Train Accuracy = 60.79136690647482\n",
            "Valid Accuracy = 49.625\n",
            "Train Loss = 1.1435969683527947\n",
            "Valid Loss = 1.5102846765518187\n",
            "Epoch # 39\n",
            "Train Accuracy = 59.77478886456053\n",
            "Valid Accuracy = 49.375\n",
            "Train Loss = 1.1460519036650658\n",
            "Valid Loss = 1.5106366348266602\n",
            "Epoch # 40\n",
            "Train Accuracy = 61.901782921488895\n",
            "Valid Accuracy = 49.375\n",
            "Train Loss = 1.1199074560403823\n",
            "Valid Loss = 1.4872148275375365\n",
            "Epoch # 41\n",
            "Train Accuracy = 60.181420081326245\n",
            "Valid Accuracy = 47.125\n",
            "Train Loss = 1.1501330402493477\n",
            "Valid Loss = 1.5489450407028198\n",
            "Epoch # 42\n",
            "Train Accuracy = 61.33875508289021\n",
            "Valid Accuracy = 51.625\n",
            "Train Loss = 1.1116343557834625\n",
            "Valid Loss = 1.4319736289978027\n",
            "Epoch # 43\n",
            "Train Accuracy = 61.63590866437285\n",
            "Valid Accuracy = 48.125\n",
            "Train Loss = 1.1088747763633728\n",
            "Valid Loss = 1.639384331703186\n",
            "Epoch # 44\n",
            "Train Accuracy = 61.72974663747263\n",
            "Valid Accuracy = 49.375\n",
            "Train Loss = 1.1162685874104499\n",
            "Valid Loss = 1.52619243144989\n",
            "Epoch # 45\n",
            "Train Accuracy = 62.05817954332186\n",
            "Valid Accuracy = 49.75\n",
            "Train Loss = 1.1119444453716278\n",
            "Valid Loss = 1.5137344551086427\n",
            "Epoch # 46\n",
            "Train Accuracy = 62.198936502971534\n",
            "Valid Accuracy = 47.875\n",
            "Train Loss = 1.1110587272047996\n",
            "Valid Loss = 1.5480306196212767\n",
            "Epoch # 47\n",
            "Train Accuracy = 62.3709727869878\n",
            "Valid Accuracy = 48.0\n",
            "Train Loss = 1.0998142513632774\n",
            "Valid Loss = 1.5399676275253296\n",
            "Epoch # 48\n",
            "Train Accuracy = 62.23021582733813\n",
            "Valid Accuracy = 47.5\n",
            "Train Loss = 1.101449371278286\n",
            "Valid Loss = 1.5035084581375122\n",
            "Epoch # 49\n",
            "Train Accuracy = 62.40225211135439\n",
            "Valid Accuracy = 49.25\n",
            "Train Loss = 1.0928438261151314\n",
            "Valid Loss = 1.5644547748565674\n",
            "Epoch # 50\n",
            "Train Accuracy = 63.43446981545198\n",
            "Valid Accuracy = 49.75\n",
            "Train Loss = 1.072022425532341\n",
            "Valid Loss = 1.531477885246277\n",
            "Epoch # 51\n",
            "Train Accuracy = 63.80982170785111\n",
            "Valid Accuracy = 48.5\n",
            "Train Loss = 1.06103610008955\n",
            "Valid Loss = 1.59251980304718\n",
            "Epoch # 52\n",
            "Train Accuracy = 63.825461370034404\n",
            "Valid Accuracy = 46.875\n",
            "Train Loss = 1.0610826256871224\n",
            "Valid Loss = 1.6544213962554932\n",
            "Epoch # 53\n",
            "Train Accuracy = 63.70034407256803\n",
            "Valid Accuracy = 50.125\n",
            "Train Loss = 1.0605199763178825\n",
            "Valid Loss = 1.5919661903381348\n",
            "Epoch # 54\n",
            "Train Accuracy = 63.3562715045355\n",
            "Valid Accuracy = 47.875\n",
            "Train Loss = 1.0570588380098342\n",
            "Valid Loss = 1.6079143047332765\n",
            "Epoch # 55\n",
            "Train Accuracy = 64.27901157335002\n",
            "Valid Accuracy = 48.375\n",
            "Train Loss = 1.0444820562005044\n",
            "Valid Loss = 1.6139114618301391\n",
            "Epoch # 56\n",
            "Train Accuracy = 64.62308414138255\n",
            "Valid Accuracy = 48.625\n",
            "Train Loss = 1.0424263921380044\n",
            "Valid Loss = 1.5568287706375121\n",
            "Epoch # 57\n",
            "Train Accuracy = 63.91929934313419\n",
            "Valid Accuracy = 49.25\n",
            "Train Loss = 1.0483551931381225\n",
            "Valid Loss = 1.5214324712753295\n",
            "Epoch # 58\n",
            "Train Accuracy = 64.12261495151705\n",
            "Valid Accuracy = 49.125\n",
            "Train Loss = 1.0510832077264787\n",
            "Valid Loss = 1.564335012435913\n",
            "Epoch # 59\n",
            "Train Accuracy = 64.57616515483265\n",
            "Valid Accuracy = 51.625\n",
            "Train Loss = 1.032011087834835\n",
            "Valid Loss = 1.5586472988128661\n",
            "Epoch # 60\n",
            "Train Accuracy = 65.35814826399749\n",
            "Valid Accuracy = 49.125\n",
            "Train Loss = 1.0263567864894867\n",
            "Valid Loss = 1.6918562030792237\n",
            "Epoch # 61\n",
            "Train Accuracy = 65.23303096653112\n",
            "Valid Accuracy = 48.25\n",
            "Train Loss = 1.0214714831113816\n",
            "Valid Loss = 1.6304879236221312\n",
            "Epoch # 62\n",
            "Train Accuracy = 64.60744447919924\n",
            "Valid Accuracy = 48.0\n",
            "Train Loss = 1.0256067776679993\n",
            "Valid Loss = 1.6287075281143188\n",
            "Epoch # 63\n",
            "Train Accuracy = 64.92023772286518\n",
            "Valid Accuracy = 51.0\n",
            "Train Loss = 1.018745786100626\n",
            "Valid Loss = 1.5867385816574098\n",
            "Epoch # 64\n",
            "Train Accuracy = 65.15483265561464\n",
            "Valid Accuracy = 47.25\n",
            "Train Loss = 1.015542279779911\n",
            "Valid Loss = 1.5841632413864135\n",
            "Epoch # 65\n",
            "Train Accuracy = 65.51454488583046\n",
            "Valid Accuracy = 46.75\n",
            "Train Loss = 1.010894753932953\n",
            "Valid Loss = 1.6039809703826904\n",
            "Epoch # 66\n",
            "Train Accuracy = 65.35814826399749\n",
            "Valid Accuracy = 46.25\n",
            "Train Loss = 1.0014139375090598\n",
            "Valid Loss = 1.623742413520813\n",
            "Epoch # 67\n",
            "Train Accuracy = 65.43634657491398\n",
            "Valid Accuracy = 47.0\n",
            "Train Loss = 1.0032963466644287\n",
            "Valid Loss = 1.606364130973816\n",
            "Epoch # 68\n",
            "Train Accuracy = 66.43728495464498\n",
            "Valid Accuracy = 49.375\n",
            "Train Loss = 0.987038579583168\n",
            "Valid Loss = 1.5726533126831055\n",
            "Epoch # 69\n",
            "Train Accuracy = 66.17141069752893\n",
            "Valid Accuracy = 46.125\n",
            "Train Loss = 0.9915107649564743\n",
            "Valid Loss = 1.6690641117095948\n",
            "Finished Training\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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Anj17Un2s9evXY//+/QgICEBAQABOnTqF9evXAwA8PT2xePFiABxMhYaGolWrVtiwYUPs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\nYm8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PPPEEtNZQSuGDDz5AamoqHnvsMVy+fBnp6ekYM2YMSpcunescTZs2Ra9evXD69Gk8/vjj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\nERERkRgoCBMRERGJwf8DNDtL/lCtKRgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy:  40.125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gZHVOg5-cqYw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "53e016bc-de19-43eb-8f6f-763942b80980"
      },
      "source": [
        "epochs = 70\n",
        "net_rnn = RNN().cuda()\n",
        "optimizer = torch.optim.Adam(net_rnn.parameters(),weight_decay=0.01)\n",
        "train_accuracy_rnn, valid_accuracy_rnn, train_loss_rnn, valid_loss_rnn = train(net_rnn,train_dataloader, epochs, optimizer)\n",
        "plot_accuracies(train_accuracy_rnn,valid_accuracy_rnn,epochs,\"RNN\")\n",
        "plot_loss(train_loss_rnn, valid_loss_rnn, epochs, \"RNN\")\n",
        "test_accuracy_rnn = calc_test_accuracy(net_rnn)\n",
        "print(\"Test accuracy: \", test_accuracy_rnn)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch # 0\n",
            "Train Accuracy = 12.777604003753519\n",
            "Valid Accuracy = 11.75\n",
            "Train Loss = 2.098012029528618\n",
            "Valid Loss = 2.081027250289917\n",
            "Epoch # 1\n",
            "Train Accuracy = 12.66812636847044\n",
            "Valid Accuracy = 11.75\n",
            "Train Loss = 2.086566331386566\n",
            "Valid Loss = 2.089074192047119\n",
            "Epoch # 2\n",
            "Train Accuracy = 12.746324679386925\n",
            "Valid Accuracy = 10.5\n",
            "Train Loss = 2.084738301038742\n",
            "Valid Loss = 2.0830338859558104\n",
            "Epoch # 3\n",
            "Train Accuracy = 12.449171097904285\n",
            "Valid Accuracy = 14.875\n",
            "Train Loss = 2.0835785055160523\n",
            "Valid Loss = 2.0844626522064207\n",
            "Epoch # 4\n",
            "Train Accuracy = 12.730685017203628\n",
            "Valid Accuracy = 11.75\n",
            "Train Loss = 2.082304697036743\n",
            "Valid Loss = 2.079803638458252\n",
            "Epoch # 5\n",
            "Train Accuracy = 12.699405692837034\n",
            "Valid Accuracy = 13.75\n",
            "Train Loss = 2.080368685722351\n",
            "Valid Loss = 2.079638662338257\n",
            "Epoch # 6\n",
            "Train Accuracy = 13.027838598686268\n",
            "Valid Accuracy = 13.0\n",
            "Train Loss = 2.0807734656333925\n",
            "Valid Loss = 2.0795178413391113\n",
            "Epoch # 7\n",
            "Train Accuracy = 12.8714419768533\n",
            "Valid Accuracy = 12.875\n",
            "Train Loss = 2.0801919984817503\n",
            "Valid Loss = 2.081947994232178\n",
            "Epoch # 8\n",
            "Train Accuracy = 12.636847044103847\n",
            "Valid Accuracy = 11.875\n",
            "Train Loss = 2.079953393936157\n",
            "Valid Loss = 2.0788038063049314\n",
            "Epoch # 9\n",
            "Train Accuracy = 12.543009071004066\n",
            "Valid Accuracy = 13.5\n",
            "Train Loss = 2.0799818217754362\n",
            "Valid Loss = 2.079504232406616\n",
            "Epoch # 10\n",
            "Train Accuracy = 12.292774476071317\n",
            "Valid Accuracy = 12.125\n",
            "Train Loss = 2.0802471220493315\n",
            "Valid Loss = 2.0800082588195803\n",
            "Epoch # 11\n",
            "Train Accuracy = 12.480450422270879\n",
            "Valid Accuracy = 12.5\n",
            "Train Loss = 2.079876123666763\n",
            "Valid Loss = 2.078928213119507\n",
            "Epoch # 12\n",
            "Train Accuracy = 12.855802314670003\n",
            "Valid Accuracy = 12.125\n",
            "Train Loss = 2.079822143316269\n",
            "Valid Loss = 2.0792733001708985\n",
            "Epoch # 13\n",
            "Train Accuracy = 12.073819205505162\n",
            "Valid Accuracy = 13.75\n",
            "Train Loss = 2.0799215161800384\n",
            "Valid Loss = 2.078957748413086\n",
            "Epoch # 14\n",
            "Train Accuracy = 11.823584610572413\n",
            "Valid Accuracy = 12.125\n",
            "Train Loss = 2.0800598752498627\n",
            "Valid Loss = 2.0794052600860597\n",
            "Epoch # 15\n",
            "Train Accuracy = 11.542070691273068\n",
            "Valid Accuracy = 13.375\n",
            "Train Loss = 2.0796614468097685\n",
            "Valid Loss = 2.079299764633179\n",
            "Epoch # 16\n",
            "Train Accuracy = 12.652486706287144\n",
            "Valid Accuracy = 12.5\n",
            "Train Loss = 2.0796868228912353\n",
            "Valid Loss = 2.079357509613037\n",
            "Epoch # 17\n",
            "Train Accuracy = 12.32405380043791\n",
            "Valid Accuracy = 12.75\n",
            "Train Loss = 2.079901820421219\n",
            "Valid Loss = 2.0792007637023926\n",
            "Epoch # 18\n",
            "Train Accuracy = 11.854863934939006\n",
            "Valid Accuracy = 12.125\n",
            "Train Loss = 2.079853527545929\n",
            "Valid Loss = 2.0795909786224365\n",
            "Epoch # 19\n",
            "Train Accuracy = 12.980919612136377\n",
            "Valid Accuracy = 11.875\n",
            "Train Loss = 2.0797817194461823\n",
            "Valid Loss = 2.079924259185791\n",
            "Epoch # 20\n",
            "Train Accuracy = 12.261495151704723\n",
            "Valid Accuracy = 12.5\n",
            "Train Loss = 2.079884331226349\n",
            "Valid Loss = 2.0803940296173096\n",
            "Epoch # 21\n",
            "Train Accuracy = 12.464810760087582\n",
            "Valid Accuracy = 13.25\n",
            "Train Loss = 2.079869611263275\n",
            "Valid Loss = 2.0811169147491455\n",
            "Epoch # 22\n",
            "Train Accuracy = 12.980919612136377\n",
            "Valid Accuracy = 11.5\n",
            "Train Loss = 2.07953412771225\n",
            "Valid Loss = 2.079008255004883\n",
            "Epoch # 23\n",
            "Train Accuracy = 11.823584610572413\n",
            "Valid Accuracy = 12.0\n",
            "Train Loss = 2.0803406512737275\n",
            "Valid Loss = 2.079713697433472\n",
            "Epoch # 24\n",
            "Train Accuracy = 11.901782921488897\n",
            "Valid Accuracy = 11.75\n",
            "Train Loss = 2.08037673830986\n",
            "Valid Loss = 2.0801384544372556\n",
            "Epoch # 25\n",
            "Train Accuracy = 12.245855489521427\n",
            "Valid Accuracy = 11.75\n",
            "Train Loss = 2.0798819231987\n",
            "Valid Loss = 2.080220603942871\n",
            "Epoch # 26\n",
            "Train Accuracy = 12.105098529871755\n",
            "Valid Accuracy = 13.25\n",
            "Train Loss = 2.0801303243637084\n",
            "Valid Loss = 2.0805556869506834\n",
            "Epoch # 27\n",
            "Train Accuracy = 12.245855489521427\n",
            "Valid Accuracy = 12.5\n",
            "Train Loss = 2.0801174902915953\n",
            "Valid Loss = 2.08027813911438\n",
            "Epoch # 28\n",
            "Train Accuracy = 11.901782921488897\n",
            "Valid Accuracy = 10.125\n",
            "Train Loss = 2.0801101541519165\n",
            "Valid Loss = 2.0798583698272703\n",
            "Epoch # 29\n",
            "Train Accuracy = 12.058179543321865\n",
            "Valid Accuracy = 14.875\n",
            "Train Loss = 2.080122183561325\n",
            "Valid Loss = 2.080215826034546\n",
            "Epoch # 30\n",
            "Train Accuracy = 12.370972786987801\n",
            "Valid Accuracy = 13.0\n",
            "Train Loss = 2.0799921226501463\n",
            "Valid Loss = 2.0794672107696535\n",
            "Epoch # 31\n",
            "Train Accuracy = 12.23021582733813\n",
            "Valid Accuracy = 11.75\n",
            "Train Loss = 2.079825975894928\n",
            "Valid Loss = 2.0800859546661377\n",
            "Epoch # 32\n",
            "Train Accuracy = 12.27713481388802\n",
            "Valid Accuracy = 13.375\n",
            "Train Loss = 2.0799171209335325\n",
            "Valid Loss = 2.0811081314086914\n",
            "Epoch # 33\n",
            "Train Accuracy = 12.32405380043791\n",
            "Valid Accuracy = 12.25\n",
            "Train Loss = 2.080181497335434\n",
            "Valid Loss = 2.0799474048614504\n",
            "Epoch # 34\n",
            "Train Accuracy = 12.308414138254614\n",
            "Valid Accuracy = 12.625\n",
            "Train Loss = 2.0803548276424406\n",
            "Valid Loss = 2.078904962539673\n",
            "Epoch # 35\n",
            "Train Accuracy = 12.62120738192055\n",
            "Valid Accuracy = 12.0\n",
            "Train Loss = 2.0799904966354372\n",
            "Valid Loss = 2.0788894367218016\n",
            "Epoch # 36\n",
            "Train Accuracy = 11.854863934939006\n",
            "Valid Accuracy = 12.75\n",
            "Train Loss = 2.079996620416641\n",
            "Valid Loss = 2.078917398452759\n",
            "Epoch # 37\n",
            "Train Accuracy = 11.667187988739443\n",
            "Valid Accuracy = 12.5\n",
            "Train Loss = 2.0803214037418365\n",
            "Valid Loss = 2.0806280326843263\n",
            "Epoch # 38\n",
            "Train Accuracy = 12.27713481388802\n",
            "Valid Accuracy = 12.25\n",
            "Train Loss = 2.0800708925724027\n",
            "Valid Loss = 2.079581518173218\n",
            "Epoch # 39\n",
            "Train Accuracy = 12.32405380043791\n",
            "Valid Accuracy = 12.5\n",
            "Train Loss = 2.0799339985847474\n",
            "Valid Loss = 2.0803339862823487\n",
            "Epoch # 40\n",
            "Train Accuracy = 12.91836096340319\n",
            "Valid Accuracy = 12.0\n",
            "Train Loss = 2.079838684797287\n",
            "Valid Loss = 2.079695701599121\n",
            "Epoch # 41\n",
            "Train Accuracy = 12.464810760087582\n",
            "Valid Accuracy = 12.0\n",
            "Train Loss = 2.079828100204468\n",
            "Valid Loss = 2.079787712097168\n",
            "Epoch # 42\n",
            "Train Accuracy = 12.339693462621208\n",
            "Valid Accuracy = 11.25\n",
            "Train Loss = 2.0800589334964754\n",
            "Valid Loss = 2.0800368976593018\n",
            "Epoch # 43\n",
            "Train Accuracy = 12.824522990303409\n",
            "Valid Accuracy = 13.0\n",
            "Train Loss = 2.079688552618027\n",
            "Valid Loss = 2.0800430583953857\n",
            "Epoch # 44\n",
            "Train Accuracy = 12.152017516421646\n",
            "Valid Accuracy = 12.25\n",
            "Train Loss = 2.0807976841926576\n",
            "Valid Loss = 2.079896230697632\n",
            "Epoch # 45\n",
            "Train Accuracy = 12.308414138254614\n",
            "Valid Accuracy = 10.875\n",
            "Train Loss = 2.0800591671466826\n",
            "Valid Loss = 2.079829607009888\n",
            "Epoch # 46\n",
            "Train Accuracy = 12.464810760087582\n",
            "Valid Accuracy = 11.125\n",
            "Train Loss = 2.0797960102558135\n",
            "Valid Loss = 2.0810546112060546\n",
            "Epoch # 47\n",
            "Train Accuracy = 12.496090084454176\n",
            "Valid Accuracy = 12.25\n",
            "Train Loss = 2.0799910581111907\n",
            "Valid Loss = 2.0801649475097657\n",
            "Epoch # 48\n",
            "Train Accuracy = 12.136377854238349\n",
            "Valid Accuracy = 13.0\n",
            "Train Loss = 2.0803659856319427\n",
            "Valid Loss = 2.0803250885009765\n",
            "Epoch # 49\n",
            "Train Accuracy = 12.496090084454176\n",
            "Valid Accuracy = 13.25\n",
            "Train Loss = 2.079669796228409\n",
            "Valid Loss = 2.079658899307251\n",
            "Epoch # 50\n",
            "Train Accuracy = 12.433531435720989\n",
            "Valid Accuracy = 12.0\n",
            "Train Loss = 2.0801488149166105\n",
            "Valid Loss = 2.078245372772217\n",
            "Epoch # 51\n",
            "Train Accuracy = 11.463872380356584\n",
            "Valid Accuracy = 11.75\n",
            "Train Loss = 2.080562958717346\n",
            "Valid Loss = 2.079600019454956\n",
            "Epoch # 52\n",
            "Train Accuracy = 12.605567719737254\n",
            "Valid Accuracy = 12.625\n",
            "Train Loss = 2.0800010073184967\n",
            "Valid Loss = 2.0809756565093993\n",
            "Epoch # 53\n",
            "Train Accuracy = 12.214576165154833\n",
            "Valid Accuracy = 16.25\n",
            "Train Loss = 2.080016738176346\n",
            "Valid Loss = 2.080680456161499\n",
            "Epoch # 54\n",
            "Train Accuracy = 12.386612449171098\n",
            "Valid Accuracy = 12.375\n",
            "Train Loss = 2.0800154602527616\n",
            "Valid Loss = 2.0789981842041017\n",
            "Epoch # 55\n",
            "Train Accuracy = 13.184235220519236\n",
            "Valid Accuracy = 13.0\n",
            "Train Loss = 2.0800818741321563\n",
            "Valid Loss = 2.0796956634521484\n",
            "Epoch # 56\n",
            "Train Accuracy = 12.808883328120112\n",
            "Valid Accuracy = 13.125\n",
            "Train Loss = 2.0796855068206788\n",
            "Valid Loss = 2.079404420852661\n",
            "Epoch # 57\n",
            "Train Accuracy = 12.699405692837034\n",
            "Valid Accuracy = 13.375\n",
            "Train Loss = 2.0798815798759462\n",
            "Valid Loss = 2.0798677158355714\n",
            "Epoch # 58\n",
            "Train Accuracy = 12.370972786987801\n",
            "Valid Accuracy = 13.125\n",
            "Train Loss = 2.079959902763367\n",
            "Valid Loss = 2.0790741062164306\n",
            "Epoch # 59\n",
            "Train Accuracy = 11.714106975289333\n",
            "Valid Accuracy = 11.25\n",
            "Train Loss = 2.0801139998435976\n",
            "Valid Loss = 2.0797513580322264\n",
            "Epoch # 60\n",
            "Train Accuracy = 11.964341570222084\n",
            "Valid Accuracy = 11.625\n",
            "Train Loss = 2.0799253952503203\n",
            "Valid Loss = 2.0812808418273927\n",
            "Epoch # 61\n",
            "Train Accuracy = 12.887081639036596\n",
            "Valid Accuracy = 11.5\n",
            "Train Loss = 2.080026470422745\n",
            "Valid Loss = 2.080093126296997\n",
            "Epoch # 62\n",
            "Train Accuracy = 11.8861432593056\n",
            "Valid Accuracy = 12.125\n",
            "Train Loss = 2.0799786388874053\n",
            "Valid Loss = 2.0798754119873046\n",
            "Epoch # 63\n",
            "Train Accuracy = 12.449171097904285\n",
            "Valid Accuracy = 12.375\n",
            "Train Loss = 2.0801929688453673\n",
            "Valid Loss = 2.0800059413909913\n",
            "Epoch # 64\n",
            "Train Accuracy = 11.307475758523616\n",
            "Valid Accuracy = 12.5\n",
            "Train Loss = 2.0801044261455535\n",
            "Valid Loss = 2.0803716659545897\n",
            "Epoch # 65\n",
            "Train Accuracy = 11.588989677822958\n",
            "Valid Accuracy = 12.375\n",
            "Train Loss = 2.0802318239212036\n",
            "Valid Loss = 2.079466543197632\n",
            "Epoch # 66\n",
            "Train Accuracy = 12.66812636847044\n",
            "Valid Accuracy = 13.375\n",
            "Train Loss = 2.0800714790821075\n",
            "Valid Loss = 2.0795679759979246\n",
            "Epoch # 67\n",
            "Train Accuracy = 11.260556771973725\n",
            "Valid Accuracy = 13.375\n",
            "Train Loss = 2.0799814212322234\n",
            "Valid Loss = 2.0796870517730714\n",
            "Epoch # 68\n",
            "Train Accuracy = 12.57428839537066\n",
            "Valid Accuracy = 11.625\n",
            "Train Loss = 2.0799907100200654\n",
            "Valid Loss = 2.080039548873901\n",
            "Epoch # 69\n",
            "Train Accuracy = 12.902721301219893\n",
            "Valid Accuracy = 11.75\n",
            "Train Loss = 2.079903094768524\n",
            "Valid Loss = 2.0800163650512697\n",
            "Finished Training\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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jxZgACy+GYRiGscc6u5qFF2MGLLwYhmEYxh4luCoqgPLywLaFqXOYJryEEB8KIY4JIbY4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\n4izRXRVRNSDHa9cu0h7bttnt05nwUm1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Ly73L73LG9OlklDt1vQwSXmVl9DMEhfDq2rWG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sH1sugQBEGwjQivQNHC66ef+LdMPkK84OlqlEWHIAiCbUR4BYoILyFeSU9nwVVVJe2yBEEQHCLC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\nEAQhQvw/DDZ22+7n+TQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bFehERESk8ehOESmUGOhEREREGosCXQop0ImIiEgcFOhSSIFORERE4qBAl0IK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\nFEVRIhwV6BRFURRFUSKc/wfAPM8lXz3N8wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy:  11.125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uLPCLKHED47W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "901a96de-df50-4bdf-e33a-873c69805092"
      },
      "source": [
        "epochs = 70\n",
        "net_cnn_2d_deep = CNN_2D_Deep().cuda()\n",
        "optimizer = torch.optim.Adam(net_cnn_2d_deep.parameters(),weight_decay=0.01)\n",
        "train_accuracy_2d_deep, valid_accuracy_2d_deep, train_loss_2d_deep, valid_loss_2d_deep = train(net_cnn_2d_deep,train_dataloader, epochs, optimizer)\n",
        "plot_accuracies(train_accuracy_2d_deep,valid_accuracy_2d_deep,epochs,\"2D CNN-Deep\")\n",
        "plot_loss(train_loss_2d_deep, valid_loss_2d_deep, epochs, \"2D CNN-Deep\")\n",
        "test_accuracy_2d_deep = calc_test_accuracy(net_cnn_2d_deep)\n",
        "print(\"Test accuracy: \", test_accuracy_2d_deep)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch # 0\n",
            "Train Accuracy = 29.15233030966531\n",
            "Valid Accuracy = 37.5\n",
            "Train Loss = 1.8760982221364975\n",
            "Valid Loss = 1.7584519815444946\n",
            "Epoch # 1\n",
            "Train Accuracy = 36.143259305599\n",
            "Valid Accuracy = 39.875\n",
            "Train Loss = 1.7343204671144485\n",
            "Valid Loss = 1.6423648834228515\n",
            "Epoch # 2\n",
            "Train Accuracy = 40.569283703472\n",
            "Valid Accuracy = 43.5\n",
            "Train Loss = 1.6435627591609956\n",
            "Valid Loss = 1.666958532333374\n",
            "Epoch # 3\n",
            "Train Accuracy = 42.33656553018455\n",
            "Valid Accuracy = 50.5\n",
            "Train Loss = 1.6162633693218231\n",
            "Valid Loss = 1.5646692752838134\n",
            "Epoch # 4\n",
            "Train Accuracy = 44.135126681263685\n",
            "Valid Accuracy = 47.25\n",
            "Train Loss = 1.568979400396347\n",
            "Valid Loss = 1.5761700963974\n",
            "Epoch # 5\n",
            "Train Accuracy = 45.855489521426335\n",
            "Valid Accuracy = 48.875\n",
            "Train Loss = 1.5310518929362298\n",
            "Valid Loss = 1.508931131362915\n",
            "Epoch # 6\n",
            "Train Accuracy = 47.0128245229903\n",
            "Valid Accuracy = 46.625\n",
            "Train Loss = 1.5137915074825288\n",
            "Valid Loss = 1.5869790697097779\n",
            "Epoch # 7\n",
            "Train Accuracy = 47.5445730372224\n",
            "Valid Accuracy = 45.0\n",
            "Train Loss = 1.4942262542247773\n",
            "Valid Loss = 1.5506305360794068\n",
            "Epoch # 8\n",
            "Train Accuracy = 48.17015952455427\n",
            "Valid Accuracy = 47.0\n",
            "Train Loss = 1.4824316000938416\n",
            "Valid Loss = 1.52753634929657\n",
            "Epoch # 9\n",
            "Train Accuracy = 48.357835470753834\n",
            "Valid Accuracy = 47.25\n",
            "Train Loss = 1.4744579753279685\n",
            "Valid Loss = 1.5136851835250855\n",
            "Epoch # 10\n",
            "Train Accuracy = 49.483891147951205\n",
            "Valid Accuracy = 50.375\n",
            "Train Loss = 1.4567002779245377\n",
            "Valid Loss = 1.5148519659042359\n",
            "Epoch # 11\n",
            "Train Accuracy = 50.23459493274945\n",
            "Valid Accuracy = 49.75\n",
            "Train Loss = 1.454496805667877\n",
            "Valid Loss = 1.4756278610229492\n",
            "Epoch # 12\n",
            "Train Accuracy = 51.09477635283078\n",
            "Valid Accuracy = 51.625\n",
            "Train Loss = 1.4276087683439256\n",
            "Valid Loss = 1.449658179283142\n",
            "Epoch # 13\n",
            "Train Accuracy = 52.31467000312793\n",
            "Valid Accuracy = 52.875\n",
            "Train Loss = 1.4117337983846665\n",
            "Valid Loss = 1.4626587152481079\n",
            "Epoch # 14\n",
            "Train Accuracy = 51.92367844854551\n",
            "Valid Accuracy = 52.125\n",
            "Train Loss = 1.4073401060700417\n",
            "Valid Loss = 1.446393313407898\n",
            "Epoch # 15\n",
            "Train Accuracy = 52.78385986862684\n",
            "Valid Accuracy = 50.25\n",
            "Train Loss = 1.3905832555890083\n",
            "Valid Loss = 1.474851884841919\n",
            "Epoch # 16\n",
            "Train Accuracy = 52.59618392242727\n",
            "Valid Accuracy = 52.25\n",
            "Train Loss = 1.3857586038112641\n",
            "Valid Loss = 1.451764807701111\n",
            "Epoch # 17\n",
            "Train Accuracy = 53.31560838285893\n",
            "Valid Accuracy = 49.25\n",
            "Train Loss = 1.3767569255828858\n",
            "Valid Loss = 1.459713306427002\n",
            "Epoch # 18\n",
            "Train Accuracy = 52.36158898967782\n",
            "Valid Accuracy = 52.25\n",
            "Train Loss = 1.3856136518716813\n",
            "Valid Loss = 1.434613537788391\n",
            "Epoch # 19\n",
            "Train Accuracy = 53.988113856740696\n",
            "Valid Accuracy = 51.5\n",
            "Train Loss = 1.3593733885884285\n",
            "Valid Loss = 1.449786467552185\n",
            "Epoch # 20\n",
            "Train Accuracy = 53.78479824835784\n",
            "Valid Accuracy = 52.25\n",
            "Train Loss = 1.3503578388690949\n",
            "Valid Loss = 1.426893892288208\n",
            "Epoch # 21\n",
            "Train Accuracy = 53.894275883640915\n",
            "Valid Accuracy = 56.0\n",
            "Train Loss = 1.3505796229839324\n",
            "Valid Loss = 1.4231518936157226\n",
            "Epoch # 22\n",
            "Train Accuracy = 54.4729433844229\n",
            "Valid Accuracy = 50.75\n",
            "Train Loss = 1.345319770872593\n",
            "Valid Loss = 1.4804304695129396\n",
            "Epoch # 23\n",
            "Train Accuracy = 54.973412574288396\n",
            "Valid Accuracy = 54.25\n",
            "Train Loss = 1.337126331925392\n",
            "Valid Loss = 1.3802721118927002\n",
            "Epoch # 24\n",
            "Train Accuracy = 56.38098217078511\n",
            "Valid Accuracy = 54.0\n",
            "Train Loss = 1.3064109975099563\n",
            "Valid Loss = 1.4083478116989137\n",
            "Epoch # 25\n",
            "Train Accuracy = 55.082890209571474\n",
            "Valid Accuracy = 52.25\n",
            "Train Loss = 1.3261425957083701\n",
            "Valid Loss = 1.461830859184265\n",
            "Epoch # 26\n",
            "Train Accuracy = 55.771035345636534\n",
            "Valid Accuracy = 52.375\n",
            "Train Loss = 1.3115955606102943\n",
            "Valid Loss = 1.4463110303878783\n",
            "Epoch # 27\n",
            "Train Accuracy = 56.115107913669064\n",
            "Valid Accuracy = 53.75\n",
            "Train Loss = 1.2989759159088134\n",
            "Valid Loss = 1.4970296382904054\n",
            "Epoch # 28\n",
            "Train Accuracy = 55.73975602126994\n",
            "Valid Accuracy = 53.875\n",
            "Train Loss = 1.3023387369513513\n",
            "Valid Loss = 1.36173415184021\n",
            "Epoch # 29\n",
            "Train Accuracy = 56.95964967156709\n",
            "Valid Accuracy = 55.0\n",
            "Train Loss = 1.2882871693372726\n",
            "Valid Loss = 1.376738600730896\n",
            "Epoch # 30\n",
            "Train Accuracy = 57.96058805129809\n",
            "Valid Accuracy = 52.75\n",
            "Train Loss = 1.2752952441573142\n",
            "Valid Loss = 1.411333384513855\n",
            "Epoch # 31\n",
            "Train Accuracy = 57.64779480763215\n",
            "Valid Accuracy = 54.375\n",
            "Train Loss = 1.2635418272018433\n",
            "Valid Loss = 1.399561333656311\n",
            "Epoch # 32\n",
            "Train Accuracy = 57.00656865811698\n",
            "Valid Accuracy = 52.375\n",
            "Train Loss = 1.2793005302548408\n",
            "Valid Loss = 1.429441213607788\n",
            "Epoch # 33\n",
            "Train Accuracy = 57.788551767281824\n",
            "Valid Accuracy = 53.25\n",
            "Train Loss = 1.2550039198994636\n",
            "Valid Loss = 1.4279379844665527\n",
            "Epoch # 34\n",
            "Train Accuracy = 55.817954332186424\n",
            "Valid Accuracy = 52.875\n",
            "Train Loss = 1.2763547977805139\n",
            "Valid Loss = 1.4236710500717162\n",
            "Epoch # 35\n",
            "Train Accuracy = 57.585236158898965\n",
            "Valid Accuracy = 54.125\n",
            "Train Loss = 1.25174380838871\n",
            "Valid Loss = 1.3917567682266236\n",
            "Epoch # 36\n",
            "Train Accuracy = 57.679074131998746\n",
            "Valid Accuracy = 56.0\n",
            "Train Loss = 1.2445932030677795\n",
            "Valid Loss = 1.4191078662872314\n",
            "Epoch # 37\n",
            "Train Accuracy = 58.19518298404754\n",
            "Valid Accuracy = 52.875\n",
            "Train Loss = 1.236106208860874\n",
            "Valid Loss = 1.435756049156189\n",
            "Epoch # 38\n",
            "Train Accuracy = 58.66437284954645\n",
            "Valid Accuracy = 54.75\n",
            "Train Loss = 1.2254235988855362\n",
            "Valid Loss = 1.3781205415725708\n",
            "Epoch # 39\n",
            "Train Accuracy = 58.58617453862997\n",
            "Valid Accuracy = 53.125\n",
            "Train Loss = 1.2242845112085343\n",
            "Valid Loss = 1.4106499242782593\n",
            "Epoch # 40\n",
            "Train Accuracy = 58.414138254613704\n",
            "Valid Accuracy = 53.625\n",
            "Train Loss = 1.2140677931904793\n",
            "Valid Loss = 1.3749271726608276\n",
            "Epoch # 41\n",
            "Train Accuracy = 59.587112918360965\n",
            "Valid Accuracy = 51.125\n",
            "Train Loss = 1.2071157583594323\n",
            "Valid Loss = 1.3915404605865478\n",
            "Epoch # 42\n",
            "Train Accuracy = 59.18048170159525\n",
            "Valid Accuracy = 51.875\n",
            "Train Loss = 1.2006032833456992\n",
            "Valid Loss = 1.4575911808013915\n",
            "Epoch # 43\n",
            "Train Accuracy = 59.587112918360965\n",
            "Valid Accuracy = 56.5\n",
            "Train Loss = 1.211330718100071\n",
            "Valid Loss = 1.368283019065857\n",
            "Epoch # 44\n",
            "Train Accuracy = 60.10322177040976\n",
            "Valid Accuracy = 51.0\n",
            "Train Loss = 1.1795350670814515\n",
            "Valid Loss = 1.467283706665039\n",
            "Epoch # 45\n",
            "Train Accuracy = 60.10322177040976\n",
            "Valid Accuracy = 57.625\n",
            "Train Loss = 1.1825586435198785\n",
            "Valid Loss = 1.3536132454872132\n",
            "Epoch # 46\n",
            "Train Accuracy = 60.72880825774163\n",
            "Valid Accuracy = 51.375\n",
            "Train Loss = 1.175812151134014\n",
            "Valid Loss = 1.4244276618957519\n",
            "Epoch # 47\n",
            "Train Accuracy = 60.16578041914295\n",
            "Valid Accuracy = 53.5\n",
            "Train Loss = 1.1844682341814041\n",
            "Valid Loss = 1.3814862203598022\n",
            "Epoch # 48\n",
            "Train Accuracy = 60.16578041914295\n",
            "Valid Accuracy = 51.25\n",
            "Train Loss = 1.1625493139028549\n",
            "Valid Loss = 1.398549017906189\n",
            "Epoch # 49\n",
            "Train Accuracy = 60.68188927119174\n",
            "Valid Accuracy = 55.5\n",
            "Train Loss = 1.160221976339817\n",
            "Valid Loss = 1.3900932312011718\n",
            "Epoch # 50\n",
            "Train Accuracy = 62.386612449171096\n",
            "Valid Accuracy = 52.875\n",
            "Train Loss = 1.1353774189949035\n",
            "Valid Loss = 1.4349512267112732\n",
            "Epoch # 51\n",
            "Train Accuracy = 61.18235846105724\n",
            "Valid Accuracy = 53.875\n",
            "Train Loss = 1.1433866718411445\n",
            "Valid Loss = 1.406411418914795\n",
            "Epoch # 52\n",
            "Train Accuracy = 61.213637785423835\n",
            "Valid Accuracy = 52.125\n",
            "Train Loss = 1.1482730177044869\n",
            "Valid Loss = 1.3978471422195435\n",
            "Epoch # 53\n",
            "Train Accuracy = 61.698467313106036\n",
            "Valid Accuracy = 51.0\n",
            "Train Loss = 1.1457143107056618\n",
            "Valid Loss = 1.4589310479164124\n",
            "Epoch # 54\n",
            "Train Accuracy = 61.79230528620582\n",
            "Valid Accuracy = 51.625\n",
            "Train Loss = 1.1346215841174125\n",
            "Valid Loss = 1.4281313180923463\n",
            "Epoch # 55\n",
            "Train Accuracy = 61.47951204253988\n",
            "Valid Accuracy = 51.375\n",
            "Train Loss = 1.1291676563024522\n",
            "Valid Loss = 1.5133616352081298\n",
            "Epoch # 56\n",
            "Train Accuracy = 60.9008445417579\n",
            "Valid Accuracy = 53.375\n",
            "Train Loss = 1.1285315749049187\n",
            "Valid Loss = 1.395381236076355\n",
            "Epoch # 57\n",
            "Train Accuracy = 62.76196434157022\n",
            "Valid Accuracy = 53.125\n",
            "Train Loss = 1.107778071463108\n",
            "Valid Loss = 1.4604287481307983\n",
            "Epoch # 58\n",
            "Train Accuracy = 63.324992180168906\n",
            "Valid Accuracy = 54.625\n",
            "Train Loss = 1.101224779188633\n",
            "Valid Loss = 1.4283969974517823\n",
            "Epoch # 59\n",
            "Train Accuracy = 62.07381920550516\n",
            "Valid Accuracy = 54.75\n",
            "Train Loss = 1.1171128484606743\n",
            "Valid Loss = 1.375705180168152\n",
            "Epoch # 60\n",
            "Train Accuracy = 62.980919612136375\n",
            "Valid Accuracy = 53.5\n",
            "Train Loss = 1.0920818877220153\n",
            "Valid Loss = 1.4202432680130004\n",
            "Epoch # 61\n",
            "Train Accuracy = 63.12167657178605\n",
            "Valid Accuracy = 52.25\n",
            "Train Loss = 1.0932090553641318\n",
            "Valid Loss = 1.4853615427017213\n",
            "Epoch # 62\n",
            "Train Accuracy = 63.49702846418517\n",
            "Valid Accuracy = 53.625\n",
            "Train Loss = 1.088789083659649\n",
            "Valid Loss = 1.4268378162384032\n",
            "Epoch # 63\n",
            "Train Accuracy = 63.09039724741945\n",
            "Valid Accuracy = 50.625\n",
            "Train Loss = 1.0862391683459283\n",
            "Valid Loss = 1.5191658735275269\n",
            "Epoch # 64\n",
            "Train Accuracy = 64.12261495151705\n",
            "Valid Accuracy = 51.875\n",
            "Train Loss = 1.0841764172911643\n",
            "Valid Loss = 1.4405149173736573\n",
            "Epoch # 65\n",
            "Train Accuracy = 63.74726305911792\n",
            "Valid Accuracy = 48.25\n",
            "Train Loss = 1.0839401564002038\n",
            "Valid Loss = 1.5396281242370606\n",
            "Epoch # 66\n",
            "Train Accuracy = 63.528307788551764\n",
            "Valid Accuracy = 52.375\n",
            "Train Loss = 1.0703653424978257\n",
            "Valid Loss = 1.4419322776794434\n",
            "Epoch # 67\n",
            "Train Accuracy = 63.91929934313419\n",
            "Valid Accuracy = 50.25\n",
            "Train Loss = 1.073853280544281\n",
            "Valid Loss = 1.5022295570373536\n",
            "Epoch # 68\n",
            "Train Accuracy = 63.715983734751326\n",
            "Valid Accuracy = 52.25\n",
            "Train Loss = 1.074395545423031\n",
            "Valid Loss = 1.505642066001892\n",
            "Epoch # 69\n",
            "Train Accuracy = 63.3562715045355\n",
            "Valid Accuracy = 54.0\n",
            "Train Loss = 1.0706352657079696\n",
            "Valid Loss = 1.4219347190856935\n",
            "Finished Training\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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wXNfDhw/N7lsQBPN8/DFP/6LTAYGBPDVMZuTLL/nv+PG2b/v220C9esDIkTxB\n9R9/AN9+y5NJCxkTEV7ppGfPnjhx4kQy4RUYGIigoCB4eXlhxYoVqFSpktk+hg0bhkePHqFy5cqY\nOnVqouXMx8cH1apVQ6VKldCrVy/UrVs3cZvBgwejVatWicH1Gn5+fujfvz9q1KiBmjVrYtCgQahW\nrZpVx7Jv3z4UK1YMRYsWTVzWoEEDhISE4NatW1i5ciW++uoreHt7o06dOrh9+zZatWqFDh06ICAg\nAL6+vpg9ezYA4O2338bChQtRrVq1xKB/Y5g6V1WqVMH777+Phg0bwsfHB+MNrkiBgYG4f/++2QzS\nkiVLokaNGmjdujUWLVoENzc3DBo0CJ6envDz80PVqlUxZMgQi1mMhQoVwrJly9CzZ094e3ujdu3a\nOHfuXOL6+/fvw9vbG/PmzcOcOXMAAPPnz8fSpUvh7e2NlStXYt68eQCAefPmYdeuXfDy8oK/v79F\nt7EgCKa5dAlYtozdbN98w2UXPvnE1aOynYgIFkq9enFFeFvJmpWzHBMSONB+3jye21DIuMgk2UKm\nY8OGDfjll1+wcuVKo+v79++Pdu3amayTZi88PDwSY+IcjXzfBSE5/foB69cDly8Dr77Kda1Wr+aa\nVAbPqBmeDz8Epk8HTp8GqlRJez87dwK3b7OAE1yPuUmyn48YL+GFYdSoUdi2bRu2bt3q6qEIguAi\nzp/nwPJx41h0AcDXX7PVKzAQCA627/Q1T58Ct25xFfnQUODmTXZrduoElC+f9n5jYoD584F27dIn\nugAukipkDsTiJQiZAPm+C0ISgYGczXj5MlC4cNLyf//leKcuXThYPY35Lrh7l6flOXCAhdbdu0lZ\ngympWRPo3Rvo3t32gqTz5wOjRwP792cuK51gGXMWL4nxEgRBEDINISEsqkaNSi66AJ67cMYMnh5n\nxQrb+46LAz79FChXji1oBQoA7dsDU6dyHNa2bcCpU0BkJAuy2bOBx495LEWLctu1a7kfSzx7xhNN\n160routFI1O7GokozSn8gpBZyAxWaUFwFtOnc/mIt982vv6dd4Dt27k0Q5061rkCdTrgp5+Ad9/l\n0g7t23NdLXN5UfnzAxMm8OvUKS7fsGoVV4nPnRt47z0eYzYTd9l164Br19jqJbxYZFqLl5ubGyIi\nIuSmJDzXEBEiIiLg5ubm6qEIgss5eZID6seONV3vKmtWYOVK4KWXONBcP6ubSfbvZ0tZYCD3uXMn\n8Ouv5kVXSry8gJkzWUjt3MnxVu++y5YsY8nLRMBnn3FcV9u21u9HeD7ItBav4sWLIzQ0FOHh4a4e\niiA4FDc3NxQvXtzVwxAEl9nj8h8AACAASURBVDN9OpAnj+V6VyVKAN99B3TuDAwezBNNx8UBsbH8\nV3tdvsx1r4oV49IUffoAWdJhjsiSBWjcGGjUiC1aI0YA1aqx+3PChCTrl+ayXL48ffsTMieZNrhe\nEARBeHE4dgzw92fxNW2adduMHMmxWoYoBeTIwdPz5M4NDBjAokg/65lduXsXGD4c2LgRqFEDWLoU\n8PTkSa6vXOFaZNmz23+/gusxF1wvwksQBEHI8HTowG7BK1cA/TSnFiECbtxgS1POnCy4Xnop7dmO\naYEoyfoVHQ28+SawaBHPdDdmjPPGITgXyWoUBEEQMi2HDwO//cbB6taKLoAFVsmSnHGYLx/w8svO\nFV3aGLp3B86c4XpdixZxtuSgQc4dh5BxyLQxXoIgCELG4d49rhzfs6ft9awsMW0aB76PGmXffp1J\nkSLAhg1JWY/u7q4ekeAqxOIlCIIgpIudOwEfH3adVa7MAsweUSzh4Tzv4B9/ABMnsmDJzCjFpSoa\nNXL1SARXIsJLEARBSBPPnnHZhGbNWBRt3Mh1swIDOSYrNDRt/cbHAwsWABUqcCHUt9/m6YEE4XlA\nhJcgCIJgM5cu8fQ8M2dyvNLRo8Abb3AA/Ny5bAXz9AQWL+YCpdayfz8QEMBuRX9/rt31+eccFC8I\nzwMOFV5KqXxKqQ1KqXNKqbNKqdpKqQJKqb+UUv/p/+Z35BgEQRAE+/Ljj1yf6sIFLmi6ZElSzFLW\nrOxyPHWKSygMHcoFRf/7z3yft25xHa369XlKnvXrgb/+YtelIDxPOLSchFJqOYB9RPSdUuolADkB\nvAcgkohmKqUmA8hPRO+Y60fKSQiCIDiH0FDg9m3j64iAefN4apwGDViAlShhui8irl01fjzw5AlP\n4WMsq5AIOHKE20ycyO5LCT4XMjMuqeOllMoLIBhAGTLYiVLqPIBGRHRLKfUagN1EVNFcXyK8BEEQ\nHMvDh5w9uGABkJBgul3WrNzuvff4f2sIC2MxdemS6TYlSnCFd2vmVhSEjI454eXIchKlAYQDWKqU\n8gFwFMAYAEWI6Ja+zW0ARYxtrJQaDGAwAJQsWdKBwxQEQXhx0el4bsNJkziLcPBgrjdlirJlbXf/\nFS3K0+MIguBY4ZUNgB+AUUR0SCk1D8BkwwZEREopoyY3IloCYAnAFi8HjlMQBOGF5Phxnlbn4EGg\nZk1g61YOaBcEwXE4Mrg+FEAoER3Sv98AFmJ39C5G6P/edeAYBEEQhBRERvIcggEBHPS+dCmLLxFd\nguB4HCa8iOg2gBtKKS1+qymAEAC/AuinX9YPwC+OGoMgCMLzyKNHQMeOPLmzLbWyHj7k8g8VKnCZ\nh5EjOTOxf38gixQXEgSn4Ogpg0YBWKXPaLwMYABY7K1TSg0EcA1ANwePQRAE4blixAieuzBLFmD+\nfC7DMHEiUKmS8fa3b3NtrYULgagooGVLYNYswNvbueMWBMHBdbyIKJiIAojIm4heJ6L7RBRBRE2J\nqDwRNSOiSEeOQRAE4XlixQp+TZ3KbsLBg3mKHk9PoHNnLsugcfEi19Hy8OAipK1bA8eO8RQ8IroE\nwTU4tI6XvZByEoIgCMD58xyHFRAA/P13UjmHu3eBr74Cvv4aePCAC5YWLMiTMmfLBgwYwNPulCvn\n2vELwouCS+p42RMRXoIgvOjExQG1anFNrOBgoFix1G2ioriK/Jw5QHQ0B9CPGQO89przxysILzKu\nquMlCIIg2IkJE3jewi1bjIsuAMiThy1bY8dyfS6Z31AQMh4ivARBEDI4GzdyYPyECUCbNpbbZ5Mr\nuyBkWCSBWBAEIQNz5QowcCBPOP3JJ64ejSAI6UWeiwRBEBxAbCxPKD17Ns99WKSI8VeZMoCfH1Cg\nQOo+nj4FevTg/3/6SVyHgvA8IMJLEATBjsTHA8uW8UTSYWFA27Ysru7c4Xpap04BO3Zw9qEhHh6c\nsWj4mjkTOHwYWL8eKF3aFUcjCIK9EeElCIJgB4iAX38F3n0XOHsWqF2brVT16xtv/+QJl4E4fx44\nepRfx45xPJchQ4cCXbo4fvyCIDgHEV6CIAjp5OBBYNIk4MABoGJF4OefgddfB5Qyvc3LLwMlSvCr\nWbOk5ffv8+TVx44B9+6x5UwQhOcHEV6CIAhpJDSUyzesXQu8+irPf/jmm+nLKsyfnwugNmliv3EK\ngpBxEOElCILTITJvDcroPHnCRUo/+ojrZU2fzgLM3d3VIxMEIaMj5SQEQXAq4eFA5cpA+fLAhx/y\nfIKZiT/+ALy8OJarZUuO55o2TUSXIAjWIcJLEASnERcHdOgAXLsGFC/Owqt8eQ5E/+YbjmlKK0TA\nqlUcZ+UIrl4FOnXiiaYBFmA//8zZiIIgCNYirkZBEJyCTgf06QMcOsSTN7/xBsdIrV4NrFwJjBjB\n8wq2aQO89RbQrp1tfU+YAMydC5QqxVa0tMRZEXGZh5s3eWyhofz/lSscx5U1K5d4GDuWg+NfOJ48\nAR4/BvLmdfVIBCHTIpNkC4LgFCZO5GKiX3wBjB+fev3Jk8CPP7LVKiwM6N8fWLDAsgvvyRNu+9NP\nQOPGwK5dLOw6d7Z+bJGRQMeOnEkYG5t8nVJc6LRJE+Czz9hS98Ly9tvA1q1ASIirRyIIGRpzk2SL\n8BIEweEsXAgMH85WrfnzzQfWx8dz0PpHH3FphrVrAW9v422jothy9vffLIomTGDXZdGiwP791o9v\n6lTe36hR7DosXpxfxYoBr70mFeMTadKElW1sLJAjh6tHIwgZFhFegiC4jK1bgfbt2YW4aZP1LsCd\nO4HAQHb9zZ0LDB6cXLDducPxVidPAj/8APTty8vnzgXGjeOK79WrW97PgwfsnmzenC1lghlKl+Zg\nt9OngSpVXD0aQciwmBNeElwvCILDOH4c6NYN8PUF1qyxLe6qSRPgxAmgQQOu3t6jB/DwIa+7eBGo\nU4ervv/2W5LoAriOVu7cLMCsYf58tpxNmWL92F5Inj0Drl/n/zNbKqogZCBEeAmC4BBu3OB5CgsU\nAH7/HciVy/Y+ChcGtm3jgPaNG3ky6eXLgbp1WYTt3JmUZaiRJw8wcCCwbh0HxpsjKorrcXXowOJQ\nMMONG5zFAACXLrl2LIKQiRHhJQiC3YmOZtEVEwNs2cJxUmklSxbgnXeAvXs5/qt/fw4vOnAAqFnT\n+DajR7NGWLDAfN/ffMNT9HzwQdrH98Jw+XLS/2LxEoQ0I8JLEAS7M3o0cOYMx0x5edmnzzp12HX5\n2Wc8N2LFiqbbli7NcyUuXpw6S1Hj0SPOsGzdGggwGokhJOPKFf5buLAIL0FIByK8BEGwK+vWAcuW\nAe+/zwHr9qRAAZ6MumhRy23HjmVr1ooVxtcvWsQFW8XaZSWXL3OQXoMG4moUhHQgwksQBLtx4wYw\nZAi7AF0taOrVA/z9OcheC03SiI3lmmLNmnHVfMEKrlzh9M+KFXnqgWfPXD0iQciUiPASBMEuJCRw\nZfr4eC6Cmj27a8ejFJeVOH8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1m0oJKgut+z6KDh5kfZLs9/7aa0T9+lnesab2QkPNDy5f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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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GwtfXF4MHD8agQYPsXtubGRHxdB1cEhUVJbGxsW69xs03A35+wPLlbr0MEREV0I4dO9C0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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy:  40.375\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HY0uVckzD49v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bcf088fc-6da1-4d7f-f759-b50e87a03e83"
      },
      "source": [
        "epochs = 70\n",
        "net_cnn_2d_shallow = CNN_2D_Shallow().cuda()\n",
        "optimizer = torch.optim.Adam(net_cnn_2d_shallow.parameters(),weight_decay=0.01)\n",
        "train_accuracy_per_epoch_2d_shallow, valid_accuracy_per_epoch_2d_shallow, train_loss_2d_shallow, valid_loss_2d_shallow = train(net_cnn_2d_shallow,train_dataloader, epochs, optimizer)\n",
        "plot_accuracies(train_accuracy_per_epoch_2d_shallow,valid_accuracy_per_epoch_2d_shallow,epochs,\"2D CNN-Shallow\")\n",
        "plot_loss(train_loss_2d_shallow, valid_loss_2d_shallow, epochs, \"2D CNN-Shallow\")\n",
        "test_accuracy_2d_shallow = calc_test_accuracy(net_cnn_2d_shallow)\n",
        "print(\"Test accuracy: \", test_accuracy_2d_shallow)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch # 0\n",
            "Train Accuracy = 31.654676258992804\n",
            "Valid Accuracy = 35.125\n",
            "Train Loss = 1.8195014011859894\n",
            "Valid Loss = 1.7538940191268921\n",
            "Epoch # 1\n",
            "Train Accuracy = 39.28683140444166\n",
            "Valid Accuracy = 41.125\n",
            "Train Loss = 1.6537211871147155\n",
            "Valid Loss = 1.6069148874282837\n",
            "Epoch # 2\n",
            "Train Accuracy = 42.758836409133565\n",
            "Valid Accuracy = 43.5\n",
            "Train Loss = 1.5766329568624498\n",
            "Valid Loss = 1.5631998348236085\n",
            "Epoch # 3\n",
            "Train Accuracy = 45.08914607444479\n",
            "Valid Accuracy = 48.875\n",
            "Train Loss = 1.528760256767273\n",
            "Valid Loss = 1.4957845306396484\n",
            "Epoch # 4\n",
            "Train Accuracy = 46.62183296840788\n",
            "Valid Accuracy = 49.125\n",
            "Train Loss = 1.503468628525734\n",
            "Valid Loss = 1.468125476837158\n",
            "Epoch # 5\n",
            "Train Accuracy = 47.79480763215515\n",
            "Valid Accuracy = 45.875\n",
            "Train Loss = 1.4729834389686585\n",
            "Valid Loss = 1.5016724157333374\n",
            "Epoch # 6\n",
            "Train Accuracy = 48.87394432280263\n",
            "Valid Accuracy = 48.0\n",
            "Train Loss = 1.439123187661171\n",
            "Valid Loss = 1.4695663452148438\n",
            "Epoch # 7\n",
            "Train Accuracy = 49.530810134501095\n",
            "Valid Accuracy = 49.375\n",
            "Train Loss = 1.438443130850792\n",
            "Valid Loss = 1.50089768409729\n",
            "Epoch # 8\n",
            "Train Accuracy = 50.82890209571473\n",
            "Valid Accuracy = 45.875\n",
            "Train Loss = 1.423067591190338\n",
            "Valid Loss = 1.5282359361648559\n",
            "Epoch # 9\n",
            "Train Accuracy = 50.29715358148264\n",
            "Valid Accuracy = 52.0\n",
            "Train Loss = 1.4133797496557237\n",
            "Valid Loss = 1.4149627351760865\n",
            "Epoch # 10\n",
            "Train Accuracy = 52.01751642164529\n",
            "Valid Accuracy = 50.375\n",
            "Train Loss = 1.386320441365242\n",
            "Valid Loss = 1.4212891149520874\n",
            "Epoch # 11\n",
            "Train Accuracy = 51.90803878636221\n",
            "Valid Accuracy = 50.5\n",
            "Train Loss = 1.3864032870531082\n",
            "Valid Loss = 1.4360231924057008\n",
            "Epoch # 12\n",
            "Train Accuracy = 53.42508601814201\n",
            "Valid Accuracy = 52.75\n",
            "Train Loss = 1.3502118268609047\n",
            "Valid Loss = 1.4194238328933715\n",
            "Epoch # 13\n",
            "Train Accuracy = 53.90991554582421\n",
            "Valid Accuracy = 51.125\n",
            "Train Loss = 1.3414973011612892\n",
            "Valid Loss = 1.4390706348419189\n",
            "Epoch # 14\n",
            "Train Accuracy = 54.394745073506414\n",
            "Valid Accuracy = 51.0\n",
            "Train Loss = 1.3153969472646714\n",
            "Valid Loss = 1.452893238067627\n",
            "Epoch # 15\n",
            "Train Accuracy = 55.473881764153894\n",
            "Valid Accuracy = 52.25\n",
            "Train Loss = 1.2933899900317192\n",
            "Valid Loss = 1.4211174583435058\n",
            "Epoch # 16\n",
            "Train Accuracy = 56.14638723803566\n",
            "Valid Accuracy = 51.875\n",
            "Train Loss = 1.2662950623035432\n",
            "Valid Loss = 1.402718071937561\n",
            "Epoch # 17\n",
            "Train Accuracy = 57.147325617766654\n",
            "Valid Accuracy = 51.625\n",
            "Train Loss = 1.2499540850520134\n",
            "Valid Loss = 1.4398200750350951\n",
            "Epoch # 18\n",
            "Train Accuracy = 58.226462308414135\n",
            "Valid Accuracy = 52.75\n",
            "Train Loss = 1.2330600494146347\n",
            "Valid Loss = 1.4271124744415282\n",
            "Epoch # 19\n",
            "Train Accuracy = 58.58617453862997\n",
            "Valid Accuracy = 49.5\n",
            "Train Loss = 1.2096921202540398\n",
            "Valid Loss = 1.443840217590332\n",
            "Epoch # 20\n",
            "Train Accuracy = 58.97716609321239\n",
            "Valid Accuracy = 51.375\n",
            "Train Loss = 1.1913350501656532\n",
            "Valid Loss = 1.4096963500976563\n",
            "Epoch # 21\n",
            "Train Accuracy = 60.80700656865812\n",
            "Valid Accuracy = 51.75\n",
            "Train Loss = 1.1594470468163491\n",
            "Valid Loss = 1.411964497566223\n",
            "Epoch # 22\n",
            "Train Accuracy = 60.93212386612449\n",
            "Valid Accuracy = 52.625\n",
            "Train Loss = 1.1317625260353088\n",
            "Valid Loss = 1.4712719440460205\n",
            "Epoch # 23\n",
            "Train Accuracy = 61.54207069127307\n",
            "Valid Accuracy = 49.625\n",
            "Train Loss = 1.1173350086808205\n",
            "Valid Loss = 1.4823809957504273\n",
            "Epoch # 24\n",
            "Train Accuracy = 61.995620894588676\n",
            "Valid Accuracy = 54.0\n",
            "Train Loss = 1.1041463083028793\n",
            "Valid Loss = 1.4167875576019286\n",
            "Epoch # 25\n",
            "Train Accuracy = 63.38755082890209\n",
            "Valid Accuracy = 52.5\n",
            "Train Loss = 1.082577411532402\n",
            "Valid Loss = 1.4938184118270874\n",
            "Epoch # 26\n",
            "Train Accuracy = 64.27901157335002\n",
            "Valid Accuracy = 51.625\n",
            "Train Loss = 1.039333499968052\n",
            "Valid Loss = 1.4989941501617432\n",
            "Epoch # 27\n",
            "Train Accuracy = 65.04535502033156\n",
            "Valid Accuracy = 48.75\n",
            "Train Loss = 1.0256947284936906\n",
            "Valid Loss = 1.500728907585144\n",
            "Epoch # 28\n",
            "Train Accuracy = 65.95245542696277\n",
            "Valid Accuracy = 47.875\n",
            "Train Loss = 0.9858607015013695\n",
            "Valid Loss = 1.5788202905654907\n",
            "Epoch # 29\n",
            "Train Accuracy = 66.64060056302783\n",
            "Valid Accuracy = 47.75\n",
            "Train Loss = 0.9802662405371666\n",
            "Valid Loss = 1.525364966392517\n",
            "Epoch # 30\n",
            "Train Accuracy = 68.3296840788239\n",
            "Valid Accuracy = 48.0\n",
            "Train Loss = 0.9424111180007457\n",
            "Valid Loss = 1.5449587869644166\n",
            "Epoch # 31\n",
            "Train Accuracy = 68.76759461995621\n",
            "Valid Accuracy = 48.375\n",
            "Train Loss = 0.9189872121810914\n",
            "Valid Loss = 1.6269014644622803\n",
            "Epoch # 32\n",
            "Train Accuracy = 70.17516421645293\n",
            "Valid Accuracy = 46.75\n",
            "Train Loss = 0.8896152061223984\n",
            "Valid Loss = 1.5763917398452758\n",
            "Epoch # 33\n",
            "Train Accuracy = 70.31592117610259\n",
            "Valid Accuracy = 50.375\n",
            "Train Loss = 0.8736471404135228\n",
            "Valid Loss = 1.5529164314270019\n",
            "Epoch # 34\n",
            "Train Accuracy = 69.47137941820456\n",
            "Valid Accuracy = 49.625\n",
            "Train Loss = 0.8752218510210514\n",
            "Valid Loss = 1.541891746520996\n",
            "Epoch # 35\n",
            "Train Accuracy = 71.98936502971536\n",
            "Valid Accuracy = 50.875\n",
            "Train Loss = 0.8270488114655018\n",
            "Valid Loss = 1.7264432001113892\n",
            "Epoch # 36\n",
            "Train Accuracy = 71.67657178604942\n",
            "Valid Accuracy = 48.0\n",
            "Train Loss = 0.8302161014080047\n",
            "Valid Loss = 1.6568526554107665\n",
            "Epoch # 37\n",
            "Train Accuracy = 72.94338442289647\n",
            "Valid Accuracy = 48.875\n",
            "Train Loss = 0.8020570714771748\n",
            "Valid Loss = 1.711336989402771\n",
            "Epoch # 38\n",
            "Train Accuracy = 73.64716922114482\n",
            "Valid Accuracy = 51.375\n",
            "Train Loss = 0.7742645071446895\n",
            "Valid Loss = 1.5641294097900391\n",
            "Epoch # 39\n",
            "Train Accuracy = 74.7419455739756\n",
            "Valid Accuracy = 47.75\n",
            "Train Loss = 0.7534931015968322\n",
            "Valid Loss = 1.7227436923980712\n",
            "Epoch # 40\n",
            "Train Accuracy = 75.60212699405693\n",
            "Valid Accuracy = 48.625\n",
            "Train Loss = 0.7301211471855641\n",
            "Valid Loss = 1.667679033279419\n",
            "Epoch # 41\n",
            "Train Accuracy = 75.2424147638411\n",
            "Valid Accuracy = 48.375\n",
            "Train Loss = 0.7300137993693352\n",
            "Valid Loss = 1.7974704313278198\n",
            "Epoch # 42\n",
            "Train Accuracy = 75.77416327807319\n",
            "Valid Accuracy = 44.875\n",
            "Train Loss = 0.7150313870608807\n",
            "Valid Loss = 1.8725975847244263\n",
            "Epoch # 43\n",
            "Train Accuracy = 77.6978417266187\n",
            "Valid Accuracy = 47.0\n",
            "Train Loss = 0.6659064245223999\n",
            "Valid Loss = 1.8217082548141479\n",
            "Epoch # 44\n",
            "Train Accuracy = 76.47794807632155\n",
            "Valid Accuracy = 47.375\n",
            "Train Loss = 0.6782045675814152\n",
            "Valid Loss = 1.8947774505615234\n",
            "Epoch # 45\n",
            "Train Accuracy = 78.10447294338442\n",
            "Valid Accuracy = 46.0\n",
            "Train Loss = 0.6662059721350669\n",
            "Valid Loss = 1.8198498725891112\n",
            "Epoch # 46\n",
            "Train Accuracy = 77.97935564591805\n",
            "Valid Accuracy = 46.25\n",
            "Train Loss = 0.6462847179174424\n",
            "Valid Loss = 1.7586950778961181\n",
            "Epoch # 47\n",
            "Train Accuracy = 78.90209571473257\n",
            "Valid Accuracy = 44.875\n",
            "Train Loss = 0.619848373606801\n",
            "Valid Loss = 1.8343901777267455\n",
            "Epoch # 48\n",
            "Train Accuracy = 79.82483578354707\n",
            "Valid Accuracy = 47.625\n",
            "Train Loss = 0.6054460660368204\n",
            "Valid Loss = 1.8654094552993774\n",
            "Epoch # 49\n",
            "Train Accuracy = 79.93431341883016\n",
            "Valid Accuracy = 43.125\n",
            "Train Loss = 0.594432081952691\n",
            "Valid Loss = 1.9913713693618775\n",
            "Epoch # 50\n",
            "Train Accuracy = 81.138567406944\n",
            "Valid Accuracy = 44.375\n",
            "Train Loss = 0.5633238322287798\n",
            "Valid Loss = 1.9731591510772706\n",
            "Epoch # 51\n",
            "Train Accuracy = 81.01345010947763\n",
            "Valid Accuracy = 41.875\n",
            "Train Loss = 0.5771596479415894\n",
            "Valid Loss = 1.9997175025939942\n",
            "Epoch # 52\n",
            "Train Accuracy = 80.95089146074444\n",
            "Valid Accuracy = 43.25\n",
            "Train Loss = 0.5681548471748828\n",
            "Valid Loss = 2.0175475883483887\n",
            "Epoch # 53\n",
            "Train Accuracy = 80.87269314982797\n",
            "Valid Accuracy = 46.125\n",
            "Train Loss = 0.5668818424642086\n",
            "Valid Loss = 1.9132002353668214\n",
            "Epoch # 54\n",
            "Train Accuracy = 81.56083828589303\n",
            "Valid Accuracy = 46.625\n",
            "Train Loss = 0.5405915726721287\n",
            "Valid Loss = 1.9664754819869996\n",
            "Epoch # 55\n",
            "Train Accuracy = 82.51485767907413\n",
            "Valid Accuracy = 45.0\n",
            "Train Loss = 0.5298169347643852\n",
            "Valid Loss = 2.0157111835479737\n",
            "Epoch # 56\n",
            "Train Accuracy = 82.48357835470753\n",
            "Valid Accuracy = 44.75\n",
            "Train Loss = 0.5173835276812315\n",
            "Valid Loss = 1.9931734275817872\n",
            "Epoch # 57\n",
            "Train Accuracy = 82.81201126055677\n",
            "Valid Accuracy = 42.25\n",
            "Train Loss = 0.5097933226078749\n",
            "Valid Loss = 1.9670144653320312\n",
            "Epoch # 58\n",
            "Train Accuracy = 83.57835470753832\n",
            "Valid Accuracy = 45.25\n",
            "Train Loss = 0.5017358212918043\n",
            "Valid Loss = 2.037917399406433\n",
            "Epoch # 59\n",
            "Train Accuracy = 83.53143572098843\n",
            "Valid Accuracy = 43.25\n",
            "Train Loss = 0.48995339289307593\n",
            "Valid Loss = 2.0914583873748778\n",
            "Epoch # 60\n",
            "Train Accuracy = 84.34469815451986\n",
            "Valid Accuracy = 42.125\n",
            "Train Loss = 0.4922959151864052\n",
            "Valid Loss = 2.037123303413391\n",
            "Epoch # 61\n",
            "Train Accuracy = 84.18830153268689\n",
            "Valid Accuracy = 44.5\n",
            "Train Loss = 0.48496346674859525\n",
            "Valid Loss = 2.0926149463653565\n",
            "Epoch # 62\n",
            "Train Accuracy = 85.04848295276823\n",
            "Valid Accuracy = 44.5\n",
            "Train Loss = 0.4686885447800159\n",
            "Valid Loss = 2.1190773677825927\n",
            "Epoch # 63\n",
            "Train Accuracy = 84.78260869565217\n",
            "Valid Accuracy = 44.375\n",
            "Train Loss = 0.45325251266360284\n",
            "Valid Loss = 2.0502999019622803\n",
            "Epoch # 64\n",
            "Train Accuracy = 84.79824835783548\n",
            "Valid Accuracy = 43.125\n",
            "Train Loss = 0.47324127674102784\n",
            "Valid Loss = 1.9369049882888794\n",
            "Epoch # 65\n",
            "Train Accuracy = 85.61151079136691\n",
            "Valid Accuracy = 48.375\n",
            "Train Loss = 0.43938993096351625\n",
            "Valid Loss = 1.9130716276168824\n",
            "Epoch # 66\n",
            "Train Accuracy = 85.92430403503285\n",
            "Valid Accuracy = 43.125\n",
            "Train Loss = 0.4406712730228901\n",
            "Valid Loss = 2.095189332962036\n",
            "Epoch # 67\n",
            "Train Accuracy = 85.2048795746012\n",
            "Valid Accuracy = 45.75\n",
            "Train Loss = 0.4463224021345377\n",
            "Valid Loss = 2.0364813566207887\n",
            "Epoch # 68\n",
            "Train Accuracy = 86.58116984673131\n",
            "Valid Accuracy = 42.5\n",
            "Train Loss = 0.4156855673342943\n",
            "Valid Loss = 2.2590314817428587\n",
            "Epoch # 69\n",
            "Train Accuracy = 86.6437284954645\n",
            "Valid Accuracy = 44.75\n",
            "Train Loss = 0.4113124957680702\n",
            "Valid Loss = 2.1076035833358766\n",
            "Finished Training\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PH8/nn3+e4P5u3brRsmXLROukpZQyZcrEzYkLNf2+i2QuO3b4YcOBA/1ahU8+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\ngg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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II5rtv2PH5LfnBzZyZowxxmQUS5ZoQNa6tQZo3rj7bnj3XR1p+/JLGD5cyzGl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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy:  31.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GnpY52iwIg_7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a654c8a3-7238-481d-804f-e21c154971e8"
      },
      "source": [
        "epochs = 70\n",
        "net_cnn_1d_deep = CNN_1D_Deep().cuda()\n",
        "optimizer = torch.optim.Adam(net_cnn_1d_deep.parameters(),weight_decay=0.01)\n",
        "train_accuracy_per_epoch_1d_deep, valid_accuracy_per_epoch_1d_deep, train_loss_1d_deep, valid_loss_1d_deep  = train(net_cnn_1d_deep,train_dataloader, epochs, optimizer)\n",
        "plot_accuracies(train_accuracy_per_epoch_1d_deep,valid_accuracy_per_epoch_1d_deep,epochs,\"1D CNN-Deep\")\n",
        "plot_loss(train_loss_1d_deep, valid_loss_1d_deep, epochs, \"1D CNN-Deep\")\n",
        "test_accuracy_1d_deep = calc_test_accuracy(net_cnn_1d_deep)\n",
        "print(\"Test accuracy: \", test_accuracy_1d_deep)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch # 0\n",
            "Train Accuracy = 29.918673756646857\n",
            "Valid Accuracy = 38.5\n",
            "Train Loss = 1.8195237171649934\n",
            "Valid Loss = 1.6719623041152953\n",
            "Epoch # 1\n",
            "Train Accuracy = 39.05223647169221\n",
            "Valid Accuracy = 40.125\n",
            "Train Loss = 1.6478660893440247\n",
            "Valid Loss = 1.6330195331573487\n",
            "Epoch # 2\n",
            "Train Accuracy = 43.13418830153269\n",
            "Valid Accuracy = 44.625\n",
            "Train Loss = 1.5904286420345306\n",
            "Valid Loss = 1.569496874809265\n",
            "Epoch # 3\n",
            "Train Accuracy = 43.77541445104786\n",
            "Valid Accuracy = 42.5\n",
            "Train Loss = 1.567538744211197\n",
            "Valid Loss = 1.5451992416381837\n",
            "Epoch # 4\n",
            "Train Accuracy = 45.073506412261494\n",
            "Valid Accuracy = 46.75\n",
            "Train Loss = 1.5335226601362228\n",
            "Valid Loss = 1.5168287181854248\n",
            "Epoch # 5\n",
            "Train Accuracy = 47.184860807006565\n",
            "Valid Accuracy = 49.625\n",
            "Train Loss = 1.4892945659160615\n",
            "Valid Loss = 1.486042356491089\n",
            "Epoch # 6\n",
            "Train Accuracy = 47.23177979355646\n",
            "Valid Accuracy = 48.5\n",
            "Train Loss = 1.4747293102741241\n",
            "Valid Loss = 1.5375769186019896\n",
            "Epoch # 7\n",
            "Train Accuracy = 48.404754457303724\n",
            "Valid Accuracy = 48.625\n",
            "Train Loss = 1.4554327553510666\n",
            "Valid Loss = 1.4843746042251587\n",
            "Epoch # 8\n",
            "Train Accuracy = 49.280575539568346\n",
            "Valid Accuracy = 46.625\n",
            "Train Loss = 1.442680139541626\n",
            "Valid Loss = 1.528050241470337\n",
            "Epoch # 9\n",
            "Train Accuracy = 50.563027838598686\n",
            "Valid Accuracy = 47.75\n",
            "Train Loss = 1.4145135906338693\n",
            "Valid Loss = 1.5069542980194093\n",
            "Epoch # 10\n",
            "Train Accuracy = 50.84454175789803\n",
            "Valid Accuracy = 51.125\n",
            "Train Loss = 1.4035235157608985\n",
            "Valid Loss = 1.4513417816162109\n",
            "Epoch # 11\n",
            "Train Accuracy = 53.112292774476074\n",
            "Valid Accuracy = 51.875\n",
            "Train Loss = 1.3614675605297089\n",
            "Valid Loss = 1.4659085750579834\n",
            "Epoch # 12\n",
            "Train Accuracy = 53.988113856740696\n",
            "Valid Accuracy = 53.0\n",
            "Train Loss = 1.3580365657806397\n",
            "Valid Loss = 1.388506588935852\n",
            "Epoch # 13\n",
            "Train Accuracy = 55.129809196121364\n",
            "Valid Accuracy = 51.5\n",
            "Train Loss = 1.3191734245419502\n",
            "Valid Loss = 1.4691576480865478\n",
            "Epoch # 14\n",
            "Train Accuracy = 55.59899906162027\n",
            "Valid Accuracy = 50.625\n",
            "Train Loss = 1.3219604989886284\n",
            "Valid Loss = 1.4498914432525636\n",
            "Epoch # 15\n",
            "Train Accuracy = 57.06912730685017\n",
            "Valid Accuracy = 49.875\n",
            "Train Loss = 1.3005451941490174\n",
            "Valid Loss = 1.5150958395004273\n",
            "Epoch # 16\n",
            "Train Accuracy = 56.97528933375039\n",
            "Valid Accuracy = 49.625\n",
            "Train Loss = 1.2800583902001381\n",
            "Valid Loss = 1.484825053215027\n",
            "Epoch # 17\n",
            "Train Accuracy = 57.632155145448856\n",
            "Valid Accuracy = 49.125\n",
            "Train Loss = 1.2727423319220543\n",
            "Valid Loss = 1.5455701303482057\n",
            "Epoch # 18\n",
            "Train Accuracy = 57.8980294025649\n",
            "Valid Accuracy = 52.0\n",
            "Train Loss = 1.2596326524019241\n",
            "Valid Loss = 1.4393171072006226\n",
            "Epoch # 19\n",
            "Train Accuracy = 58.88332812011261\n",
            "Valid Accuracy = 50.125\n",
            "Train Loss = 1.2444635045528412\n",
            "Valid Loss = 1.4594601249694825\n",
            "Epoch # 20\n",
            "Train Accuracy = 58.554895214263375\n",
            "Valid Accuracy = 51.75\n",
            "Train Loss = 1.2486786106228829\n",
            "Valid Loss = 1.4034916496276855\n",
            "Epoch # 21\n",
            "Train Accuracy = 58.805129809196124\n",
            "Valid Accuracy = 50.25\n",
            "Train Loss = 1.2244808939099312\n",
            "Valid Loss = 1.5301105928421022\n",
            "Epoch # 22\n",
            "Train Accuracy = 58.992805755395686\n",
            "Valid Accuracy = 52.125\n",
            "Train Loss = 1.228748479783535\n",
            "Valid Loss = 1.4906635761260987\n",
            "Epoch # 23\n",
            "Train Accuracy = 60.322177040975916\n",
            "Valid Accuracy = 51.5\n",
            "Train Loss = 1.1913661974668504\n",
            "Valid Loss = 1.530606174468994\n",
            "Epoch # 24\n",
            "Train Accuracy = 60.69752893337504\n",
            "Valid Accuracy = 49.875\n",
            "Train Loss = 1.1950510764122009\n",
            "Valid Loss = 1.518736810684204\n",
            "Epoch # 25\n",
            "Train Accuracy = 60.97904285267438\n",
            "Valid Accuracy = 49.75\n",
            "Train Loss = 1.1828909385204316\n",
            "Valid Loss = 1.488021230697632\n",
            "Epoch # 26\n",
            "Train Accuracy = 60.525492649358775\n",
            "Valid Accuracy = 50.375\n",
            "Train Loss = 1.1718115976452828\n",
            "Valid Loss = 1.4746321725845337\n",
            "Epoch # 27\n",
            "Train Accuracy = 61.4013137316234\n",
            "Valid Accuracy = 47.875\n",
            "Train Loss = 1.1554270792007446\n",
            "Valid Loss = 1.5479796934127807\n",
            "Epoch # 28\n",
            "Train Accuracy = 61.47951204253988\n",
            "Valid Accuracy = 50.625\n",
            "Train Loss = 1.1453472086787224\n",
            "Valid Loss = 1.492584104537964\n",
            "Epoch # 29\n",
            "Train Accuracy = 61.74538629965593\n",
            "Valid Accuracy = 49.125\n",
            "Train Loss = 1.143801386654377\n",
            "Valid Loss = 1.5385618829727172\n",
            "Epoch # 30\n",
            "Train Accuracy = 62.71504535502033\n",
            "Valid Accuracy = 50.75\n",
            "Train Loss = 1.1318797370791436\n",
            "Valid Loss = 1.5060347986221314\n",
            "Epoch # 31\n",
            "Train Accuracy = 62.543009071004064\n",
            "Valid Accuracy = 51.75\n",
            "Train Loss = 1.1208288875222205\n",
            "Valid Loss = 1.4936028814315796\n",
            "Epoch # 32\n",
            "Train Accuracy = 63.15295589615264\n",
            "Valid Accuracy = 52.25\n",
            "Train Loss = 1.1102683442831038\n",
            "Valid Loss = 1.4755366325378418\n",
            "Epoch # 33\n",
            "Train Accuracy = 62.386612449171096\n",
            "Valid Accuracy = 51.25\n",
            "Train Loss = 1.110611642897129\n",
            "Valid Loss = 1.5388829565048219\n",
            "Epoch # 34\n",
            "Train Accuracy = 63.38755082890209\n",
            "Valid Accuracy = 49.625\n",
            "Train Loss = 1.0863716790080071\n",
            "Valid Loss = 1.5395520210266114\n",
            "Epoch # 35\n",
            "Train Accuracy = 63.63778542383484\n",
            "Valid Accuracy = 47.25\n",
            "Train Loss = 1.0973344150185584\n",
            "Valid Loss = 1.495696439743042\n",
            "Epoch # 36\n",
            "Train Accuracy = 64.29465123553331\n",
            "Valid Accuracy = 51.375\n",
            "Train Loss = 1.0717607891559602\n",
            "Valid Loss = 1.5672943687438965\n",
            "Epoch # 37\n",
            "Train Accuracy = 63.70034407256803\n",
            "Valid Accuracy = 48.375\n",
            "Train Loss = 1.0781327521800994\n",
            "Valid Loss = 1.6275073099136352\n",
            "Epoch # 38\n",
            "Train Accuracy = 64.29465123553331\n",
            "Valid Accuracy = 49.875\n",
            "Train Loss = 1.0698177751898765\n",
            "Valid Loss = 1.5114205694198608\n",
            "Epoch # 39\n",
            "Train Accuracy = 64.8420394119487\n",
            "Valid Accuracy = 51.625\n",
            "Train Loss = 1.0469075307250022\n",
            "Valid Loss = 1.5486781263351441\n",
            "Epoch # 40\n",
            "Train Accuracy = 65.23303096653112\n",
            "Valid Accuracy = 53.75\n",
            "Train Loss = 1.047071381509304\n",
            "Valid Loss = 1.518969407081604\n",
            "Epoch # 41\n",
            "Train Accuracy = 66.37472630591179\n",
            "Valid Accuracy = 48.25\n",
            "Train Loss = 1.0179341644048692\n",
            "Valid Loss = 1.6151006317138672\n",
            "Epoch # 42\n",
            "Train Accuracy = 65.17047231779793\n",
            "Valid Accuracy = 51.125\n",
            "Train Loss = 1.03169783949852\n",
            "Valid Loss = 1.535406231880188\n",
            "Epoch # 43\n",
            "Train Accuracy = 65.8273381294964\n",
            "Valid Accuracy = 52.0\n",
            "Train Loss = 1.0349042490124702\n",
            "Valid Loss = 1.5479707670211793\n",
            "Epoch # 44\n",
            "Train Accuracy = 66.09321238661245\n",
            "Valid Accuracy = 49.375\n",
            "Train Loss = 1.0046200531721114\n",
            "Valid Loss = 1.550076766014099\n",
            "Epoch # 45\n",
            "Train Accuracy = 65.43634657491398\n",
            "Valid Accuracy = 52.125\n",
            "Train Loss = 1.031504515707493\n",
            "Valid Loss = 1.4572347736358642\n",
            "Epoch # 46\n",
            "Train Accuracy = 66.15577103534564\n",
            "Valid Accuracy = 51.375\n",
            "Train Loss = 1.0093122285604477\n",
            "Valid Loss = 1.5915448474884033\n",
            "Epoch # 47\n",
            "Train Accuracy = 66.35908664372849\n",
            "Valid Accuracy = 48.375\n",
            "Train Loss = 0.9973780179023742\n",
            "Valid Loss = 1.5887688779830933\n",
            "Epoch # 48\n",
            "Train Accuracy = 66.23396934626211\n",
            "Valid Accuracy = 50.625\n",
            "Train Loss = 1.010521085858345\n",
            "Valid Loss = 1.5275358533859253\n",
            "Epoch # 49\n",
            "Train Accuracy = 66.57804191429464\n",
            "Valid Accuracy = 49.75\n",
            "Train Loss = 0.9881502425670624\n",
            "Valid Loss = 1.5771944332122803\n",
            "Epoch # 50\n",
            "Train Accuracy = 67.68845792930873\n",
            "Valid Accuracy = 50.25\n",
            "Train Loss = 0.9741300898790359\n",
            "Valid Loss = 1.5776484870910645\n",
            "Epoch # 51\n",
            "Train Accuracy = 67.18798873944323\n",
            "Valid Accuracy = 49.625\n",
            "Train Loss = 0.9791464278101921\n",
            "Valid Loss = 1.6675437831878661\n",
            "Epoch # 52\n",
            "Train Accuracy = 66.96903346887707\n",
            "Valid Accuracy = 53.125\n",
            "Train Loss = 0.9840980061888694\n",
            "Valid Loss = 1.5274817991256713\n",
            "Epoch # 53\n",
            "Train Accuracy = 67.23490772599312\n",
            "Valid Accuracy = 49.875\n",
            "Train Loss = 0.9660821622610092\n",
            "Valid Loss = 1.5757222366333008\n",
            "Epoch # 54\n",
            "Train Accuracy = 66.71879887394432\n",
            "Valid Accuracy = 51.25\n",
            "Train Loss = 0.9828404316306114\n",
            "Valid Loss = 1.5605432462692261\n",
            "Epoch # 55\n",
            "Train Accuracy = 68.61119799812325\n",
            "Valid Accuracy = 50.0\n",
            "Train Loss = 0.945012421309948\n",
            "Valid Loss = 1.5482723426818847\n",
            "Epoch # 56\n",
            "Train Accuracy = 68.20456678135753\n",
            "Valid Accuracy = 51.875\n",
            "Train Loss = 0.9426339569687844\n",
            "Valid Loss = 1.603055510520935\n",
            "Epoch # 57\n",
            "Train Accuracy = 68.37660306537379\n",
            "Valid Accuracy = 48.875\n",
            "Train Loss = 0.9438962531089783\n",
            "Valid Loss = 1.5744756197929382\n",
            "Epoch # 58\n",
            "Train Accuracy = 69.283703472005\n",
            "Valid Accuracy = 50.25\n",
            "Train Loss = 0.926181997358799\n",
            "Valid Loss = 1.6223132610321045\n",
            "Epoch # 59\n",
            "Train Accuracy = 69.09602752580544\n",
            "Valid Accuracy = 50.5\n",
            "Train Loss = 0.9172996151447296\n",
            "Valid Loss = 1.6785600900650024\n",
            "Epoch # 60\n",
            "Train Accuracy = 68.87707225523928\n",
            "Valid Accuracy = 52.125\n",
            "Train Loss = 0.9289828005433083\n",
            "Valid Loss = 1.542271728515625\n",
            "Epoch # 61\n",
            "Train Accuracy = 68.64247732248984\n",
            "Valid Accuracy = 49.0\n",
            "Train Loss = 0.9367656986415386\n",
            "Valid Loss = 1.6167012357711792\n",
            "Epoch # 62\n",
            "Train Accuracy = 69.2993431341883\n",
            "Valid Accuracy = 48.5\n",
            "Train Loss = 0.9249414305388928\n",
            "Valid Loss = 1.6008950185775757\n",
            "Epoch # 63\n",
            "Train Accuracy = 68.8145136065061\n",
            "Valid Accuracy = 50.75\n",
            "Train Loss = 0.9276591458916664\n",
            "Valid Loss = 1.6041387033462524\n",
            "Epoch # 64\n",
            "Train Accuracy = 69.65905536440413\n",
            "Valid Accuracy = 49.25\n",
            "Train Loss = 0.9064187204837799\n",
            "Valid Loss = 1.7040415763854981\n",
            "Epoch # 65\n",
            "Train Accuracy = 69.90928995933687\n",
            "Valid Accuracy = 49.875\n",
            "Train Loss = 0.9007917517423629\n",
            "Valid Loss = 1.7117238283157348\n",
            "Epoch # 66\n",
            "Train Accuracy = 69.34626212073819\n",
            "Valid Accuracy = 51.5\n",
            "Train Loss = 0.9196542558073998\n",
            "Valid Loss = 1.541776568889618\n",
            "Epoch # 67\n",
            "Train Accuracy = 69.45573975602127\n",
            "Valid Accuracy = 50.875\n",
            "Train Loss = 0.9043173708021641\n",
            "Valid Loss = 1.6309584188461304\n",
            "Epoch # 68\n",
            "Train Accuracy = 70.23772286518611\n",
            "Valid Accuracy = 53.625\n",
            "Train Loss = 0.8815965408086777\n",
            "Valid Loss = 1.5258443069458008\n",
            "Epoch # 69\n",
            "Train Accuracy = 70.59743509540193\n",
            "Valid Accuracy = 47.125\n",
            "Train Loss = 0.8772389841079712\n",
            "Valid Loss = 1.7005070877075195\n",
            "Finished Training\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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8+HFAe9f27duX6vNRuXJlduzYwZ9//gnA6dOn+fvvv+O2T5o0CYD58+dTunRpChcuTMOG\nDZkwYQKggWWZMmUoXLgwLVq04L333gM0oDt48GCq1zbGGJNzrVkDTz8N11wDDRvCp59Cq1YwYwZ0\n7x7ctgUs8BKRdSISLiLhQE3gBDAVGAL8LCIVgJ+9P5+zatSoQeXKlalUqRI9evSgfv36GTrPpk2b\n2LVr11lDbhUqVKBAgQIsXbqUd999l1mzZlGtWjUiIiJYu3YtYWFhPP744zRq1Ijw8HAee+wxAO69\n915++uknwsLCWL58ORdeeGGy12zbti0nTpygcuXKDB06lDp16gBQunRpRo0aRfv27QkLC6Nr165x\nx3To0IHDhw/Tq1evFB9L1apVady4MfXq1WPEiBGULl2aNm3a0LFjR2644QaqVatG586dOXbsWKrP\nyYUXXsiUKVN4+OGHCQ0NpXr16nGTCgDy5ctHeHg4gwYN4uOPPwbgueeeY9GiRYSGhvLss8/GzSwd\nNmwYe/bsoWrVqoSHh9uQojHG5ELffw81akCVKvDKK3DddTB+POzZA198AW3bQrBXX3MiEviLONcS\nGCYi9Z1z64AbRWSXc+5yYL6IXJfa8REREeKblebzzz//cP311weu0SZDFi9ezJNPPsm8efOS3T50\n6FBKlizJgw8+GNB2NGjQgHfffZfw8PCAXie72OvdGGNSFhsLzz4LL70E118P/fvDHXdA6dLBaY9z\nbqmIJE1OJvtyvO4EJnq/Ly0ivpoAu4EgPS0mq7344ot89NFHZ5W5MMYYYxKLioKYGLj22syfa+9e\n6NIFfv4Z7rkH3n4bChTI/HkDJeA9Xs65/MBOoIqI7HHOHRKRYgm2HxSRJHlezrl+QD+AcuXK1fTl\nE/lYD4A5n9jr3RiTW4nAli3wyy8wf75+9f1L79pVe6kyWgt88WLo1An27YP33wdvucqgS63HKzsK\nqLYGlonIHu/Pe7xDjHi/JluRUkQ+EpEIEYkoVapUNjTTGGOMMVll2jRNZL/qKk1y79MHfvgBIiLg\nnXfgySfh6681D+upp+DIEf/PLQLvvaelIfLl05mLOSXoSkt2BF5diB9mBJgO9PR+3xP4NhvaYIwx\nxphsEBOjhc07dIDZs+GGGzRIWr0a/vsPpkyBgQO1p2vdOujYEV5+WYcdR43S41Nz/LgGdAMHQsuW\nsHQpeEtA5goBzfFyzhUGWgD3Jrj7FeAr59zdwFagcyDbYIwxxpjscfgw3HmnFip96CH4v/+DvHlT\n3r9cOfjsMxg8GB55BO67T3O0nn8eihaF7dthxw796rtt3QpHj8ILL2ivWW5bvjeggZeIHAcuSXTf\nfqBZIK9rjDHGnEsOH9a6VKBL2oWHa8mEggUDd81jx2DECM3PGjRIh/VSq0O9aRPcfDNs2AAffaSJ\n7v6KiND8r+nT4bHHNG8rodKloWxZuPpqrcvVqRPceGNGHlXw5bI4Medo0qRJkmKob731FgMGDEj1\nON+C1Dt37qRjx47J7nPjjTeSuHxGYm+99RYnTpyI+7lNmzZx6wtmhfDwcO68884sO59JXlRUFFWr\nVg12M4wxOdjOnRr0fPih1qS65x6oVQuKFNHgq2tX+N//YM4cDdCywk8/QbVq8NprMG+eBjkNG8LM\nmZpfldiCBVCnDuzercOL6Qm6fJyD9u3h77/1OgsXatB36pSeNzJS88beey/3Bl1ggVeGdenSJUnZ\nhC+//DJufcC0XHHFFUyZMiXD108ceP3www8Uy6LFp/755x9iY2NZuHBhXCX5QMiJy+/kxDYZY85f\na9dC3bqwebMmph86BBs3ap6UrzL7r7/CE09AixZQvLgGY3ffrb1Oq1ZpjSt/HTqkx7ZsCfnza/Cz\nfbsmw2/bBm3aaO/U11/rcjwAY8ZA8+ZQsiT88Qc0aZK5x5wvH9x0EzRoACEh2o5ziQVeGdSxY0e+\n//57Tp8+DWjPxc6dO2nYsCHHjh2jWbNm1KhRg2rVqvHtt0nnDyTs6Th58iR33nkn119/PR06dODk\nyZNx+w0YMICIiAiqVKnCsGHDAHj77bfZuXMnTZo0oYn3FR4SEhK3xM4bb7xB1apVqVq1Km95F7KM\niori+uuv55577qFKlSq0bNnyrOskNHHiRLp3707Lli3PavvGjRtp3rw5YWFh1KhRg02bNgHw6quv\nUq1aNcLCwhgyRBciSNhrl3B9yXHjxnHLLbfQtGlTmjVrlupzNX78eEJDQwkLC6N79+4cPXqU8uXL\nc+bMGUCXY0r4s0+vXr3o378/ERERVKxYkRkzZgC6lNJjjz1GrVq1CA0N5cMPPwR0SaGGDRtyyy23\nJFm8HGD27NnUrVuXGjVq0KlTp7iK+iEhITz++ONUq1aN2rVrs3HjxrjnumnTpoSGhtKsWTO2bdsG\nwJ49e+jQoQNhYWGEhYXx+++/x7XLn9+LMeb8smgR1K8P0dFagqFFC81nuuYauP12eO45HZrbuhX2\n79eephEjoHx5+PZbuPdeCAuDiy/WYGjoUM29SqlX7NtvoXJlXV5nyBBYsUKDn4IFNZF940Zd5/Do\nUU2Ir1oVevbUQO3GG7W0Q4UK2foU5U4ikuNvNWvWlMTWrFkT/8PgwSKNG2ftbfDgJNdMrG3btjJt\n2jQREXn55ZflkUceERGRM2fOyOHDh0VEZO/evXLNNdeIx+MREZHChQuLiMiWLVukSpUqIiLy+uuv\nS+/evUVEZOXKlZI3b15ZsmSJiIjs379fRERiYmKkcePGsnLlShERueqqq2Tv3r1xbfH9HBkZKVWr\nVpVjx47J0aNHpXLlyrJs2TLZsmWL5M2bV5YvXy4iIp06dZLPPvss2cdVsWJF2bp1q8yaNUvatWsX\nd3/t2rXlm2++ERGRkydPyvHjx+WHH36QunXryvHjx89qb+PGjeMew969e+Wqq64SEZGxY8dKmTJl\n4vZL6blavXq1VKhQIe4x+vbv1auXTJ06VUREPvzwQ3n44YeTtL9nz57SqlUriY2NlfXr10uZMmXk\n5MmT8uGHH8rzzz8vIiLR0dFSs2ZN2bx5s8ybN08KFSokmzdvTnKuvXv3SsOGDeXYsWMiIvLKK6/I\niBEj4p7zF154QUREPv30U2nbtq2IiLRr107GjRsnIiKjR4+W9u3bi4hI586d5c0334z7fR46dMjv\n38tZr3djzDnvu+9EChYUufZakY0b03+8xyOyYYPI55+LDBwoUrOmSN68IiDinEhoqMiAASITJois\nXClyxx26LSxMJDIy9XPHxIhMnChSrZoec//9ImfOZOxxnquASEkhprEer0xIONyYcJhRRHjqqacI\nDQ2lefPm7Nixgz179qR4ngULFtCtWzcAQkNDCQ0Njdv21VdfUaNGDapXr87ff//NmjVrUm3Tr7/+\nSocOHShcuDBFihThtttui1t3sHz58nFL6NSsWZOoqKgkx0dGRlKyZEnKlStHs2bNWL58OQcOHODo\n0aPs2LGDDh06AFCgQAEKFSrEnDlz6N27N4UKFQKgRIkSaT5vLVq0iNsvpedq7ty5dOrUiZIlS551\n3r59+8atrzh27Fh6p1C4pXPnzuTJk4cKFSpw9dVXs3btWmbPns348eMJDw+nTp067N+/nw0bNgBQ\nu3Ztypcvn+Q8ixcvZs2aNdSvX5/w8HA+/fRTEhbz9f3Ou3TpwqJFiwBYtGgRd911FwDdu3fn119/\nBWDu3LlxOYB58+bl4osvBvz7vRhjcodjx+DWW7XHaPPmjJ1jzBg9R+XK8Ntv2sOVXs5peYauXXWY\nMDJShxF//hmGD4fLLoPPP9ftYWEwdarOJFyyBGrWTP3cefPqzMUVK7QX7N134YLsWgfnHHBuPFXe\n4bTs1r59ex566CGWLVvGiRMnqOl9tU6YMIG9e/eydOlS8uXLR0hICNHR0ek+/5YtW3jttddYsmQJ\nxYsXp1evXhk6j0/CxbDz5s2b7JDWxIkTWbt2bdzQ4JEjR/j666/TnWh/wQUX4PEmACRuc+HCheO+\nT+9zVb9+faKiopg/fz6xsbEpJqa7RFNvnHOICO+88w6tWrU6a9v8+fPPalNCIkKLFi2YOHFistsT\nXifxNf3lz+/FGJM7vPmmDtl99x28+qoOD/bvr7P90lqc2ePRelZDh2qO1ddfawJ9VilSBJo21Rto\n7tdff2lQ1qABVKqUvvP5hj1N+liPVyYUKVKEJk2a0KdPn7OS6g8fPsyll15Kvnz5mDdvHomXO0qs\nUaNGfPHFFwCsXr2aVatWARr0FC5cmIsvvpg9e/Ywc+bMuGOKFi3K0aNHk5yrYcOGTJs2jRMnTnD8\n+HGmTp1Kw4YN/Xo8Ho+Hr776ir/++ouoqCiioqL49ttvmThxIkWLFqVs2bJMmzYNgFOnTnHixAla\ntGjB2LFj4xL9Dxw4AGj+09KlSwFSnUSQ0nPVtGlTJk+ezP79+886L0CPHj246667UuztApg8eTIe\nj4dNmzaxefNmrrvuOlq1asWoUaPicsLWr1+f5uSBG264gd9++y0uf+v48eOsX78+bvukSZPivtat\nWxeAevXqxfWETpgwIe75b9asGaNGjQI0r+twVk0/MsbkCPv2ad2qW2/VRPQRI+CffzQfq1w5Dai2\nbtVZgdu3a77Va69pxfVatbRu1dCh0K2bBm5ZGXQlJ29eLUvRt2/6gy6TcRZ4ZVKXLl1YuXLlWYFX\n165diYyMpFq1aowfP55KabyiBwwYwLFjx7j++ut59tln43rOwsLCqF69OpUqVeKuu+6ifv36ccf0\n69ePm266KS653qdGjRr06tWL2rVrU6dOHfr27Ut1P0v6Lly4kDJlynDFFVfE3deoUSPWrFnDrl27\n+Oyzz3j77bcJDQ2lXr167N69m5tuuolbbrmFiIgIwsPDee211wB49NFHGTVqFNWrV49L+k9OSs9V\nlSpVePrpp2ncuDFhYWE8/PDDZx1z8ODBVGeQlitXjtq1a9O6dWs++OADChQoQN++falcuTI1atSg\natWq3HvvvWnOYixVqhTjxo2jS5cuhIaGUrduXdauXRu3/eDBg4SGhjJy5EjefPNNAN555x3Gjh1L\naGgon332GSNHjgRg5MiRzJs3j2rVqlGzZs00h42NMbnLSy9pVfUXX4QyZeDZZ3Ux6O++0+G7l17S\nxPdixeDKK6F1a61Z9eOPel+/fvDFF5rcfq7N5DPxAr5IdlaIiIiQxHWtbNHg89eUKVP49ttv+eyz\nz5Ld3qtXL9q1a5dinbSsEhISEpcTF2j2ejcmZ9u2TWf0de2qOVop7TNmjC6bU7Wq3qpUgUsuSX5/\nk3ultkj2uZHjZc4bgwYNYubMmfzwww/BbooxxsQZPlwT2ocPT3mfcuVS327ODxZ4mVzlnXfeSXOf\ncePGBb4hYLMPjckBzpzRXKVgrte3Zo0ODz74oAZXxqTGcryMMcbkSrt2acmFihVh7FgNwoLh6ac1\nEf6pp4JzfZO75OrAKzfkpxmTWfY6Nyapw4c1OX3XLp0N2KePzswbPTp7A7BFi3T9wMces1wt459c\nG3gVKFCA/fv32z8lc04TEfbv30+BAgWC3RRjcozo6PjFlL/5BpYt06VzihfX0ggVK8Inn4B3RbeA\nEdFCqaVL6zCjMf7ItTleZcuWZfv27ezduzfYTTEmoAoUKEDZsmWD3QxjcoTYWJ05+MsvMGGCFhoF\nLVDarp0uJD1iBNxzD7zwAjz0kK5TWKWK5oKlRgTWrdPq7vPmweWX6+LTKf35/fgjLFigldsDXXPL\nnDtybTkJY4wx5xcRGDAAPvxQK8Sn1MskokHRiBHwxx96X+HCEBEBN9wAdero18svhx07NND6+WeY\nMwd27tT9y5XTYUzndLHpJ5/U/X08HqhRQxeM/ucfq7tlzmblJIwxxuR6I0Zo0DVkSOpDe85p/tdN\nN8GmTRp8/fEHLF4Mb7wRnwN2ySXgXRyDkiV1KZ3mzaFZM7j6aq0y//zz8P77OnR5333aA1aqFEya\nBCtXaq+bBV0mPazHyxhjTI43apQGPn36aBCUwaVRiY6G5cs1EFu1SocgmzWD0NCUS1Js3AjPPadB\nVsGC8MADGngVLar5ZcEsZWFyptR6vCzwMsYYk6NNmQKdO2sO1zffwAVBGqtZu1YLoHqXaOWHH7Rn\nzZjEUgu8LE43xhiT45w4AZMnQ8eO0KUL1KsHX34ZvKALtFzFl19qT9mECTqUaUx6WY6XMcbkMiK6\nmHL9+hASEuzWpE4ENm/W4T5239gAACAASURBVL08ebS9ISFa+iHxcGF0tCbFT5qkC0sfP66lGu67\nT3uaChUKwgNIRrVqejMmIyzwMsaYXOaFF+DZZ+GaazRXKacU7hSBqCiIjISlS+O/HjqUdN8iReKD\nsJAQLYj67bdw5Ig+nm7d4I47oFGjtMtAGJObWOBljDG5yLhxGnS1aqW1pm6/HWbPzvzMOhFNIv/9\ndy23UKlS+o799FOd8ffff3pfvnyasH7HHVCzpt6c08As8W3BAt12++26f9Omerwx5yILvIwxJpeY\nNUsLg7ZooZXaJ0/WnqGBA7XMQnpn+u3eDXPnav2qn3+Gbdv0/nz54JlnNJBKK6A7eFDrXE2eDA0b\n6uy/iAioWhUuvDDp/tWrJ38ekYzPVDQmN7HAyxhjcoFlyzTRvGpVneWXP79WcP/nH3jxRbj+eq3S\nnpbt27WW1U8/werVel/x4lrdfcgQqFULXn9de9WmTNG1DyOSnZul1eO7ddMA7uWXdb3CjA4LWtBl\nzhdWTsIYY3K4qCioW1eDrUWL4Ior4rd5PNCpky7UPH06tG2b/Dk8Hvj4Yw2OTp3S3ClfsdDq1ZMG\nTNOnQ//+sGcPPPqoJrcXLKjbzpyBYcPglVfg2mt1hl+tWoF45MbkTlbHyxhjcqkDB3T24u7d8Ntv\nULly0n2OH9dAasMGzdGqWvXs7Rs36hDl/PmaP/Xxx1qZPS2HDmnQNXo0VKigXy+7THvaliyBu++G\nt96ydQqNSczqeBljTC4UHQ3t22s5hunTkw+6QNch/PZbDYBuvjk+wT02VocNQ0N1qPLjjzWfy5+g\nC6BYMa0SP2cOxMRocBcaqoHclCm6zYIuY9InoIGXc66Yc26Kc26tc+4f51xd59xw59wO59wK761N\nINtgjDG5UWwsdO8Ov/4Kn3+uieupKVtWg6/du+G22zTQqldPe6yaN4c1a6Bv34zlUjVrBn/9BQ8/\nrJXaV63SGYjGmPQL6FCjc+5TYKGIfOKcyw8UAh4EjonIa/6ex4YajTHniy1bYMwYLRvhS4T3J2ne\n56uvtCQD6MLP77yjP1vyujHZJ7WhxoDNanTOXQw0AnoBiMhp4LSzv35jjDlLdDRMnao5VD//rEHS\nTTfBu+/qUGN6dO6sQ41//aWFVkuVCkybjTEZE8hyEuWBvcBY51wYsBQY7N020DnXA4gEHhGRg4kP\nds71A/oBlCtXLoDNNMaY4Fi7Ft5/X4cSDx7UCu7PPQe9esGVV2b8vAMHZlULjTFZLZA5XhcANYBR\nIlIdOA4MAUYB1wDhwC7g9eQOFpGPRCRCRCJK2Uc2Y8w5ZMcOzbeqUgU++kh7t+bMgU2btHBpZoIu\nY0zOFsger+3AdhH5w/vzFGCIiOzx7eCc+xiYEcA2GGNMjnH4MLz6qpZgiImBBx6Ap56y4UBjzicB\n6/ESkd3Av86567x3NQPWOOcuT7BbB2B1oNpgjDE5walTGmxdc41WeL/tNli3Dt5804IuY843gV4y\naBAwwTujcTPQG3jbORcOCBAF3BvgNhhjTKasWaOV3a+7Lu19ExKBL7/UXq2oKF1j8dVXU16v0Bhz\n7gto4CUiK4DE0ym7B/KaxhiTVU6ehBEj4DVv8Zv77tOfixdP+9hly2DQIK0kHx4Os2dr4GWMOb9Z\n5XpjjEnGr79qwPTqq9CzJ/TrB++9BxUrasV2jyf54/bt0zUOIyJ0CZ/Ro2HpUgu6jDHKAi9jjEng\n6FHtqWrUCE6fhp9+0uDp/fchMhIqVdJ1D+vUgcWL44+LiTk7MHvgAVi/Hvr0gTz2TmuM8bK3A2OM\n8Zo9WxeYfu89DZz++kuX2/GpXh0WLNC6Wzt2QN26WnPru++gZk2tnxUeDitWaDJ9sWJBeyjGmBzK\nAi9jzHnt4EFdgLpbN2jVCgoV0mHGt95KfgFo56BrV52V+MQT8MUXcMstep6vvtLK81WrZv/jMMbk\nDoGe1WiMMTnKgQPaa/XLLzB/PqxcqbMPCxTQ2YfPPKPfp6VoUXjlFR1K/OMPXTS6UKGAN98Yk8tZ\n4GWMOS/MnAlPPgmrVsUHWnXrwvDh0Lix5mz5E3AlVrGi3owxxh8WeBljchURHe5Lj+nToWNHuPZa\nXQuxcWOoXRsuvDAwbTTGmJRYjpcxJlc4eRKGDdOE9WefhdhY/46bNk2DrurVtabW0KHQsKEFXcaY\n4LDAyxiT482YoQtKP/ecLrvz/PPQsiXs2ZP6cVOnQqdOUKOGzli0WYbGmGCzwMsYk2NFRUH79nDz\nzZp/NXeuVoQfMwYWLYov75Ccr7+Gzp2hVi0Nui6+OFubbowxybLAyxiTrY4dgx49tD7W4MHw8cca\nRB0+HL/PqVPw4otQubKWZ3j1Va2N1aSJbu/dW2cSFi0KTZvq9oSV5CdPhjvu0DyuH3+Eiy7K3sdo\njDEpseR6Y0y2OXwY2raN760aPRqOH4/ffuWVOqS4aZMut9OxI7zxht6fWLVqWkn+nntgyBCtvfXp\npzBnDtx1F9xwg85kLFo0+x6fMcakxQIvY0y2OHBAC5SuWAGTJmlQ5fHA1q2wejX8/bd+Xb1aC5f+\n+KPun5qiRWHiRF3e56GHNBjbswfq1YPvv7egyxiT8zgRCXYb0hQRESGRkZHBboYxJoP++08XiV63\nTnOv2rbN+mtERurw4lVXafmI5KrOG2NMdnDOLRWRiOS2WY+XMSagduzQfK6tW3V2YsK1D7NSRIQu\nSp0nT/rrfBljTHaxwMsYEzBbt2ry+969MGuW1s8KpLx5A3t+Y4zJLAu8jDEBsXGjBl1Hj2rCe+3a\nwW6RMcYEnwVexpgsEx2t1eHnzNFaW7GxMG8ehIcHu2XGGJMzWOBljMmw2FhYvlwDrZ9/1pIO0dE6\n5FevHnzwgdbiMsYYoyzwMsakaNMmLe+wb1/yt3Xr4OBB3bdaNejfH5o10/IOVrTUGGOSssDLGJOs\n8eOhb184cyb+vgIFoGTJ+Nvtt2seV9OmULp08NpqjDG5hQVexpiziOgi1MOGxS/Hc+mlGmgVKhTs\n1hljTO5mgZcx57gjRzQXq3jxtPc9fRruvRfGjYOePeGjjyB//oA30Rhjzhu2SLYx5xiPB5Yu1UWm\nGzWCEiV0GLBXL/jrr5SPO3QI2rTRoGv4cBg71oIuY4zJatbjZcw54L//tEDprFkwe7YWLAWoUQMe\nf1x7vcaO1UWkW7SARx6Bli3jK7xv3arL+KxbF9/bZYwxJutZ4GVMLhUdrWsSjhunAZfHA6VK6cLS\nrVppgJUw4f255+DDD+Gdd+Cmm6BqVXj4YahUCW67DU6e1PM0bRq0h2SMMec8WyTbmFxEBP74Q4Ot\nSZN0eLBsWejRQ2cYhofrWoWpOXUKvvwSXn89fuixXDn44QeoUiXgD8EYY855QVsk2zlXDPgEqAoI\n0AdYB0wCQoAooLOIHAxkO4zJ7U6ehLfe0oBr/XooWFADrZ49oUmT9K1ReOGFelyPHlr4dOZMHY68\n7LKANd8YY4xXQHu8nHOfAgtF5BPnXH6gEPAUcEBEXnHODQGKi8gTqZ3HerzM+UwEunWDL77QZPle\nvaBjRyhaNNgtM8YYk5yg9Hg55y4GGgG9AETkNHDaOdceuNG726fAfCDVwMuY89lHH2nQ9cIL8PTT\nwW6NMcaYzAhkOYnywF5grHNuuXPuE+dcYaC0iOzy7rMbsHrXxqRg2TJ44AFNln/yyWC3xhhjTGYF\nMvC6AKgBjBKR6sBxYEjCHUTHOZMd63TO9XPORTrnIvf65sYbcx45fBg6ddKq8Z9/nnbSvDHGmJwv\nkG/l24HtIvKH9+cpaCC2xzl3OYD363/JHSwiH4lIhIhElCpVKoDNNCbnEYHevWHbNp29WLJksFtk\njDEmKwQs8BKR3cC/zrnrvHc1A9YA0wFfecaewLeBaoMxudXIkTB1qq6TWK9esFtjjDEmqwS6gOog\nYIJ3RuNmoDca7H3lnLsb2Ap0DnAbjMlVFi+Gxx6DW2+Fhx4KdmuMMcZkpYAGXiKyAkhuOmWzQF7X\nmNxq/37o3BmuvFKX+PEt6WOMMebcYEsGGZNDeDzQvTvs2QO//w7FigW7RcYYY7KaBV7GBNHhw7Bq\nFaxYAXPnahX599+HmjWD3TJjjDGBYIGXMelw6pTONty6FTp00GV7ypf379iDB+G337Q214oVetuy\nJX57yZLwyCPQv39g2m6MMSb4bJFsY/x08iTcdhv8+KMuJv3333p/jRoagHXsCBUrxu9/4AAsWADz\n58Mvv8DKlVomwjndLyxMF7X2fb38csvpMsaYc0FqSwZZ4GWMH06cgFtu0eHAjz+Gu++GzZvhm2/g\n6691JiJA1apQpw4sWaJDiAAFCmhJiBtvhMaNdRixcOGgPRRjjDEBZoGXMZlw7Bi0awcLF+pMwx49\nku7z779ad+vrr3UIsXbt+ECrVi248MJsb7YxxpggscDLmAw6cgTatNEerfHj4a67gt0iY4wxOV1q\ngZcl1xuTgkOH4KabYOlSmDhR1000xhhjMsMCL2OSceAAtGypeVqTJ2sVeWOMMSazLPAyxsvjgb/+\ngp9/hk8+gU2bNG+rbdtgt8wYY8y5wgIvc16LioI5c/Q2dy7s3av3X3cdfPed9noZY4wxWcUCL3Pe\n8Xjggw/gjTe0Vwu0hlarVtC8OTRrBmXLBreNxhhjzk0WeJnzyvr10LevloZo0AAGD9ZA6/rrrXip\nMcaYwLPAy5wXYmK0h2vYMC1oOmYM9OplwZYxxpjsZYGXOeetWqWV5iMjdXbi++/r0KIxxhiT3Szw\nMrnWkSPwzDMQHQ2XXZb0VqKE9nK99JJ+/9VXup6i9XIZY4wJFgu8TK710EMwbhyULKmzEVNahKFb\nN3jrLbjkkmxtnjHGGJOEBV4mV5o+XfO0nnxSe7RiYjT42r0b9uzRr7t3Q0SEzlQ0xhhjcgILvEyu\ns3cv3HMPhIXB8OF63wUXaN6W5W4ZY4zJySzwMrmKCPTvr+so/vQT5M8f7BYZY4wx/rPAy+QqEybA\nN9/AK69AaGiwW2OMMcakT55gN8AYf/37LwwcCPXqwaOPBrs1xhhjTPpZ4GVyBY8H+vTRJPrx4yFv\n3mC3yBhjjEk/G2o0ucKoUbqQ9QcfwDXXBLs1xhhjTMZYj5fJ8davh8ceg5tugn79gt0aY4wxJuMs\n8DI5WkwM9Oyp6yt+8olVnTfGGJO7WeBlcqxff4W2bWHxYl1fsUyZYLfIGGOMyRwLvEyO4vFoVfr6\n9aFhQ1i6FF57De68M9gtM8YYYzIvoIGXcy7KOfeXc26Fcy7Se99w59wO730rnHNtAtkGkzucPg2f\nfgrVqkH79rBjB7zzDmzbBo88EuzWGWOMMVkjO2Y1NhGRfYnue1NEXsuGa5tcYNw4eOYZ2L5dA6/P\nP4fOnSFfvmC3zBhjjMlaVk7CBNW4cdC7N9StCx99pDMXLYHeGGPMuSrQOV4CzHbOLXXOJSwEMNA5\nt8o5N8Y5Vzy5A51z/Zxzkc65yL179wa4mSYYZs3Sxa5btID586F1awu6jDHGnNuciATu5M6VEZEd\nzrlLgZ+AQcA6YB8alD0PXC4ifVI7T0REhERGRgasnSb7LVsGjRvDtdfCL7/ARRcFu0XGGGNM1nDO\nLRWRiOS2BbTHS0R2eL/+B0wFaovIHhGJFREP8DFQO5BtMDlPVJSWiShRAr7/3oIuY4wx54+ABV7O\nucLOuaK+74GWwGrn3OUJdusArA5UG0zOc+CADilGR8PMmXDFFcFukTHGGJN9AplcXxqY6jRp5wLg\nCxH50Tn3mXMuHB1qjALuDWAbTA4SHQ233AKbN8NPP0HlysFukTHGGJO9AhZ4ichmICyZ+7sH6pom\n5/J4oFs3+O03mDQJGjUKdouMMcaY7GeV603AiWgR1K+/htdf1xpdxhhjzPnI6niZgIqNhYce0ir0\ngwfr98YYY8z5ynq8TMAcPw633aZB10MPwRtvWJ0uY/x25Ah89pl2GRtjzhkWeJmA2L0bbrwRZszQ\nwOuNNyCPvdqM8d+wYdCjB6xaFeyWGJN7rF8PY8YEuxWpsn+FJsutWQM33KBfp06FgQOD3SJjcpl9\n+3QNLYAVK4LbFmNykyeegLvvhqNHg92SFFngZbLUvHlQr56WjvjlFy0fYYxJp7ffhhMndKX4lSuD\n3Rpjcod9+7QqN2jPVw5lgZfJMp99Bq1aaVHUxYshItnFEowxqTpyRMfnO3SA8HALvIzx16RJcOaM\nfr9uXXDbkgoLvEyWGDNG01EaNIDff4eQkEQ7WIKwMf758EM4dAiefBLCwjTwsr+frLd/P1SvDn/8\nEeyWmKwyfrxW5s6TB9auDXZrUpRm4OWcG+ScK54djTG505kzmgdcrx78+CMUK5Zoh5kzoUwZmD07\nKO0zJteIjtaZKM2bQ61aGnjt3w87dwa7ZeeeGTM0f2706GC3xGSFdevgzz81v6t8+dwdeKFL/yxx\nzn3lnLvJOSsIYM42ZQps364f0PPnT7Txzz+hY0fYtQv69NFP8saY5I0bp1OCn3xSfw7zLv5hw41Z\n74cf9Ov06bq0hsndPvtMe7ruugsqVcrdgZeIDAUqAKOBXsAG59xLzrlrAtw2kwuI6Af0ihWhTZtE\nGzdsgLZtoXRp/XS5e7dVUDUmJTEx8L//QZ060KSJ3hcaql8t8MpaMTHaA1+6NOzZY8ONuZ3HE59k\nfNllGnht2KAVvHMgv3K8RESA3d5bDFAcmOKc+18A22Zygd9+g8hIjafOqtO1e7f+EQDMmqUB2JNP\n6if6GTOC0VRjcrZJk2DLFv078Q0sXHyxJkxa4JW1Fi/W3vcXX4QLLoBp04LdIpMZCxbAtm2aaAwa\neEVH6305kD85XoOdc0uB/wG/AdVEZABQE7g9wO0zOdwbb0CJEvGvd0Drp7Rtq58kv/8eKlTQ+595\nRj/B33MPHDgQlPYakyN5PPDyy1ClCtx889nbfAn2Juv88IMGXB07aqXnb78NdotMZowfD0WLQvv2\n+vN11+nXHDrc6E+PVwngNhFpJSKTReQMgIh4gHYBbZ3J0TZt0g+K/ftDoULeO0+fhttv138UU6ZA\n7drxB+TPrz1e+/bBAw8Eo8kmt9uyRQOU3buD3ZKsNWMG/P03DBmSdImHsDCtSXTyZHDadi764Qeo\nX197FG+9VROzc+g/aZOGEydg8mTo1AkKFtT7KlXSrzm0pIQ/gddMIK57wjl3kXOuDoCI/BOohpmc\nb+RI/dB4//3eOzweTaD/6Sf45BNo3TrpQdWrw9ChMGGClrU38aZPh9df1y5yc7ZNm/S1VaECPPWU\n5kDt2hXsVmUNER3yCgmBO+9Muj0sTP+2Vq/O9qYl8dtv8H//px+wcqsdO/SDoS8p1Vfl+Vzu9dq+\nXSu6n4vvLdOmwbFjZw+7lCypQzE5NZgWkVRvwHLAJfg5D7AsreOy8lazZk0xOcuBAyKFC4v06JHg\nzsceEwGRF19M/eDTp0WqVxe59FKRvXsD2s4MiY4W6dtX5K+/su+a+/aJXHSRPn8VK4r8/HP2XTsn\n27BBpGdPkbx5RQoUEHngAZGvv9YXX8WKIjt2BLuFmffzz/p7f//95Ldv3KjbP/44a64XGSnSr5/I\noUPpP7ZmTW1LRITIpk1Z057s9skn+hgS/n3XqCFSt27w2hRIHo9I69b6mL/6KtityXqtWolcdZVI\nbOzZ99erJ9K4cTBaJCIiQKSkFFeltCFuB1iRzH2r0jouK28WeOU8r76qr57ly0UkJkZk6FC94/77\n9Q89LatWieTLJ3LHHQFva7p9/bU+lkaN/HssWeHhh0Xy5NF/vtdco9fv1k1kz57suX5Os26dSPfu\n+pwUKCDy4IMiO3fGb//1V5EiRUQqVBDZvj147cwKzZuLlC4tcvJk8ttjY/WxDhyY+WtNny5SqJC+\nvt5+O33Hrl6tx3XqJFKsmH5QmDw5823KbrfdJlK27Nl/2889J+KcyK5dgb/+iy+m/eE0K02erL83\nyJnvt5mxY4e+RwwdmnRb7976dxUkmQ28vgEeAPJ5b4OBaWkdl5U3C7xyltOnRcqUEWnaVPSfYZMm\n+lLq00eDMH+9+GLO/BR2++3xb1Tff5++Y2fOFGnQ4OwgIS1RUSL58+sbhYjIiRP6RpIvn/6D+/DD\npJ/mzlUej8jzz+ubacGCGpCm9M/wt99EihYVufZakX//zd52ZpU//9TX2auvpr5fvXoiDRtm7lrv\nvKPPa0SESNWqIuHh6Tv+iSe053HPHpEtW0Tq1NG233dfykFjTnPqlL5m+vU7+/6VK/WxfPRRYK//\n99/6OyhUSOT48cBeS0Tk8GGRK67Q33WfPhrA55bflT/+7//097ZuXdJtvt6Bgwezv12S+cDrUuBL\n4D9gD/AFcGlax2XlzQKvnOWLL/SVs/i5WSKlSumbyNix6T/RmTP6T6BkyZzTs3P4sMiFF4oMGKA9\nT9Wq+R9M+t7kQOTmm/3vLevZU6+5bdvZ9//zj3aVgw6DrFqVnkeS+3g8Is88o4/3rrtEdu9O+5hF\ni7Tn5Zprkj5/uUG7dhpcHz6c+n4DBohcfHHGemBjYkQeekif1/btRY4dE3nvPf156VL/z1GmjLbX\n5/Tp+PSCsLDk//nlNHPnanunTTv7fo9HpHx5kTZtAnv9W2/VwAuyp7dw0CDtyfvjD5Eff9TrTp8e\n+OtmhTNn0t4nNFTkhhuS3/btt95/VIuztl1+ylTglRNuFnjlHB6PSJ2aZ2RUiafE45xIlSr6KS6j\n/v5bg4569TTPKdjGjdM/i0WLRL78Ur8fP96/Yx94QN/kevb0/7hVq/SYRx9NfrvHo20qWVJ7wCIj\n/X4ouYrHI/L00/q89e2bvh6+xYs1+Lr6apGtWwPTvmPHsr6HYvZsfbwvvZT2vh98oPtu2ZK+axw/\nLtKhgx47eHD8h4iDB3UI9/77/TvPTz+l3Ds9Y4bIJZdob8qECelrX3Z79FH9Ozp6NOm2Bx/U96Ij\nRwJz7d9+0+dw+HAdAuvUKTDX8VmyRN9bfL/jU6c0eO/ZM7DXzQrz5+vvonfvlPOAV6yQVHMj163T\n7ePGBa6dqchsj1cB4H7gfWCM75bWcVl5s8Ar51g8eZssoEH8P8is+Gc0ZYr+kVWooMnUwdSypX7y\n9Xj0n3/NmiLlyqXdPR8ZqZ9kBwzQf24NGuibXFr5R+3a6X7796e+33//iVx2mfYQpmc4NzfweESG\nDNHX1D33ZGxY9Y8/9HksX16HbrNas2ZZ2xty5ox+aClf3r+hn99/T76nJjV79ojUrq3/fN96K+n2\nu+7S3rYTJ9I+V/fu+vym1NZ//9XXPIj88ov/bcxuVaro7zI58+f73xN15ozI5s3+X9fj0eendGkN\n4u+/X4fSjx3z/xzpceaMThi4/PKzJ1F07y5SvLj2VuZU0dE6ceaSS0QuuEC/jh2btLf34Yc1iE7p\nvfP0aT3+yScD3uTkZDbwmgw8D2wCegKzgZFpHZeVNwu8coiZM+VIvhJylCISPSaLP9n+9pv+gZUs\nqf9kgmH3bg2enn46/j7fJ/033kj5uJgYDdAuuyw+n2DDBn1jbd065eGhX37Rc7/8sn/tmzhRMpQU\nnZN5PCKPP66P6957M5fLtmSJBhI1avg3TOGvY8f0DTxPHv+GP/3x7rv6mL/5xr/9jx7VAOq55/zb\nf906DeoKFkw5WPPNpvzii7SvXahQ0ryoxI4f19mm997rXxuzW1SUPt7XX09++5kzIiVK6KSWtPTo\nob+PGTP8u/Z33+m1R43Sn31/+5Mm+Xd8eo0cmfz5p03T+2fPDsx1s8KIEdrGH3/Umaf160vcZKc1\na3SfM2c0iL3tttTPVamS9vgGQWYDr+Xer6u8X/MBi9M6LitvFnjlACdPSmzhIrKSavJG/wDlcmzY\noInSF14YnNlSb7+tfxKJh05btNA35JSm3/ve5CZOTP58o0cnPcbj0dyEK67wv9fQ49EeuaJFz40y\nCh6PDv2A9hRmxQSCSZP0fCNHZv5cPr7gO7VhjfTYv19fT02apC9n69prdeKHP5o00Q8yf/6Z8j6x\nsSIhITqrMjWffqqP/ddf077unXfqh6esDHyzyqhR+jj++SflfXr2TLtHyPd8XHSR3tauTf26MTHa\n01ahQvx5Y2K0N8rf32d6bN+u7xGtWiV9fZ04ocFx//5Zf92ssH69vv8nnH0ZG6ulVIoX1x6up5+O\nn3k+dWrq57v1VpHrrw9sm1OQ2cDrT+/XBUBVoCSwOa3jsvJmgVfwnZ4+UwSkXd4f0jVhL9327o3/\nhPN//5d95RxENIE9LCzp/UuXansS9oT5+N7kWrZM2tbYWE2Ov+iipLlHU6dKhmozbdyob0yBzg8J\nNI9HhwpAyyRk1e/Z4xG56Sb9nWRVmYmhQ3U239VXZ01doEGDtPds5cr0HXf77Rp8pWX7du2NGT48\n7X19vQup5Y41b66P3Z/f0Tff6Pl++intfbPbzTfHpxGkxNf+lOrorVungUujRjrUWKqUyHXXpV4T\nbexYSTY/btAgzbPL6pyy22/X86ZUZ61TJ+0tymkpCx6PDgNfdFHys8L/+097GkFf3yVKaN5aap54\nQoO1IHwQyGzg1RddFLsRsNk7u/HetI7LypsFXsGxebN+wG/fXuTDfPfJMQpJ/17ZMBX55En9xOPr\nCcmOP5pNm/R6r7yS/PYuXXTYJvEbQseO+ia3cWPK5y1cWHvNfG/4Z85oF3ilShl7bM8/r2394Yf0\nH5tT+GbDPfBA1gfXmzbp7ySrehMaNRKpVUtk2DB9w8/MJ4/VqzWIGzAg/cf6ak0llxie0OuvS4pT\n7BPbulXPOWxY8tv//Tf17YmdOKFJ9vfc49/+2SU6WodL77sv9f2OHYsv1JvcOcLD9R++r3zJ/Pk6\nDH3zzcn32J48KXLllfr6Sfw6X7hQ/BrqTY8ZMyTNIta+SUMLFmT+eocO6eSQjBTjTezzz7Vd772X\n+n5z52o6gT/D7r6gqqzAQAAAIABJREFUd/36zLcvnTIceHmr1HdObZ/suFnglT08Hh1WHzRIe8V9\noytXlfPIviLlZEft9ml+wMgysbHxCdetW6edfJ5ZvppiKSVmb9yob7AJ81e+/16PeeGF1M/9/vu6\n34cf6s8ffyzpyu9JLDpag7aQkOypBZSSWbO0Nyi9gdO8efr4+/cPXI+m7/fpbw5OSk6e1B7GRx/V\nIWjQelgZ4fFoAF6sWMZWbPBNj08rB7JmTZ2E4a+WLXUCSXI9IK+8otdM6YNFcu66S4c505PAvWCB\nPseBej37ZpD683q4+WZ9PhK/Nh94QJItx+DL13vmmaTneu013TZ3btJtsbGaanDrrf4/jtQcO6YV\n3CtXTr0n6MgRfU0/+GDmr+krnJ3ZmZIHDuhKJrVrZ21P3KJFyf/OskFme7xSPDi7bhZ4ZQ/f/ypf\nTvjIkZq+4FnhLS6YVUuWpMdHH2lX8ZVXagJ+IHg8+mbVoEHq+w0cqL0Va9fqP4iQEM0fSCsajY3V\nLvQiRTQ59IorNL8rM0GHbwbWkCEZP0dmVa8u6Z6uHR2tQzPlywc2aDx1Sn+nV12VuZljvufZ98Zd\ntWrGC5lOn67nSm6GoT98yeG+BO3krF0rqSaQJ8fXA5I44dr3d1G/fvra6QsQZ83yb3+PR3OgQP/x\nBqKmn69UhD+vOd+SQsuXx9/ne0yDByfd3+PR4qSguUc+Bw9qXlKrVilfa/BgbVdaddxSEhurZRVG\njtSeWX97slIKLtPj+HENsH1LnWUmuOnXT99bEz7nWeHAAW3b//6Xtef1Q2YDr1eAR4ErgRK+W1rH\neY+NAv4CVvga4T3+J2CD92vxtM5jgVfg/fijjih06ZLMjHFfRBbQ5K5ULFmiOSZ58+on8Kyu4u6r\nWp1W4vSePRo8deyouQPpmTofFaV5RxdfnL7jUtOrl/bCZXRNyZgY7b2pWzf9xUdXrdLHUaSIPiZ/\nK8c/95weN3Nm+tubXgsW6LUefzzj5/AN7x04cPbP6c0fi47W/KxKlTI+ld/j0d6y1BKjfcOh6Wlf\ndLQOn91559n3R0ae3VPrr5Mn9Z9xnz7+7e8r7Nmrl37qK18+9QT4jKhYUXP//LFnz9nDq//+q89P\n9er6XCUnOlo/TBUuHP/36OuxTy2Y8NX2+vxz/9oWEyOybJnOsm7fXgM739BE+fKaF+sPX73C1CZf\npMXXkz9njhaavuyyjI1M+J6Dhx/OeFtSc+mlInffHZhzpyKzgdeWZG5+Jdd7A6+Sie77HzDE+/0Q\n4NW0zmOBV2Bt3qzvK9WqpdA5cMMN6Ru6CIRDh0Q6d9aXbKtWWfupeMgQDWD8Gf4ZNkzbkDdv/BI/\n/vINMbZtm6FmJrF3r37irF8//cHo0qX6O/W9aae31s1jj+lztmiR5s7cdFPan559M5Y6d07ftTKj\nTx9tZ0ar/jdrdvaEi3/+kQzNmvQtbZLZgLNRo5QXc/Z4NEegSZP0n3fQIP3dJPzH+cADep8v6EwP\nX70of3ITWrTQXuBTp7Qe26WX6rFpfTg5c0ZnGIaFaRd9SjltvkXG0/M7q19f87nOnNEezsKF086Z\n27FDZypefbUGXwULinTtmvoxsbG6buQtt6Tdpp07Nbjy/c1ec42+vsePT3/h4P379e8ioz3msbH6\nWvPlri1bpudL6/Emdvq09iJfeWXauYsZ1ahR+ntts0DQKtenEHitAy73fn85sC6t81jgFTgnTuj7\nS7FiKaRx+D79jRiR7W1LwuPRCt4XXqhvcPPnZ/6csbE6HNW6tX/7HzmiM5kuuST9eToej/6jyMqe\nwzFj9M/4k0/82//wYf2HmiePfkL98ksddihd2r9/kiL6qfuKK/Q4kfgcl9Ta4PHo7LiUZiwFyr59\nWt6gbt30B6enTuk/z8SJ1qGh6Xsj371bezuzIuAeNEiDgOQei6+HKiMpAcuXy1n5a6dP6/OW0dmz\nvrpVaU0A8fU2J6xlt3mz9gzmz598JfwzZzRp+tpr9diqVbXXNX9+ncmZuMv+nXd0v/QUZ/YFyr4h\nRH9Xr1i0SNtRsKCmSPhTZPWhh/SY1BLUY2J0cdxChbS3KivWJm3RQoOnjAw3+oZev/wy/r7hwyXd\nuau+9RTTUxg4vfr10/frbJbZHq8eyd3SOk7ie8uWAUuBft77DiXY7hL+nNLNAq/A8HjiZ+emmHPq\nmxXi75pu2WHFCh06yJNH/9gzmh8horWJQOSzz/w/ZvVqveUEHo9+oiteXJ+XlKqQezxaG+2KKzSQ\nvu+++GKvvkkC/i5W7ktU9tVai43VXpaiRVP+5D1hgh7z7rvpe3xZwTeskt4hM1+1+IR5OyI6mQL8\n/+fXt6/2BqRV78kfvvyj5IIIXyXvjPRQiehMserV9XtfPtp332XsXNHRGgz16pX6fj17ajCReIjq\nwAGRG2+UuMkrHo8Gg6NHa48SaFunTtXX365dOlQK+t6QsBxE69YaYKTH+vXxPUs9eqTvWN/vKLmZ\nkcnxJYCnFtz50j2SqwmYUb66ZhnpDW7USD+wJpyVffq0/k4uvdS/D6VbtmiA2r59+q+fHm+8oY8z\nIxNaMiGzgdc7CW4fe0tKTEnrOO+xZbxfLwVWektSHEq0z8EUju0HRAKR5cqVC/yzdB7yrZOb6kzx\n22/Xf9bZWU/LH0ePaoVp0ACsVi2dFTVjRvqmNt93n/7xB2p9tuywZo1+Yvb9oyhdWqROHR3Se/xx\n/UW3aaPbwsOTLhobE6OJtiktpZJYt27aRZqwZ2HLFs33at486WvFN2OpVq3g1A7yePSfeLFi6as8\n//LL+pz999/Z9/vWgHvzzbTP8euvGug+9FD62pySJUv02lOmnH2/rxfSnyGrlPh6Lpct0zzGUqUy\nt7RMz54afKXUk7pzpwaKAwcmvz06Ov5vvH37+GG2mjU1MEzuPWnWLB2C+//27jxeyrL+//jrw2EN\nzIVFEVTEBcRKVBZxA1H7qbllbmRGhqF+i9wiRW39uuRSmpYZaYrfpDTTLEvTFFkCwZPiihsInQMo\nmLLvZz6/P6779gzDWWbOmXvus7yfj8c85sw999zz4WKWz1zXdX8uCI99771QHqKmSfH1+dznQsLW\nkCGw2bPz70HOZML7L3sB8mwzZoSpDaNGFfdzeOnS/Ou9ZYtfgzWt5vHKK+H/NLsAaq5MJvzI69kz\n9N4mtb5qLP5hmU8B4CIq6lAjsAPwZAMe98Nokr6GGvPx3nvFP8Mjy8yZ4f1xwgl1jMBs2BC+TOtb\nKiQtmUyYB/K974V5GHHy0aZN+PV+2WV1n3ofD6fU9SHRXLz1Vui1+9//DRNJjzkmDMW0axfapHPn\n8EFZW92wuDZYfcMxq1aFHoqaloWJF3LOPevuggvC/0mavabz5oW2KGQOyvHHh7P6ajJwYO1zrWJr\n14b/gz59ipfYr1sX2jK3dMGzz/o2Qz+F+uijMIx/zjnhuiHJSrb4C6+27vSrrgpf/HWVqshk3L//\n/XCcwYPDsepLPtatC2UO2rWr/kx48snC41+6NAxVl8Lll9fcW/nRRyEp69u3cT37tTniiDC5txBn\nnx2mDNQWT9w7V1MP+vz54X0V91iWlxcec6HiGo35TscokmInXu3ySZaAzsB2WX/PBI4Dbs6ZXH9T\nfcdqlYnX0KHhjVhbBeVGWLo0/Dju27eeUYl4SKmhww2ltm5d+AL6wQ9ChfEOHUL8xxwTihXmeiJU\n4/fHHit1pKVTVRUm/cbDirVZsiT8qh4/vu794mG7mkp7xHWqOneuntsSD9cVo2ZQY111VYgld0mo\nmmzeHIZOazuD8Prrw7Hq+rV+8cVhn5pqODXGfvtt27P1jW+Edm9siY5Ro6p7ThubKG/cGHoZaxqq\nW7MmDI/nu47ekiWF9/bMmxd6OnfdNb+FyNM0e3Zo83vvrd6WyYS1CNu2bdzZh3W59dbwvPkWGF20\nKHxOXH557fts3hxO3OnatbqHeePG8J7p2DH8mL/tttJVk9+ypboWXwk1dqjxr8Bfosvj0VDjT/J4\nXN9oePFl4HXg6mh7V+CZqJzEP8mjNEWrS7zeeSf817RvHz78i9jztWlT+JHTqVMeK5bES1qkWaSz\nMdasCQUMe/QI7XnUUVtPyC/kzKvW4LTTQg9gbafMu4cJvnvtVfuX4H/+E34NjxgR2vWznw1nbTWF\nodxly8IHcH3Vy92rJ6rnrr8Zi9+jtdXLiut/1TaM1hhnnx3m18Q2bAiv43wWd65PvC7l/vsXZ1jr\nvPPC6yH3NRUPa5Zi+KfY5WeSkMlse5JPXK7hlluSe95Fi8Jz1LZiR67LLw+JV33Dg6+/Ht5rX/xi\nKOsyYEB4ni99qXhLeRXis5+tPhmoRBqbeA3PuhwG9K7vMcW+tLrEKx72mTkzfGntskvda6nladOm\nMO0ir7IxmUyYU1HbvIPmZO3aMMy2yy7hHz98eDjbqkuXMPFZgn/8I7RPbUuYxMvL1HeG6z33hOMM\nG+YFn+WUtNGjQ89QffMA42V36lqM/KCDQs90rtWrQ3dy376NK95am3juWdyLGZ9hVowlpKqqwpd/\nvmfx1SfuVc4urrllSxiCHTq06c0dTVNcouW//w2/ijt0CP8XSSeOgweHS31WrgxJ9KhR+R33ppuq\ne0/32KPxq0g0xumnF36CRSM1NvHaE+iYdbsT0Ke+xxXz0qoSr0wmnEp95JHh9muvhe76fv0adVbG\nypVhZRDIcy7la6+Fne+6q8HP2eSsWxe6uHv2rP5AKPYwUHNWVVX3QtDx8Fpti+/GMpnqeRwnndS0\nvlzjnqz6qseffHL9C1LHy+nk/ij6n/8JCWox1sKryd//Hp43rnN11lmFL9FTKps2bdsbFy8Qn+9Z\ntK1FPGn99tvDd0DPnslU8c8VJ/L19WLFP0ZeeCG/427ZEs5qnTAhmR8ghYgXuq+rN7/IGpt4lQPt\ns263B16o73HFvLSqxCuup5Od8EyfHn79HHJIg4b9KipCT2tZWQHzC+MvlTS6hZO2fn34cPvWt9I5\ny64pi//fcyuHxz8I6ltWKbZ4cZhUX4x6Q8U2bFj49VtbT0JVVUgW6qt2HU/aza4W/s9/euJz2hYv\nrv6CXr06zBtoyKLbpTJmTJgyEc+zOvzwbUsRSPUoQ1lZSNwTmN9bo/gs3S99qfbka/PmMMk/7hBo\nbuIFuPOZ31kkjU285taw7eX6HlfMS6tKvOLu5tyzaR55JJzNdOKJBX1gvfRSmFu63Xb5L53m7qFA\nZFzTR1qPDz4IJ3XkJg5z5oSPi4kT04mrmOKaYrVVkY+Lek6aVP+xBg2qXtVh5crw5bTvvsnOi8xk\nwly8MWOqv1BqOnmkqYiHsP/85+pJ5PmU4miN4qXIrr66tM975ZXhfd+uXTiLPbcXN17Ps7meiBT3\nJpZw2kNjE6+ngZOzbp8CPFPf44p5aTWJV7x8RG0VruPJlmPG1D58U1UVvjyrqvyJJ8I0pt69C6yR\nt3x5SPK+//2C/wnSApx5ZujxyS7G+q1vhV7X+s6ObA42bgzz/U44oeb7b789vM8WLqz/WPE8lvnz\nwxdWmzZ1lzAplqOPDgnfCSeEZK8pTyDftCkMhX75y+G1tf32TeNki6bo/ffrLvuSpEWLQs9p+/bh\nx//554ezkzOZMAesrl7ipm7VqvA+vf76kj1lYxOvvYDngf9El5nA3vU9rpiXVpN4xYv61rRMRuya\na8I+l14afu1ee214gxxzTHhjRHVrXvr8d72sLJQbqmt+cI3uvz88R1KnMEvTFteEiidYb9wYelhK\nucZi0uLFpGuqW3b66SGZycfChaGt4jlt9ZXjKJbLLguJcNu2oZekqRs7NtR/a9OmdG0kDVNR4f7N\nb1YnYCedFF7bd96ZdmSN06tX4asQNEJR6ngBXYAu+e5fzEurSbwuvDB8ONVVKTmTqV4/LLtS+ZAh\n7mee6Znx3/V3+hzt6+jo54xc0rAflmecEXoEmuuvG2mcTCYMl8XrEcZnzaV5VlKxLVkSvlRyK8pn\nMqH8yLnn5n+soUND+/TvX7p6UXE9NQhLRTV18dy3tm1DyRFp+iorqxdK79at+ZYVih19dM1nISek\nrsSrDfUws+vNbAd3X+Pua8xsRzO7tr7HSYE2bYKHHoJTToEuXWrfzwwmToTnn4c334R16+D992H2\nbHjwQX628438v4W/pr1t5v79b2S77RoQxz/+AV/4ArSp9+UhLZEZjB0L//oXvPYa3H8/9OgBn/98\n2pEVT8+ecPrp8Nvfwpo11dvfeguWLYMjj8z/WF/5CrRtC5MmQceOxY+1JgccEK4HDIDPfa40z9kY\nw4dD797w5S/DbrulHY3ko1cv+PnPYdEiKC+HT30q7Ygap1+/8J0ZOpJSlc836/HuviK+4e4fAyck\nF1Ir9fTT8NFH4YOpPmVlMHRoeCF16vTJ5kcegfHj4eAz9qLN6K/SZuJdsGRJYXFMnw6rVsFJJxX4\nD5AWZfRo6NABfvIT+Otfw+uyXbu0oyquceNg5Ur43e+qt02dGq6HD8//OBddBBUVMGRIceOry4AB\nIRkeOzYkyk1d27bw8svhR6M0LzvvDHvskXYUjde/f3i/f/BB2pHklXiVmVmH+IaZdQI61LG/NMTk\nybDjjg3uVZgzJ/zwHjo0/PC2710DVVXhi7MQjz8evnCPOaZBcUgL0a1b6BF64IHQC/rVr6YdUfEN\nGwYHHQS/+EX1r+CpU0Nv2N5753+csjLYZZdkYqxN+/Yh2fv2t0v7vI2x007hs0UkDf37h+s330w3\nDvJLvB4AnjGzMWZ2PuEsx0nJhtXKrF0Ljz0GZ5wRPlALtHBh6KDaZZdwmE6dgL59Q6/FxIlQWZnf\ngdxD78bIkdC5c8FxSAtzwQXh+jOfgYED040lCWah1+v11+G558Lrf9q0MMzYHHqR2rdvHnGKNAVx\n4vXWW+nGQR6Jl7vfCFwL7Af0A/4BtIB+xybkr38NyVc+w4w5VqwI07E2bYK//S2MPnzimqjX64Yb\n8jvYiy/C/PkaZpTg8MPh3HPD66ilfsGfdRZ07Qp33AELFsDixYUNM4pI89CrV5in1kx6vAA+ABw4\nAxgJzEssotZo8uTwojjiiIIetnlzGA16++0wv2u//XJ26NMHzjsP7r47DEvUZckSOO20kLl96UsF\nxSEtlFmYWH/WWWlHkpxOneAb3whdxf/3f2FbIRPrRaR5aNOmeoJ92qHUdoeZ7WtmPzCzN4E7CDW8\nzN2PcvdflCzClu6//4UnnoBRowo6i9A9zOl95hn4zW/gqKNq2fHqq8PO119f+8FWroTjjw+T+//+\n95xuM5EW7qKLwvX114e5bQMGpBuPiCSjf/8mP9T4JqF360R3P9zd7wCqShNWK/KnP8GWLSHxKsCN\nN8I994RRoK99rY4d99gDvv71sPOiRdvev3EjnHoqvPFG6DY7+OCC4hBp9nbfPbwHNm8Ovc4tdVhV\npLXr3z9Mil6/PtUw6kq8TgOWAlPM7DdmdjSgT6Ri+/3vQ/fngQdutfnZZ0MudtJJoTdr8OAwlLjb\nbuHkxwkTwv0//nEez3HVVeHLJLfXK5MJZ6s99xzcey8ce2zR/lkizcq4ceG61q5jEWn2+vULI0Dv\nvJNqGG1ru8Pd/wz82cw6E9ZnvAToYWa/Ah5196dKFGPLVVkZTl//4Q+3+pW9fn1IqqqqQodVly7Q\nvTvsuWf4u0uXMCXs4ovz/HG+++5w/vnhDMcJE8LcL3e49NJQtPXmm0MtCpHWasSIUDhY87tEWq7s\nMxtTLDxca+IVc/e1wGRgspntSJhgfwWgxKuxHnwwJEA5w4z33BOKZ0+dWsTvgQkTwiT7664Lk8Ju\nvhluvx0uuQQuv7xITyLSjLWkyvwisq1+/eCPfwxnbKfIvAmUz6/PoEGDvLy8PO0wiu/gg0PxxTlz\nPtm0aVOo3bj77qGIfFGnm4wbB3fdFXrYrrkmnK02ebKWBhIRESkiM/u3uw+q6T5946ZlyZJQN+uM\nM7baPHlyqPxw9dUJzPGdMCEketdcE+ayTJqkpEtERKSE9K2blunTw3XWZN641unAgXDccQk85667\nwg9+ECrTP/qolu8QEREpMSVeaZk+PSzLk7UUyyOPhGKo8UmIiZgwIRT/2n77hJ5AREREaqPEKy0z\nZoRFetuG8xviGqf9+oUC8iIiItLyKPFKw4oV8MorWy0R9MQTMHcuXHllmIYlIiIiLY8SrzTMnBm6\nuKLEyz1Uedh9dzjnnJRjExERkcTUW8dLEjB9ehhiHDr0k5szZ8Idd0C7dinHJiIiIolRj1caZswI\nNbw+9SkgzO3q0QPGjEk5LhEREUmUEq9S27AhFEyNhhnLy8NKJZddBp06pRybiIiIJEqJV6m98EIo\nTx8lXjfcECo7XHRRynGJiIhI4hJPvMyszMxeMrPHo9v3mdl7ZjY3ugys7xgtSlw49bDDeOONULtr\n3Dj49KfTDUtERESSV4rJ9RcD84Ds1GK8uz9cguduembMgAEDoGtXfnJpmOZ18cVpByUiIiKlkGiP\nl5n1Br4A3J3k8zQbVVXwr3/BEUcwbRo88ABccAF065Z2YCIiIlIKSQ813gZ8F8jkbL/OzF4xs1vN\nrMYFA81srJmVm1n58uXLEw6zRF59FVat4uPPHMGZZ8I++8APf5h2UCIiIlIqiSVeZnYisMzd/51z\n1wSgPzAY2Am4oqbHu/tEdx/k7oO6d++eVJilFc3vOn/SEaxeDX/6k+Z2iYiItCZJ9ngdBpxsZguB\nPwAjzex37r7Ug43AvcCQBGNI3iWXwOjR+e07YwYfbbc7j5Tvzt13w/77JxuaiIiINC2JJV7uPsHd\ne7t7H+Bs4Fl3/4qZ9QQwMwNOBV5LKobEucPkyXD//fDSS/Xuu/7p6Tyx+nDGjYNRo0oTooiIiDQd\nadTxesDMXgVeBboB16YQQ3HMnw/x/LMbbqhz13efWkCnj5eyeM8juOWWEsQmIiIiTU5J1mp09+eA\n56K/R5biOUti1qxwfeKJ8PDD8NZb0K/fNrutXg33fG06NwCj7z6C9u1LG6aIiIg0Dapc3xgzZ8J2\n28HEidChA9x00za7uIc1GPd5fzqbP70TO4/YL4VARUREpClQ4tUYs2bBIYdAz55w/vlhrldFxVa7\n3HYb/PGPcGq3GbQbfhi0UZOLiIi0VsoCGmr16lCXa9iwcPs73wnXP/0pEHq67rsPxo+H0cd9wE4f\nvv3J+owiIiLSOinxaqg5cyCTgUMPDbf32APOOQcmTuS9Ocv5/OfhvPNCh9gvR80I+yjxEhERadWU\neDVUPLF+6NBPNm2+7Ap8wwYePOx2Zs+GO++EadOg84vToVMnOOiglIIVERGRpkCJV0PNnBkWu95h\nBwDKy2HwV/fjEf8i47iDN+es4qKLoildM2aEBE2nM4qIiLRqSrwaIpOB55+HQw9lzRq47LKQVy1b\nBjvdNIHOW1ay61/uCvuuXh2Kq2qYUUREpNVT4tUQb78NH38Mw4bxta/BrbfC2LEwbx4cNX4QHHss\n/OxnsH59GJLMZJR4iYiIiBKvBpk5E4DNg4bx5JNw4YXwq1/B9ttH90+YAB98EE5rnD4dysrCLHsR\nERFp1UpSub7FmTULdtyRf6/px9q1cPTROfePGBESrZtugt69YeDAUGhVREREWjX1eDVEVDh16vTQ\nfEcemXO/Wej1WrgwTKzXMKOIiIigxKtwK1bA66/DsGFMnQr77Qc9etSw34knwmc+E/5W4iUiIiIo\n8Src7NkAVA09lBkzYPjwWvZr0wauvRZ23bWOnURERKQ1UeJVqJkzoU0b5rYfwurV9eRUp5wCixdD\n164lC09ERESaLiVehZo1Cz77WaaUh8ny6swSERGRfCnxKkRVVRhqjOZ37bsv9OyZdlAiIiLSXCjx\nKsQbb8CqVWSGDmP6dPV2iYiISGGUeBUiWhh73o6HsnKlEi8REREpjBKvQsyaBd268dT8vQAlXiIi\nIlIYJV6FmDkzzO+aZuy1VyhKLyIiIpIvJV75+vBDePttMsMOZdo09XaJiIhI4ZR45ev55wFY0GMY\nH3+sxEtEREQKp8QrX7NmQVkZT300CFDiJSIiIoVT4pWvWbNg4ECeeb4zffrAHnukHZCIiIg0N0q8\n8rFlC8yejR8yTPO7REREpMGUeOXj1Vdh3Toqdz+UDz9U4iUiIiINo8QrHzNnAjBlwzAARoxIMRYR\nERFpthJPvMyszMxeMrPHo9t7mtlsM3vXzB40s/ZJx9Bos2bBLrvw+Kt7sNtu0KdP2gGJiIhIc1SK\nHq+LgXlZt28EbnX3vYGPgTEliKFxZs3CDz2UqdOM4cPBLO2AREREpDlKNPEys97AF4C7o9sGjAQe\njnaZBJyaZAyNNncuLFjAsr0OZdkyze8SERGRhku6x+s24LtAJrrdFVjh7lui25VAr5oeaGZjzazc\nzMqXL1+ecJi1qKqCCy6AHj34285fB5R4iYiISMMllniZ2YnAMnf/d0Me7+4T3X2Quw/q3r17kaPL\n069/DXPmwM9+xtPlO7LrrrD33umEIiIiIs1f2wSPfRhwspmdAHQEPg38HNjBzNpGvV69gcUJxtBw\n778PEybAMcfgo77M1PHhbEbN7xIREZGGSqzHy90nuHtvd+8DnA086+7nAFOA06PdRgOPJRVDo1x6\nKWzcCHfeyTvvGkuXaphRREREGieNOl5XAJeZ2buEOV/3pBBD3Z56Cv7wB7jqKthnH6ZODZuVeImI\niEhjJDnU+Al3fw54Lvp7ATCkFM/bIOvXw0UXQb9+cMUVAEydCjvvHDaJiIiINFRJEq9m5brrYMEC\nePZZ6NAB95B4qX6XiIiINJaWDMo2bx7cdBOcey4cdRQQ5thXVsJhh6Ucm4iIiDR7Srxi7nDhhdCl\nC9xyyyebKyrCdd++KcUlIiIiLYaGGmP33QfTpsFvfgM9enyyubIyXPfunU5YIiIi0nKoxwvgww9h\n/Pgwnvj1r2/oD8JEAAAOF0lEQVR1V9zjpcRLREREGkuJF8Cdd8LKlaFSfZutm6SyEjp2hK5dU4pN\nREREWgwlXgBXXx2GGffff5u7KitDb5fOaBQREZHGUuIFUFYGw4bVeFdFhYYZRUREpDiUeNUj7vES\nERERaSwlXnXIZGDxYthtt7QjERERkZZAiVcdli2DLVvU4yUiIiLFocSrDiolISIiIsWkxKsOKp4q\nIiIixaTEqw5x4qU5XiIiIlIMSrzqUFEB7dtDt25pRyIiIiItgRKvOqh4qoiIiBSTEq86VFZqmFFE\nRESKR4lXHVQ8VURERIpJiVctMhklXiIiIlJcSrxqsXw5bN6sxEtERESKR4lXLVRKQkRERIpNiVct\nVDxVREREik2JVy20XJCIiIgUmxKvWlRWhuKp3bunHYmIiIi0FEq8alFZCb16QRu1kIiIiBSJ0opa\nVFRomFFERESKS4lXLVTDS0RERIotscTLzDqa2Rwze9nMXjezH0Xb7zOz98xsbnQZmFQMDeWu5YJE\nRESk+NomeOyNwEh3X2Nm7YAZZvZEdN94d384wedulA8/hE2b1OMlIiIixZVY4uXuDqyJbraLLp7U\n8xWTSkmIiIhIEhKd42VmZWY2F1gGPO3us6O7rjOzV8zsVjPrkGQMDaHiqSIiIpKERBMvd69y94FA\nb2CImX0GmAD0BwYDOwFX1PRYMxtrZuVmVr58+fIkw9yGlgsSERGRJJTkrEZ3XwFMAY5z96UebATu\nBYbU8piJ7j7I3Qd1L3EV04oKaNsWevQo6dOKiIhIC5fkWY3dzWyH6O9OwLHAm2bWM9pmwKnAa0nF\n0FAqnioiIiJJSPKsxp7AJDMrIyR4D7n742b2rJl1BwyYC1yYYAwNolISIiIikoQkz2p8BTiwhu0j\nk3rOYqmshEGD0o5CREREWhoNpuWIi6fqjEYREREpNiVeOf77X9iwQYmXiIiIFJ8SrxwqJSEiIiJJ\nUeKVQ1XrRUREJClKvHKoar2IiIgkRYlXjsrKUDx1553TjkRERERaGiVeOSorYdddoaws7UhERESk\npVHilaOiQsOMIiIikgwlXjlUw0tERESSosQrS1w8VaUkREREJAlKvLJ89BGsX68eLxEREUmGEq8s\nKiUhIiIiSVLilUWJl4iIiCRJiVcWLRckIiIiSVLilaWiItTv2mWXtCMRERGRlkiJV5bKSujZU8VT\nRUREJBlKvLKohpeIiIgkSYlXlooKze8SERGR5CjxisTFU9XjJSIiIklR4hVZsQLWrVPiJSIiIslR\n4hVRKQkRERFJmhKvSEVFuFaPl4iIiCRFiVdEVetFREQkaUq8IpWV0KZNqOMlIiIikgQlXpGKipB0\ntW2bdiQiIiLSUinxiqiUhIiIiCRNiVdEiZeIiIgkTYkXoXhqRYUSLxEREUlWYomXmXU0szlm9rKZ\nvW5mP4q272lms83sXTN70MzaJxVDvlauhLVrVcNLREREkpVkj9dGYKS7HwAMBI4zs0OAG4Fb3X1v\n4GNgTIIx5EWlJERERKQUEku8PFgT3WwXXRwYCTwcbZ8EnJpUDPlS4iUiIiKlkOgcLzMrM7O5wDLg\naWA+sMLdt0S7VAK9annsWDMrN7Py5cuXJxmmlgsSERGRkkg08XL3KncfCPQGhgD9C3jsRHcf5O6D\nunfvnliMADvtBCNGqHiqiIiIJKskZzW6+wpgCjAM2MHM4jKlvYHFpYihLqedBlOmQLt2aUciIiIi\nLVmSZzV2N7Mdor87AccC8wgJ2OnRbqOBx5KKQURERKQpSXKBnJ7AJDMrIyR4D7n742b2BvAHM7sW\neAm4J8EYRERERJqMxBIvd38FOLCG7QsI871EREREWhVVrhcREREpESVeIiIiIiWixEtERESkRJR4\niYiIiJSIEi8RERGRElHiJSIiIlIiSrxERERESkSJl4iIiEiJKPESERERKRElXiIiIiIlYu6edgz1\nMrPlwKIiHKob8GERjtOSqY3yo3bKj9qpfmqj/Kid6qc2yk8p2mkPd+9e0x3NIvEqFjMrd/dBacfR\nlKmN8qN2yo/aqX5qo/yoneqnNspP2u2koUYRERGRElHiJSIiIlIirS3xmph2AM2A2ig/aqf8qJ3q\npzbKj9qpfmqj/KTaTq1qjpeIiIhImlpbj5eIiIhIalpF4mVmx5nZW2b2rpldmXY8TYWZ/dbMlpnZ\na1nbdjKzp83sneh6xzRjTJuZ7WZmU8zsDTN73cwujrarnbKYWUczm2NmL0ft9KNo+55mNjt67z1o\nZu3TjjVtZlZmZi+Z2ePRbbVRDjNbaGavmtlcMyuPtuk9l8PMdjCzh83sTTObZ2bD1E7VzKxf9BqK\nL6vM7JK026jFJ15mVgb8EjgeGACMMrMB6UbVZNwHHJez7UrgGXffB3gmut2abQEud/cBwCHAN6PX\nj9ppaxuBke5+ADAQOM7MDgFuBG51972Bj4ExKcbYVFwMzMu6rTaq2VHuPjDrtH+957b1c+BJd+8P\nHEB4XamdIu7+VvQaGggcDKwDHiXlNmrxiRcwBHjX3Re4+ybgD8ApKcfUJLj7NOCjnM2nAJOivycB\np5Y0qCbG3Ze6+4vR36sJH2y9UDttxYM10c120cWBkcDD0fZW305m1hv4AnB3dNtQG+VL77ksZrY9\ncCRwD4C7b3L3FaidanM0MN/dF5FyG7WGxKsXUJF1uzLaJjXb2d2XRn+/D+ycZjBNiZn1AQ4EZqN2\n2kY0hDYXWAY8DcwHVrj7lmgXvffgNuC7QCa63RW1UU0ceMrM/m1mY6Ntes9tbU9gOXBvNHR9t5l1\nRu1Um7OB30d/p9pGrSHxkgbycMqrTnsFzKwL8CfgEndflX2f2ilw96qoS783oae5f8ohNSlmdiKw\nzN3/nXYszcDh7n4QYYrIN83syOw79Z4DoC1wEPArdz8QWEvOkJnaKYjmTZ4M/DH3vjTaqDUkXouB\n3bJu9462Sc0+MLOeANH1spTjSZ2ZtSMkXQ+4+yPRZrVTLaLhjinAMGAHM2sb3dXa33uHASeb2ULC\nlIeRhDk6aqMc7r44ul5GmJMzBL3nclUCle4+O7r9MCERUztt63jgRXf/ILqdahu1hsTrBWCf6Myh\n9oTuxr+kHFNT9hdgdPT3aOCxFGNJXTQH5x5gnrv/LOsutVMWM+tuZjtEf3cCjiXMh5sCnB7t1qrb\nyd0nuHtvd+9D+Bx61t3PQW20FTPrbGbbxX8DnwdeQ++5rbj7+0CFmfWLNh0NvIHaqSajqB5mhJTb\nqFUUUDWzEwhzK8qA37r7dSmH1CSY2e+BEYSV2j8AfgD8GXgI2B1YBJzp7rkT8FsNMzscmA68SvW8\nnKsI87zUThEz+xxhkmoZ4QfdQ+7+YzPrS+jd2Ql4CfiKu29ML9KmwcxGAN9x9xPVRluL2uPR6GZb\nYLK7X2dmXdF7bitmNpBwokZ7YAFwHtH7D7UT8Eny/h+gr7uvjLal+lpqFYmXiIiISFPQGoYaRURE\nRJoEJV4iIiIiJaLES0RERKRElHiJiIiIlIgSLxEREZESUeIlIkVhZm5mP826/R0z+2GRjn2fmZ1e\n/56Nfp4zzGyemU3J2d7HzNab2dysy1eL+LwjzOzxYh1PRJqutvXvIiKSl43AaWZ2g7t/mHYwMTNr\nm7UWYn3GAN9w9xk13Dc/WhJJRKTB1OMlIsWyBZgIXJp7R26PlZmtia5HmNlUM3vMzBaY2U/M7Bwz\nm2Nmr5rZXlmHOcbMys3s7Wjdw3hh7pvN7AUze8XMLsg67nQz+wuhmnduPKOi479mZjdG274PHA7c\nY2Y35/uPNrM1Znarmb1uZs+YWfdo+0Azez6K61Ez2zHavreZ/dPMXjazF7P+jV3M7GEze9PMHohW\nTSBqkzei49ySb1wi0jQp8RKRYvolcI6ZbV/AYw4ALgT2A84F9nX3IYSK3OOy9utDWLPvC8BdZtaR\n0EO10t0HA4OBb5jZntH+BwEXu/u+2U9mZrsCNxLWShwIDDazU939x0A5cI67j68hzr1yhhqPiLZ3\nBsrdfX9gKmEFCID7gSvc/XOElQ/i7Q8Av3T3A4BDgaXR9gOBS4ABQF/gsKjC9heB/aPjXFtfY4pI\n06bES0SKxt1XERKObxfwsBfcfWm0TM584Klo+6uEZCv2kLtn3P0dwvIo/Qnr+H3VzOYSlnHqCuwT\n7T/H3d+r4fkGA8+5+/JoCPIB4Mg84pzv7gOzLtOj7Rngwejv3wGHR4nnDu4+Ndo+CTgyWoOwl7s/\nCuDuG9x9XVa8le6eAeZG//aVwAZCL9xpQLyviDRTSrxEpNhuI/REdc7atoXo88bM2hDWlotlr0uY\nybqdYet5qLnrmzlgwLisZGhPd48Tt7WN+lc0XEPXYctuhyognps2BHgYOBF4spGxiUjKlHiJSFFF\ni80+REi+YguBg6O/TwbaNeDQZ5hZm2hOVF/gLeAfwEVm1g7AzPaNFsWtyxxguJl1M7MyYBRhiLCh\n2gDx/LUvAzOixXg/zhqOPBeY6u6rgUozOzWKt4OZfaq2A5tZF2B7d/87Ye7cAY2IU0SaAJ3VKCJJ\n+CnwrazbvwEeM7OXCb02DemN+g8hafo0cKG7bzCzuwlDci9Gk9GXA6fWdRB3X2pmVwJTCD1mf3P3\nx/J4/r2iIc3Yb939dsK/ZYiZXQMsA86K7h9NmIv2KcLQ6HnR9nOBX5vZj4HNwBl1POd2hHbrGMV6\nWR5xikgTZu4N7RUXEREzW+PuXdKOQ0SaBw01ioiIiJSIerxERERESkQ9XiIiIiIlosRLREREpESU\neImIiIiUiBIvERERkRJR4iUiIiJSIkq8RERERErk/wMzGmQ2juBncQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WF8eBrjVDkRp/f64tdu2a/dsFuFSNMECCMMtkSNIfHroXBQrwFw8hhBAuLmM+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\nd921b9eBgkZJmIiIiOQ6M7jjDp+1+vBDOPFE3981fDgsWuQzdum9954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            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy:  33.125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uq2j7ETxMlCs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2f4b7253-341f-4c4a-a2b9-0b234b599822"
      },
      "source": [
        "epochs = 70\n",
        "net_cnn_1d_shallow= CNN_1D_Shallow().cuda()\n",
        "optimizer = torch.optim.Adam(net_cnn_1d_shallow.parameters(),weight_decay=0.01)\n",
        "train_accuracy_per_epoch_1d_shallow, valid_accuracy_per_epoch_1d_shallow, train_loss_1d_shallow, valid_loss_1d_shallow  = train(net_cnn_1d_shallow,train_dataloader, epochs, optimizer)\n",
        "plot_accuracies(train_accuracy_per_epoch_1d_shallow,valid_accuracy_per_epoch_1d_shallow,epochs,\"1D CNN-Shallow\")\n",
        "plot_loss(train_loss_1d_shallow, valid_loss_1d_shallow, epochs, \"1D CNN-Shallow\")\n",
        "test_accuracy_1d_shallow = calc_test_accuracy(net_cnn_1d_shallow)\n",
        "print(\"Test accuracy: \", test_accuracy_1d_shallow)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch # 0\n",
            "Train Accuracy = 21.567094150766344\n",
            "Valid Accuracy = 20.625\n",
            "Train Loss = 2.4078990799188613\n",
            "Valid Loss = 2.0357896280288696\n",
            "Epoch # 1\n",
            "Train Accuracy = 25.28933375039099\n",
            "Valid Accuracy = 23.125\n",
            "Train Loss = 1.9248912620544434\n",
            "Valid Loss = 1.9583972358703614\n",
            "Epoch # 2\n",
            "Train Accuracy = 26.994056928370348\n",
            "Valid Accuracy = 23.125\n",
            "Train Loss = 1.9137018322944641\n",
            "Valid Loss = 1.949570746421814\n",
            "Epoch # 3\n",
            "Train Accuracy = 28.198310916484203\n",
            "Valid Accuracy = 30.0\n",
            "Train Loss = 1.8768904900550842\n",
            "Valid Loss = 1.8933915662765504\n",
            "Epoch # 4\n",
            "Train Accuracy = 31.341883015326868\n",
            "Valid Accuracy = 32.25\n",
            "Train Loss = 1.8098538362979888\n",
            "Valid Loss = 1.7840623807907106\n",
            "Epoch # 5\n",
            "Train Accuracy = 32.499218016890836\n",
            "Valid Accuracy = 35.625\n",
            "Train Loss = 1.7821377670764924\n",
            "Valid Loss = 1.8286171913146974\n",
            "Epoch # 6\n",
            "Train Accuracy = 34.40725680325305\n",
            "Valid Accuracy = 36.75\n",
            "Train Loss = 1.7436901247501373\n",
            "Valid Loss = 1.7241625452041627\n",
            "Epoch # 7\n",
            "Train Accuracy = 37.34751329371286\n",
            "Valid Accuracy = 37.875\n",
            "Train Loss = 1.6890760952234267\n",
            "Valid Loss = 1.785120162963867\n",
            "Epoch # 8\n",
            "Train Accuracy = 39.6152643102909\n",
            "Valid Accuracy = 39.375\n",
            "Train Loss = 1.6591791313886644\n",
            "Valid Loss = 1.6779539012908935\n",
            "Epoch # 9\n",
            "Train Accuracy = 40.412887081639035\n",
            "Valid Accuracy = 44.25\n",
            "Train Loss = 1.6292926156520844\n",
            "Valid Loss = 1.61900851726532\n",
            "Epoch # 10\n",
            "Train Accuracy = 41.41382546137003\n",
            "Valid Accuracy = 44.5\n",
            "Train Loss = 1.6099164056777955\n",
            "Valid Loss = 1.66705500125885\n",
            "Epoch # 11\n",
            "Train Accuracy = 42.49296215201752\n",
            "Valid Accuracy = 40.25\n",
            "Train Loss = 1.599683965444565\n",
            "Valid Loss = 1.620419087409973\n",
            "Epoch # 12\n",
            "Train Accuracy = 43.306224585548954\n",
            "Valid Accuracy = 43.5\n",
            "Train Loss = 1.5666280531883239\n",
            "Valid Loss = 1.5627067375183106\n",
            "Epoch # 13\n",
            "Train Accuracy = 44.69815451986237\n",
            "Valid Accuracy = 49.25\n",
            "Train Loss = 1.55664959192276\n",
            "Valid Loss = 1.532094202041626\n",
            "Epoch # 14\n",
            "Train Accuracy = 43.66593681576478\n",
            "Valid Accuracy = 44.125\n",
            "Train Loss = 1.5652375185489655\n",
            "Valid Loss = 1.6007299137115478\n",
            "Epoch # 15\n",
            "Train Accuracy = 44.573037222395996\n",
            "Valid Accuracy = 47.625\n",
            "Train Loss = 1.5484020459651946\n",
            "Valid Loss = 1.5172703981399536\n",
            "Epoch # 16\n",
            "Train Accuracy = 44.5573975602127\n",
            "Valid Accuracy = 44.375\n",
            "Train Loss = 1.5528017085790635\n",
            "Valid Loss = 1.5718375301361085\n",
            "Epoch # 17\n",
            "Train Accuracy = 45.761651548326554\n",
            "Valid Accuracy = 46.875\n",
            "Train Loss = 1.5286044526100158\n",
            "Valid Loss = 1.5361687326431275\n",
            "Epoch # 18\n",
            "Train Accuracy = 45.40193931811073\n",
            "Valid Accuracy = 45.375\n",
            "Train Loss = 1.5227247381210327\n",
            "Valid Loss = 1.54282452583313\n",
            "Epoch # 19\n",
            "Train Accuracy = 45.65217391304348\n",
            "Valid Accuracy = 45.125\n",
            "Train Loss = 1.5036949354410172\n",
            "Valid Loss = 1.5594770193099976\n",
            "Epoch # 20\n",
            "Train Accuracy = 46.87206756334063\n",
            "Valid Accuracy = 45.75\n",
            "Train Loss = 1.4928069663047792\n",
            "Valid Loss = 1.540800371170044\n",
            "Epoch # 21\n",
            "Train Accuracy = 46.95026587425711\n",
            "Valid Accuracy = 47.25\n",
            "Train Loss = 1.4875386762619018\n",
            "Valid Loss = 1.5476757621765136\n",
            "Epoch # 22\n",
            "Train Accuracy = 47.607131685955586\n",
            "Valid Accuracy = 47.875\n",
            "Train Loss = 1.4732160079479217\n",
            "Valid Loss = 1.5175817966461183\n",
            "Epoch # 23\n",
            "Train Accuracy = 47.137941820456675\n",
            "Valid Accuracy = 46.75\n",
            "Train Loss = 1.4761703544855118\n",
            "Valid Loss = 1.566465744972229\n",
            "Epoch # 24\n",
            "Train Accuracy = 47.904285267438226\n",
            "Valid Accuracy = 48.75\n",
            "Train Loss = 1.470973705649376\n",
            "Valid Loss = 1.4962486171722411\n",
            "Epoch # 25\n",
            "Train Accuracy = 48.201438848920866\n",
            "Valid Accuracy = 48.75\n",
            "Train Loss = 1.4492202982306481\n",
            "Valid Loss = 1.5603627204895019\n",
            "Epoch # 26\n",
            "Train Accuracy = 49.71848608070066\n",
            "Valid Accuracy = 46.25\n",
            "Train Loss = 1.4330266976356507\n",
            "Valid Loss = 1.5350306510925293\n",
            "Epoch # 27\n",
            "Train Accuracy = 49.31185486393494\n",
            "Valid Accuracy = 49.0\n",
            "Train Loss = 1.4281593242287636\n",
            "Valid Loss = 1.541708664894104\n",
            "Epoch # 28\n",
            "Train Accuracy = 50.172036284016265\n",
            "Valid Accuracy = 47.0\n",
            "Train Loss = 1.4276038718223572\n",
            "Valid Loss = 1.5351932334899903\n",
            "Epoch # 29\n",
            "Train Accuracy = 51.18861432593056\n",
            "Valid Accuracy = 50.5\n",
            "Train Loss = 1.4178949296474457\n",
            "Valid Loss = 1.461312050819397\n",
            "Epoch # 30\n",
            "Train Accuracy = 51.57960588051298\n",
            "Valid Accuracy = 52.0\n",
            "Train Loss = 1.3995038440823555\n",
            "Valid Loss = 1.45090877532959\n",
            "Epoch # 31\n",
            "Train Accuracy = 51.39192993431342\n",
            "Valid Accuracy = 45.875\n",
            "Train Loss = 1.3955233228206634\n",
            "Valid Loss = 1.648151707649231\n",
            "Epoch # 32\n",
            "Train Accuracy = 52.17391304347826\n",
            "Valid Accuracy = 50.75\n",
            "Train Loss = 1.3756790527701377\n",
            "Valid Loss = 1.5498530530929566\n",
            "Epoch # 33\n",
            "Train Accuracy = 53.4876446668752\n",
            "Valid Accuracy = 50.625\n",
            "Train Loss = 1.3708685341477393\n",
            "Valid Loss = 1.4756750774383545\n",
            "Epoch # 34\n",
            "Train Accuracy = 52.89333750390992\n",
            "Valid Accuracy = 51.375\n",
            "Train Loss = 1.365374396443367\n",
            "Valid Loss = 1.4953685998916626\n",
            "Epoch # 35\n",
            "Train Accuracy = 53.67532061307476\n",
            "Valid Accuracy = 48.875\n",
            "Train Loss = 1.3537223914265633\n",
            "Valid Loss = 1.532180061340332\n",
            "Epoch # 36\n",
            "Train Accuracy = 54.50422270878949\n",
            "Valid Accuracy = 48.625\n",
            "Train Loss = 1.3371458476781846\n",
            "Valid Loss = 1.506053638458252\n",
            "Epoch # 37\n",
            "Train Accuracy = 54.26962777604004\n",
            "Valid Accuracy = 47.875\n",
            "Train Loss = 1.3425976207852364\n",
            "Valid Loss = 1.5673836278915405\n",
            "Epoch # 38\n",
            "Train Accuracy = 54.75445730372224\n",
            "Valid Accuracy = 52.375\n",
            "Train Loss = 1.3291059973835946\n",
            "Valid Loss = 1.4608287620544433\n",
            "Epoch # 39\n",
            "Train Accuracy = 54.488583046606195\n",
            "Valid Accuracy = 51.0\n",
            "Train Loss = 1.3256291133165359\n",
            "Valid Loss = 1.577983946800232\n",
            "Epoch # 40\n",
            "Train Accuracy = 55.317485142320926\n",
            "Valid Accuracy = 48.5\n",
            "Train Loss = 1.3278183734416962\n",
            "Valid Loss = 1.4955755138397218\n",
            "Epoch # 41\n",
            "Train Accuracy = 54.582421019705976\n",
            "Valid Accuracy = 48.75\n",
            "Train Loss = 1.3142243745923041\n",
            "Valid Loss = 1.520987296104431\n",
            "Epoch # 42\n",
            "Train Accuracy = 55.48952142633719\n",
            "Valid Accuracy = 48.0\n",
            "Train Loss = 1.3131721791625024\n",
            "Valid Loss = 1.5516170167922974\n",
            "Epoch # 43\n",
            "Train Accuracy = 55.75539568345324\n",
            "Valid Accuracy = 51.0\n",
            "Train Loss = 1.2940034592151641\n",
            "Valid Loss = 1.471573338508606\n",
            "Epoch # 44\n",
            "Train Accuracy = 55.78667500781983\n",
            "Valid Accuracy = 54.375\n",
            "Train Loss = 1.2885747742652893\n",
            "Valid Loss = 1.47162006855011\n",
            "Epoch # 45\n",
            "Train Accuracy = 55.48952142633719\n",
            "Valid Accuracy = 50.0\n",
            "Train Loss = 1.2923668259382248\n",
            "Valid Loss = 1.5012163782119752\n",
            "Epoch # 46\n",
            "Train Accuracy = 57.11604629340006\n",
            "Valid Accuracy = 52.75\n",
            "Train Loss = 1.2678592598438263\n",
            "Valid Loss = 1.475826759338379\n",
            "Epoch # 47\n",
            "Train Accuracy = 56.427901157335\n",
            "Valid Accuracy = 50.5\n",
            "Train Loss = 1.2777447482943536\n",
            "Valid Loss = 1.521724157333374\n",
            "Epoch # 48\n",
            "Train Accuracy = 56.944010009383796\n",
            "Valid Accuracy = 53.25\n",
            "Train Loss = 1.2672363337874413\n",
            "Valid Loss = 1.4529606771469117\n",
            "Epoch # 49\n",
            "Train Accuracy = 56.77197372536753\n",
            "Valid Accuracy = 52.25\n",
            "Train Loss = 1.2512693092226983\n",
            "Valid Loss = 1.5008739757537841\n",
            "Epoch # 50\n",
            "Train Accuracy = 57.47575852361589\n",
            "Valid Accuracy = 45.875\n",
            "Train Loss = 1.2570985653996467\n",
            "Valid Loss = 1.655287823677063\n",
            "Epoch # 51\n",
            "Train Accuracy = 56.78761338755083\n",
            "Valid Accuracy = 52.25\n",
            "Train Loss = 1.2717813220620156\n",
            "Valid Loss = 1.5063671827316285\n",
            "Epoch # 52\n",
            "Train Accuracy = 58.226462308414135\n",
            "Valid Accuracy = 52.625\n",
            "Train Loss = 1.249322298169136\n",
            "Valid Loss = 1.5035068321228027\n",
            "Epoch # 53\n",
            "Train Accuracy = 57.61651548326556\n",
            "Valid Accuracy = 50.125\n",
            "Train Loss = 1.2446569120883941\n",
            "Valid Loss = 1.5305977535247803\n",
            "Epoch # 54\n",
            "Train Accuracy = 58.273381294964025\n",
            "Valid Accuracy = 51.75\n",
            "Train Loss = 1.2335018816590309\n",
            "Valid Loss = 1.4696532011032104\n",
            "Epoch # 55\n",
            "Train Accuracy = 58.08570534876446\n",
            "Valid Accuracy = 50.0\n",
            "Train Loss = 1.2269159311056137\n",
            "Valid Loss = 1.50616482257843\n",
            "Epoch # 56\n",
            "Train Accuracy = 58.00750703784798\n",
            "Valid Accuracy = 50.5\n",
            "Train Loss = 1.2405803030729294\n",
            "Valid Loss = 1.519988489151001\n",
            "Epoch # 57\n",
            "Train Accuracy = 58.758210822646234\n",
            "Valid Accuracy = 48.625\n",
            "Train Loss = 1.2131925785541535\n",
            "Valid Loss = 1.6043014669418334\n",
            "Epoch # 58\n",
            "Train Accuracy = 58.68001251172975\n",
            "Valid Accuracy = 47.625\n",
            "Train Loss = 1.2170848789811135\n",
            "Valid Loss = 1.6032101106643677\n",
            "Epoch # 59\n",
            "Train Accuracy = 58.33593994369722\n",
            "Valid Accuracy = 47.25\n",
            "Train Loss = 1.2259879833459855\n",
            "Valid Loss = 1.5772121381759643\n",
            "Epoch # 60\n",
            "Train Accuracy = 58.898967782295905\n",
            "Valid Accuracy = 51.375\n",
            "Train Loss = 1.2192499482631682\n",
            "Valid Loss = 1.5202423906326294\n",
            "Epoch # 61\n",
            "Train Accuracy = 59.790428526743824\n",
            "Valid Accuracy = 49.125\n",
            "Train Loss = 1.1917855322360993\n",
            "Valid Loss = 1.521624917984009\n",
            "Epoch # 62\n",
            "Train Accuracy = 59.22740068814514\n",
            "Valid Accuracy = 48.375\n",
            "Train Loss = 1.1972580707073213\n",
            "Valid Loss = 1.5275289297103882\n",
            "Epoch # 63\n",
            "Train Accuracy = 59.243040350328435\n",
            "Valid Accuracy = 48.375\n",
            "Train Loss = 1.2014547783136367\n",
            "Valid Loss = 1.6250857591629029\n",
            "Epoch # 64\n",
            "Train Accuracy = 60.228339067876135\n",
            "Valid Accuracy = 48.25\n",
            "Train Loss = 1.1795974576473236\n",
            "Valid Loss = 1.5322733879089356\n",
            "Epoch # 65\n",
            "Train Accuracy = 59.8999061620269\n",
            "Valid Accuracy = 49.75\n",
            "Train Loss = 1.1813358661532403\n",
            "Valid Loss = 1.5354430294036865\n",
            "Epoch # 66\n",
            "Train Accuracy = 59.790428526743824\n",
            "Valid Accuracy = 50.375\n",
            "Train Loss = 1.1913922873139382\n",
            "Valid Loss = 1.5688918018341065\n",
            "Epoch # 67\n",
            "Train Accuracy = 60.572411635908665\n",
            "Valid Accuracy = 51.0\n",
            "Train Loss = 1.181314473748207\n",
            "Valid Loss = 1.5587406158447266\n",
            "Epoch # 68\n",
            "Train Accuracy = 59.634031904910856\n",
            "Valid Accuracy = 53.625\n",
            "Train Loss = 1.1799815732240677\n",
            "Valid Loss = 1.5202624607086181\n",
            "Epoch # 69\n",
            "Train Accuracy = 60.181420081326245\n",
            "Valid Accuracy = 51.0\n",
            "Train Loss = 1.1766973972320556\n",
            "Valid Loss = 1.550415987968445\n",
            "Finished Training\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8DgYAbNoEVKvGJR527ACmTUsqkjJn5twsV1fO05o7lwXY3r3cnmfzZu5TWKwY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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yzwG8JiK/GWM+BPCViCy2s5t00dHRsmXLFq+112IBAgKACROATCevEBFRPrR//37Uq1fP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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Test accuracy:  33.125\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-miXs8giF_kD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}