coursera_deep_neural_network/8.1.1mist2layer_v2.ipynb

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   "source": [
    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DL0110EN-SkillsNetwork/Template/module%201/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Hidden Layer Deep Network: Sigmoid, Tanh and Relu Activations Functions MNIST Dataset</h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Objective for this Notebook<h3>    \n",
    "<h5> 1. Define Several Neural Network, Criterion function, Optimizer.</h5>\n",
    "<h5> 2. Test Sigmoid ,Tanh and Relu. </h5>\n",
    "<h5> 3. Analyse Results. </h5>     \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "<p>In this lab, you will test Sigmoid, Tanh and Relu activation functions on the MNIST dataset with two hidden Layers.</p>\n",
    "\n",
    "<ul>\n",
    "    <li><a href=\"#Model\">Neural Network Module and Training Function</a></li>\n",
    "    <li><a href=\"#Makeup_Data\">Make Some Data</a></li>\n",
    "    <li><a href=\"#Train\">Define Several Neural Network, Criterion function, Optimizer</a></li>\n",
    "    <li><a href=\"#Test\">Test Sigmoid ,Tanh and Relu </a></li>\n",
    "    <li><a href=\"#Result\">Analyse Results</a></li>\n",
    "</ul>\n",
    "<p>Estimated Time Needed: <strong>25 min</strong></p>\n",
    "\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll need the following libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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     "end_time": "2021-07-15T12:26:21.621752Z",
     "start_time": "2021-07-15T12:26:21.606227Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torch._C.Generator at 0x7f02f01958f0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the libraries we need for this lab\n",
    "\n",
    "# Using the following line code to install the torchvision library\n",
    "# !conda install -y torchvision\n",
    "\n",
    "# !pip install torchvision==0.9.1 torch==1.8.1 \n",
    "import torch \n",
    "import torch.nn as nn\n",
    "import torchvision.transforms as transforms\n",
    "import torchvision.datasets as dsets\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "torch.manual_seed(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Model\">Neural Network Module and Training Function</h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the neural network module or class, with two hidden Layers \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://ibm.box.com/shared/static/5wtclahun0f70qlwkn2kxzh3amnbq4zg.png\" width=\"200\" alt=\"Neural Network Model\" />\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:26:24.183554Z",
     "start_time": "2021-07-15T12:26:24.180211Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the model class using sigmoid as the activation function\n",
    "\n",
    "class Net(nn.Module):\n",
    "    \n",
    "    # Constructor\n",
    "    def __init__(self, D_in, H1, H2, D_out):\n",
    "        super(Net, self).__init__()\n",
    "        self.linear1 = nn.Linear(D_in, H1)\n",
    "        self.linear2 = nn.Linear(H1, H2)\n",
    "        self.linear3 = nn.Linear(H2, D_out)\n",
    "    \n",
    "    # Prediction\n",
    "    def forward(self,x):\n",
    "        x = torch.sigmoid(self.linear1(x)) \n",
    "        x = torch.sigmoid(self.linear2(x))\n",
    "        x = self.linear3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the class with the Tanh activation function \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:26:27.780359Z",
     "start_time": "2021-07-15T12:26:27.773753Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the model class using Tanh as a activation function\n",
    "\n",
    "class NetTanh(nn.Module):\n",
    "    \n",
    "    # Constructor\n",
    "    def __init__(self, D_in, H1, H2, D_out):\n",
    "        super(NetTanh, self).__init__()\n",
    "        self.linear1 = nn.Linear(D_in, H1)\n",
    "        self.linear2 = nn.Linear(H1, H2)\n",
    "        self.linear3 = nn.Linear(H2, D_out)\n",
    "    \n",
    "    # Prediction\n",
    "    def forward(self, x):\n",
    "        x = torch.tanh(self.linear1(x))\n",
    "        x = torch.tanh(self.linear2(x))\n",
    "        x = self.linear3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the class for the Relu activation function \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:26:28.802182Z",
     "start_time": "2021-07-15T12:26:28.788703Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the model class using Relu as a activation function\n",
    "\n",
    "class NetRelu(nn.Module):\n",
    "    \n",
    "    # Constructor\n",
    "    def __init__(self, D_in, H1, H2, D_out):\n",
    "        super(NetRelu, self).__init__()\n",
    "        self.linear1 = nn.Linear(D_in, H1)\n",
    "        self.linear2 = nn.Linear(H1, H2)\n",
    "        self.linear3 = nn.Linear(H2, D_out)\n",
    "    \n",
    "    # Prediction\n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.linear1(x))  \n",
    "        x = torch.relu(self.linear2(x))\n",
    "        x = self.linear3(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to  train the model, in this case the function returns a Python dictionary to store the training loss and accuracy on the validation data \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:27:28.349129Z",
     "start_time": "2021-07-15T12:27:28.341228Z"
    }
   },
   "outputs": [],
   "source": [
    "# Train the model\n",
    "from tqdm import tqdm\n",
    "\n",
    "def train(model, criterion, train_loader, validation_loader, optimizer, epochs=100):\n",
    "    i = 0\n",
    "    useful_stuff = {'training_loss': [], 'validation_accuracy': []}  \n",
    "    \n",
    "    for epoch in tqdm(range(epochs)):\n",
    "        for i, (x, y) in enumerate(train_loader):\n",
    "            optimizer.zero_grad()\n",
    "            z = model(x.view(-1, 28 * 28))\n",
    "            loss = criterion(z, y)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            useful_stuff['training_loss'].append(loss.data.item())\n",
    "        \n",
    "        correct = 0\n",
    "        for x, y in validation_loader:\n",
    "            z = model(x.view(-1, 28 * 28))\n",
    "            _, label = torch.max(z, 1)\n",
    "            correct += (label == y).sum().item()\n",
    "    \n",
    "        accuracy = 100 * (correct / len(validation_dataset))\n",
    "        useful_stuff['validation_accuracy'].append(accuracy)\n",
    "    \n",
    "    return useful_stuff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Makeup_Data\">Make Some Data</h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the training dataset by setting the parameters <code>train</code> to <code>True</code> and convert it to a tensor  by placing a transform object int the argument <code>transform</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:26:32.956773Z",
     "start_time": "2021-07-15T12:26:32.936063Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the training dataset\n",
    "\n",
    "train_dataset = dsets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the testing dataset by setting the parameters <code>train</code> to <code>False</code> and convert it to a tensor  by placing a transform object int the argument <code>transform</code>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:26:34.144944Z",
     "start_time": "2021-07-15T12:26:34.140230Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the validating dataset\n",
    "\n",
    "validation_dataset = dsets.MNIST(root='./data', train=False, download=True, transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the criterion function  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:26:35.061686Z",
     "start_time": "2021-07-15T12:26:35.059789Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the criterion function\n",
    "\n",
    "criterion = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the training-data loader and the validation-data loader object \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:26:36.139528Z",
     "start_time": "2021-07-15T12:26:36.133793Z"
    }
   },
   "outputs": [],
   "source": [
    "# Create the training data loader and validation data loader object\n",
    "\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=2000, shuffle=True)\n",
    "validation_loader = torch.utils.data.DataLoader(dataset=validation_dataset, batch_size=5000, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Train\">Define Neural Network, Criterion function, Optimizer and Train the Model</h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create  the model with 100 hidden layers  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:26:38.358038Z",
     "start_time": "2021-07-15T12:26:38.355345Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set the parameters for create the model\n",
    "\n",
    "input_dim = 28 * 28\n",
    "hidden_dim1 = 50\n",
    "hidden_dim2 = 50\n",
    "output_dim = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The epoch number in the video is 35. You can try 10 for now. If you try 35, it may take a long time.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:26:39.597694Z",
     "start_time": "2021-07-15T12:26:39.595607Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set the number of iterations\n",
    "\n",
    "cust_epochs = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Test\">Test Sigmoid ,Tanh and Relu</h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the network using the Sigmoid activation function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:28:15.917043Z",
     "start_time": "2021-07-15T12:27:32.893422Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:43<00:00,  4.30s/it]\n"
     ]
    }
   ],
   "source": [
    "# Train the model with sigmoid function\n",
    "\n",
    "learning_rate = 0.01\n",
    "model = Net(input_dim, hidden_dim1, hidden_dim2, output_dim)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
    "training_results = train(model, criterion, train_loader, validation_loader, optimizer, epochs=cust_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the network using the Tanh activation function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:29:01.615795Z",
     "start_time": "2021-07-15T12:28:15.918239Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:45<00:00,  4.57s/it]\n"
     ]
    }
   ],
   "source": [
    "# Train the model with tanh function\n",
    "\n",
    "learning_rate = 0.01\n",
    "model_Tanh = NetTanh(input_dim, hidden_dim1, hidden_dim2, output_dim)\n",
    "optimizer = torch.optim.SGD(model_Tanh.parameters(), lr=learning_rate)\n",
    "training_results_tanch = train(model_Tanh, criterion, train_loader, validation_loader, optimizer, epochs=cust_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the network using the Relu activation function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:29:46.826392Z",
     "start_time": "2021-07-15T12:29:01.617027Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10/10 [00:45<00:00,  4.52s/it]\n"
     ]
    }
   ],
   "source": [
    "# Train the model with relu function\n",
    "\n",
    "learning_rate = 0.01\n",
    "modelRelu = NetRelu(input_dim, hidden_dim1, hidden_dim2, output_dim)\n",
    "optimizer = torch.optim.SGD(modelRelu.parameters(), lr=learning_rate)\n",
    "training_results_relu = train(modelRelu, criterion, train_loader, validation_loader, optimizer, epochs=cust_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"Result\">Analyze Results</h2> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the training loss for each activation \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:29:46.968746Z",
     "start_time": "2021-07-15T12:29:46.827649Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f01d72839d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2Sh7qeS9DY0fz5db3uFsFYOlzK7h5/xZM6nKjlhAYbxRIPmHGl3b390YhNuxBCEuGmZPh4C/Q8XKjVpJ8hfGjWfKM8SMf+FfY9CnsrTvD6X8HfD7BKFhSlxs1krHPwfyHjAL5qKAEKD5s1HJCOsCFzxgFxucTjMLWp41RUNutxg+p5/VGYVuaafwAMzcaBQoAyriSa/cPRiGoXCBhNIR3hq1fGgVQxnqj1gTHvQ+jkKwpM5rBXFyh6wTYNst4T0W+se3jKRcjIbn7G/Mctt/mhSYbhcTx04KTjP20VUPbocYxOZGLm/GvveZ/5/nHGoWLtfIk73M1jh8YiT5vtxGfm48Rm0egsW9Hl3H3g6pC47XJ/FucZnfjOGu7cYySxhrJZfNnGC23Jzh64nFUQJxRQNprwWQxaj4AoZ2MAq6q2GjaPP49ZnfjmJz42tXHKFytVUahVnTIeF9ostFMerSGBYAC7zCjsCysK7y7ToSDy4yTD2UyTlaOF9XHSKz2WqOw1XYjceXsMBJ/QBwcXlt3kuNrHJeY/sZ+pa+Fyvzf1qVMxsmA2d34Xmr7b/N8o4x1pZ3Qmm32MJp3/WOMxBDdHw6vNmLI32ckg46XGUn4aA3ePwa6T4L17xvH4uh+OWzG5+0ZZPy2et8Ml77yv59XPTg1EdQ1/ywDntVan/LOLaXUUOBxrfXok8y7FbgVICYmplda2ikftHPGVqfkM+W/67gwOZzRyaHcM3MrAIMSgvjs5n6oc6yGZ5Vn4WZ2w9fVl2Ezh2F1WPllwi8sTV/K46sep9ZRi6vJlekXTye1JJVlGctYmbGSMmsZN3a6kem7pzMgrDeRvnGU1JRQZatiacZSfCw+JAUmYVZm1mav5elBT3NFwhUcLDnIk6uf5M4ed1JWW0a/Nv3wsnj9caBl2cYP7kz3t/iwkQi8go0vcEmm8YM3uUB4N+MMuygVet1oFBhg/ABydho/aKWMQqSqyDhz3PktrH4Trp9jnKXtnQdHtkCXq42zyepSI2nFDPjtTBGgshCWvwR9phoJavlLRmF3tDlMa/j2L8b8TlcYZ2gms1F4bfrUONPK3QkRPY1mr+LDxplnVZHRZJW9HfyijJj3zDXOuhNGG7UVZTISWEm6cca88BFju23q9v/Sfxu1reJ0o8M/dxdE9TWO985vjBqKu7+RiDPWQ1A7Y52/vmW8N3WZcYx7TAJbjfHaWgnLXjTO9kc9btTWNn9mFKSRvYwaX852I9lG9zOOna3GOL5pq2Dnd8Zx636dUePxi4aDS439dViNmk5ED6PwqimDtsMgY50xvcsEo7ZQnAYjHvt9X1F5nnFxgzIZZ/RKwbaZxhl514nGca0qNpo6TWbI328c+zbdYOsXRnK4/HXY9T1E94H8FCg+ZDSPxg0yTmQ2/tcoOK96H9a8bZxk1JQZJw/7FhjfLb8o43vgEWh8npG9jBrn4dXGNgPjjTPwqmIjqWZvN2q2wQlGbfFof1jar8aVgbYaY1pET+Nzy95qnIBkbTVOqHwjIX+vcRxzd0NoB+g83uir278I2o0wYm/Tzfh97PjWiHHsc8Zn9e00IylE9jKO29HkFjMAMjYYCSWmv3EMj9ZQzpDTEoFSygLMBRZqrf8wjSmlDgJ9tdb5p1qmIWsER9XaHLiajTbj3VmlLN+Xx/Pz9/DJTX2x2R10i/Yn2NvtnLfzyc5PUCiu73Q9AIdKDrE+Zz0dAzvSObjzseXSS9NJL09nYMRAnlnzDDP3zsTT7Im3xdtIEJ1vRGvNO1vfAcDNxY0wzzCuSbqGnMocPtv92bF1BXsE8+lFn4KG23++nZs630RWRRZ/6vAnqmxVhHmG4WJyodZey48Hf2RI1BCCPYLPeV9FHa2bZpt+baVREB9Nzs2F1kbSjez5v00pzojFVt0wfUJaG0nWP7bRLsJwVmexAj4BCrXWd59imQTgQF1ncU/gByBKnyaoxkgEJ6qx2Rn9yjJKKq2UVtvw97Tw9nU9GZgQ/LukcT5Y7VaOVBwhyjsKkzLh0I5jBfesvbMorikmzi+Oh1c8DIDZZCbQLZC+bfoyMGIgT/36FKNjRxPiEcJ/d/732HpHRo9kacZShkcNp1dYL77a9xWHSg8xNGoob41663cxfLXvK7oGd6V9YPvztt9CiIblrEQwGFgBbMe4PBTgESAGQGv9rlLqQeB6wApUAQ801uWjZ2rOlkzu+nIL/doGUlRZS3phFS9P6MZj3+1gcv9Y7rkgqdFjOBMV1gqmzJ/C3qK93N/7fqZ0mgLAKxte4ZNdn+Bl8aJ9QHsivSPZWbCTlOIUXJQL9ro2z46BHUkKSGLOgTlMbD+RHfk7mNh+Ij1Ce3DZd5cxLGoYb456k9c3vU5yUDKjY/+nBU8I0YSdLhE02oXgdQX6aevEWusXgBcaK4ZzcVnXCMwmE4MTgqmx27n6nV+5fcYmAN5Ysp8ukX50jfYjt7SG2CBPfNyde4mYl8WLO3vcyQPLHmBkzMhj02/ucjM7C3ayLnsdU7tMZVDkIGbtncXTa55mTNwY7ux+J2aTmXCvcGzaRpWtill7Z+Hm4sbTa55mYMRAAFYfWc26rHW8v/19QjxC6BzcGT83PzzMDXSppBDCac7b5aMN5XzVCE60L6eM695fw5QBcXy3JZMDeRXH5nm6uvD8VV24vFvEOXcunyu7w47LSa6/LqwuJNDdaFMtqSnhnqX38EDvB+gY9L9X65bVllFjr2HSvElklmcS5R1FRnkGwR7BFNcUY6u7EqVbSDfeHPkmeVV5PLv2WRL8E7i7590opbCYLGg0bi7n3rcihDh3Tr98tCE5KxEAOBwak0lRbbXz3eZMrHYHgV5uvLf8AIcKKgnzdaNDuC8vXt0Vd0szvhmmTpWtigWpC+gd1ps7ltxBakkq07pNI68yj4KqApZmLP3d8iZloo1XG7TWeLl6UVJTwqvDX6VLSBfAuLt78eHFFFQV0CO0BzaHjQ6BHXhx/YusOrKKl4a+dNLEJIQ4d5IIGtme7FIufm0FHhYXKmrtXNcvhueu7OLssBqU3WHHoR1YjrtLcsbuGRRUFRDtE024Vzi3Lb4Nu7bjanJFownyCKKwqpCrEq8izi+ONUfW/E/yCPMMI6cyBx+LD0op5lwxh0Mlh+gdftLvqxDiLEkiOA9W7M8j0t+DD1em8tXGDP7vko5szyxhZIdQxnZu4+zwzot3tr6Di3JhYMRAbA4bcb5xPLLyEdZmraXWUYtC8UCfB2gf0J7UklRyKnPYWbCTKxOvxNfiy18W/4V4v3gOlhzk+yu+J843jnJrOR5mD4qqi1h8eDFXJ12NxWTB6rBiMRlJSWvNjvwddA7uTJWtCk+Lp5OPhBBNjySC8+hAXjmjXl4GgKuLCTeLiXtGJxHo5cq47s7vQ3CWnfk7qXXU0iO0x0nn19prGfLlECptxt21QyKHUFhdyM6CnXhZvAhwCyCjPIObOt9ERlkGv6T/wv2972dUzCh2FOzg7l/u5roO1zFr3yxeHf4qw6KHnc/dE6LJk0Rwnr23/AAeFhf6tg3ioteW46g7xNf0iiIxzJsFO7K5IDmc24a3c26gTcy9S+/lp7SfiPWNJa00jUjvSK5MuJLNeZtZl7WOOL849hftx8PsQbxfPDsLdqJQhHiGkFv529AWyUHJjI4Zjb+7P8XVxVyVeBVBHicZ5E6IVsQpl4+2ZrcO/a2Af+7KLmjgcGEl7yw1xkqJ8HPnhQV76BUbQN+2Tr47sgmZ0mkKIR4hTEqexILUBVzX8Tq8LF5oramyVVFcU8zHOz9mcsfJtPFuw+ojq3l/2/tsydtCYkAi+4v2kxiQyK6CXewqODbILfuL9vPisBcB4+qpe5fei4+rD3d2v5OC6gKSApLkbmrRqkmN4DxxODT3zzbGMXpqXGfG/Hs5pVVW7hqdyNQh8U6OrvnKrcxl+q7p3NDpBlYdWcWI6BF8vvtzRsSMwM3Fje9SvuOD7R/QOagzAyIGsOrIKg4UH8DT7Ild2ymtLaV9QHuGRg0l2COYqxKvYn7qfOL84liXtQ67tjMpeRK+rr7O3lUhzok0DTVBKbnlPPPjLpbuzeO1a7szLCmE0iobQd6ueLlJRa2hVForeeLXJ8iuyGZz7mb83Px4cuCThHmGcf3862nr15Z9RftQKDSapIAk9hXtw83FjZq6kUK7Bnflk4s+wWySz0U0X5IImqgam53x76xmR+ZvI3MHe7vy7e2DiA6UK18aWl5lHv5u/scugc2uyCbII4j1Wetp492G9dnreXrN04R6hlJprSTeL56JHSby6MpHCfUMZXTMaB7u9/Cx9aWVplFjryEpoGkNNyLEyUgiaMLKqq0s2ZNLXlkNHq4u/HP+HrxczYzrHkGHNj6E+bgzMEHar8+Xr/d9TYegDoR4hOBt8cbD7MGL619kffZ69hbt5c2RbzIsehiV1kou/+5yymrLGBY1DBTc1+s+qmxVxPnFYXVY2ZG/A1cXVzoFdXL2bgkhiaA52ZhWyGs/p/DrgXysduOzeXdSL4YmBZNbWkNccD2eLSAaXI29hgk/TOBgyUES/BMwm8zsKdyDt8WbcqvxYBuLyYKH2YMF4xdw39L7+DXLeMbS8Ojh/GvYv6i11+LjeuYPOxKiIUgiaIZKqqxklVTx4OxtbM8swcvNTFm1jSGJwbx/fe8WMYRFc1NSU8KsvbPYlr+NnIoceoX14uqkqym3ljNj9wy25G4hqyLr2E1x9/a6F6vDyhub3yDALYBqezVXJlxJbmUuj/V/jED3QAqrCwnyCKLGXsPO/J30CO1x7F6T4upi/N39nbzXoqWQRNCMFVbU8sGKg2QWVxEX5MVrP+9nYLsgSqqstAvxbjHjGjV3R39H1/14HTsKdnB799u5rdttALy4/kV+OPADwR7BpBSnYFZmonyi6BjUkZ/SfuLTsZ/ywfYPWJK+hBeHvshFbS9iQeoCHlzxINMvmk7XkK7kVuby6MpHebz/40T7RjtzV0UzJYmgBblv1la+3pRB1yg/tmWU8I/LkhmUEExCiDcmU+u8a7kpSS9NJ6M8gwERA3433e6wU+uoJbMsk9LaUu78+U7KrGWYlRmTMlHrqCXQPZDy2nL83f2xO+wUVBcwLGoYI2NGUlBVwOubX2dcu3E8M/gZJ+2daM4kEbQgNTY7KbnlJLfx5cq3V7MlvRiAQC9XIv09uKRrG24ZEo+LJIUmLaUohZWZK0kKSOKdre9wVeJVdAvtxttb3ia7IputeVvpFtKNrXnGvSdmZcambZiVmfcufI+CqgKGRw/H3Xx2z68VrY8kghZqzcEC/jl/D5d2bcO+nDIO5FWwMa2IF8d3ZXRyGD9sPcLEPtHSdNTMOLSDrIosXE2uvLH5DQ6VHmJz7mbGJ45necZy8qryALgq8SoujL2QMM8wnl/3PMlBydi1nWuSrqGtX1tSS1KJ8o763YixovWSRNBKaK254q1VZJdW4+Vq5mB+BU9clswNg9o6OzRxDg4UH+DhFQ/z8rCX8Xb15vsD37O3cC8/HPzh2DKuJlesDisaTcfAjgS6B7LqyComtp/IY/0fo7C6EF9X33rfFLencA9aa3k+RAsiiaAVWXOwgKmfbCDY2xWTUtgcmhlT+/HhylQm9Y8lIdTb2SGKBlBtq+aD7R8Q7RPN/EPzuanTTSQGJLLqyCoeXvEwXhYvkgKS2Ja3jf4R/VmduZoBEQNI8E+gV1iv3z3O9GTGfTcOF5ML31z+zXnaI9HYJBG0Ugt2ZDPts42YFDg0eLuZ+fLW/nSO9HN2aKKRaK1ZeGghnYM742525/JvL8fd7M6AiAF8f+B7wHiS3KCIQQyKHMSE9hOwmCzcsugWIr0jeWLgE2SWZzL267EArLx2JX5u8n1pCSQRtGLzt2fxw7YjXNM7moe+3kaApyvf3D4QT1cZN6c1KK0txcPsgcVkYV3WOvzc/Ji+azrb8reRWpLK+MTx3Nr1VsZ8PQZXkytLJixhXuo8nlv7HACvj3idETEjnLwXoiFIIhAA/LQrh1s+3YCb2UQbP3f+PrYDqfkVjO4Yxp7sUoYlheDv6ersMMV5oLXmqTVPMSdlDpM6TuK/O/8LgK+rL6W1pbTxakN2RTYB7gH8c31VD64AACAASURBVMg/GRAxgC25W1ibtZZh0cM4WHyQHQU7+Hufvzt5T0R9SSIQx2xMK2T+9myW7ssjJbf8d/Mu6xbBG386+RPERMuTVprGpd9eikKRFJCESZnIr8rnkvhL6NemHx/t+Ij12evxsnixcPxCpsyfwoGSA7i7uGM2mSm3lvPmyDdJCEgg0jvS2bsj/oAkAvE/8spqeGruLka0D2HZvjwqauws3p3D+9f35oLkMGeHJ86Td7a+w8Hig1yTdA3JQcm4mFzwMHsAxmitW/O2cs/SexgaNZTlGcu5rdttzNw7k5KaErwsXpTWluJj8WHxNYvxtHhidVh5ecPLrD6ymvcueI9wr3An76E4ShKB+EMVNTaufHsV+3LKGdguiL+P7cAXaw8ztks4I9qHOjs84UQPLn+QeanzcHdxZ8mEJWRVZJFdkU1pbSnf7P+G9dnreXzA4wyJHML0XdP5dNenuJpcifeP5+VhL2PTNjZkb2B84nhcTHJPi7NIIhD1Um218+HKVD5efYj88hq0Bh93M9/fOZi2Muppq2Vz2Jh7cC7uLu6MbTv2d/O01lzy7SWkl6Ub9yhoGJcwjlExo3hg+QNUWCuOPfTn5s4387eef2N5xnJSilO4Pvl6XF2kT+p8kUQgzkhKbjmTPljLiA6h/LD1CJW1NoYkhnDPBUl0jzZGw6yosWF2UbiZ5Qyvtftm/zfM3DsTL4sX+4v28+24bwn2CCanIoe5B+dSaaskuyKb7w98T5/wPqzPXg/AlOQpTE6eTKhn6LERV0XjkUQgzpjDoTGZFBlFlcxan86X69MprrLyzp974uNuYeon6xnWPlQ6l8UxWmuq7dXH+hiOZ7VbmbpoKptyN3Fdh+uoslXxbcq3AIR6hHJN+2u4ufPNWFwsVFor8bTIE/oamiQCcc4KK2qZ8tE6DuSVY3No0GBzOFj2wAh5rKaol+LqYpZlLOOS+EuwOWwsOLSASmslq46sYnnGcsYnjqd/RH8eWv4QvcN7c1Onm5h7cC7Tuk0jxjfG2eE3e5IIRIPILqnmirdWER3owVPjOnPpGyu5tk80z17ZxdmhiWbutU2v8cH2DwBI8E8gryqPkpoSAC6Nv5TnhzwPgNVhxYRJOp3PwukSgdxeKuot3M+dpQ8Mx81sQinF5P6xfLz6EJsPF2NxUQR7uzGsfQhdIv0wKUW3aHm6lqifv/b4K+0D27MrfxdTOk3B6rDy6a5Pya7IZsGhBQyJHEKUTxR3/HwHpbWlDIoYxCP9HiGvKo9on2iCPeS53udCagTirNXaHEz9dAPFlbW4mU1klVSTUVSFi0kR6OXK6odGYnExOTtM0Yyll6Vz7dxrKa0tBSDYI5hL2l7C7P2zCfEI4VDpIXxdfbmj+x2MTxqPm4ubkyNuupzSNKSUigY+BcIADbyntX7thGX+DDwIKKAMuE1rvfV065VE0HTV2hzc/Ml6MoqqSM2v4N1JPekZG4CLUgR5yw9UnJ1KayXb87fz+e7PubHzjXQP7c6svbN4es3TRHlHEe4VzoacDYR6hvL0wKfxd/fnkRWPMDRqKPf2vtfZ4TcZzkoEbYA2WutNSikfYCNwhdZ613HLDAR2a62LlFIXAU9orfudbr2SCJo2rTV2h2bIi7+QU1qNQ4PFRXHToLY8dFEHuUxQNAi7w867295lVMwo2ge0Z232Wl5Y9wKZ5ZnYHXY0GqvDyjuj32Fw5GAA7lpyF1vztnJDpxu4ofMNzt0BJ2gSncVKqTnAm1rrn04xPwDYobU+7aAlkgiah9Up+Szbl0eIjxu7s8r4elMGd45I4L4LkyQZiEaRU5HD5PmTifeP54kBT3Db4ts4Un6ECO8Ion2i+SX9F0I8QiisLmTWZbPIr8yn3FrOhXEXOjv088LpiUApFQcsBzprrUtPscz9QAet9dSTzLsVuBUgJiamV1paWuMFKxqc1pq/z97GVxszGNkhlDtGtKPWpjEpqLY5GJYU4uwQRQthd9iPXVGUW5nLrYtupaimiMLqQiwmC19f/jWT50+mS3AXVmauBGBql6nc0f2Oej+9rblyaiJQSnkDy4BntdYnfdyRUmoE8DYwWGtdcLr1SY2gedJaM31NGs/8uJtam+PYdKXgx78OITnC14nRiZZKa6OJ6M/z/kzHwI48Negp3tz8Jv/Z9h8A3F3cqbZX0zO0Jz6uPgC8MfKNY7VWu8NOraP2pDfJNTdOSwRKKQswF1iotX7lFMt0Bb4FLtJa7/ujdUoiaN5ScsvYlVWGl6sLJVVWnp67iw7hvtw2vB0Ldmbz7BWdpelINDi7w45JGZc9Z5ZnctHXF2E2mVk2cRlL05fy7NpnqbBWAPDx2I/pFdYLgOfXPs/S9KXMvWouABaTxWn7cK6cch+BMn7NH2J0Bp8qCcQA3wCT65MERPOXEOpDQqjPsf9XWe08+u0ONqYVUWt3MKF39LHxjIRoKMffgBbpHckl8Zfg5uKGj6sPl7W7jL7hfSmuKWbqoqm8v/19eob2pKC6gK/2fYXVYWXK/CnkVObwxSVfEOrZ8kbjbcyrhgYDK4DtwNG2gEeAGACt9btKqQ+A8cDRRn/bqTLWUVIjaFm01tz8yQZW7s8HYEKfKC7pEoHZRdE7NkBqB+K8+mTnJ/xrw7/oEtyFlOIUqm3V+Ln5UVxTDMDAiIG8O/pdlFJU2arYW7iXbiHdmsX31OmdxQ1JEkHLU2tzkFdewz/m7GTx7pxj03vHBhAV4MGdIxP4bM1h7r0wCV/35ls1F02fQzu44+c72Jy7mbFxY+ka0pWy2jLmHpzLmLgxvLbpNd4a9RaDIwfz1yV/ZXnGcvq36c8tXW6hb5u+zg7/tCQRiGZhS3oxn69NY0yncPbllPPVxnQO5lUQ7utOdmk1YzuF886kns3i7Es0XzaHDZvDhrvZ/XfTrQ4r474bh0mZ6BDYgYWHFjI2bizrstdRVF3EzEtnklKcwi/pv/CPAf/Az83PSXtwcpIIRLPkcGhGvLyUtIJKIv09yCyuYsbUfgxKkHFlhHOsPrKaR1c+SnF1MVO7TuX2brdTZi1jzOwxmE3mY01Il7e7nGcHP+vkaH9PBp0TzZLJpJgyII5n5+3mP5N7MenDtbz+837mbMnEoeEflyXjI01F4jwaGDGQRVcvosZWg7erNwC+rr5c1/E63tv2Hjd3vhmTMvH+9vcJ8wzjQPEBpnaZSpeQLlgd1iZ71ZHUCEST5nBoMouriA705Jm5u/hgZSo+bmbKamz8ZWg87hYXesUGMCQxWJqMhNPYHDb2F+2nY1BHrHYrNy+6mc25mwEwKzN397qb1ze9zvSLpxPvF8/MvTO5JukaDpYcJDkoGZNq/MEZpWlItAgF5TV88msak/vHcu+sLayou9II4PoBsdw5MoFQH/fTrEGI8yO/Kp/vUr5jTNwYrvvxumNNRjd0uoHcylzmpc7j6qSrmb1vNg/2eZBJyZMaPSZJBKLFWX+okGnTN/LUuM5sOlzEhytTAYjwc+e963vTObJpddSJ1uvoJaleFi9MmCizlgHganKl1lFLhFcETwx8gu6h3Rv1DmZJBKJF0lqjlEJrzabDxWxNL+btpSn4eViwOzSXd4tg/o5s/nFZJwYnSgezcA6bw8aKjBXkVObw7Npn6RLcBU+LJ2uz1mJWZmzaBkDP0J5c1u4yfkn/hW4h3fCyeHFB7AUNdgObdBaLFulon4BSil6xAfSKDcDLzYUHv96Om9nE60tSAHjmx13M+9sQTCbpQxDnn9lkZkTMCCqtlRRUFzCx/US+S/mOtVlruTj+YgZEDKCkpoR/rf8Xm3I3EegeyPKM5QAcKD7A4wMeb/wYG30LQpxH1/SKJtTHna5RfszemIFDwwsL9rBwZzYXdWnj7PBEK+Zp8eSO7ncA0CO0BwC9w3pzafylAFzc9mKKaoqI9YklozyDf2/8N4vSFvFw34exuDTu1UbyHEHRophMihEdQgnyduMvw9px69B44kO8eHXxfhyO5tUMKlqunqE9eXX4q1za7tJj0wLcA4j3i8fF5EKsbyxXJlxJSU0Jiw8vBmDG7hkcKD7QKPFIIhAtmotJcdeoRPbmlNH/+Z/5z7IDNLd+MdHyKKUYFTvqtPcVDIwYSLxfPI+seIS3t7zNC+teYM6BOY0TT3P7UUhnsThTdofmo5WpLNuXx8qUfHrHBtArLoAhCSEMSgiS+w9Ek1VaW8qdP9/J5tzN+Lj6MP+q+Wc9dIVcNSQExlVGM9en8/rP+8krr8Fq1/x1ZAL3Xdje2aEJcUr5Vfncvvh2JrSfwNVJV5/1eiQRCHGCGpudB2dv44dtWQR7uxLp70FptY2re0UxbVg7Z4cnRIM7XSKQPgLRKrmZXXjy8s7EBnoSH+xNSZWVsmor/1q4l73ZZc4OT4jzSmoEQtQpKK9h1CvLaB/mg9bQNcqPxy5NdnZYQjQIqREIUQ9B3m78bWQia1MLWXeokA9WpvLTrt8elNPcTpqEqC9JBEIcZ1L/WDpH+vLnfjEkt/Hl4W+2sSOzhHFvruT+r7Y5OzwhGoU0DQlxAodDYzIp9maXcdkbK6m1O47NG9c9gt6xAUweEOe8AIU4CzLWkBBn4OiYRO3Dffji1v5sTS+mT1wg0z7byJwtR5i/PZtRHcNo4+eO1sgYRqLZkxqBEPWUXVJNZnEl1763hou7tKGo0kqN1c7nt/THRZKBaOKkRiBEAwj3cyfcz53bhifw+s/7j02/d9YWLuocztjOMqidaJ4kEQhxhu4ZnYjZpNAatqQXMWfLERbvymFIYghebvKTEs2PfGuFOENKKf42KhEwxjH69UABkz5cy7ztWQR5u1JYYeXqXlFOjlKI+pNEIMQ5cDEpBiUE0TbYi6fm7qKs2oZJQY8Yf9qFeDs7PCHqRe4jEOIcKaV44vJODE0K4ZYhbXE1m3jqh12k5MpQFaJ5kBqBEA1gWFIIw5JCAPD3dOWlhXtZ+Wo+912YRN+4QA4VVEpzkWiyJBEI0cDuGJHANb2ieHLuLl5csPfYdJOCJXtyee3aHnK5qWhSpGlIiEYQ6uvOm3/qwYtXd6VHjD8AD3+znbnbsliyJ5dvNmUw9MVfKK22OjlSISQRCNFolFJM6B3N19MGEuBpocZmDFXx/vKDvLhgL4cLK5mzOdPJUQohiUCIRmcyKQYnGv0H1/aJZt2hQrJLqwn2dmXG2sOUVFqZsyVTRjcVTiN9BEKcB9OGxdM5wpdbh8ZzZY9IskqqqbU7+PvsbVzzn9XsyyknNb+CLenFvPGnHvi4n/qh5kI0tEZLBEqpaOBTIAzQwHta69dOWKYD8F+gJ/Co1vpfjRWPEM7UKcKPThHGQ8f7xQcBYLM7+GDFQfbllAPw6mJj2IoV+/O5uIsMVyHOn8ZsGrIB92mtk4H+wB1KqRMf91QI/A2QBCBaHbOLiRfGd+WybhE8dklHLC4KV7OJ5fvyyCurcXZ4ohVptBqB1joLyKp7XaaU2g1EAruOWyYXyFVKXdJYcQjRlPWICeCNmAAAJvSJ5v5ZW/lyfTqzNqTzwviu/Lg9ixfGdyXM193JkYqW7Lx0Fiul4oAewNqzfP+tSqkNSqkNeXl5DRmaEE2Gr7uFIXU3pTk0PDB7G0v35vH2LylOjky0dI2eCJRS3sDXwN1a69KzWYfW+j2tdW+tde+QkJCGDVCIJuS6vjF8PrUfd4xoB0CwtxtfrE8nraCC9MJKLnpthQxdIRpco141pJSyYCSBGVrrbxpzW0K0BC4mxcCEYDpH+eHnYWFUxzCuens1kz9cx4D4IHZnlfLd5iPcP6a9s0MVLUi9agRKqbuUUr7K8KFSapNS6sI/eI8CPgR2a61faYhghWgtfN0t3Dq0He1CvPn4xj7klFYzc0M6AD/vyQWMZyvP2ZJJRY3NmaGKFqC+TUM31TXrXAgEAJOBf/7BewbVLTdSKbWl7u9ipdQ0pdQ0AKVUuFIqA7gXeEwplaGU8j27XRGiZeoRE8BT4zphUjA4IZjdWaX8Y84OXl+yn7u+3ML7Kw46O0TRzNW3aejoCFkXA9O11jvrzvhPSWu98rj3nWqZbECGZBTiD0zsE8OYTuGUVtkY+9pyZqw9jM1h3Ik8e2MGfxuZiEkGshNnqb41go1KqUUYiWChUsoHcDReWEKIE/l7uhIT5MnOJ8ew4O4hjOwQyrRh7cgoqmLRrhxnhyeasfrWCG4GugMHtdaVSqlA4MbGC0sIcSpKKRJCffjohj5UW+0s3ZvLPTO3EBUwgM6Rfs4OTzRD9a0RDAD2aq2LlVKTgMeAksYLSwhRH+4WFz69qS8+7mb+Pnsbb/2SwtqDBc4OSzQz9U0E7wCVSqluwH3AAYxxhIQQThbq687jlyWzK6uUlxbuZeJ7a5izRYa3FvVX30Rg08YYueOAN7XWbwE+jReWEOJMXNKlDe9O6sXie4cSG+TJN5syKa+xcd+srfxSd7mpEKdS3z6CMqXUwxiXgw5RSpkAGSdXiCZCKcXYzuEAjO4YxvRf07jpv+tZd6iQhTuzubJHJON7RdE92t/JkYqmqL41golADcb9BEcv+Xyp0aISQpy1UR1DqbU7WJ9WyKMXG6OaTl+TxrtLDzg7NNFE1SsR1BX+MwA/pdSlQLXWWvoIhGiC+sQFcnGXcF6Z0I1bhsaz+qFRXN0ritUH8sktrabGZnd2iKKJqe8QExOAdcA1wARgrVLq6sYMTAhxdiwuJt7+cy+u7GHcq+nh6sLQpBBKq230fe5nrn7nV0oqrU6OUjQl9W0aehToo7WeorW+HugL/F/jhSWEaEiD2gUde703u4zn5+92YjSiqalvZ7Gp7iEyRxUgD74XotkI8nbjsUs60i3an+m/pvHTrhxuHlzGN5sz6RHtzy97c/F0NfPwRR0wu8hPu7WpbyJYoJRaCHxR9/+JwLzGCUkI0RimDokHILukmu+3HuGi11YcG6/oKIfW/OOyTs4ITzhRfTuLHwDeA7rW/b2ntX6wMQMTQjSO4e1DsLgovNzM/Pi3wXSL8uPKHpFc1Dmcuduy0FqzPaOEbRnFzg5VnCf1fjCN1vprjIfMCCGaMR93C/+8qisxQZ50ivDjuzsGoZTii3WHmb8jm3055fxl+gYAVj44UkY1bQVOmwiUUmWAPtksQGut5dkBQjRD43v9Nvr70RHlB9Z1KD83bzdHSqoBWH2ggMGJwec/QHFenTYRaK1lGAkhWomYQE8i/T1Yti+PEB83aqx2Zm1IJ9zPjapaB12iZGTTlqpRn1kshGg+lFL8c3wXVu7PZ0hiCD/vyeHTX9NYm1qA1a5Z/dBI3C0uzg5TNAJJBEKIY4YkhjAkMQSAdqFezFhzmJzSGgDmbc/iqp7yQMGWSBKBEOKk2vh58NBFHSisqGX+jixeXrSPylo78SFeDGwn/QYtiSQCIcQp3TS4LQAjOoRyx4xNPPbdDjxdXVj/6Gi83KT4aCnkFkIhxB/qFRvAL/cP583relBZa2fBjmzyy2vYdaTU2aGJBiCJQAhRLx6uLlzSpQ2xQZ7M3JDOw99s55p3V1NtldFMmztJBEKIelNKMbl/LOtSC/lpVw4VtXbWyDOSmz1JBEKIMzKpfyyR/h4oBW5mE28uSeHjVamsSsl3dmjiLElvjxDijLhbXHhlQjf255azdG8ui3fnsiGtCIBbh8bj6mLivguTjt2xLJo+SQRCiDPWLz6IfvFB9I8PYlj7UEZ2COX2GZt4b/lBAEqqrOSX1/DatT1wNUvDQ1MniUAIcdYSQr1JCPUG4JMb+7A9s4SHv9nO9DVpAFy1L48LksOcGaKoB0nVQogG4e/pypDEEB65uCM9YvwJ9HLlu82Zzg5L1IMkAiFEg7q4Sxu+vX0Ql3Vtw0+7c8gqqXJ2SOIPSCIQQjSKmwfHY1Jw78ytvLJoL1M+WkdFjc3ZYYmTkEQghGgUMUGePHpJMmtSC3h9SQrL9uXxy97cP36jOO8aLREopaKVUr8opXYppXYqpe46yTJKKfW6UipFKbVNKdWzseIRQpx/k/vHsumxC1j54AiCvFx5f0Uq983aSrnUDJqUxqwR2ID7tNbJQH/gDqVU8gnLXAQk1v3dCrzTiPEIIZwgwMuVqABPRncMY2t6MV9vymDu1iPY7A7255Q5OzxBIyYCrXWW1npT3esyYDcQecJi44BPtWEN4K+UatNYMQkhnGdS/1h6xwYQ6e/Bx6sPccXbq7jg38tZlZKP1e7g640ZWO0OZ4fZKp2XPgKlVBzQA1h7wqxIIP24/2fwv8lCCNECdInyY/ZtA7muXwx7ssvIKa3B193MjLVpfLc5k/u+2sqSPdKH4AyNfkOZUsob+Bq4W2t9VmPWKqVuxWg6IiYmpgGjE0KcbzcOisPf08KlXSN4/ef9fLL6EGkFlQDsPFLKmE7hTo6w9WnUGoFSyoKRBGZorb85ySKZQPRx/4+qm/Y7Wuv3tNa9tda9Q0JCGidYIcR54elq5s/9YvHzsDBlQByeri7srHuuwa4jJU6OrnVqzKuGFPAhsFtr/copFvseuL7u6qH+QInWOquxYhJCNC0xQZ5Mv7kfFySHMSA+SB504ySNWSMYBEwGRiqlttT9XayUmqaUmla3zDzgIJACvA/c3ojxCCGaoG7R/rx/fW9GdAjhSEk1d325mZ935zBt+kYO1zUZicbVaH0EWuuVwGnHodVaa+COxopBCNF8dIn0B2DOliPM2XIEgPbhPtxzQZIzw2oVZPRRIUST0D8+kOk39yXUx51Pfz3E15syWJWSj6vZxMQ+0QR7uzk7xBZLEoEQoklQSjEk0bgY5Nkru+BhceGDlalsSCuistbGA2M6ODnClkvGGhJCNEkD2gUde51eWEVuWTVTP9nA1vRiJ0bVMkkiEEI0SYMSgvlT32g6hPuwMa2IqZ9sYPHuHN5fcdDZobU4kgiEEE2Su8WF56/qymXdIsgsrmJbRglJYd78vDuXyloZtK4hSSIQQjRpnSJ8j71+8vLOVFnt/LxbhqJoSJIIhBBNWs/YANqH+fDJTX3p2zaQCD93Zm1Ix+HQzg6txZBEIIRo0nzdLSy8ZyjDkkJwMSkm9olhxf58ej3zE28vTXF2eC2CJAIhRLMyoU8UbmYTVrvmXwv38qf31jBt+kb2Zv/2bIPVKflyV/IZkEQghGhW2vh5sOLvI1j54Ah6xwZSZbWz6kA+/zdnBwDVVjs3fbKeV37a6+RImw+5oUwI0eyE+roDMGvaAADeX36QZ+ftZv72LCwuJqqtDlLyyp0ZYrMiiUAI0exN6B3Nq4v3cduMTcemHcitwOHQmEynHfJMIE1DQogWwM/Twry7hvDAmPbHplVZ7Vzzn195eu4uJ0bWPEiNQAjRIsQGeXHHiAT8PS1UWx08PXcXG9OK2JhWRIdwH67pHf3HK2mlpEYghGhR/twvliu6RwDgYlL0iPHn/+bsYF9O2R+8s/WSRCCEaHECvVwJ8nJlUEIw/5ncC09XMy8tlKuITkWahoQQLY5Sig9v6EOIjxuhPu6M6hDK4t05zFx/GF93Cxd1aePsEJsUqREIIVqk7tH+RPp7ANA7LoCiSiuPfLuDFxbscXJkTY8kAiFEi9crNhAAu0NzqKCStIIKJ0fUtLSIpiGr1UpGRgbV1dXODqXJc3d3JyoqCovF4uxQhDhv2oV4EeBpwaGhpMrK7TM20a9tEAPaBXFBcpizw3M6ZTw/vvno3bu33rBhw++mpaam4uPjQ1BQEErJzSOnorWmoKCAsrIy2rZt6+xwhDiv5mzJxNfdwkPfbCOntAY3s4kam4O/j23P7cMTnB1eo1NKbdRa9z7ZvBZRI6iuriYuLk6SwB9QShEUFEReXp6zQxHivBvXPRKAj27oQ2mVjV6xAdw7awsvLdxLr5gA+sUH/cEaWq4W00cgSaB+5DiJ1q5ThB8D2gXhajbxwviuRAd4cv/srZTXtN6nnrWYRCCEEGfKy83MKxO6kVFUxT/n73Z2OE4jiaABFBcX8/bbb5/1+4cPH86J/R5CiPOjd1wgE3tHM3tjBlW1dmeH4xSSCBrAuSYCIYRzXdo1gmqrg5Up+f8zz2p3UFZtdUJU50+L6Cw+3pM/7GTX/7d371FVlnsCx78Pd7koKRcRTBD1eAEhRcQwlcpjmnlbdpus03RbZ3Rap8ZOOmYz2m3hyWOdUzNak6bOMmyy0crsZhqWYgi4VUQUNBgRQ0RQ8IboM3/sFwTlIsbm3Zv9+6zF4t3P++79/n48W3/7ed7LLj7Tpq85sEdn/v2+QU2unzt3LocPHyY2NpakpCT27t1LeXk5ly5d4rXXXmPy5MkUFBQwfvx4Ro4cyY4dOwgNDeWzzz6jUyfrBS+ffPIJM2fOpKKiguXLl3PHHXe0aQ5CiKYN792Vzl5ufLP/V8YODOZYxXlOVl4kpqc/b313iM8sxfw0J6nDHmOTEUEbSE5OJjIyEovFwptvvsn69evJyspi69atzJ49m9pTdPPy8pg1axb79+/H39+fTz/9tO41ampqSE9P5+2332bhwoVmpSKEU3J3dWHcoO58ufc4JWcu8PxaCw+9v5OKc9VsyT3BsYrzlJ/ruKOCDjciaO6Te3vQWjNv3jy2bduGi4sLx44do6SkBICIiAhiY2MBGDp0KAUFBXXPmzZtWqPtQoj28eydfdlgOcbMNVlkFpYDsDT1MLnGdyEXlp2lq4+HmSHajIwI2tiaNWsoLS0lMzMTi8VCcHBw3RXPnp6eddu5urpSU3P1dLXadde2CyHax63dvHnu7n5kFpbj6+nGbbf6817qkbr1hWXnTIzOtjrciMAMfn5+VFZaPzWcPn2aoKAg3N3d2bp1K4WFhSZHJ4S4UbOS+jBuUDDVNRoPNxfuXpIKgFLwU/5JNu07TlH5ed57dCg9u3qbHG3bkULQBrp160ZiYiJRUVEMGzaM3NxcoqOjiYuLo3///maHJ4RoqcQ+RwAADdFJREFUhT5BfnXL6fPu4mRVNU+vzmBdZhHeHq64uijGvpVKVI8upDyTgLur40+s2KwQKKVWABOBE1rrqEbW3wKsACKBC8ATWutsW8Vjax999FGL22RnX03vhRdeqFv+4Ycf6pYDAgLkGIEQdiKosxdBnb3w9nAFYEZCL6YNCeWdLfl8ufc4u345xe19AkyO8rezZSlbCdzTzPp5gEVrPRh4DPibDWMRQoib5mKcNvrgsJ70796ZN6cPxtPNhW9zSkyOrG3YrBBorbcBp5rZZCCwxdg2FwhXSsn9YIUQduedf7iN5GnRRAb6AuDt4cYdfQP4LqcER7uDc2PMnNzaA0wDUErFA72AsMY2VEo9o5TKUEplyJ0zhRDtrV+wHw/F39qg7c7+1gvPjpx0/C+5MbMQJAP+SikL8CywG2j0Rh9a6/e11nFa67jAwMD2jFEIIRo1ItJ62+qdR8pMjuS3M60QaK3PaK3/UWsdi/UYQSBwpIWnCSGEXQjv5k1wZ092HjnFlSuaI6VVXLh0mUMllWaH1mqmnT6qlPIHzmmtq4GngG1a67a9SZAQQtiIUoqE3t3YcbiMlF3/x0vrr54V+M1zo/hdd79mnm1fbDYiUEqlAGnA75RSRUqpJ5VSf1RK/dHYZACQrZQ6CIwH/mSrWMzy1FNPkZOTY9N9TJgwgYqKiuvaFyxYwOLFi226byGcXWKfAEorL7Is9TAAMT39Adh8wLHOJrLZiEBr/XAL69OAfrbavz344IMPbL6PTZs22XwfQojGjR0QjJuL4uip8zya0ItXp0Rx3zs/8eH2X3B1UTyRGIGHm/1fcNbxriz+ai78uq9tX7N7NIxPbnaTs2fP8sADD1BUVMTly5d5+eWXWbp0KYsXLyYuLo7ly5ezaNEi/P39iYmJwdPTk3fffZfHH3+cTp06sXv3bk6cOMGKFStYvXo1aWlpDB8+nJUrVwKQkpLCG2+8gdaae++9l0WLFgEQHh5ORkYGAQEBvP7666xatYqgoCB69uzJ0KFD2/bvIIRo4BYfDxL7BJB6qJSRfa0XliX1D+Lv3+eR/FUu3Xw8uD+up8lRtsz+S5WD+Prrr+nRowd79uwhOzube+65ei1dcXExr776Kjt37mT79u3k5uY2eG55eTlpaWm89dZbTJo0ieeff579+/ezb98+LBYLxcXFzJkzhy1btmCxWNi1axcbNmxo8BqZmZmsXbsWi8XCpk2b2LVrV7vkLYSzezShF+HdvLndOItoRsKtPDkygt4BPqxKK3CI6ww63oighU/uthIdHc3s2bOZM2cOEydObPDFMunp6YwePZquXbsCcP/993Po0KG69ffddx9KKaKjowkODiY6OhqAQYMGUVBQQGFhIWPGjKH21NlHHnmEbdu2MWXKlLrX+PHHH5k6dSre3tYbYU2aNMnmOQsh4O6Bwdw98Oq1sEF+Xrw8cSDhAT68vCGb3UcrGHLrLSZG2DIZEbSRfv36kZWVRXR0NPPnz+eVV1654efW3oLaxcWlwa2qXVxc5JbUQjioabeF4ufpxqodBQ3ar1zRbNh9jOqaK+YE1ggpBG2kuLgYb29vZsyYwZ///GeysrLq1g0bNozU1FTKy8upqalp8M1kNyI+Pp7U1FROnjzJ5cuXSUlJYfTo0Q22GTVqFBs2bOD8+fNUVlbyxRdftEleQoib4+PpxvS4ML7YU0z865v5xbgCec3PhTz3sYWPM46aHOFVUgjayL59+4iPjyc2NpaFCxcyf/78unWhoaHMmzeP+Ph4EhMTCQ8Pp0uXLjf82iEhISQnJ5OUlERMTAxDhw5l8uTJDbYZMmQIDz74IDExMYwfP55hw4a1WW5CiJvz9B29GTswmNKqi3xuKQbg+9wTAJy9WMPfNudRca7azBABUI5wIKO+uLg4nZGR0aDtwIEDDBgwwKSIbkxVVRW+vr7U1NQwdepUnnjiCaZOnWpKLI7w9xKiI5m+dAdHy8/x2IhwlqUepvJCDfHhXUkvOMVrU6KYkdDL5jEopTK11nGNrZMRQTtZsGABsbGxREVFERER0eBArxCiY7snqjslZy7y5jcHqbxgPe5nOWq9EDTPDm5J0fHOGrJTcpWvEM5r2pAwDpVUMiOhF5293Hlx3V7SC6x36T8ohUAIITq+rj4e/GV6TN3j4C5edct5JVVmhNSATA0JIUQ769756mniZWerKau6yKXLV/jrtwc5cLz9770phUAIIdpZcGfriCDQz1oQDv5aydfZv/LOlnweXf4zReXn2jUeKQRCCNHOuhtTQ/dGh+Dr6caS7w7xwY9H6NHFi7Kz1azLLGrXeKQQtLMxY8Zw7emvQgjnEmIUgqjQLrw+NYqMwnL2FJ1mZlIfIgN9yT52ul3jkYPFNqC1RmuNi4vUWSHE9WLC/Jk9th/jBgXj5+VOZKAvYC0MGQWnSGvnr7/scIVgUfoick/ltrxhK/Tv2p858XOa3aagoIBx48YxfPhwMjMzefHFF1m2bBkXL14kMjKSDz/8EF9f3wbP8fX1parKesbAunXr2LhxY91tp4UQHZebqwvP3tW37nFUaJcGyxssxfxy8izh3bxRStk8HvnI2oby8vKYOXMmqampLF++nM2bN5OVlUVcXBxLliwxOzwhhAOINopC0uIfeHp1JueqbX/jyQ43Imjpk7st9erVi4SEBDZu3EhOTg6JiYkAVFdXM2LECNPiEkI4jkGhXejm40Ggnyff55awLPUI/zLWtl/m2OEKgZl8fHwA6zGCsWPHkpKS0uz29Yd8Fy5csGlsQgjH4OvpRvpLd+PqonjgvTQ255TYvBDI1JANJCQksH37dvLz8wHr11jW/yKaWsHBwRw4cIArV66wfv369g5TCGGnXF2sHxLv6h9EzvEzLPxiP+syi7hw6bJN9ieFwAYCAwNZuXIlDz/8MIMHD2bEiBHXfT0lQHJyMhMnTuT2228nJCTEhEiFEPbsrgFBAHy4vYAXPtnDa1/m2GQ/chtqJyR/LyEcg9aa//zhMIN6dMbL3ZVAP8+6U01bq7nbUMsxAiGEsFNKKWYl9bH5fmRqSAghnFyHKQSONsVlFvk7CSGu1SEKgZeXF2VlZfKfXAu01pSVleHl5dXyxkIIp9EhjhGEhYVRVFREaWmp2aHYPS8vL8LCwswOQwhhRzpEIXB3dyciIsLsMIQQwiF1iKkhIYQQN08KgRBCODkpBEII4eQc7spipVQpUHiTTw8ATrZhOGaSXOyT5GKfJBfopbUObGyFwxWC30IpldHUJdaORnKxT5KLfZJcmidTQ0II4eSkEAghhJNztkLwvtkBtCHJxT5JLvZJcmmGUx0jEEIIcT1nGxEIIYS4hhQCIYRwck5TCJRS9yilDiql8pVSc82Op7WUUgVKqX1KKYtSKsNo66qU+k4plWf8vsXsOBujlFqhlDqhlMqu19Zo7Mrq70Y/7VVKDTEv8us1kcsCpdQxo28sSqkJ9db9q5HLQaXUOHOivp5SqqdSaqtSKkcptV8p9Sej3eH6pZlcHLFfvJRS6UqpPUYuC432CKXUz0bMHyulPIx2T+NxvrE+/KZ2rLXu8D+AK3AY6A14AHuAgWbH1cocCoCAa9r+Asw1lucCi8yOs4nYRwFDgOyWYgcmAF8BCkgAfjY7/hvIZQHwQiPbDjTea55AhPEedDU7ByO2EGCIsewHHDLidbh+aSYXR+wXBfgay+7Az8bf+3+Ah4z2ZcA/GcszgWXG8kPAxzezX2cZEcQD+VrrI1rramAtMNnkmNrCZGCVsbwKmGJiLE3SWm8DTl3T3FTsk4HV2mon4K+UCmmfSFvWRC5NmQys1Vpf1Fr/AuRjfS+aTmt9XGudZSxXAgeAUBywX5rJpSn23C9aa11lPHQ3fjRwJ7DOaL+2X2r7ax1wl1JKtXa/zlIIQoGj9R4X0fwbxR5p4FulVKZS6hmjLVhrfdxY/hUINie0m9JU7I7aV/9sTJmsqDdF5xC5GNMJt2H99OnQ/XJNLuCA/aKUclVKWYATwHdYRywVWusaY5P68dblYqw/DXRr7T6dpRB0BCO11kOA8cAspdSo+iu1dWzokOcCO3LshqVAJBALHAf+am44N04p5Qt8CjyntT5Tf52j9UsjuThkv2itL2utY4EwrCOV/rbep7MUgmNAz3qPw4w2h6G1Pmb8PgGsx/oGKakdnhu/T5gXYas1FbvD9ZXWusT4x3sF+C+uTjPYdS5KKXes/3Gu0Vr/r9HskP3SWC6O2i+1tNYVwFZgBNapuNovEqsfb10uxvouQFlr9+UshWAX0Nc48u6B9aDK5ybHdMOUUj5KKb/aZeD3QDbWHP5gbPYH4DNzIrwpTcX+OfCYcZZKAnC63lSFXbpmrnwq1r4Bay4PGWd2RAB9gfT2jq8xxjzycuCA1npJvVUO1y9N5eKg/RKolPI3ljsBY7Ee89gKTDc2u7ZfavtrOrDFGMm1jtlHydvrB+tZD4ewzre9ZHY8rYy9N9azHPYA+2vjxzoX+D2QB2wGupodaxPxp2Adml/COr/5ZFOxYz1r4j+MftoHxJkd/w3k8t9GrHuNf5gh9bZ/ycjlIDDe7PjrxTUS67TPXsBi/ExwxH5pJhdH7JfBwG4j5mzg34z23liLVT7wCeBptHsZj/ON9b1vZr9yiwkhhHByzjI1JIQQoglSCIQQwslJIRBCCCcnhUAIIZycFAIhhHByUgiEEMLJSSEQQggn9/8o/p7Hr+hfagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the training loss\n",
    "\n",
    "plt.plot(training_results_tanch['training_loss'], label='tanh')\n",
    "plt.plot(training_results['training_loss'], label='sigmoid')\n",
    "plt.plot(training_results_relu['training_loss'], label='relu')\n",
    "plt.ylabel('loss')\n",
    "plt.title('training loss iterations')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the validation loss for each model  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-07-15T12:29:47.094793Z",
     "start_time": "2021-07-15T12:29:46.970237Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f01d7348850>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compare the validation loss\n",
    "\n",
    "plt.plot(training_results_tanch['validation_accuracy'], label = 'tanh')\n",
    "plt.plot(training_results['validation_accuracy'], label = 'sigmoid')\n",
    "plt.plot(training_results_relu['validation_accuracy'], label = 'relu') \n",
    "plt.ylabel('validation accuracy')\n",
    "plt.xlabel('Iteration')   \n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://dataplatform.cloud.ibm.com/registration/stepone?context=cpdaas&apps=data_science_experience,watson_machine_learning\"><img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DL0110EN-SkillsNetwork/Template/module%201/images/Watson_Studio.png\"/></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>About the Authors:</h2> \n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/\">Joseph Santarcangelo</a> has a PhD in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other contributors: <a href=\"https://www.linkedin.com/in/michelleccarey/\">Michelle Carey</a>, <a href=\"www.linkedin.com/in/jiahui-mavis-zhou-a4537814a\">Mavis Zhou</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description                                          |\n",
    "| ----------------- | ------- | ---------- | ----------------------------------------------------------- |\n",
    "| 2020-09-23        | 2.0     | Srishti    | Migrated Lab to Markdown and added to course repo in GitLab |\n",
    "\n",
    "<hr>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}