6 2 lab_predicting _MNIST_using_Softmax_v2__M.ipynb

{
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   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\">\n",
    "    </a>\n",
    "</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Softmax Classifier</h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Objective</h2><ul><li> How to classify handwritten digits from the MNIST database by using Softmax classifier.</li></ul> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "<p>In this lab, you will use a single layer Softmax to classify handwritten digits from the MNIST database.</p>\n",
    "\n",
    "<ul>\n",
    "    <li><a href=\"#Make-Some-Data\">Make some Data</a></li>\n",
    "    <li><a href=\"#Build-a-Softmax-Classifer\">Build a Softmax Classifer</a></li>\n",
    "    <li><a href=\"#Define-the-Softmax-Classifier,-Criterion-Function,-Optimizer,-and-Train-the-Model\">Define Softmax, Criterion Function, Optimizer, and Train the Model</a></li>\n",
    "    <li><a href=\"#Analyze-Results\">Analyze Results</a></li>\n",
    "</ul>\n",
    "<p>Estimated Time Needed: <strong>25 min</strong></p>\n",
    "\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Preparation</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll need the following libraries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting torchvision==0.9.1\n",
      "  Downloading torchvision-0.9.1-cp37-cp37m-manylinux1_x86_64.whl (17.4 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.4/17.4 MB\u001b[0m \u001b[31m65.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hCollecting torch==1.8.1\n",
      "  Downloading torch-1.8.1-cp37-cp37m-manylinux1_x86_64.whl (804.1 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m804.1/804.1 MB\u001b[0m \u001b[31m402.3 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: numpy in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from torchvision==0.9.1) (1.21.6)\n",
      "Requirement already satisfied: pillow>=4.1.1 in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from torchvision==0.9.1) (8.1.0)\n",
      "Requirement already satisfied: typing-extensions in /home/jupyterlab/conda/envs/python/lib/python3.7/site-packages (from torch==1.8.1) (4.5.0)\n",
      "Installing collected packages: torch, torchvision\n",
      "  Attempting uninstall: torch\n",
      "    Found existing installation: torch 1.13.1+cpu\n",
      "    Uninstalling torch-1.13.1+cpu:\n",
      "      Successfully uninstalled torch-1.13.1+cpu\n",
      "  Attempting uninstall: torchvision\n",
      "    Found existing installation: torchvision 0.14.1+cpu\n",
      "    Uninstalling torchvision-0.14.1+cpu:\n",
      "      Successfully uninstalled torchvision-0.14.1+cpu\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "torchaudio 0.13.1+cpu requires torch==1.13.1, but you have torch 1.8.1 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed torch-1.8.1 torchvision-0.9.1\n"
     ]
    }
   ],
   "source": [
    "# Import the libraries we need for this lab\n",
    "\n",
    "# Using the following line code to install the torchvision library\n",
    "# !mamba 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 matplotlib.pylab as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the following function to plot out the parameters of the Softmax function:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The function to plot parameters\n",
    "\n",
    "def PlotParameters(model): \n",
    "    W = model.state_dict()['linear.weight'].data\n",
    "    w_min = W.min().item()\n",
    "    w_max = W.max().item()\n",
    "    fig, axes = plt.subplots(2, 5)\n",
    "    fig.subplots_adjust(hspace=0.01, wspace=0.1)\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        if i < 10:\n",
    "            \n",
    "            # Set the label for the sub-plot.\n",
    "            ax.set_xlabel(\"class: {0}\".format(i))\n",
    "\n",
    "            # Plot the image.\n",
    "            ax.imshow(W[i, :].view(28, 28), vmin=w_min, vmax=w_max, cmap='seismic')\n",
    "\n",
    "            ax.set_xticks([])\n",
    "            ax.set_yticks([])\n",
    "\n",
    "        # Ensure the plot is shown correctly with multiple plots\n",
    "        # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the following function to visualize the data: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot the data\n",
    "\n",
    "def show_data(data_sample):\n",
    "    plt.imshow(data_sample[0].numpy().reshape(28, 28), cmap='gray')\n",
    "    plt.title('y = ' + str(data_sample[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make Some Data\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 in the argument <code>transform</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
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     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
      "\n",
      "Processing...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jupyterlab/conda/envs/python/lib/python3.7/site-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n",
      "Print the training dataset:\n",
      "  Dataset MNIST\n",
      "    Number of datapoints: 60000\n",
      "    Root location: ./data\n",
      "    Split: Train\n",
      "    StandardTransform\n",
      "Transform: ToTensor()\n"
     ]
    }
   ],
   "source": [
    "# Create and print the training dataset\n",
    "\n",
    "train_dataset = dsets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())\n",
    "print(\"Print the training dataset:\\n \", train_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the testing dataset and convert it to a tensor by placing a transform object in the argument <code>transform</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the validating dataset:\n",
      "  Dataset MNIST\n",
      "    Number of datapoints: 60000\n",
      "    Root location: ./data\n",
      "    Split: Train\n",
      "    StandardTransform\n",
      "Transform: ToTensor()\n"
     ]
    }
   ],
   "source": [
    "# Create and print the validating dataset\n",
    "\n",
    "validation_dataset = dsets.MNIST(root='./data', download=True, transform=transforms.ToTensor())\n",
    "print(\"Print the validating dataset:\\n \", validation_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the data type is long:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Type of data element:  <class 'int'>\n"
     ]
    }
   ],
   "source": [
    "# Print the type of the element\n",
    "\n",
    "print(\"Type of data element: \", type(train_dataset[0][1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each element in the rectangular tensor corresponds to a number that represents a pixel intensity as demonstrated by the following image:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/chapter3/3.32_image_values.png\" width=\"550\" alt=\"MNIST elements\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this image, the values are inverted i.e back represents wight.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print out the label of the fourth element:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The label:  1\n"
     ]
    }
   ],
   "source": [
    "# Print the label\n",
    "\n",
    "print(\"The label: \", train_dataset[3][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result shows the number in the image is 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot  the fourth sample:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The image:  None\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the image\n",
    "\n",
    "print(\"The image: \", show_data(train_dataset[3]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You see that it is a 1. Now, plot the third sample:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the image\n",
    "\n",
    "show_data(train_dataset[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a Softmax Classifer\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a Softmax classifier class: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define softmax classifier class\n",
    "\n",
    "class SoftMax(nn.Module):\n",
    "    \n",
    "    # Constructor\n",
    "    def __init__(self, input_size, output_size):\n",
    "        super(SoftMax, self).__init__()\n",
    "        self.linear = nn.Linear(input_size, output_size)\n",
    "        \n",
    "    # Prediction\n",
    "    def forward(self, x):\n",
    "        z = self.linear(x)\n",
    "        return z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Softmax function requires vector inputs. Note that the vector shape is 28x28.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the shape of train dataset\n",
    "\n",
    "train_dataset[0][0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Flatten the tensor as shown in this image: \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/chapter3/3.3.2image_to_vector.gif\" width=\"550\" alt=\"Flattern Image\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The size of the tensor is now 784.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/chapter3/3.3.2Imagetovector2.png\" width=\"550\" alt=\"Flattern Image\">\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the input size and output size: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set input size and output size\n",
    "\n",
    "input_dim = 28 * 28\n",
    "output_dim = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the Softmax Classifier, Criterion Function, Optimizer, and Train the Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Print the model:\n",
      "  SoftMax(\n",
      "  (linear): Linear(in_features=784, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Create the model\n",
    "\n",
    "model = SoftMax(input_dim, output_dim)\n",
    "print(\"Print the model:\\n \", model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the size of the model parameters: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W:  torch.Size([10, 784])\n",
      "b:  torch.Size([10])\n"
     ]
    }
   ],
   "source": [
    "# Print the parameters\n",
    "\n",
    "print('W: ',list(model.parameters())[0].size())\n",
    "print('b: ',list(model.parameters())[1].size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can cover the model parameters for each class to a rectangular grid:  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a>     <img src=\"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DL0110EN/notebook_images%20/chapter3/3.3.2paramaters_to_image.gif\" width=\"550,\" align=\"center\"></a> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the model parameters for each class as a square image: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the model parameters for each class\n",
    "\n",
    "PlotParameters(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the learning rate, optimizer, criterion, data loader:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the learning rate, optimizer, criterion and data loader\n",
    "\n",
    "learning_rate = 0.1\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100)\n",
    "validation_loader = torch.utils.data.DataLoader(dataset=validation_dataset, batch_size=5000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model and determine validation accuracy **(should take a few minutes)**: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train the model\n",
    "\n",
    "n_epochs = 10\n",
    "loss_list = []\n",
    "accuracy_list = []\n",
    "N_test = len(validation_dataset)\n",
    "\n",
    "def train_model(n_epochs):\n",
    "    for epoch in range(n_epochs):\n",
    "        for x, y in 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",
    "            \n",
    "        correct = 0\n",
    "        # perform a prediction on the validationdata  \n",
    "        for x_test, y_test in validation_loader:\n",
    "            z = model(x_test.view(-1, 28 * 28))\n",
    "            _, yhat = torch.max(z.data, 1)\n",
    "            correct += (yhat == y_test).sum().item()\n",
    "        accuracy = correct / N_test\n",
    "        loss_list.append(loss.data)\n",
    "        accuracy_list.append(accuracy)\n",
    "\n",
    "train_model(n_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyze Results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the loss and accuracy on the validation data:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss and accuracy\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "color = 'tab:red'\n",
    "ax1.plot(loss_list,color=color)\n",
    "ax1.set_xlabel('epoch',color=color)\n",
    "ax1.set_ylabel('total loss',color=color)\n",
    "ax1.tick_params(axis='y', color=color)\n",
    "    \n",
    "ax2 = ax1.twinx()  \n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('accuracy', color=color)  \n",
    "ax2.plot( accuracy_list, color=color)\n",
    "ax2.tick_params(axis='y', color=color)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "View the results of the parameters for each class after the training. You can see that they look like the corresponding numbers. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the parameters\n",
    "\n",
    "PlotParameters(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We Plot the first five misclassified  samples and the probability of that class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([7])\n",
      "probability of class  0.6782503128051758\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([7])\n",
      "probability of class  0.715557873249054\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([5])\n",
      "probability of class  0.7210285067558289\n"
     ]
    },
    {
     "data": {
      "image/png": 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bNm3ShAkTlJ2dLZ/PpzVr1sSsnzJlinw+X8wYPnx4ovoFAHQgnkPozJkzGjJkiMrKyi67zfjx41VTUxMd8bzvCwDo+DzfmFBYWHjFC4N+v1/BYDDupgAAnUNSrglt3LhRmZmZ6t+/vx599FHV1dVddtuGhgZFIpGYAQDoHBIeQoWFhXrjjTe0YcMGLVy4UJWVlbrrrrvU0NDQ7PalpaUKBALR0adPn0S3BABopxL+OaHJkydH/zxo0CANGzZMOTk5eueddzRp0qQm28+ePVvFxcXR15FIhCACgE4i6R9WDYVCysnJ0YEDB5pd7/f75ff7k90GAKAdSvrnhE6ePKnq6mqFQqFk7woAkGI8z4ROnz6tgwcPRl9XVVXp008/VY8ePdSjRw+VlJTo/vvvVygU0uHDh/X000+rV69euu+++xLaOAAg9XkOoR07dujOO++Mvr50PaeoqEiLFy/Wnj17tHz5cn3xxRcKhUK68847tWrVKqWnpyeuawBAh+A5hMaOHSvn3GXXv/vuu1fVEIDOY//+/Z5rJk6c6Lkm3ssBNTU1cdWh9Xh2HADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADATNK/WRUd14ABA9pkP5WVlW2yH6SGCxcueK758ssvk9AJEoGZEADADCEEADBDCAEAzBBCAAAzhBAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADM8wBRxO3LkSJvsJy8vz3NNRUVFEjpBok2bNs1zjd/v91wTCoU810jSqVOn4qpD6zETAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYHmCJuZ8+e9Vzj8/k818ydO9dzzfbt2z3XSNKWLVviquto+vfv77lm8+bNnmt69erluebw4cOea/bt2+e5Bm2DmRAAwAwhBAAwQwgBAMwQQgAAM4QQAMAMIQQAMEMIAQDMEEIAADOEEADADCEEADBDCAEAzBBCAAAzPMAUcdu6davnmoMHD3quufnmmz3XrFmzxnONJL344ouea+bPn++55ty5c55r4tG3b9+46n75y196runZs6fnmrq6Os81EyZM8FyD9ouZEADADCEEADDjKYRKS0uVl5en9PR0ZWZmauLEifrss89itnHOqaSkRNnZ2UpLS9PYsWO1d+/ehDYNAOgYPIVQRUWFpk+fru3bt6u8vFznz59XQUGBzpw5E91mwYIFWrRokcrKylRZWalgMKhx48apvr4+4c0DAFKbpxsT1q9fH/N66dKlyszM1M6dOzV69Gg55/T8889rzpw5mjRpkiRp2bJlysrK0ooVKzR16tTEdQ4ASHlXdU0oHA5Lknr06CFJqqqqUm1trQoKCqLb+P1+jRkzRtu2bWv2ZzQ0NCgSicQMAEDnEHcIOedUXFysUaNGadCgQZKk2tpaSVJWVlbMtllZWdF131RaWqpAIBAdffr0ibclAECKiTuEZsyYod27d2vlypVN1vl8vpjXzrkmyy6ZPXu2wuFwdFRXV8fbEgAgxcT1YdWZM2dq7dq12rRpk3r37h1dHgwGJV2cEYVCoejyurq6JrOjS/x+v/x+fzxtAABSnKeZkHNOM2bM0Ntvv60NGzYoNzc3Zn1ubq6CwaDKy8ujyxobG1VRUaGRI0cmpmMAQIfhaSY0ffp0rVixQv/4xz+Unp4evc4TCASUlpYmn8+nWbNmad68eerXr5/69eunefPm6brrrtPDDz+clF8AAJC6PIXQ4sWLJUljx46NWb506VJNmTJFkvTUU0/p3Llzevzxx3Xq1Cnl5+frvffeU3p6ekIaBgB0HD7nnLNu4v+LRCIKBALWbSBJHnjgAc81q1at8lzTlqf1Rx995Llm4cKFnmsee+wxzzW33nqr5xpJMdd0W6umpsZzzeuvv+655plnnvFcAxvhcFgZGRktbsOz4wAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZniKNtq94cOHe675y1/+Ete+vvlFje2Jz+fzXNOW/3vPmTPHc81rr73muea///2v5xrY4CnaAIB2jRACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBkeYIoOKd5z6JZbbvFc88wzz3iuOXz4sOeagQMHeq755z//6blGkpYtW+a55n//+5/nmvPnz3uuQergAaYAgHaNEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGR5gCgBICh5gCgBo1wghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYMZTCJWWliovL0/p6enKzMzUxIkT9dlnn8VsM2XKFPl8vpgxfPjwhDYNAOgYPIVQRUWFpk+fru3bt6u8vFznz59XQUGBzpw5E7Pd+PHjVVNTEx3r1q1LaNMAgI7hGi8br1+/Pub10qVLlZmZqZ07d2r06NHR5X6/X8FgMDEdAgA6rKu6JhQOhyVJPXr0iFm+ceNGZWZmqn///nr00UdVV1d32Z/R0NCgSCQSMwAAnYPPOefiKXTO6d5779WpU6e0efPm6PJVq1bphhtuUE5OjqqqqvTMM8/o/Pnz2rlzp/x+f5OfU1JSot/97nfx/wYAgHYpHA4rIyOj5Y1cnB5//HGXk5PjqqurW9zu+PHjrlu3bu6tt95qdv2XX37pwuFwdFRXVztJDAaDwUjxEQ6Hr5glnq4JXTJz5kytXbtWmzZtUu/evVvcNhQKKScnRwcOHGh2vd/vb3aGBADo+DyFkHNOM2fO1OrVq7Vx40bl5uZesebkyZOqrq5WKBSKu0kAQMfk6caE6dOn669//atWrFih9PR01dbWqra2VufOnZMknT59Wk8++aQ++ugjHT58WBs3btSECRPUq1cv3XfffUn5BQAAKczLdSBd5n2/pUuXOuecO3v2rCsoKHA33nij69atm+vbt68rKipyR48ebfU+wuGw+fuYDAaDwbj60ZprQnHfHZcskUhEgUDAug0AwFVqzd1xPDsOAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYIYQAgCYIYQAAGYIIQCAGUIIAGCm3YWQc866BQBAArTm7/N2F0L19fXWLQAAEqA1f5/7XDubenz99dc6fvy40tPT5fP5YtZFIhH16dNH1dXVysjIMOrQHsfhIo7DRRyHizgOF7WH4+CcU319vbKzs9WlS8tznWvaqKdW69Kli3r37t3iNhkZGZ36JLuE43ARx+EijsNFHIeLrI9DIBBo1Xbt7u04AEDnQQgBAMykVAj5/X7NnTtXfr/fuhVTHIeLOA4XcRwu4jhclGrHod3dmAAA6DxSaiYEAOhYCCEAgBlCCABghhACAJghhAAAZlIqhF566SXl5ubq2muv1dChQ7V582brltpUSUmJfD5fzAgGg9ZtJd2mTZs0YcIEZWdny+fzac2aNTHrnXMqKSlRdna20tLSNHbsWO3du9em2SS60nGYMmVKk/Nj+PDhNs0mSWlpqfLy8pSenq7MzExNnDhRn332Wcw2neF8aM1xSJXzIWVCaNWqVZo1a5bmzJmjXbt26Y477lBhYaGOHj1q3VqbGjhwoGpqaqJjz5491i0l3ZkzZzRkyBCVlZU1u37BggVatGiRysrKVFlZqWAwqHHjxnW4h+Fe6ThI0vjx42POj3Xr1rVhh8lXUVGh6dOna/v27SovL9f58+dVUFCgM2fORLfpDOdDa46DlCLng0sR3//+9920adNiln3nO99xv/71r406antz5851Q4YMsW7DlCS3evXq6Ouvv/7aBYNBN3/+/OiyL7/80gUCAffyyy8bdNg2vnkcnHOuqKjI3XvvvSb9WKmrq3OSXEVFhXOu854P3zwOzqXO+ZASM6HGxkbt3LlTBQUFMcsLCgq0bds2o65sHDhwQNnZ2crNzdWDDz6oQ4cOWbdkqqqqSrW1tTHnht/v15gxYzrduSFJGzduVGZmpvr3769HH31UdXV11i0lVTgcliT16NFDUuc9H755HC5JhfMhJULoxIkTunDhgrKysmKWZ2Vlqba21qirtpefn6/ly5fr3Xff1ZIlS1RbW6uRI0fq5MmT1q2ZufTfv7OfG5JUWFioN954Qxs2bNDChQtVWVmpu+66Sw0NDdatJYVzTsXFxRo1apQGDRokqXOeD80dByl1zod291UOLfnm9ws555os68gKCwujfx48eLBGjBihb3/721q2bJmKi4sNO7PX2c8NSZo8eXL0z4MGDdKwYcOUk5Ojd955R5MmTTLsLDlmzJih3bt3a8uWLU3Wdabz4XLHIVXOh5SYCfXq1Utdu3Zt8i+Zurq6Jv/i6Uyuv/56DR48WAcOHLBuxcyluwM5N5oKhULKycnpkOfHzJkztXbtWn344Ycx3z/W2c6Hyx2H5rTX8yElQqh79+4aOnSoysvLY5aXl5dr5MiRRl3Za2ho0P79+xUKhaxbMZObm6tgMBhzbjQ2NqqioqJTnxuSdPLkSVVXV3eo88M5pxkzZujtt9/Whg0blJubG7O+s5wPVzoOzWm354PhTRGevPnmm65bt27u1Vdfdfv27XOzZs1y119/vTt8+LB1a23miSeecBs3bnSHDh1y27dvd/fcc49LT0/v8Megvr7e7dq1y+3atctJcosWLXK7du1yR44ccc45N3/+fBcIBNzbb7/t9uzZ4x566CEXCoVcJBIx7jyxWjoO9fX17oknnnDbtm1zVVVV7sMPP3QjRoxwN910U4c6Do899pgLBAJu48aNrqamJjrOnj0b3aYznA9XOg6pdD6kTAg559yLL77ocnJyXPfu3d3tt98ecztiZzB58mQXCoVct27dXHZ2tps0aZLbu3evdVtJ9+GHHzpJTUZRUZFz7uJtuXPnznXBYND5/X43evRot2fPHtumk6Cl43D27FlXUFDgbrzxRtetWzfXt29fV1RU5I4ePWrddkI19/tLckuXLo1u0xnOhysdh1Q6H/g+IQCAmZS4JgQA6JgIIQCAGUIIAGCGEAIAmCGEAABmCCEAgBlCCABghhACAJghhAAAZgghAIAZQggAYOb/AESEXrisme2hAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([8])\n",
      "probability of class  0.3786318600177765\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([6])\n",
      "probability of class  0.5159559845924377\n"
     ]
    }
   ],
   "source": [
    "# Plot the misclassified samples\n",
    "Softmax_fn=nn.Softmax(dim=-1)\n",
    "count = 0\n",
    "for x, y in validation_dataset:\n",
    "    z = model(x.reshape(-1, 28 * 28))\n",
    "    _, yhat = torch.max(z, 1)\n",
    "    if yhat != y:\n",
    "        show_data((x, y))\n",
    "        plt.show()\n",
    "        print(\"yhat:\", yhat)\n",
    "        print(\"probability of class \", torch.max(Softmax_fn(z)).item())\n",
    "        count += 1\n",
    "    if count >= 5:\n",
    "        break       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--Empty Space for separating topics-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We Plot the first five correctly classified samples and the probability of that class, we see the probability is much larger.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([5])\n",
      "probability of class  0.8449264168739319\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([0])\n",
      "probability of class  0.9997221827507019\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([4])\n",
      "probability of class  0.8642290234565735\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([1])\n",
      "probability of class  0.9659700989723206\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yhat: tensor([9])\n",
      "probability of class  0.9218413829803467\n"
     ]
    }
   ],
   "source": [
    "# Plot the classified samples\n",
    "Softmax_fn=nn.Softmax(dim=-1)\n",
    "count = 0\n",
    "for x, y in validation_dataset:\n",
    "    z = model(x.reshape(-1, 28 * 28))\n",
    "    _, yhat = torch.max(z, 1)\n",
    "    if yhat == y:\n",
    "        show_data((x, y))\n",
    "        plt.show()\n",
    "        print(\"yhat:\", yhat)\n",
    "        print(\"probability of class \", torch.max(Softmax_fn(z)).item())\n",
    "        count += 1\n",
    "    if count >= 5:\n",
    "        break  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://dataplatform.cloud.ibm.com/registration/stepone?utm_source=skills_network&utm_content=in_lab_content_link&utm_id=Lab-IBMDeveloperSkillsNetwork-DL0110EN-SkillsNetwork&context=cpdaas&apps=data_science_experience%2Cwatson_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": [
    "<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=\"https://www.linkedin.com/in/jiahui-mavis-zhou-a4537814a\">Mavis Zhou</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description                                          |\n",
    "| ----------------- | ------- | ---------- | ----------------------------------------------------------- |\n",
    "| 2020-09-23        | 2.0     | Shubham    | Migrated Lab to Markdown and added to course repo in GitLab |\n",
    "-->\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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