{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torchvision.datasets as datasets\n",
    "import torchvision.transforms as transforms\n",
    "#from torch.autograd import Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_size = 784\n",
    "hidden_size = 400\n",
    "out_size = 10\n",
    "epochs = 10\n",
    "batch_size = 100\n",
    "learning_rate = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = datasets.MNIST(root='./data',\n",
    "                              train=True,\n",
    "                              transform=transforms.ToTensor(),\n",
    "                              download=True)\n",
    "\n",
    "test_dataset = datasets.MNIST(root='./data',\n",
    "                              train=False,\n",
    "                              transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# inserting data into a loder class to makeit iterable\n",
    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset,\n",
    "                                          batch_size=batch_size,\n",
    "                                          shuffle=True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset,\n",
    "                                          batch_size=batch_size,\n",
    "                                          shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    \n",
    "    def __init__(self,input_size,hidden_size,out_size):\n",
    "        super(Net,self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size,hidden_size)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(hidden_size,hidden_size)\n",
    "        self.fc3 = nn.Linear(hidden_size,out_size)\n",
    "        \n",
    "    def forward(self,x):\n",
    "        out = self.fc1(x)\n",
    "        out = self.relu(out)\n",
    "        out = self.fc2(out)\n",
    "        out = self.relu(out)\n",
    "        out = self.fc3(out)\n",
    "        \n",
    "        return out\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a Net class object\n",
    "net = Net(input_size,hidden_size,out_size)\n",
    "CUDA = torch.cuda.is_available()\n",
    "if CUDA:\n",
    "    net = net.cuda()\n",
    "    \n",
    "# Select the loss function and optimization method\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = torch.optim.Adam(net.parameters(),lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 0., 0.,  ..., 0., 0., 0.],\n",
      "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "        ...,\n",
      "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "        [0., 0., 0.,  ..., 0., 0., 0.],\n",
      "        [0., 0., 0.,  ..., 0., 0., 0.]])\n"
     ]
    }
   ],
   "source": [
    "for i, (images, labels) in enumerate(train_loader):\n",
    "    print(images.view(-1,28*28))\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Done training!!!\n"
     ]
    }
   ],
   "source": [
    "# Train the network\n",
    "correct_train = 0\n",
    "total_train = 0\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    for i, (images, labels) in enumerate(train_loader):\n",
    "        # Flatten the image from (batch,1,28,28) --> (100,1,28,28), where 1 is the number of channels (grayscale --> 1) and wrap it in a variable\n",
    "        #images = Variable(images.view(-1,28*28))\n",
    "        #labels = Variable(labels)\n",
    "        images = images.view(-1,28*28)\n",
    "        \n",
    "        # Send the images and the lables to our graphics card memory\n",
    "        if CUDA:\n",
    "            images = images.cuda()\n",
    "            labels = labels.cuda()\n",
    "        \n",
    "        # Set the gradient to zero\n",
    "        optimizer.zero_grad()\n",
    "        # Pass our trainig set to the object net (which is the nn)\n",
    "        outputs = net(images)\n",
    "        _, predicted = torch.max(outputs.data,1) # We select the maximum value got and return its index\n",
    "        \n",
    "        total_train += labels.size(0)\n",
    "        \n",
    "        if CUDA:\n",
    "            correct_train += (predicted.cpu() == labels.cpu()).sum()\n",
    "        else:\n",
    "            correct_train += (predicted == labels).sum()\n",
    "        \n",
    "        loss = criterion(outputs,labels) # Calculate the loss\n",
    "        loss.backward() # Backpropagate\n",
    "        optimizer.step() # Update the weights\n",
    "        \n",
    "        #if i+1 % 100 == 0:\n",
    "        print(\"Epoch: [{}/{}], Iteration [{}/{}], Training Loss: {:.3f}, Training Accuracy: {:.3f}%\".format\n",
    "                  (epoch+1, epochs, i+1, len(train_dataset)//batch_size, loss.item(), (100*correct_train.double()/total_train)))\n",
    "            \n",
    "print(\"Done training!!!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final test accuracy: 97 %\n"
     ]
    }
   ],
   "source": [
    "# Test the NN\n",
    "correct = 0\n",
    "total = 0\n",
    "\n",
    "for images, labels in test_loader:\n",
    "    images = images.view(-1,28*28)\n",
    "    if CUDA:\n",
    "        images = images.cuda()\n",
    "        \n",
    "    # for each input (sample/row) in the batch the output will contain 10 elements\n",
    "    outputs = net(images)\n",
    "    _, predicted = torch.max(outputs.data,1)\n",
    "    \n",
    "    total += labels.size(0)\n",
    "    if CUDA:\n",
    "        correct += (predicted.cpu() == labels.cpu()).sum()\n",
    "    else:\n",
    "        correct += (predicted == labels).sum()\n",
    "    \n",
    "\n",
    "print('Final test accuracy: %d %%' % (100*correct/total))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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