PyTorch Basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pytorch tutorial P1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Introduce the basic knowledge of deep learning. Install the PyTorch package and run several simple codes to check if it is successfully installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([10.,  3.])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "x = torch.Tensor([5,3])\n",
    "y = torch.Tensor([2,1])\n",
    "\n",
    "print(x*y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.zeros([2,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0., 0., 0., 0., 0.],\n",
      "        [0., 0., 0., 0., 0.]])\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 5])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = torch.rand([2,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5872, 0.6762, 0.6928, 0.3393, 0.6189],\n",
       "        [0.4555, 0.7420, 0.7430, 0.9857, 0.9999]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5872, 0.6762, 0.6928, 0.3393, 0.6189, 0.4555, 0.7420, 0.7430, 0.9857,\n",
       "         0.9999]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.view([1,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5872, 0.6762, 0.6928, 0.3393, 0.6189],\n",
       "        [0.4555, 0.7420, 0.7430, 0.9857, 0.9999]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.view([1,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0.5872, 0.6762, 0.6928, 0.3393, 0.6189, 0.4555, 0.7420, 0.7430, 0.9857,\n",
       "         0.9999]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the dataset and check if it is balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision\n",
    "from torchvision import transforms, datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to MNIST\\raw\\train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f65539b0f8164db78fa4a3a2e85786ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST\\raw\\train-images-idx3-ubyte.gz to MNIST\\raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to MNIST\\raw\\train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
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       "model_id": "3e1ff5b9ed264b93bd54973fdbb74d34",
       "version_major": 2,
       "version_minor": 0
      },
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       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST\\raw\\train-labels-idx1-ubyte.gz to MNIST\\raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to MNIST\\raw\\t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "723972de15da471c8611b63e79ebc466",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST\\raw\\t10k-images-idx3-ubyte.gz to MNIST\\raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to MNIST\\raw\\t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "696d90d71327494b8e76fb0b7c0bc09d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=1, bar_style='info', max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST\\raw\\t10k-labels-idx1-ubyte.gz to MNIST\\raw\n",
      "Processing...\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "train = datasets.MNIST(\"\", train=True, download=True, transform = transforms.Compose([transforms.ToTensor()])) \n",
    "\n",
    "test = datasets.MNIST(\"\", train=False, download=True, transform = transforms.Compose([transforms.ToTensor()])) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset = torch.utils.data.DataLoader(train, batch_size = 10, shuffle=True)\n",
    "## batch_size->how many data to feed into the model at a time\n",
    "\n",
    "testset = torch.utils.data.DataLoader(test, batch_size = 10, shuffle=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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",
      "\n",
      "\n",
      "        [[[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",
      "\n",
      "\n",
      "        [[[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",
      "\n",
      "\n",
      "        ...,\n",
      "\n",
      "\n",
      "        [[[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",
      "\n",
      "\n",
      "        [[[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",
      "\n",
      "\n",
      "        [[[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.]]]]), tensor([7, 2, 9, 3, 2, 0, 5, 9, 6, 2])]\n"
     ]
    }
   ],
   "source": [
    "for data in trainset:\n",
    "    print(data)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(7)\n"
     ]
    }
   ],
   "source": [
    "x, y = data[0][0], data[1][0]\n",
    "\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
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      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
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      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
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      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
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      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
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      "          0.0118, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
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      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
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      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.4549, 0.9961, 0.9804, 0.0157,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0314, 0.8196, 0.9961, 0.9804, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.1490, 0.9961, 0.9961, 0.5922, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.1569, 0.9961, 0.9569, 0.1451, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000]]])\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 28, 28])\n",
      "tensor([[[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]],\n",
      "\n",
      "        [[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]],\n",
      "\n",
      "        [[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.6941, 1.0000, 0.8510, 0.7176, 0.7176, 0.5176],\n",
      "         [0.4392, 0.4392, 0.4275, 0.1569, 0.0588, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0588,\n",
      "          0.9686, 0.9961, 0.9725, 0.9686, 0.9922, 0.9961],\n",
      "         [0.9961, 0.9961, 0.9961, 0.9961, 0.9176, 0.8706, 0.4706, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.3412,\n",
      "          0.9961, 0.9333, 0.0510, 0.0000, 0.2118, 0.2510],\n",
      "         [0.2510, 0.2510, 0.3451, 0.6667, 0.9804, 0.9961, 0.7647, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.5098,\n",
      "          0.9961, 0.5608, 0.0000, 0.0000, 0.0000, 0.0000]],\n",
      "\n",
      "        [[0.0000, 0.0000, 0.0000, 0.0118, 0.9176, 0.9961, 0.3647, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0392, 0.7765,\n",
      "          0.9961, 0.2118, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.2000, 0.9961, 0.9961, 0.1176, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1843, 0.9961,\n",
      "          0.7725, 0.0078, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.5608, 0.9961, 0.8471, 0.0196, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0902, 0.6314,\n",
      "          0.2431, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0353, 0.9490, 0.9961, 0.4784, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]],\n",
      "\n",
      "        [[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.4588, 0.9961, 0.9137, 0.0706, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0471, 0.9137, 0.9961, 0.4902, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.4078, 0.9961, 0.9686, 0.1529, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]],\n",
      "\n",
      "        [[0.0118, 0.8078, 0.9961, 0.7098, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.5020, 0.9961, 0.9647, 0.0667, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0588],\n",
      "         [0.8706, 0.9961, 0.5176, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.4235],\n",
      "         [0.9961, 0.9961, 0.1137, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]],\n",
      "\n",
      "        [[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.6980],\n",
      "         [0.9961, 0.7412, 0.0118, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.2118, 0.9922],\n",
      "         [0.9961, 0.3255, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.4549, 0.9961],\n",
      "         [0.9804, 0.0157, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0314, 0.8196, 0.9961]],\n",
      "\n",
      "        [[0.9804, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.1490, 0.9961, 0.9961],\n",
      "         [0.5922, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.1569, 0.9961, 0.9569],\n",
      "         [0.1451, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],\n",
      "         [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
      "          0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]])\n"
     ]
    }
   ],
   "source": [
    "print(data[0][0].shape)\n",
    "data_m = data[0][0].view(8,7,14)\n",
    "print(data_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([28, 28])\n",
      "torch.Size([1, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "data1 = data[0][0].view(28,28)\n",
    "print(data1.shape)\n",
    "print(data[0][0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAOZUlEQVR4nO3df5BddXnH8c+HZRMgkEISwDSJgEwcZKpGXYIdLKUyKsYfgSId0hmaCkOcDjgypR0pnVYUpxOxwIAizFJSYwcQERjyB1UzGS2lCrJAIAlRoDRgSCYRQUhSCEn26R970llh7/du7u/N837N7Nx7z3PPPc+e2c+ee+/3nvt1RAjA/u+AbjcAoDMIO5AEYQeSIOxAEoQdSOLATm5skifHQZrSyU0CqbyuHXojdnqsWlNht32GpOsk9Un6l4hYWrr/QZqik316M5sEUPBQrKpZa/hpvO0+STdI+rikEyUtsn1io48HoL2aec0+X9IzEfFsRLwh6buSFramLQCt1kzYZ0n61ajbG6tlv8P2EttDtod2aWcTmwPQjGbCPtabAG/57G1EDEbEQEQM9GtyE5sD0Ixmwr5R0pxRt2dL2tRcOwDapZmwPyxpru3jbE+SdK6kFa1pC0CrNTz0FhG7bV8s6YcaGXpbFhHrWtYZgJZqapw9Iu6TdF+LegHQRnxcFkiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5BEU1M2294gaZukPZJ2R8RAK5oC0HpNhb3yJxHxYgseB0Ab8TQeSKLZsIekH9l+xPaSse5ge4ntIdtDu7Szyc0BaFSzT+NPiYhNto+StNL2LyLi/tF3iIhBSYOSNNXTosntAWhQU0f2iNhUXW6VdI+k+a1oCkDrNRx221NsH7b3uqSPSlrbqsYAtFYzT+OPlnSP7b2Pc1tE/KAlXQFouYbDHhHPSnpvC3sB0EYMvQFJEHYgCcIOJEHYgSQIO5BEK06EwQS2c8FJxfpzn3ax/rUP31Gsnz3l5X3uaa+bX5lTrC//6qeK9am3PdjwtvdHHNmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnG2fcDuz/8gZq1M67/SXHdJYdfX6wf4knF+q3bZhbrJz2yqGZt4TFriutePqNcX3Hh88X69h21v0tl06nl49zcfyxve3jHjmK9F3FkB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkGGfvAX1z31GsP/3lqcX6Y6feULM22f3FdW/ddkyxvuyLZxbrhz7wTLF+5G9+WbP24EHl3+uEpRcV6784p/bvLUnPX/9azdrbDzy4uO77t3y+WP/9q35arPcijuxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kATj7B1w4JzZxfpVK28t1k/on1ysP797d83agn/96+K6x123vlg/+OWfF+t7itWy4ddfL9Yn/6a5Y9GxBx5Ss/YPW+cV1519w+PF+nBDHXVX3b1pe5ntrbbXjlo2zfZK209Xl0e0t00AzRrPv85vSzrjTcsuk7QqIuZKWlXdBtDD6oY9Iu6X9NKbFi+UtLy6vlxS+TOVALqu0RdFR0fEZkmqLo+qdUfbS2wP2R7apZ0Nbg5As9r+bnxEDEbEQEQM9Kv8RhOA9mk07Ftsz5Sk6nJr61oC0A6Nhn2FpMXV9cWS7m1NOwDape44u+3bJZ0maYbtjZK+JGmppO/ZvkDS85LOaWeTva5vxvRi/W13/rZYf2d/+bvZV71WfvlzzTnn1ay9/bHyedfNjJM3a+cnynPDLzn3vqYev7TfHj3/3cV1Y8e6prbdi+qGPSJqfcv/6S3uBUAb8XFZIAnCDiRB2IEkCDuQBGEHkuAU1xbY/qHji/XBOTfVeQQXq9eeeXaxHmt7d5jof5b+Yc3avy/6enHd0imqI8r77W9uvLBmbWadIcn9EUd2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCcfYWeOGPy/8zhxVNPX4cXJ52uZ18UvlU0Fe+8r/F+pr3XF+zdoDK0yZvj/LXmJ3yjUuL9VlX5xtLL+HIDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJMM7eAsff+Vqx/uLZ5fqMvvJ48+BdNxbrl2w4q2btE0euKa5bz0emPFCsz+orn3M+3MTxZN4PP1+sv/NrjKPvC47sQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AE4+wt4J8+Xqx/6st/W6wfdu6mYv3OE24r1u84/gc1a1v2lMf4t+wpTxf9mTWfLdZ/Nu+OYr247s6+Yv1dV79arHdzuumJqO6R3fYy21ttrx217ArbL9heXf0saG+bAJo1nqfx35Z0xhjLr42IedXPfa1tC0Cr1Q17RNwv6aUO9AKgjZp5g+5i209UT/OPqHUn20tsD9ke2qXyd4oBaJ9Gw36jpOMlzZO0WdLVte4YEYMRMRARA/2a3ODmADSrobBHxJaI2BMRw5JuljS/tW0BaLWGwm575qibZ0laW+u+AHpD3XF227dLOk3SDNsbJX1J0mm250kKSRskfa6NPU5402/5WfkOt5TLi/7o4mJ9++zaL48OX18eqx5e/WSx/uJ104t1zSuXHym8TfOV888vrtv35KPlB8c+qRv2iFg0xuI6f54Aeg0flwWSIOxAEoQdSIKwA0kQdiAJTnGdAA74z8eK9amF2nCdx37qW+XPQz2z8FvFep/Lx4uL/+mvatam/6TOkCRaiiM7kARhB5Ig7EAShB1IgrADSRB2IAnCDiTBOPt+7qmbyuPoqz95XbE+rPJXTZ94S+1xdEk6dtnPi3V0Dkd2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCcfb9wfx31yx9/2PfLK7aJxfrJ19Z/hrrY2/inPSJgiM7kARhB5Ig7EAShB1IgrADSRB2IAnCDiTBOPsEcMBBBxXrn1m+smbtPZP6iusu3vCxYv1IxtH3G3WP7Lbn2P6x7fW219n+QrV8mu2Vtp+uLo9of7sAGjWep/G7JV0aEe+S9EFJF9k+UdJlklZFxFxJq6rbAHpU3bBHxOaIeLS6vk3SekmzJC2UtLy623JJZ7arSQDN26c36GwfK+l9kh6SdHREbJZG/iFIOqrGOktsD9ke2qWdzXULoGHjDrvtQyXdJemSiHh1vOtFxGBEDETEQL8mN9IjgBYYV9ht92sk6LdGxN3V4i22Z1b1mZK2tqdFAK1Qd+jNtiXdIml9RFwzqrRC0mJJS6vLe9vSIfTy3bOL9b+Y+l81a6teO6S47m//tL+hnjDxjGec/RRJ50laY3t1texyjYT8e7YvkPS8pHPa0yKAVqgb9oh4QKr5DQent7YdAO3Cx2WBJAg7kARhB5Ig7EAShB1IglNcJ4AH532/WN8Ttb8O+srLP1tc99AtDzbUEyYejuxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kATj7B3g/knF+rNXfqBY3zPyFYA13bl9es3a7/3Hs+XHLlaxP+HIDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJMM7eAc/93UCxvu68b9R5hNrnq0vStV89t2bt8C1MuYwRHNmBJAg7kARhB5Ig7EAShB1IgrADSRB2IInxzM8+R9J3JL1N0rCkwYi4zvYVki6U9OvqrpdHxH3tanQiO+C9rzS1/l07jijWp9+7rmaN89Wx13g+VLNb0qUR8ajtwyQ9YntlVbs2Iv65fe0BaJXxzM++WdLm6vo22+slzWp3YwBaa59es9s+VtL7JD1ULbrY9hO2l9ke87mm7SW2h2wP7dLOppoF0Lhxh932oZLuknRJRLwq6UZJx0uap5Ej/9VjrRcRgxExEBED/ZrcgpYBNGJcYbfdr5Gg3xoRd0tSRGyJiD0RMSzpZknz29cmgGbVDbttS7pF0vqIuGbU8pmj7naWpLWtbw9Aq4zn3fhTJJ0naY3t1dWyyyUtsj1PUkjaIOlzbelwP3DovVPLd/hguXzV1/+8WJ/+Kqexor7xvBv/gMY+oZoxdWAC4RN0QBKEHUiCsANJEHYgCcIOJEHYgSQcER3b2FRPi5N9ese2B2TzUKzSq/HSmN89zpEdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Lo6Di77V9Lem7UohmSXuxYA/umV3vr1b4kemtUK3s7JiKOHKvQ0bC/ZeP2UESUJy/vkl7trVf7kuitUZ3qjafxQBKEHUii22Ef7PL2S3q1t17tS6K3RnWkt66+ZgfQOd0+sgPoEMIOJNGVsNs+w/YvbT9j+7Ju9FCL7Q2219hebXuoy70ss73V9tpRy6bZXmn76eqyPJ9zZ3u7wvYL1b5bbXtBl3qbY/vHttfbXmf7C9Xyru67Ql8d2W8df81uu0/SU5I+ImmjpIclLYqIJzvaSA22N0gaiIiufwDD9qmStkv6TkT8QbXsKkkvRcTS6h/lERHxxR7p7QpJ27s9jXc1W9HM0dOMSzpT0l+qi/uu0NefqQP7rRtH9vmSnomIZyPiDUnflbSwC330vIi4X9JLb1q8UNLy6vpyjfyxdFyN3npCRGyOiEer69sk7Z1mvKv7rtBXR3Qj7LMk/WrU7Y3qrfneQ9KPbD9ie0m3mxnD0RGxWRr545F0VJf7ebO603h30pumGe+ZfdfI9OfN6kbYx/p+rF4a/zslIt4v6eOSLqqermJ8xjWNd6eMMc14T2h0+vNmdSPsGyXNGXV7tqRNXehjTBGxqbrcKuke9d5U1Fv2zqBbXW7tcj//r5em8R5rmnH1wL7r5vTn3Qj7w5Lm2j7O9iRJ50pa0YU+3sL2lOqNE9meIumj6r2pqFdIWlxdXyzp3i728jt6ZRrvWtOMq8v7ruvTn0dEx38kLdDIO/L/Lenvu9FDjb7eIenx6mddt3uTdLtGntbt0sgzogskTZe0StLT1eW0Hurt3yStkfSERoI1s0u9fUgjLw2fkLS6+lnQ7X1X6Ksj+42PywJJ8Ak6IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUji/wAofCumXboNbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.imshow(data[0][0].view(28,28))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check if the dataset is balanced."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = 0\n",
    "counter_dict = {0:0, 1:0, 2:0, 3:0, 4:0, 5:0, 6:0, 7:0, 8:0, 9:0}\n",
    "\n",
    "counter_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 5923, 1: 6742, 2: 5958, 3: 6131, 4: 5842, 5: 5421, 6: 5918, 7: 6265, 8: 5851, 9: 5949}\n"
     ]
    }
   ],
   "source": [
    "for data in trainset:\n",
    "    Xs, ys = data\n",
    "    for i in ys:\n",
    "        counter_dict[int(i)] += 1\n",
    "        total += 1\n",
    "\n",
    "print(counter_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0: 9.871666666666666\n",
      "1: 11.236666666666666\n",
      "2: 9.93\n",
      "3: 10.218333333333334\n",
      "4: 9.736666666666666\n",
      "5: 9.035\n",
      "6: 9.863333333333333\n",
      "7: 10.441666666666666\n",
      "8: 9.751666666666667\n",
      "9: 9.915000000000001\n"
     ]
    }
   ],
   "source": [
    "for i in counter_dict:\n",
    "    print(f\"{i}: {counter_dict[i]/total*100}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (fc1): Linear(in_features=784, out_features=64, bias=True)\n",
      "  (fc2): Linear(in_features=64, out_features=64, bias=True)\n",
      "  (fc3): Linear(in_features=64, out_features=64, bias=True)\n",
      "  (fc4): Linear(in_features=64, out_features=10, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# Define the layer\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.fc1 = nn.Linear(28*28, 64)    #fc -> fully connected (input, output)\n",
    "        self.fc2 = nn.Linear(64, 64)\n",
    "        self.fc3 = nn.Linear(64, 64)\n",
    "        self.fc4 = nn.Linear(64, 10)\n",
    "        \n",
    "    # Define the paths for data to take through these layers\n",
    "    def forward(self, x):\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = F.relu(self.fc3(x))\n",
    "        x = self.fc4(x)\n",
    "        return F.log_softmax(x, dim=1)\n",
    "    \n",
    "        \n",
    "net = Net()\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = torch.rand((28,28))\n",
    "X = X.view(1, 28*28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-2.3059, -2.3086, -2.2137, -2.2390, -2.3124, -2.3289, -2.3282, -2.3265,\n",
       "         -2.2897, -2.3833]], grad_fn=<LogSoftmaxBackward>)"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output = net(X)\n",
    "output\n",
    "\n",
    "# grad_fn -> gradian function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 10])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  P4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.2204, grad_fn=<NllLossBackward>)\n",
      "tensor(0.0760, grad_fn=<NllLossBackward>)\n",
      "tensor(0.0151, grad_fn=<NllLossBackward>)\n"
     ]
    }
   ],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "optimizer = optim.Adam(net.parameters(), lr = 0.001)\n",
    "\n",
    "EPOCHS = 3\n",
    "\n",
    "for epoch in range(EPOCHS):\n",
    "    for data in trainset:\n",
    "        #\n",
    "        X, y = data\n",
    "        #print(X[0])\n",
    "        #print(y[0])\n",
    "        #break\n",
    "        net.zero_grad()\n",
    "        output = net(X.view(-1, 28*28))\n",
    "        loss = F.nll_loss(output, y)\n",
    "        loss.backward()\n",
    "        optimizer.step()  #adjust the weight\n",
    "    print(loss)\n",
    "        \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.049\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total1 = 0\n",
    "\n",
    "with torch.no_grad():  # no gradient\n",
    "    for data in trainset:\n",
    "        X, y = data\n",
    "        outpyt = net(X.view(-1, 784))\n",
    "        for idx, i in enumerate(output):\n",
    "            if torch.argmax(i) == y[idx]:\n",
    "                correct += 1\n",
    "            total += 1\n",
    "\n",
    "print(\"Accuracy: \", round(correct/total, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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",
       "\n",
       "\n",
       "        [[[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",
       "\n",
       "\n",
       "        [[[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",
       "\n",
       "\n",
       "        ...,\n",
       "\n",
       "\n",
       "        [[[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",
       "\n",
       "\n",
       "        [[[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",
       "\n",
       "\n",
       "        [[[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.]]]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAANMElEQVR4nO3df4wc9XnH8c8H29ixAckO2LqaH4bUVUOg2OTqtHXV0JAighKZRCXCUVNXojJpQQoVUoqooqD2j7pRSJpKKZJTrDgVEEUlFIfSBteKQsgPw5ka/6hp7domGF99pFZkE4Txmad/3BAd5mZ2vTu7s/bzfkmr3Z1nZ+fR6j43s/ud3a8jQgDOfGc13QCA/iDsQBKEHUiCsANJEHYgien93NjZnhmzNKefmwRSeU0/1+txzFPVugq77eslfVnSNEn/EBFrqh4/S3P0Pl/bzSYBVNgcm0prHR/G254m6SuSPiTpckkrbV/e6fMB6K1u3rMvk7QnIvZGxOuSviFpRT1tAahbN2FfKOnFSfcPFMvewvZq2yO2R47rWBebA9CNbsI+1YcAbzv3NiLWRsRwRAzP0MwuNgegG92E/YCkiybdv1DSwe7aAdAr3YT9GUmLbV9q+2xJN0vaUE9bAOrW8dBbRIzbvl3SdzQx9LYuInbW1hmAWnU1zh4Rj0t6vKZeAPQQp8sCSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kERfp2w+nY3e+VultaHvH61e+entNXcDnDr27EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBOPshRc/Wz6OLkn/8sefL63de/O1levu+/gllfXxfS9U1jE1//qVlfVXLp5dWpvz8Oa62xl4XYXd9n5JRyWdkDQeEcN1NAWgfnXs2X83In5aw/MA6CHeswNJdBv2kPSE7S22V0/1ANurbY/YHjmuY11uDkCnuj2MXx4RB23Pl7TR9vMR8eTkB0TEWklrJek8z4sutwegQ13t2SPiYHE9JukRScvqaApA/ToOu+05ts9987ak6yTtqKsxAPXq5jB+gaRHbL/5PA9GxL/V0lUDxt9R/Q7j4unlY7Zf/qUfVa77gSs+VVmfxTj7lPze91TWP/XgI5X1J352RWnthR8sqFx3/H8PVdZPRx2HPSL2Srqqxl4A9BBDb0AShB1IgrADSRB2IAnCDiTBV1wL4xdyKu+g2bPy3Mr6R2Yfqawvmv690tpnLp3y7O5f8Bk49MaeHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSYJy9sPeD6yrrJ/iNndPOnzz/idLaeT96ro+dDAb27EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSaBl22+tsj9neMWnZPNsbbe8uruf2tk0A3Wpnz/41SdeftOwuSZsiYrGkTcV9AAOsZdgj4klJh09avELS+uL2ekk31twXgJp1+p59QUSMSlJxPb/sgbZX2x6xPXJczKcGNKXnH9BFxNqIGI6I4Rma2evNASjRadgP2R6SpOJ6rL6WAPRCp2HfIGlVcXuVpEfraQdAr7T83XjbD0m6RtL5tg9I+pykNZK+afsWST+RdFMvmwQGzWsfXlZZn/XY033qpH0twx4RK0tK19bcC4Ae4gw6IAnCDiRB2IEkCDuQBGEHkmDK5sJlD99aWd/9sfs6fu6xpdUv88Xf7vipT2tnzZ5dWf/OTV9o8QzV6z96xddLa8sfvK1y3a8se6CyvnjGU5X1D1/5mcr6hX/9w8p6L7BnB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkGGcvTD/au/9705f+rGfP3bTxD7y3sv7a+TNKayfKS5KkRdOrx7JbeedZ7yitPf/+dV09d6sx/uO/9vMun79+7NmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnG2fvgI4t2VNY3PvarlfX/21s9Se7w1XtOuae63L3w7yvrV57dYjD9NPXtV8+rrI+/PKtPnbSPPTuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJME4ex/81fytXdW1tMZmand6jqP/+Fh1/V+PXFVZ3/KH76msL962+VRb6rmWe3bb62yP2d4xadk9tl+yvbW43NDbNgF0q53D+K9Jun6K5V+KiCXF5fF62wJQt5Zhj4gnJR3uQy8AeqibD+hut72tOMwvPXnb9mrbI7ZHjqvFGyUAPdNp2O+T9C5JSySNSrq37IERsTYihiNieIZmdrg5AN3qKOwRcSgiTkTEG5K+KmlZvW0BqFtHYbc9NOnuRyVVf4cTQONajrPbfkjSNZLOt31A0uckXWN7iaSQtF9S9eTmQAM+O7aktPbcxy6tXHd83wstnv35DjpqVsuwR8TKKRbf34NeAPQQp8sCSRB2IAnCDiRB2IEkCDuQBF9xLQz98ERl/bplN5bWnnj3P9fdzhnj/dt/v7T2m/P3Va67ZsGWrrb9H6suL629se/0GzrrFnt2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCcfbCrMeerqx7Y/mv7PzK3/xp9boX9PbnuOb+e/n0wCdazBx8zsHq8wvGrq7+E7ns76rHq8955aXS2s5z51Wuq+eqyzg17NmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnG2dsUx8rHyn/5jh/3sZP+uuTR6nr1KH21mDati7VxqtizA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASjLOjMS+vvKrFI57qSx9ZtNyz277I9ndt77K90/ani+XzbG+0vbu4ntv7dgF0qp3D+HFJd0bEuyX9hqTbbF8u6S5JmyJisaRNxX0AA6pl2CNiNCKeLW4flbRL0kJJKyStLx62XlL5/EgAGndKH9DZXiRpqaTNkhZExKg08Q9B0vySdVbbHrE9cly9/S02AOXaDrvtcyQ9LOmOiDjS7noRsTYihiNieIbKf7QRQG+1FXbbMzQR9Aci4lvF4kO2h4r6kKSx3rQIoA4th95sW9L9knZFxBcnlTZIWiVpTXHd4suQwFtd8FCL34r+y/70kUU74+zLJX1S0nbbW4tld2si5N+0fYukn0i6qTctAqhDy7BHxFOSXFK+tt52APQKp8sCSRB2IAnCDiRB2IEkCDuQBF9xxcAaPfFqZX1o2uw+dXJmYM8OJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kwzo7GvPFq9Tj6H9z6Z5X1f1r7t3W2c8Zjzw4kQdiBJAg7kARhB5Ig7EAShB1IgrADSTgi+rax8zwv3md+kBbolc2xSUfi8JS/Bs2eHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSaBl22xfZ/q7tXbZ32v50sfwe2y/Z3lpcbuh9uwA61c6PV4xLujMinrV9rqQttjcWtS9FxBd61x6AurQzP/uopNHi9lHbuyQt7HVjAOp1Su/ZbS+StFTS5mLR7ba32V5ne27JOqttj9geOa5jXTULoHNth932OZIelnRHRByRdJ+kd0laook9/71TrRcRayNiOCKGZ2hmDS0D6ERbYbc9QxNBfyAiviVJEXEoIk5ExBuSvippWe/aBNCtdj6Nt6T7Je2KiC9OWj406WEflbSj/vYA1KWdT+OXS/qkpO22txbL7pa00vYSSSFpv6Rbe9IhgFq082n8U5Km+n7s4/W3A6BXOIMOSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQRF+nbLb9sqQXJi06X9JP+9bAqRnU3ga1L4neOlVnb5dExAVTFfoa9rdt3B6JiOHGGqgwqL0Nal8SvXWqX71xGA8kQdiBJJoO+9qGt19lUHsb1L4keutUX3pr9D07gP5pes8OoE8IO5BEI2G3fb3t/7K9x/ZdTfRQxvZ+29uLaahHGu5lne0x2zsmLZtne6Pt3cX1lHPsNdTbQEzjXTHNeKOvXdPTn/f9PbvtaZL+W9LvSTog6RlJKyPiP/vaSAnb+yUNR0TjJ2DY/h1Jr0j6ekRcUSz7vKTDEbGm+Ec5NyL+fEB6u0fSK01P413MVjQ0eZpxSTdK+iM1+NpV9PVx9eF1a2LPvkzSnojYGxGvS/qGpBUN9DHwIuJJSYdPWrxC0vri9npN/LH0XUlvAyEiRiPi2eL2UUlvTjPe6GtX0VdfNBH2hZJenHT/gAZrvveQ9ITtLbZXN93MFBZExKg08ccjaX7D/Zys5TTe/XTSNOMD89p1Mv15t5oI+1RTSQ3S+N/yiLha0ock3VYcrqI9bU3j3S9TTDM+EDqd/rxbTYT9gKSLJt2/UNLBBvqYUkQcLK7HJD2iwZuK+tCbM+gW12MN9/MLgzSN91TTjGsAXrsmpz9vIuzPSFps+1LbZ0u6WdKGBvp4G9tzig9OZHuOpOs0eFNRb5C0qri9StKjDfbyFoMyjXfZNONq+LVrfPrziOj7RdINmvhE/n8k/UUTPZT0dZmk54rLzqZ7k/SQJg7rjmviiOgWSe+UtEnS7uJ63gD19o+StkvapolgDTXU229r4q3hNklbi8sNTb92FX315XXjdFkgCc6gA5Ig7EAShB1IgrADSRB2IAnCDiRB2IEk/h/dC9tcfA1tWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X[1].view(28, 28))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(4)\n"
     ]
    }
   ],
   "source": [
    "print(torch.argmax(net(X[1].view(-1, 784))[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(313)"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.argmax((X[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 1., 1., 2., 4., 2., 3.])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = torch.Tensor([1,1,1,2,4,2,3])\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(2)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.argmin(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}