Deep-Learning-with-Python-and-PyToch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torchvision import transforms, datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": "f6d464464afc4cf6a752bc1ddce8df6a",
       "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": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a56b36e3f83a4c9f92d5b9f4195cb441",
       "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-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": "0bad8ecd6bab420592384dab254ea04a",
       "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": "8559e8057592492e888977a096b10cfc",
       "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",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "train  = datasets.MNIST(\"\", train=True, download=True, \n",
    "                       transform = transforms.Compose([transforms.ToTensor()]))\n",
    "test  = datasets.MNIST(\"\", train=False, download=True, \n",
    "                       transform = transforms.Compose([transforms.ToTensor()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset = torch.utils.data.DataLoader(train, batch_size=10, shuffle=True)\n",
    "testset = torch.utils.data.DataLoader(test, batch_size=10, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      "\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([6, 2, 7, 5, 6, 6, 6, 8, 0, 1])]\n"
     ]
    }
   ],
   "source": [
    "for data in trainset:\n",
    "    print(data)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(6)\n"
     ]
    }
   ],
   "source": [
    "x, y = data[0][0], data[1][0]\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.imshow(data[0][0].view(28,28))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 28, 28])\n"
     ]
    }
   ],
   "source": [
    "print(data[0][0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "pygments_lexer": "ipython3",
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