2. Pytorch Importing and analysing datasets.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torchvision import transforms, datasets\n",
    "\n",
    "#torchvision contains a collection of datasets for vision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "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"
     ]
    },
    {
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      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), 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"
     ]
    },
    {
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     },
     "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"
     ]
    },
    {
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     },
     "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",
      "\n",
      "\n"
     ]
    },
    {
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      ]
     },
     "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "..\\torch\\csrc\\utils\\tensor_numpy.cpp:141: 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.\n"
     ]
    }
   ],
   "source": [
    "#Transform - everything we want to apply to the data\n",
    "#Shuffle - MNIST - hand drawn numbers data set from 0 - 9 28 by 28 image\n",
    "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Batch - how many at a time do we want to pass to a model\n",
    "# Nerual network advantage - when we can leverage millions - billions worth of samples\n",
    "# Neurons per layer - trial and error\n",
    "# Graident descent operation\n",
    "# as models start to optimise every weights and bias - if you pass whole data at once,\n",
    "# the machine may learn some generalisation at once, machine might find that some weights are just arbitrary\n",
    "# is this generally trough or is it because this is in sample data - machine has no way to know this\n",
    "# if we pass through batches at a time, each time it optimizes the optimization that stick around generally are going to be the\n",
    "# general the generalizations the correct things that assumed than just fitment cases - erased out when working in batches\n",
    "# not the case when you want a bigger batch size - sweet spot batch sizes ~ usually 8 to 64 regardless how big your memory is\n",
    "\n",
    "#Shuffle - Providing neural network opportunity to learn general principles rather than just simply figuring out tricks\n",
    "# if there is a quickeer route to get to increasing / decreasing loss - NN is going to take that route\n",
    "\n",
    "train_set = torch.utils.data.DataLoader(train, batch_size=10, shuffle=True)\n",
    "test_set = torch.utils.data.DataLoader(test, batch_size=10, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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([9, 5, 0, 0, 6, 9, 9, 4, 4, 3])]\n"
     ]
    }
   ],
   "source": [
    "#showing the data. firstly shown the actual data 28 x 28 and then the result in a batch of 10\n",
    "for data in train_set:\n",
    "    print(data)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 28, 28])\n",
      "tensor(9)\n",
      "torch.Size([])\n"
     ]
    }
   ],
   "source": [
    "# Data is a tensor object containing list of tensors of images and then list of tensors that are labels\n",
    "x, y = data[0][0], data[1][0]\n",
    "# extra 1 in tensor shape\n",
    "print(x.shape)\n",
    "print(y)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QxP8DobgxDiFHWeYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(data[0][0].view(28,28))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "#balancing - if a model can find a shorter path to decrease loss- the optimiser doesn'tknow how good could we get\n",
    "# just trying to decrease loss the best possible\n",
    "# what if 60% of our dataset is #3 - our model is quickly learn and adjust weight such that it will always predict a 3\n",
    "# it is stuck into a hole - getting out means the loss is alot worse than before\n",
    "# make sure data is as balanced as possible\n",
    "# attempt: modifying the weights of specific classes when calculating loss\n",
    "# confirming that the data set is balanced\n",
    "\n",
    "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",
    "for data in train_set:\n",
    "    Xs, ys = data\n",
    "    for y in ys:\n",
    "        counter_dict[int(y)] += 1\n",
    "        total += 1\n",
    "print(counter_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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