Computer_Vision/Chapter03/Preparing_our_data.ipynb

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"source": [
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"import torch\n",
"data_folder = '~/data/FMNIST' # This can be any directory you want \n",
"# to download FMNIST to\n",
"fmnist = datasets.FashionMNIST(data_folder, download=True, train=True)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
],
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"output_type": "stream",
"text": [
"Extracting /root/data/FMNIST/FashionMNIST/raw/train-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
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"text": [
"Extracting /root/data/FMNIST/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
],
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"output_type": "stream",
"text": [
"Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n",
"\n",
"\n"
],
"name": "stdout"
},
{
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{
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"text": [
"Extracting /root/data/FMNIST/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /root/data/FMNIST/FashionMNIST/raw\n",
"Processing...\n",
"Done!\n",
"\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: 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:141.)\n",
" return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QVvAwUng01NP"
},
"source": [
"tr_images = fmnist.data\n",
"tr_targets = fmnist.targets"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "waeiC6U-02_p",
"outputId": "4051ec94-f38c-4658-a64f-cf18722f9335",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 163
}
},
"source": [
"unique_values = tr_targets.unique()\n",
"print(f'tr_images & tr_targets:\\n\\tX - {tr_images.shape}\\n\\tY - {tr_targets.shape}\\n\\tY - Unique Values : {unique_values}')\n",
"print(f'TASK:\\n\\t{len(unique_values)} class Classification')\n",
"print(f'UNIQUE CLASSES:\\n\\t{fmnist.classes}') "
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"tr_images & tr_targets:\n",
"\tX - torch.Size([60000, 28, 28])\n",
"\tY - torch.Size([60000])\n",
"\tY - Unique Values : tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n",
"TASK:\n",
"\t10 class Classification\n",
"UNIQUE CLASSES:\n",
"\t['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "r46vyojT04rs"
},
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import numpy as np"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bUAU2NbA06Le",
"outputId": "94cdb9a7-e758-461f-aca1-d51587ce40b6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 729
}
},
"source": [
"R, C = len(tr_targets.unique()), 10\n",
"fig, ax = plt.subplots(R, C, figsize=(10,10))\n",
"for label_class, plot_row in enumerate(ax):\n",
" label_x_rows = np.where(tr_targets == label_class)[0]\n",
" for plot_cell in plot_row:\n",
" plot_cell.grid(False); plot_cell.axis('off')\n",
" ix = np.random.choice(label_x_rows)\n",
" x, y = tr_images[ix], tr_targets[ix]\n",
" plot_cell.imshow(x, cmap='gray')\n",
"plt.tight_layout()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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"text/plain": [
"<Figure size 720x720 with 100 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jWzqBYjE0-RK"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}