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|
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|
"data_folder = '~/data/FMNIST' # This can be any directory you want \n",
|
|||
|
"# to download FMNIST to\n",
|
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|
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"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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"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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|
"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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"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",
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|||
|
"model_id": "9b400b47d9bf46e9a5adb2dc45632c56",
|
|||
|
"version_minor": 0,
|
|||
|
"version_major": 2
|
|||
|
},
|
|||
|
"text/plain": [
|
|||
|
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
|
|||
|
]
|
|||
|
},
|
|||
|
"metadata": {
|
|||
|
"tags": []
|
|||
|
}
|
|||
|
},
|
|||
|
{
|
|||
|
"output_type": "stream",
|
|||
|
"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": {
|
|||
|
"image/png": "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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": []
|
|||
|
}
|
|||
|
]
|
|||
|
}
|