1359 lines
87 KiB
Plaintext
1359 lines
87 KiB
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"6df0e3bc02f3412382289c53a39f0432",
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"de8f6182df404305ae1dc7f6ad99a527",
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|||
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|
"1a75f61134f64f738f1083dd673a935b",
|
|||
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"908091ee8889489cbf7bc8552a2fd550",
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|||
|
|
"14804e6bf0914d488bc6eeab5fd24d8f",
|
|||
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|
"43615a6aa59547bfaaf1853653eb42a4",
|
|||
|
|
"14a63f0435f44efa8df06492ec01ebd2",
|
|||
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|
"61d5d4ad4d1e4c0b9f1840757cdd0941",
|
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|
"1b32d6ece60f47e3a261fa51348cdcc5",
|
|||
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|
"a689213d10884765a87b34dfd5a6fa40",
|
|||
|
|
"e8e91702f81f4c8bb258e125e974a4fb",
|
|||
|
|
"6fad2c31bfed4c90a21a1d7419bebaa8",
|
|||
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|
"6ae898cf5da34da28908ed4f3fdc2dd4",
|
|||
|
|
"e32bc05010b7478b8c0d22a15e720009",
|
|||
|
|
"28b89bafdb7d45e293760e3bcaea07bd",
|
|||
|
|
"17ff1ce21bd54b58bcbe73c99c48c156",
|
|||
|
|
"091e699baa1d4ba4b91cba550e681dbe",
|
|||
|
|
"432a47c8ef324bcaa1c43f075335a8cd",
|
|||
|
|
"9a217404110a4d73b997381af135ea07",
|
|||
|
|
"e2223fac1d2d4b6c860120b415ad5e92",
|
|||
|
|
"c57b490aab2642da8c363966cbde6681",
|
|||
|
|
"67b023daab4b4a16ac3b8066c2b891bb",
|
|||
|
|
"9e7e249aa6cd4830b386b0c8fdad3fad",
|
|||
|
|
"7ab8dc9d03f4490cacf9b8f76c094ba2",
|
|||
|
|
"10a1557fd6ac4208b8295ab45df74fff",
|
|||
|
|
"5198220448f3401998b592f2d2a64587",
|
|||
|
|
"7c090329320e4ab1a8407f9e7b4bffb0",
|
|||
|
|
"38cb3d3490ed40d8a60c9eaadf1efe83"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"from torchvision import datasets\n",
|
|||
|
|
"from torch.utils.data import Dataset, DataLoader\n",
|
|||
|
|
"import torch\n",
|
|||
|
|
"import torch.nn as nn\n",
|
|||
|
|
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
|
|||
|
|
"import numpy as np\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)\n",
|
|||
|
|
"tr_images = fmnist.data\n",
|
|||
|
|
"tr_targets = fmnist.targets"
|
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|
|
],
|
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|
|
"execution_count": null,
|
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|
"outputs": [
|
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|
{
|
|||
|
|
"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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"name": "stdout"
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},
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{
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"output_type": "display_data",
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"version_major": 2
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
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|
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},
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"metadata": {
|
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|
"tags": []
|
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|
|
}
|
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|
|
},
|
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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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|
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"name": "stdout"
|
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|
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},
|
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{
|
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"output_type": "display_data",
|
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"data": {
|
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"application/vnd.jupyter.widget-view+json": {
|
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"model_id": "14804e6bf0914d488bc6eeab5fd24d8f",
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"version_minor": 0,
|
|||
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"version_major": 2
|
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},
|
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"text/plain": [
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"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
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|
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},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"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"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
},
|
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|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
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|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
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|
"model_id": "6ae898cf5da34da28908ed4f3fdc2dd4",
|
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|
"version_minor": 0,
|
|||
|
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"version_major": 2
|
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|
|
},
|
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"text/plain": [
|
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"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-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"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "c57b490aab2642da8c363966cbde6681",
|
|||
|
|
"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": "d4iqXqYb14wL"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"class FMNISTDataset(Dataset):\n",
|
|||
|
|
" def __init__(self, x, y):\n",
|
|||
|
|
" x = x.float()/255\n",
|
|||
|
|
" x = x.view(-1,28*28)\n",
|
|||
|
|
" self.x, self.y = x, y \n",
|
|||
|
|
" def __getitem__(self, ix):\n",
|
|||
|
|
" x, y = self.x[ix], self.y[ix] \n",
|
|||
|
|
" return x.to(device), y.to(device)\n",
|
|||
|
|
" def __len__(self): \n",
|
|||
|
|
" return len(self.x)"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "9bH5VEys19M4",
|
|||
|
|
"outputId": "16e6bb05-b48f-4319-ee0e-0d6fdf77c7f1",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 108
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"def get_data(): \n",
|
|||
|
|
" train = FMNISTDataset(tr_images, tr_targets) \n",
|
|||
|
|
" trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
|
|||
|
|
" return trn_dl\n",
|
|||
|
|
"from torch.optim import SGD\n",
|
|||
|
|
"def get_model():\n",
|
|||
|
|
" model = nn.Sequential(\n",
|
|||
|
|
" nn.Linear(28 * 28, 1000),\n",
|
|||
|
|
" nn.ReLU(),\n",
|
|||
|
|
" nn.Linear(1000, 10)\n",
|
|||
|
|
" ).to(device)\n",
|
|||
|
|
" loss_fn = nn.CrossEntropyLoss()\n",
|
|||
|
|
" optimizer = SGD(model.parameters(), lr=1e-2)\n",
|
|||
|
|
" return model, loss_fn, optimizer\n",
|
|||
|
|
"def train_batch(x, y, model, opt, loss_fn):\n",
|
|||
|
|
" model.train()\n",
|
|||
|
|
" # call your model like any python function on your batch of inputs\n",
|
|||
|
|
" prediction = model(x)\n",
|
|||
|
|
" # compute loss\n",
|
|||
|
|
" batch_loss = loss_fn(prediction, y)\n",
|
|||
|
|
" # based on the forward pass in `model(x)` compute all the gradients of \n",
|
|||
|
|
" # 'model.parameters()'\n",
|
|||
|
|
" batch_loss.backward()\n",
|
|||
|
|
" # apply new-weights = f(old-weights, old-weight-gradients) \n",
|
|||
|
|
" # where \"f\" is the optimizer\n",
|
|||
|
|
" optimizer.step()\n",
|
|||
|
|
" # Flush memory for next batch of calculations\n",
|
|||
|
|
" optimizer.zero_grad()\n",
|
|||
|
|
" return batch_loss.item()\n",
|
|||
|
|
"def accuracy(x, y, model):\n",
|
|||
|
|
" model.eval()\n",
|
|||
|
|
" # since there's no need for updating weights, we might \n",
|
|||
|
|
" # as well not compute the gradients\n",
|
|||
|
|
" with torch.no_grad():\n",
|
|||
|
|
" # get the prediction matrix for a tensor of `x` images\n",
|
|||
|
|
" prediction = model(x)\n",
|
|||
|
|
" # compute if the location of maximum in each row coincides \n",
|
|||
|
|
" # with ground truth\n",
|
|||
|
|
" max_values, argmaxes = prediction.max(-1)\n",
|
|||
|
|
" is_correct = argmaxes == y\n",
|
|||
|
|
" return is_correct.cpu().numpy().tolist()\n",
|
|||
|
|
"trn_dl = get_data()\n",
|
|||
|
|
"model, loss_fn, optimizer = get_model()\n",
|
|||
|
|
"losses, accuracies = [], []\n",
|
|||
|
|
"for epoch in range(5):\n",
|
|||
|
|
" print(epoch)\n",
|
|||
|
|
" epoch_losses, epoch_accuracies = [], []\n",
|
|||
|
|
" for ix, batch in enumerate(iter(trn_dl)):\n",
|
|||
|
|
" x, y = batch\n",
|
|||
|
|
" batch_loss = train_batch(x, y, model, optimizer, loss_fn)\n",
|
|||
|
|
" epoch_losses.append(batch_loss)\n",
|
|||
|
|
" epoch_loss = np.array(epoch_losses).mean()\n",
|
|||
|
|
" for ix, batch in enumerate(iter(trn_dl)):\n",
|
|||
|
|
" x, y = batch\n",
|
|||
|
|
" is_correct = accuracy(x, y, model)\n",
|
|||
|
|
" epoch_accuracies.extend(is_correct)\n",
|
|||
|
|
" epoch_accuracy = np.mean(epoch_accuracies)\n",
|
|||
|
|
" losses.append(epoch_loss)\n",
|
|||
|
|
" accuracies.append(epoch_accuracy)"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"0\n",
|
|||
|
|
"1\n",
|
|||
|
|
"2\n",
|
|||
|
|
"3\n",
|
|||
|
|
"4\n"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "2tIuYppL2Bj9",
|
|||
|
|
"outputId": "075468ca-e9aa-4ddb-c7e5-22eee674e502",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 344
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"epochs = np.arange(5)+1\n",
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"%matplotlib inline\n",
|
|||
|
|
"plt.figure(figsize=(20,5))\n",
|
|||
|
|
"plt.subplot(121)\n",
|
|||
|
|
"plt.title('Loss value over increasing epochs')\n",
|
|||
|
|
"plt.plot(epochs, losses, label='Training Loss')\n",
|
|||
|
|
"plt.legend()\n",
|
|||
|
|
"plt.subplot(122)\n",
|
|||
|
|
"plt.title('Accuracy value over increasing epochs')\n",
|
|||
|
|
"plt.plot(epochs, accuracies, label='Training Accuracy')\n",
|
|||
|
|
"plt.gca().set_yticklabels(['{:.0f}%'.format(x*100) for x in plt.gca().get_yticks()]) \n",
|
|||
|
|
"plt.legend()"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.legend.Legend at 0x7facd08c46d8>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 6
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 1440x360 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": [],
|
|||
|
|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "l6YOA3y52QnM"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
""
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|