1736 lines
177 KiB
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1736 lines
177 KiB
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|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "btTNGfAhOgCk",
|
|||
|
|
"outputId": "f0126e54-1428-4d8c-e714-43d8e227c433",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 437,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"ea072e177d1e4caa9035de65006a92c5",
|
|||
|
|
"4da29921a0a34d52a8b6b92fad1ee020",
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"f9cda45fc5994f15bcbfcacce9b6e6b4",
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|||
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"fd2f745952424affb588b07e843dd01a",
|
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|
"9fbab9764037441d96f79f64b14521c0",
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"b4dec018429c4173852fdf63e1cd4567",
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|
"c6f3057a4f284b90a4638fd9db096490",
|
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|
"33fc9c37c5e14716a6f172a8c4c366f3",
|
|||
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|
"d5d654907a224d93832444b7e35eb8ee",
|
|||
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|
"66800c263cdb492cab4dd5920aa5ab17",
|
|||
|
|
"76287a4edc454f16bcd16a23f6cc3fb1",
|
|||
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|
"6f2ac3ddbc12451cbd3a4b97fa5e7d7d",
|
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"75b3b6fc935b4519938d343d6e116e91",
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"4fa16cecb9684d0e966868c6676eff42",
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|
"08551f384b0a4e84a0c93b552211a34e",
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|
"1f3bbea53c9a477b9a2e982dc1ba65e9",
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"9d864b0262c14565be271fbbb5b220af",
|
|||
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|
"c3b3f4e0232648e3a87a15fed2a3fb8d",
|
|||
|
|
"cac33fb9728d4382ace5d0ddd5150c16",
|
|||
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|
"1d13ae30976a4798b63dd86e2a930244",
|
|||
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|
"a3ae40caee0444409b77c602e6a95e8f",
|
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"fc6186a0ce2040e1a8a21a88109c937c",
|
|||
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|
"9bfae4d0957d480f8575d5778804cda2",
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|||
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"ad46377548a94f5f8f2c8533374519a2",
|
|||
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|
"2e5f5ac346414ea4a885f5058969ed8e",
|
|||
|
|
"9330d89f45a3486a92ca00ec3966493b",
|
|||
|
|
"154c1c718d1d4f2e942089e48beba5ca",
|
|||
|
|
"4ea94dfcc551493190b83924f03a3196",
|
|||
|
|
"dc8f74e2b6e14d259fc50e1e9abbc0e9",
|
|||
|
|
"036c61dcb0624aa8a086f85c43d7cbac",
|
|||
|
|
"51ca5d600977413b9c9e8e37962ba118",
|
|||
|
|
"6fdb1dce89944976af24e0052f42f321"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"from torchvision import datasets\n",
|
|||
|
|
"import torch\n",
|
|||
|
|
"data_folder = '/content/' # This can be any directory you want 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 /content/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "ea072e177d1e4caa9035de65006a92c5",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Extracting /content/FashionMNIST/raw/train-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
|
|||
|
|
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "d5d654907a224d93832444b7e35eb8ee",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Extracting /content/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /content/FashionMNIST/raw\n",
|
|||
|
|
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
|
|||
|
|
],
|
|||
|
|
"name": "stdout"
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"model_id": "9d864b0262c14565be271fbbb5b220af",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Extracting /content/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /content/FashionMNIST/raw\n",
|
|||
|
|
"Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /content/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": "2e5f5ac346414ea4a885f5058969ed8e",
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0
|
|||
|
|
},
|
|||
|
|
"text/plain": [
|
|||
|
|
"HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"text": [
|
|||
|
|
"Extracting /content/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /content/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": "f1x6oYFlVfYY"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"tr_images = fmnist.data\n",
|
|||
|
|
"tr_targets = fmnist.targets"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "8ULXRurzVgjD"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"val_fmnist = datasets.FashionMNIST(data_folder, download=True, train=False)\n",
|
|||
|
|
"val_images = val_fmnist.data\n",
|
|||
|
|
"val_targets = val_fmnist.targets"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "CUhXKInOViDg"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"import matplotlib.pyplot as plt\n",
|
|||
|
|
"%matplotlib inline\n",
|
|||
|
|
"import numpy as np\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'"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "F5qu0HNtVjvs"
|
|||
|
|
},
|
|||
|
|
"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)\n",
|
|||
|
|
"\n",
|
|||
|
|
"from torch.optim import SGD, Adam\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",
|
|||
|
|
"\n",
|
|||
|
|
" loss_fn = nn.CrossEntropyLoss()\n",
|
|||
|
|
" optimizer = Adam(model.parameters(), lr=1e-3)\n",
|
|||
|
|
" return model, loss_fn, optimizer\n",
|
|||
|
|
"\n",
|
|||
|
|
"def train_batch(x, y, model, opt, loss_fn):\n",
|
|||
|
|
" prediction = model(x)\n",
|
|||
|
|
" batch_loss = loss_fn(prediction, y)\n",
|
|||
|
|
" batch_loss.backward()\n",
|
|||
|
|
" optimizer.step()\n",
|
|||
|
|
" optimizer.zero_grad()\n",
|
|||
|
|
" return batch_loss.item()\n",
|
|||
|
|
"\n",
|
|||
|
|
"def accuracy(x, y, model):\n",
|
|||
|
|
" with torch.no_grad():\n",
|
|||
|
|
" prediction = model(x)\n",
|
|||
|
|
" max_values, argmaxes = prediction.max(-1)\n",
|
|||
|
|
" is_correct = argmaxes == y\n",
|
|||
|
|
" return is_correct.cpu().numpy().tolist()\n"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "2uV3YsRUVqcD"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"def get_data(): \n",
|
|||
|
|
" train = FMNISTDataset(tr_images, tr_targets) \n",
|
|||
|
|
" trn_dl = DataLoader(train, batch_size=32, shuffle=True)\n",
|
|||
|
|
" val = FMNISTDataset(val_images, val_targets) \n",
|
|||
|
|
" val_dl = DataLoader(val, batch_size=len(val_images), shuffle=True)\n",
|
|||
|
|
" return trn_dl, val_dl"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Y7lhpYHPVr_b"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"def val_loss(x, y, model):\n",
|
|||
|
|
" with torch.no_grad():\n",
|
|||
|
|
" prediction = model(x)\n",
|
|||
|
|
" val_loss = loss_fn(prediction, y)\n",
|
|||
|
|
" return val_loss.item()"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "2VindwEyWD-N"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"trn_dl, val_dl = get_data()\n",
|
|||
|
|
"model, loss_fn, optimizer = get_model()"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "VpDI95dnWFSG",
|
|||
|
|
"outputId": "600f9003-dc4a-40e3-8c0f-66ede8783bbd",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 108
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"train_losses, train_accuracies = [], []\n",
|
|||
|
|
"val_losses, val_accuracies = [], []\n",
|
|||
|
|
"for epoch in range(5):\n",
|
|||
|
|
" print(epoch)\n",
|
|||
|
|
" train_epoch_losses, train_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",
|
|||
|
|
" train_epoch_losses.append(batch_loss) \n",
|
|||
|
|
" train_epoch_loss = np.array(train_epoch_losses).mean()\n",
|
|||
|
|
"\n",
|
|||
|
|
" for ix, batch in enumerate(iter(trn_dl)):\n",
|
|||
|
|
" x, y = batch\n",
|
|||
|
|
" is_correct = accuracy(x, y, model)\n",
|
|||
|
|
" train_epoch_accuracies.extend(is_correct)\n",
|
|||
|
|
" train_epoch_accuracy = np.mean(train_epoch_accuracies)\n",
|
|||
|
|
"\n",
|
|||
|
|
" for ix, batch in enumerate(iter(val_dl)):\n",
|
|||
|
|
" x, y = batch\n",
|
|||
|
|
" val_is_correct = accuracy(x, y, model)\n",
|
|||
|
|
" validation_loss = val_loss(x, y, model)\n",
|
|||
|
|
" val_epoch_accuracy = np.mean(val_is_correct)\n",
|
|||
|
|
"\n",
|
|||
|
|
" train_losses.append(train_epoch_loss)\n",
|
|||
|
|
" train_accuracies.append(train_epoch_accuracy)\n",
|
|||
|
|
" val_losses.append(validation_loss)\n",
|
|||
|
|
" val_accuracies.append(val_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": "a46qfaZwbsd-",
|
|||
|
|
"outputId": "42aa6305-1299-472e-8262-74cc6b59e711",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 299
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"#ix = np.random.randint(len(tr_images))\n",
|
|||
|
|
"ix = 24300\n",
|
|||
|
|
"plt.imshow(tr_images[ix], cmap='gray')\n",
|
|||
|
|
"plt.title(fmnist.classes[tr_targets[ix]])"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Text(0.5, 1.0, 'Trouser')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 10
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": [],
|
|||
|
|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "VKEOfcdibsgc"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"img = tr_images[ix]/255.\n",
|
|||
|
|
"img = img.view(28*28)\n",
|
|||
|
|
"img = img.to(device)"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "o9FBQQbcb1q-",
|
|||
|
|
"outputId": "1fe3d97f-ef09-4a15-fab1-622f4535f18b",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 72
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"np_output = model(img).cpu().detach().numpy()\n",
|
|||
|
|
"np.exp(np_output)/np.sum(np.exp(np_output))"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"array([2.4361714e-06, 9.9999738e-01, 8.3687448e-09, 2.2200647e-08,\n",
|
|||
|
|
" 5.7493144e-10, 8.5185324e-14, 1.6882856e-07, 7.0115940e-21,\n",
|
|||
|
|
" 3.0295655e-12, 1.3271068e-13], dtype=float32)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 12
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "dzxkVp_vb5nw"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"Translation"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "o4qSbj_JhEPQ",
|
|||
|
|
"outputId": "188d478c-4f9d-4e54-90ad-b1d43c133176",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 35
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"tr_targets[ix]"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"tensor(1)"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 13
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "kENvOeLeb9T8",
|
|||
|
|
"outputId": "718eb5e8-d854-4f71-cc25-7b703f3d9c39",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 1000
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"preds = []\n",
|
|||
|
|
"for px in range(-5,6):\n",
|
|||
|
|
" img = tr_images[ix]/255.\n",
|
|||
|
|
" img = img.view(28, 28)\n",
|
|||
|
|
" #img2 = np.zeros((28,28))\n",
|
|||
|
|
" img2 = np.roll(img, px, axis=1)\n",
|
|||
|
|
" plt.imshow(img2)\n",
|
|||
|
|
" plt.show()\n",
|
|||
|
|
" img3 = torch.Tensor(img2).view(28*28).to(device)\n",
|
|||
|
|
" np_output = model(img3).cpu().detach().numpy()\n",
|
|||
|
|
" preds.append(np.exp(np_output)/np.sum(np.exp(np_output)))"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
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|
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"text/plain": [
|
|||
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"<Figure size 432x288 with 1 Axes>"
|
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]
|
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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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{
|
|||
|
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"output_type": "display_data",
|
|||
|
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"data": {
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"image/png": "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|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
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"tags": [],
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|
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"needs_background": "light"
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|
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}
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},
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|||
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{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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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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"image/png": "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"text/plain": [
|
|||
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|
"<Figure size 432x288 with 1 Axes>"
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]
|
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},
|
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"metadata": {
|
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"needs_background": "light"
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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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"text/plain": [
|
|||
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"<Figure size 432x288 with 1 Axes>"
|
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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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"needs_background": "light"
|
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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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"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
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]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": [],
|
|||
|
|
"needs_background": "light"
|
|||
|
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}
|
|||
|
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},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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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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"text/plain": [
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|||
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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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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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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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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"image/png": "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|
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"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
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]
|
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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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"needs_background": "light"
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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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|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 432x288 with 1 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": [],
|
|||
|
|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "FXFrgJ9uf8mp",
|
|||
|
|
"outputId": "2846c10e-f6ee-4b07-a477-964efe241cb3",
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 625
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
"import seaborn as sns\n",
|
|||
|
|
"fig, ax = plt.subplots(1,1, figsize=(12,10))\n",
|
|||
|
|
"plt.title('Probability of each class for various translations')\n",
|
|||
|
|
"sns.heatmap(np.array(preds), annot=True, ax=ax, fmt='.2f', xticklabels=fmnist.classes, yticklabels=[str(i)+str(' pixels') for i in range(-5,6)], cmap='gray')"
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<matplotlib.axes._subplots.AxesSubplot at 0x7fe8819940f0>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": []
|
|||
|
|
},
|
|||
|
|
"execution_count": 15
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"image/png": "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
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 864x720 with 2 Axes>"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {
|
|||
|
|
"tags": [],
|
|||
|
|
"needs_background": "light"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "ugxiRTlpgVas"
|
|||
|
|
},
|
|||
|
|
"source": [
|
|||
|
|
""
|
|||
|
|
],
|
|||
|
|
"execution_count": null,
|
|||
|
|
"outputs": []
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|