diff --git a/lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb b/lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb
index 06cf206f5f087a64101db278d705b3d19f25ba24..f96fec1bf09a79c2e256439bf84363bc78b59d4b 100644
--- a/lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb
+++ b/lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb
@@ -52,26 +52,29 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
- "height": 35
+ "height": 36
},
"id": "xwqVx9-bwHl3",
- "outputId": "38564a63-0567-4406-ee1a-1d3618f27351",
+ "outputId": "7fdf1bd8-a54b-4eb0-cb09-dbb81a42d99c",
"tags": []
},
"outputs": [
{
+ "output_type": "execute_result",
"data": {
"text/plain": [
- "'1.8.2'"
- ]
+ "'1.11.0+cu113'"
+ ],
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ }
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- "execution_count": 1,
"metadata": {},
- "output_type": "execute_result"
+ "execution_count": 10
}
],
"source": [
@@ -108,32 +111,32 @@
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- "execution_count": 2,
+ "execution_count": null,
"metadata": {
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"id": "ybpnk08HwHl4",
- "outputId": "54e2c89b-b373-4389-b285-49b0510be931",
+ "outputId": "377e6e25-bc5a-4d2e-fe8e-17dac1d20c2c",
"trusted": true
},
"outputs": [
{
- "name": "stdout",
"output_type": "stream",
+ "name": "stdout",
"text": [
"tensor([[1, 2],\n",
" [3, 4]])\n",
- "tensor([[ 1.3577, 0.7550, -1.7503],\n",
- " [-0.7006, 0.1918, -0.2571],\n",
- " [ 0.2964, -0.3188, 0.4575],\n",
- " [-0.1524, 1.3446, 0.7218],\n",
- " [-1.4642, -1.3296, -0.5098],\n",
- " [ 2.2282, 0.5065, 0.6176],\n",
- " [-0.3013, 0.9485, 0.1195],\n",
- " [ 1.0261, 1.5614, -0.1013],\n",
- " [-0.2211, -0.4294, -2.2319],\n",
- " [ 1.4257, -0.6976, 0.0656]])\n"
+ "tensor([[ 0.8995, -1.6137, 1.4489],\n",
+ " [-0.2796, -2.1443, -2.4618],\n",
+ " [-0.2358, -0.4249, -0.0716],\n",
+ " [-0.1267, -0.6382, 0.0593],\n",
+ " [-0.4956, 1.7054, 0.3874],\n",
+ " [ 1.3479, -1.6329, 0.2793],\n",
+ " [ 1.1211, -1.5430, 0.7186],\n",
+ " [-1.5197, 0.5559, -1.6421],\n",
+ " [ 0.1900, -0.4175, -0.3922],\n",
+ " [ 1.8994, 0.1497, -0.7039]])\n"
]
}
],
@@ -155,7 +158,7 @@
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@@ -200,7 +203,7 @@
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@@ -236,7 +239,7 @@
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+ "execution_count": null,
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@@ -272,7 +275,7 @@
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+ "execution_count": null,
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@@ -318,7 +321,7 @@
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@@ -358,7 +361,7 @@
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@@ -399,7 +402,7 @@
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@@ -448,7 +451,7 @@
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@@ -500,7 +503,7 @@
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@@ -522,7 +525,7 @@
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@@ -576,7 +579,7 @@
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@@ -592,7 +595,7 @@
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@@ -650,7 +653,7 @@
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@@ -689,7 +692,7 @@
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- "execution_count": 16,
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"metadata": {
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@@ -717,7 +720,7 @@
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{
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+ "execution_count": null,
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@@ -731,7 +734,7 @@
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{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": null,
"metadata": {
"colab": {
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@@ -767,14 +770,16 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "JPO9xs4bToPb"
+ },
"source": [
"We now have obtained optimized parameters $W$ and $b$. Note that their values are similar to the original values used when generating the dataset ($W=2, b=1$)"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -800,7 +805,7 @@
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{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": null,
"metadata": {
"colab": {
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@@ -854,7 +859,7 @@
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{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -929,7 +934,7 @@
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{
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- "execution_count": 23,
+ "execution_count": null,
"metadata": {
"id": "j0OTPkGpwHl7",
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@@ -952,7 +957,7 @@
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{
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- "execution_count": 24,
+ "execution_count": null,
"metadata": {
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@@ -981,7 +986,7 @@
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{
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- "execution_count": 25,
+ "execution_count": null,
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@@ -1035,7 +1040,7 @@
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{
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- "execution_count": 26,
+ "execution_count": null,
"metadata": {
"id": "J1KaixW-cMWJ"
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@@ -1079,7 +1084,7 @@
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{
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- "execution_count": 27,
+ "execution_count": null,
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"id": "kdDxWeCqwHl8",
"trusted": true
@@ -1110,17 +1115,18 @@
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{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PfyqjVb2wHl8",
- "outputId": "b3a685a9-304c-4e7e-adf9-2858cc47c3a5",
+ "outputId": "f9f5af23-005e-42e0-928b-9890b6c4e0cf",
"trusted": true
},
"outputs": [
{
+ "output_type": "execute_result",
"data": {
"text/plain": [
"[tensor([[ 1.5442, 2.5290],\n",
@@ -1142,9 +1148,8 @@
" tensor([1., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 1., 1., 1.])]"
]
},
- "execution_count": 28,
"metadata": {},
- "output_type": "execute_result"
+ "execution_count": 14
}
],
"source": [
@@ -1166,7 +1171,7 @@
},
{
"cell_type": "code",
- "execution_count": 78,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -1206,14 +1211,16 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "nnyEjYAWToPd"
+ },
"source": [
"Obtained parameters:"
]
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -1246,7 +1253,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@@ -1293,7 +1300,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -1320,7 +1327,9 @@
},
{
"cell_type": "markdown",
- "metadata": {},
+ "metadata": {
+ "id": "Fv7JxC3uToPe"
+ },
"source": [
"Let's explain what is going on here:\n",
"* `pred` is the vector of predicted probabilities for the whole validation dataset. We compute it by running original validation data `valid_x` through our network, and applying `sigmoid` to get probabilities.\n",
@@ -1357,7 +1366,7 @@
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{
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- "execution_count": 32,
+ "execution_count": null,
"metadata": {
"colab": {
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@@ -1395,7 +1404,7 @@
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{
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- "execution_count": 33,
+ "execution_count": null,
"metadata": {
"id": "B4AxyrFMozh0"
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@@ -1415,7 +1424,7 @@
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{
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- "execution_count": 34,
+ "execution_count": null,
"metadata": {
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@@ -1469,7 +1478,7 @@
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{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": null,
"metadata": {
"colab": {
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@@ -1526,7 +1535,7 @@
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{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": null,
"metadata": {
"colab": {
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@@ -1563,7 +1572,7 @@
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{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -1606,7 +1615,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": null,
"metadata": {
"colab": {
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@@ -1647,7 +1656,7 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@@ -1690,48 +1699,12191 @@
},
{
"cell_type": "markdown",
- "metadata": {},
"source": [
- "## Takeaways\n",
+ "## Defining a Network as PyTorch Lightning Module"
+ ],
+ "metadata": {
+ "id": "7THJ0lhITxDi"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Let's wrap the written PyTorch model code in PyTorch Lightining module. This allows to work with your model more conveniently and flexibly using various Lightining methods for training and accuracy testing."
+ ],
+ "metadata": {
+ "id": "bPpYZFQAXNMV"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "First we need to install and import PyTorch Lightining. This can be done with the command\n",
"\n",
- "* PyTorch allows you to operate on tensors at low level, you have most flexibility.\n",
- "* There are convenient tools to work with data, such as Datasets and Dataloaders.\n",
- "* You can define neural network architectures using `Sequential` syntax, or inheriting a class from `torch.nn.Module`\n",
- "* For even simpler approach to defining and training a network - look into PyTorch Lightning"
- ]
- }
- ],
- "metadata": {
- "accelerator": "GPU",
- "celltoolbar": "Slideshow",
- "colab": {
- "collapsed_sections": [],
- "name": "IntroPyTorch.ipynb",
- "provenance": []
+ "```\n",
+ "pip install pytorch-lightning\n",
+ "```\n",
+ "or\n",
+ "```\n",
+ "conda install -c conda-forge pytorch-lightning\n",
+ "```"
+ ],
+ "metadata": {
+ "id": "Crqwpx6gZUm3"
+ }
},
- "interpreter": {
- "hash": "0cb620c6d4b9f7a635928804c26cf22403d89d98d79684e4529119355ee6d5a5"
+ {
+ "cell_type": "code",
+ "source": [
+ "import pytorch_lightning as pl"
+ ],
+ "metadata": {
+ "id": "_bBSXSELVlRK"
+ },
+ "execution_count": null,
+ "outputs": []
},
- "kernelspec": {
- "display_name": "Python 3.8.12 64-bit (conda)",
- "name": "python3"
+ {
+ "cell_type": "markdown",
+ "source": [
+ "In order for our code to work in Lightning, we need to do the following:\n",
+ "\n",
+ "1. Create a subclass of `pl.LightningModule` and add to it model architecture in `__init__` method and `forward` pass method.\n",
+ "2. Move used optimizer to the `configure_optimizers()` method.\n",
+ "3. Define the training and validation process in methods `training_step` and `validation_step` respectively.\n",
+ "4. (Optional) Implement a testing (`test_step` method) and prediction process (`predict_step` method).\n",
+ "\n",
+ "It should also be understood that PyTorch Lightning has a built-in translation of models to different devices, depending on where the incoming data from the `DataLoaders` is located. Therefore, all calls `.cuda()` or `.to(device)` should be removed from the code."
+ ],
+ "metadata": {
+ "id": "_Aaz4FNpZjqL"
+ }
},
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
+ {
+ "cell_type": "code",
+ "source": [
+ "class MyNetPL(pl.LightningModule):\n",
+ " def __init__(self, hidden_size = 10, func = torch.nn.Sigmoid()):\n",
+ " super().__init__()\n",
+ " self.fc1 = torch.nn.Linear(2,hidden_size)\n",
+ " self.func = func\n",
+ " self.fc2 = torch.nn.Linear(hidden_size,1)\n",
+ "\n",
+ " self.val_epoch_num = 0 # for logging\n",
+ "\n",
+ " def forward(self, x):\n",
+ " x = self.fc1(x)\n",
+ " x = self.func(x)\n",
+ " x = self.fc2(x)\n",
+ " return x\n",
+ "\n",
+ " def training_step(self, batch, batch_nb):\n",
+ " x, y = batch\n",
+ " y_res = self(x).view(-1)\n",
+ " loss = torch.nn.functional.binary_cross_entropy_with_logits(y_res, y)\n",
+ " return loss\n",
+ "\n",
+ " def configure_optimizers(self):\n",
+ " optimizer = torch.optim.SGD(self.parameters(), lr = 0.005)\n",
+ " return optimizer\n",
+ " \n",
+ " def validation_step(self, batch, batch_nb):\n",
+ " x, y = batch\n",
+ " y_res = self(x).view(-1)\n",
+ " val_loss = torch.nn.functional.binary_cross_entropy_with_logits(y_res, y)\n",
+ " print(\"Epoch \", self.val_epoch_num, \": val loss = \", val_loss.item(), \" val acc = \",((torch.sigmoid(y_res.flatten())>0.5).float()==y).float().mean().item(), sep = \"\")\n",
+ " self.val_epoch_num += 1"
+ ],
+ "metadata": {
+ "id": "0vp2ROQ9UHeE"
},
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.8.12"
+ "execution_count": null,
+ "outputs": []
},
- "livereveal": {
- "start_slideshow_at": "selected"
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Let's also add validation `Dataset` and `DataLoader`:"
+ ],
+ "metadata": {
+ "id": "tuWOgQabncMG"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "valid_dataset = torch.utils.data.TensorDataset(torch.tensor(valid_x),torch.tensor(valid_labels,dtype=torch.float32))\n",
+ "valid_dataloader = torch.utils.data.DataLoader(valid_dataset, batch_size = 16)"
+ ],
+ "metadata": {
+ "id": "h3bAMM8RVckT"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "Now our model is ready for training. In Pytorch Lightning, this process is implemented through an object of the `Trainer` class, which essentially \"mixes\" the model with any datasets."
+ ],
+ "metadata": {
+ "id": "yj0Cd6OOnoyy"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "net = MyNetPL(func=torch.nn.ReLU())\n",
+ "trainer = pl.Trainer(max_epochs = 30, log_every_n_steps = 1, accelerator='gpu', devices=1)\n",
+ "trainer.fit(model = net, train_dataloaders = dataloader, val_dataloaders = valid_dataloader)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 864,
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+ "outputId": "7c60f8aa-67e6-4343-c72e-d6e356d60e6c"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "GPU available: True, used: True\n",
+ "TPU available: False, using: 0 TPU cores\n",
+ "IPU available: False, using: 0 IPUs\n",
+ "HPU available: False, using: 0 HPUs\n",
+ "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
+ "\n",
+ " | Name | Type | Params\n",
+ "--------------------------------\n",
+ "0 | fc1 | Linear | 30 \n",
+ "1 | func | ReLU | 0 \n",
+ "2 | fc2 | Linear | 11 \n",
+ "--------------------------------\n",
+ "41 Trainable params\n",
+ "0 Non-trainable params\n",
+ "41 Total params\n",
+ "0.000 Total estimated model params size (MB)\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Sanity Checking: 0it [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "1657a223e5524ca682ea081b9e4addea"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Epoch 0: val loss = 0.7213451266288757 val acc = 0.3333333432674408\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Training: 0it [00:00, ?it/s]"
+ ],
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+ "metadata": {}
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+ "Validation: 0it [00:00, ?it/s]"
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+ "Epoch 26: val loss = 0.6253498792648315 val acc = 0.7333333492279053\n"
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+ "Epoch 27: val loss = 0.6225143671035767 val acc = 0.7333333492279053\n"
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+ "Epoch 28: val loss = 0.6197248101234436 val acc = 0.7333333492279053\n"
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+ "text": [
+ "Epoch 29: val loss = 0.6169812679290771 val acc = 0.7333333492279053\n"
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+ },
+ {
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+ ],
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+ "text": [
+ "Epoch 30: val loss = 0.6142613887786865 val acc = 0.8000000715255737\n"
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+ }
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+ },
+ {
+ "cell_type": "markdown",
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+ "## Takeaways\n",
+ "\n",
+ "* PyTorch allows you to operate on tensors at low level, you have most flexibility.\n",
+ "* There are convenient tools to work with data, such as Datasets and Dataloaders.\n",
+ "* You can define neural network architectures using `Sequential` syntax, or inheriting a class from `torch.nn.Module`\n",
+ "* For even simpler approach to defining and training a network - look into PyTorch Lightning"
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+ }
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