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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"`Learn the Basics <intro.html>`_ ||\n",
"**Quickstart** ||\n",
"`Tensors <tensorqs_tutorial.html>`_ ||\n",
"`Datasets & DataLoaders <data_tutorial.html>`_ ||\n",
"`Transforms <transforms_tutorial.html>`_ ||\n",
"`Build Model <buildmodel_tutorial.html>`_ ||\n",
"`Autograd <autogradqs_tutorial.html>`_ ||\n",
"`Optimization <optimization_tutorial.html>`_ ||\n",
"`Save & Load Model <saveloadrun_tutorial.html>`_\n",
"\n",
"Quickstart\n",
"===================\n",
"This section runs through the API for common tasks in machine learning. Refer to the links in each section to dive deeper.\n",
"\n",
"Working with data\n",
"-----------------\n",
"PyTorch has two `primitives to work with data <https://pytorch.org/docs/stable/data.html>`_:\n",
"``torch.utils.data.DataLoader`` and ``torch.utils.data.Dataset``.\n",
"``Dataset`` stores the samples and their corresponding labels, and ``DataLoader`` wraps an iterable around\n",
"the ``Dataset``.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import torch\n",
"from torch import nn\n",
"from torch.utils.data import DataLoader\n",
"from torchvision import datasets\n",
"from torchvision.transforms import ToTensor, Lambda, Compose\n",
"import matplotlib.pyplot as plt\n",
"from huggingface_hub import push_to_hub_keras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"PyTorch offers domain-specific libraries such as `TorchText <https://pytorch.org/text/stable/index.html>`_,\n",
"`TorchVision <https://pytorch.org/vision/stable/index.html>`_, and `TorchAudio <https://pytorch.org/audio/stable/index.html>`_,\n",
"all of which include datasets. For this tutorial, we will be using a TorchVision dataset.\n",
"\n",
"The ``torchvision.datasets`` module contains ``Dataset`` objects for many real-world vision data like\n",
"CIFAR, COCO (`full list here <https://pytorch.org/vision/stable/datasets.html>`_). In this tutorial, we\n",
"use the FashionMNIST dataset. Every TorchVision ``Dataset`` includes two arguments: ``transform`` and\n",
"``target_transform`` to modify the samples and labels respectively.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Download training data from open datasets.\n",
"training_data = datasets.FashionMNIST(\n",
" root=\"data\",\n",
" train=True,\n",
" download=True,\n",
" transform=ToTensor(),\n",
")\n",
"\n",
"# Download test data from open datasets.\n",
"test_data = datasets.FashionMNIST(\n",
" root=\"data\",\n",
" train=False,\n",
" download=True,\n",
" transform=ToTensor(),\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We pass the ``Dataset`` as an argument to ``DataLoader``. This wraps an iterable over our dataset, and supports\n",
"automatic batching, sampling, shuffling and multiprocess data loading. Here we define a batch size of 64, i.e. each element\n",
"in the dataloader iterable will return a batch of 64 features and labels.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of X [N, C, H, W]: torch.Size([64, 1, 28, 28])\n",
"Shape of y: torch.Size([64]) torch.int64\n"
]
}
],
"source": [
"batch_size = 64\n",
"\n",
"# Create data loaders.\n",
"train_dataloader = DataLoader(training_data, batch_size=batch_size)\n",
"test_dataloader = DataLoader(test_data, batch_size=batch_size)\n",
"\n",
"for X, y in test_dataloader:\n",
" print(\"Shape of X [N, C, H, W]: \", X.shape)\n",
" print(\"Shape of y: \", y.shape, y.dtype)\n",
" break"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read more about `loading data in PyTorch <data_tutorial.html>`_.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"--------------\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creating Models\n",
"------------------\n",
"To define a neural network in PyTorch, we create a class that inherits\n",
"from `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_. We define the layers of the network\n",
"in the ``__init__`` function and specify how data will pass through the network in the ``forward`` function. To accelerate\n",
"operations in the neural network, we move it to the GPU if available.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cpu device\n",
"NeuralNetwork(\n",
" (flatten): Flatten(start_dim=1, end_dim=-1)\n",
" (linear_relu_stack): Sequential(\n",
" (0): Linear(in_features=784, out_features=512, bias=True)\n",
" (1): ReLU()\n",
" (2): Linear(in_features=512, out_features=512, bias=True)\n",
" (3): ReLU()\n",
" (4): Linear(in_features=512, out_features=10, bias=True)\n",
" )\n",
")\n"
]
}
],
"source": [
"# Get cpu or gpu device for training.\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"print(f\"Using {device} device\")\n",
"\n",
"# Define model\n",
"class NeuralNetwork(nn.Module):\n",
" def __init__(self):\n",
" super(NeuralNetwork, self).__init__()\n",
" self.flatten = nn.Flatten()\n",
" self.linear_relu_stack = nn.Sequential(\n",
" nn.Linear(28*28, 512),\n",
" nn.ReLU(),\n",
" nn.Linear(512, 512),\n",
" nn.ReLU(),\n",
" nn.Linear(512, 10)\n",
" )\n",
"\n",
" def forward(self, x):\n",
" x = self.flatten(x)\n",
" logits = self.linear_relu_stack(x)\n",
" return logits\n",
"\n",
"model = NeuralNetwork().to(device)\n",
"print(model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read more about `building neural networks in PyTorch <buildmodel_tutorial.html>`_.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"--------------\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Optimizing the Model Parameters\n",
"----------------------------------------\n",
"To train a model, we need a `loss function <https://pytorch.org/docs/stable/nn.html#loss-functions>`_\n",
"and an `optimizer <https://pytorch.org/docs/stable/optim.html>`_.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"loss_fn = nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In a single training loop, the model makes predictions on the training dataset (fed to it in batches), and\n",
"backpropagates the prediction error to adjust the model's parameters.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def train(dataloader, model, loss_fn, optimizer):\n",
" size = len(dataloader.dataset)\n",
" model.train()\n",
" for batch, (X, y) in enumerate(dataloader):\n",
" X, y = X.to(device), y.to(device)\n",
"\n",
" # Compute prediction error\n",
" pred = model(X)\n",
" loss = loss_fn(pred, y)\n",
"\n",
" # Backpropagation\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" if batch % 100 == 0:\n",
" loss, current = loss.item(), batch * len(X)\n",
" print(f\"loss: {loss:>7f} [{current:>5d}/{size:>5d}]\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We also check the model's performance against the test dataset to ensure it is learning.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def test(dataloader, model, loss_fn):\n",
" size = len(dataloader.dataset)\n",
" num_batches = len(dataloader)\n",
" model.eval()\n",
" test_loss, correct = 0, 0\n",
" with torch.no_grad():\n",
" for X, y in dataloader:\n",
" X, y = X.to(device), y.to(device)\n",
" pred = model(X)\n",
" test_loss += loss_fn(pred, y).item()\n",
" correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n",
" test_loss /= num_batches\n",
" correct /= size\n",
" print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The training process is conducted over several iterations (*epochs*). During each epoch, the model learns\n",
"parameters to make better predictions. We print the model's accuracy and loss at each epoch; we'd like to see the\n",
"accuracy increase and the loss decrease with every epoch.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1\n",
"-------------------------------\n",
"loss: 2.293067 [ 0/60000]\n",
"loss: 2.287422 [ 6400/60000]\n",
"loss: 2.265790 [12800/60000]\n",
"loss: 2.274793 [19200/60000]\n",
"loss: 2.257332 [25600/60000]\n",
"loss: 2.222204 [32000/60000]\n",
"loss: 2.240200 [38400/60000]\n",
"loss: 2.206084 [44800/60000]\n",
"loss: 2.190236 [51200/60000]\n",
"loss: 2.176934 [57600/60000]\n",
"Test Error: \n",
" Accuracy: 42.4%, Avg loss: 2.162450 \n",
"\n",
"Epoch 2\n",
"-------------------------------\n",
"loss: 2.161891 [ 0/60000]\n",
"loss: 2.160867 [ 6400/60000]\n",
"loss: 2.099223 [12800/60000]\n",
"loss: 2.127940 [19200/60000]\n",
"loss: 2.089684 [25600/60000]\n",
"loss: 2.018054 [32000/60000]\n",
"loss: 2.060461 [38400/60000]\n",
"loss: 1.981958 [44800/60000]\n",
"loss: 1.971331 [51200/60000]\n",
"loss: 1.930486 [57600/60000]\n",
"Test Error: \n",
" Accuracy: 58.1%, Avg loss: 1.909495 \n",
"\n",
"Epoch 3\n",
"-------------------------------\n",
"loss: 1.930542 [ 0/60000]\n",
"loss: 1.913976 [ 6400/60000]\n",
"loss: 1.788895 [12800/60000]\n",
"loss: 1.838503 [19200/60000]\n",
"loss: 1.757226 [25600/60000]\n",
"loss: 1.682464 [32000/60000]\n",
"loss: 1.722755 [38400/60000]\n",
"loss: 1.617113 [44800/60000]\n",
"loss: 1.632282 [51200/60000]\n",
"loss: 1.548769 [57600/60000]\n",
"Test Error: \n",
" Accuracy: 61.0%, Avg loss: 1.543196 \n",
"\n",
"Epoch 4\n",
"-------------------------------\n",
"loss: 1.601020 [ 0/60000]\n",
"loss: 1.574128 [ 6400/60000]\n",
"loss: 1.412696 [12800/60000]\n",
"loss: 1.496537 [19200/60000]\n",
"loss: 1.391789 [25600/60000]\n",
"loss: 1.360881 [32000/60000]\n",
"loss: 1.398112 [38400/60000]\n",
"loss: 1.316551 [44800/60000]\n",
"loss: 1.347136 [51200/60000]\n",
"loss: 1.253991 [57600/60000]\n",
"Test Error: \n",
" Accuracy: 62.8%, Avg loss: 1.267020 \n",
"\n",
"Epoch 5\n",
"-------------------------------\n",
"loss: 1.336873 [ 0/60000]\n",
"loss: 1.324502 [ 6400/60000]\n",
"loss: 1.153551 [12800/60000]\n",
"loss: 1.265215 [19200/60000]\n",
"loss: 1.149221 [25600/60000]\n",
"loss: 1.156962 [32000/60000]\n",
"loss: 1.194912 [38400/60000]\n",
"loss: 1.133846 [44800/60000]\n",
"loss: 1.164861 [51200/60000]\n",
"loss: 1.080542 [57600/60000]\n",
"Test Error: \n",
" Accuracy: 64.1%, Avg loss: 1.094896 \n",
"\n",
"Done!\n"
]
}
],
"source": [
"epochs = 5\n",
"for t in range(epochs):\n",
" print(f\"Epoch {t+1}\\n-------------------------------\")\n",
" train(train_dataloader, model, loss_fn, optimizer)\n",
" test(test_dataloader, model, loss_fn)\n",
"print(\"Done!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read more about `Training your model <optimization_tutorial.html>`_.\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"--------------\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Saving Models\n",
"-------------\n",
"A common way to save a model is to serialize the internal state dictionary (containing the model parameters).\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved PyTorch Model State to model.pth\n"
]
}
],
"source": [
"torch.save(model.state_dict(), \"pytorch_model.bin\")\n",
"print(\"Saved PyTorch Model State to pytorch_model.bin\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Loading Models\n",
"----------------------------\n",
"\n",
"The process for loading a model includes re-creating the model structure and loading\n",
"the state dictionary into it.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model = NeuralNetwork()\n",
"model.load_state_dict(torch.load(\"model.pth\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This model can now be used to make predictions.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: \"Shirt\", Actual: \"Shirt\"\n"
]
}
],
"source": [
"classes = [\n",
" \"T-shirt/top\",\n",
" \"Trouser\",\n",
" \"Pullover\",\n",
" \"Dress\",\n",
" \"Coat\",\n",
" \"Sandal\",\n",
" \"Shirt\",\n",
" \"Sneaker\",\n",
" \"Bag\",\n",
" \"Ankle boot\",\n",
"]\n",
"\n",
"model.eval()\n",
"x, y = test_data[4][0], test_data[4][1]\n",
"with torch.no_grad():\n",
" pred = model(x)\n",
" predicted, actual = classes[pred[0].argmax(0)], classes[y]\n",
" print(f'Predicted: \"{predicted}\", Actual: \"{actual}\"')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[[0.0000, 0.0000, 0.0000, 0.0078, 0.0000, 0.0039, 0.0039, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.2235, 0.2627, 0.2863, 0.2980, 0.2980,\n",
" 0.3255, 0.2431, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0039, 0.0039, 0.0039, 0.0000, 0.0000,\n",
" 0.0510, 0.3098, 0.5020, 0.7882, 0.6353, 0.6314, 0.6784, 0.7529,\n",
" 0.6745, 0.7098, 0.7216, 0.4235, 0.1176, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0039, 0.0000, 0.0000, 0.4000,\n",
" 0.5451, 0.5569, 0.4039, 0.4510, 0.6353, 0.6039, 0.6471, 0.6000,\n",
" 0.5451, 0.5059, 0.5882, 0.5412, 0.6706, 0.6314, 0.1020, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0039, 0.0000, 0.0000, 0.4157, 0.4863,\n",
" 0.4235, 0.4039, 0.4157, 0.3647, 0.3922, 0.7059, 0.6118, 0.5765,\n",
" 0.5412, 0.3333, 0.6157, 0.4471, 0.4863, 0.6039, 0.6157, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0078, 0.0000, 0.1137, 0.5255, 0.3961,\n",
" 0.4431, 0.4235, 0.3804, 0.4549, 0.3176, 0.5725, 0.7176, 0.6431,\n",
" 0.4353, 0.5725, 0.5137, 0.4784, 0.5176, 0.5686, 0.6627, 0.3647,\n",
" 0.0000, 0.0039, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.2549, 0.5137, 0.4118,\n",
" 0.3961, 0.4235, 0.3922, 0.4078, 0.3804, 0.2902, 0.8078, 0.6824,\n",
" 0.4510, 0.5882, 0.4235, 0.4667, 0.5725, 0.5961, 0.6353, 0.5529,\n",
" 0.0000, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.4235, 0.4824, 0.4392,\n",
" 0.4157, 0.3843, 0.3922, 0.3961, 0.4353, 0.2824, 0.5333, 0.5176,\n",
" 0.4392, 0.4510, 0.4275, 0.5569, 0.5882, 0.6275, 0.6353, 0.7647,\n",
" 0.0000, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.5294, 0.4784, 0.4667,\n",
" 0.4392, 0.3255, 0.3647, 0.3804, 0.4157, 0.4510, 0.3569, 0.4275,\n",
" 0.3255, 0.4275, 0.4902, 0.6471, 0.5490, 0.7569, 0.6275, 0.6902,\n",
" 0.0235, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0902, 0.5294, 0.5176, 0.5843,\n",
" 0.4078, 0.3059, 0.3765, 0.3804, 0.4039, 0.4235, 0.4235, 0.4510,\n",
" 0.3294, 0.4471, 0.5843, 0.6196, 0.5765, 0.8196, 0.6275, 0.6980,\n",
" 0.2039, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.2235, 0.4863, 0.5137, 0.6275,\n",
" 0.4039, 0.3765, 0.3961, 0.4275, 0.4275, 0.4353, 0.4235, 0.4471,\n",
" 0.4157, 0.4431, 0.6118, 0.6392, 0.6118, 0.7686, 0.6549, 0.6824,\n",
" 0.3333, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.3373, 0.4549, 0.4941, 0.6275,\n",
" 0.5176, 0.4000, 0.3765, 0.4078, 0.4196, 0.3843, 0.3647, 0.4824,\n",
" 0.4549, 0.4392, 0.5843, 0.6275, 0.7098, 0.7294, 0.6353, 0.6353,\n",
" 0.4824, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.4392, 0.4471, 0.4392, 0.6549,\n",
" 0.5725, 0.3922, 0.3922, 0.3961, 0.4196, 0.3765, 0.3922, 0.4941,\n",
" 0.4039, 0.4706, 0.5529, 0.6196, 0.6549, 0.7333, 0.5765, 0.5804,\n",
" 0.6667, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.4863, 0.4627, 0.3961, 0.7725,\n",
" 0.3490, 0.3961, 0.3922, 0.3765, 0.4235, 0.4039, 0.4235, 0.4784,\n",
" 0.4196, 0.4980, 0.5451, 0.5882, 0.4667, 0.7686, 0.5686, 0.5569,\n",
" 0.7020, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.5137, 0.4510, 0.3804, 0.7765,\n",
" 0.1843, 0.4235, 0.3765, 0.3765, 0.4157, 0.4667, 0.4000, 0.4706,\n",
" 0.4039, 0.4824, 0.5490, 0.5882, 0.3176, 0.8078, 0.5725, 0.5294,\n",
" 0.7608, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0157, 0.5333, 0.4627, 0.3843, 0.7569,\n",
" 0.0824, 0.4275, 0.3765, 0.4157, 0.4000, 0.5059, 0.3922, 0.4667,\n",
" 0.4000, 0.4627, 0.5529, 0.6000, 0.1765, 0.8471, 0.5804, 0.5451,\n",
" 0.8039, 0.0471, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0941, 0.5373, 0.4588, 0.3961, 0.7333,\n",
" 0.0980, 0.4431, 0.3608, 0.4392, 0.3686, 0.4706, 0.4118, 0.4980,\n",
" 0.3804, 0.4510, 0.5569, 0.5882, 0.0745, 0.8353, 0.5804, 0.5137,\n",
" 0.8000, 0.1412, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.1569, 0.5529, 0.4275, 0.4588, 0.6196,\n",
" 0.0471, 0.4863, 0.3529, 0.4549, 0.3765, 0.4588, 0.4431, 0.5333,\n",
" 0.3686, 0.4353, 0.5765, 0.6392, 0.1216, 0.7490, 0.5725, 0.5255,\n",
" 0.8078, 0.2275, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.1529, 0.5059, 0.4000, 0.5765, 0.4667,\n",
" 0.0000, 0.4706, 0.3529, 0.4667, 0.3961, 0.4549, 0.4157, 0.4980,\n",
" 0.4000, 0.4471, 0.5725, 0.7059, 0.0784, 0.5725, 0.6235, 0.5059,\n",
" 0.8000, 0.2745, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.2275, 0.4941, 0.4353, 0.6353, 0.3961,\n",
" 0.0824, 0.5176, 0.3490, 0.4824, 0.4235, 0.4157, 0.4000, 0.4941,\n",
" 0.4353, 0.4549, 0.5529, 0.6980, 0.1961, 0.4392, 0.6627, 0.5412,\n",
" 0.6431, 0.3294, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.4235, 0.5255, 0.5255, 0.7255, 0.3294,\n",
" 0.2863, 0.4824, 0.3412, 0.4784, 0.4353, 0.4000, 0.4157, 0.5020,\n",
" 0.4471, 0.4275, 0.5255, 0.6824, 0.3804, 0.3843, 0.6275, 0.5765,\n",
" 0.6863, 0.5294, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.3804, 0.5569, 0.6627, 0.7765, 0.1451,\n",
" 0.3294, 0.4196, 0.3804, 0.4784, 0.4392, 0.4275, 0.4392, 0.4941,\n",
" 0.4000, 0.3765, 0.5137, 0.6745, 0.5020, 0.2000, 0.9961, 0.6588,\n",
" 0.6431, 0.4353, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0471, 0.1804, 0.0078,\n",
" 0.4667, 0.4000, 0.4275, 0.4824, 0.3765, 0.4549, 0.4784, 0.5176,\n",
" 0.4157, 0.4157, 0.5059, 0.5922, 0.7216, 0.1020, 0.0784, 0.0314,\n",
" 0.0000, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0510,\n",
" 0.5373, 0.3961, 0.4471, 0.3922, 0.4157, 0.5255, 0.5294, 0.5059,\n",
" 0.4078, 0.4353, 0.4824, 0.5922, 0.7608, 0.2902, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0039, 0.0118, 0.0000, 0.2863,\n",
" 0.5176, 0.3961, 0.4078, 0.4000, 0.5490, 0.4235, 0.4235, 0.5137,\n",
" 0.4157, 0.4667, 0.4431, 0.5569, 0.6549, 0.5294, 0.0000, 0.0039,\n",
" 0.0000, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.4392,\n",
" 0.4627, 0.4196, 0.4078, 0.5451, 0.4275, 0.3804, 0.4824, 0.5412,\n",
" 0.4196, 0.4980, 0.4706, 0.5333, 0.6314, 0.6235, 0.0000, 0.0000,\n",
" 0.0039, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0118, 0.0000, 0.5569,\n",
" 0.5804, 0.4392, 0.4118, 0.3961, 0.3255, 0.4902, 0.4824, 0.5608,\n",
" 0.4078, 0.4510, 0.3922, 0.4941, 0.6588, 0.6980, 0.0275, 0.0000,\n",
" 0.0078, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0078, 0.0000, 0.0353,\n",
" 0.4941, 0.7216, 0.7843, 0.6549, 0.6392, 0.6706, 0.5882, 0.6549,\n",
" 0.6118, 0.6824, 0.7725, 0.7137, 0.6353, 0.2392, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000],\n",
" [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.1176, 0.2824, 0.3725, 0.4275, 0.4353, 0.4353,\n",
" 0.4157, 0.3961, 0.2784, 0.0471, 0.0000, 0.0000, 0.0000, 0.0000,\n",
" 0.0000, 0.0000, 0.0000, 0.0000]]])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_data[4][0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read more about `Saving & Loading your model <saveloadrun_tutorial.html>`_.\n",
"\n",
"\n"
]
}
],
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