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extend docs notes (#1004)

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This commit is contained in:
Jirka Borovec
2020-03-06 07:07:04 -05:00
committed by GitHub
parent b183782e83
commit 25bb33ad45
4 changed files with 55 additions and 9 deletions
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docs/source/introduction_guide.rst
+13 -7
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@@ -182,7 +182,8 @@ Here's the PyTorch code for loading MNIST
# transforms
# prepare transforms standard to MNIST
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
transform=transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
# data
mnist_train = MNIST(os.getcwd(), train=True, download=True)
@@ -201,8 +202,10 @@ the LightningModule
class LitMNIST(pl.LightningModule):
def train_dataloader(self):
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
mnist_train = MNIST(os.getcwd(), train=True, download=False, transform=transform)
transform=transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
mnist_train = MNIST(os.getcwd(), train=True, download=False,
transform=transform)
return DataLoader(mnist_train, batch_size=64)
Notice the code is exactly the same, except now the training dataloading has been organized by the LightningModule
@@ -314,7 +317,8 @@ For clarity, we'll recall that the full LightningModule now looks like this.
return x
def train_dataloader(self):
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
transform=transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
mnist_train = MNIST(os.getcwd(), train=True, download=False, transform=transform)
return DataLoader(mnist_train, batch_size=64)
@@ -424,7 +428,7 @@ First, change the runtime to TPU (and reinstall lightning).
Next, install the required xla library (adds support for PyTorch on TPUs)
.. code-block:: default
.. code-block:: python
import collections
from datetime import datetime, timedelta
@@ -627,8 +631,10 @@ sample split in the `train_dataloader` method.
return {'avg_val_loss': avg_loss, 'log': tensorboard_logs}
def val_dataloader(self):
transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
mnist_train = MNIST(os.getcwd(), train=True, download=False, transform=transform)
transform=transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
mnist_train = MNIST(os.getcwd(), train=True, download=False,
transform=transform)
_, mnist_val = random_split(mnist_train, [55000, 5000])
mnist_val = DataLoader(mnist_val, batch_size=64)
return mnist_val