Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p torch,pytorch_lightning
Author: Sebastian Raschka Python implementation: CPython Python version : 3.9.6 IPython version : 7.29.0 torch : 1.8.0 pytorch_lightning: 1.5.2
import pytorch_lightning as pl
import torch
import torch.nn as nn
from torchmetrics import Accuracy
class MultiLayerPerceptron(pl.LightningModule):
def __init__(self,image_shape=(1, 28, 28), hidden_units=(32, 16)):
super().__init__()
# new PL attributes:
self.train_acc = Accuracy()
self.valid_acc = Accuracy()
self.test_acc = Accuracy()
# Model similar to previous section:
input_size = image_shape[0] * image_shape[1] * image_shape[2]
all_layers = [nn.Flatten()]
for hidden_unit in hidden_units:
layer = nn.Linear(input_size, hidden_unit)
all_layers.append(layer)
all_layers.append(nn.ReLU())
input_size = hidden_unit
all_layers.append(nn.Linear(hidden_units[-1], 10))
all_layers.append(nn.Softmax(dim=1))
self.model = nn.Sequential(*all_layers)
def forward(self, x):
x = self.model(x)
return x
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = nn.functional.cross_entropy(self(x), y)
preds = torch.argmax(logits, dim=1)
self.train_acc.update(preds, y)
self.log("train_loss", loss, prog_bar=True)
return loss
def training_epoch_end(self, outs):
self.log("train_acc", self.train_acc.compute())
self.train_acc.reset()
def validation_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = nn.functional.cross_entropy(self(x), y)
preds = torch.argmax(logits, dim=1)
self.valid_acc.update(preds, y)
self.log("valid_loss", loss, prog_bar=True)
return loss
def validation_epoch_end(self, outs):
self.log("valid_acc", self.valid_acc.compute(), prog_bar=True)
self.valid_acc.reset()
def test_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = nn.functional.cross_entropy(self(x), y)
preds = torch.argmax(logits, dim=1)
self.test_acc.update(preds, y)
self.log("test_loss", loss, prog_bar=True)
self.log("test_acc", self.test_acc.compute(), prog_bar=True)
return loss
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
return optimizer
from torch.utils.data import DataLoader
from torch.utils.data import random_split
from torchvision.datasets import MNIST
from torchvision import transforms
class MnistDataModule(pl.LightningDataModule):
def __init__(self, data_path='./'):
super().__init__()
self.data_path = data_path
self.transform = transforms.Compose([transforms.ToTensor()])
def prepare_data(self):
MNIST(root=self.data_path, download=True)
def setup(self, stage=None):
# stage is either 'fit', 'validate', 'test', or 'predict'
# here note relevant
mnist_all = MNIST(
root=self.data_path,
train=True,
transform=self.transform,
download=False
)
self.train, self.val = random_split(
mnist_all, [55000, 5000], generator=torch.Generator().manual_seed(1)
)
self.test = MNIST(
root=self.data_path,
train=False,
transform=self.transform,
download=False
)
def train_dataloader(self):
return DataLoader(self.train, batch_size=64, num_workers=4)
def val_dataloader(self):
return DataLoader(self.val, batch_size=64, num_workers=4)
def test_dataloader(self):
return DataLoader(self.test, batch_size=64, num_workers=4)
torch.manual_seed(1)
mnist_dm = MnistDataModule()
from pytorch_lightning.callbacks import ModelCheckpoint
callbacks = [ModelCheckpoint(save_top_k=1, mode='max', monitor="valid_acc")] # save top 1 model
mnistclassifier = MultiLayerPerceptron()
if torch.cuda.is_available(): # if you have GPUs
trainer = pl.Trainer(max_epochs=300, callbacks=callbacks, gpu=1)
else:
trainer = pl.Trainer(max_epochs=300, callbacks=callbacks)
trainer.fit(model=mnistclassifier, datamodule=mnist_dm)
GPU available: False, used: False TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs | Name | Type | Params ----------------------------------------- 0 | train_acc | Accuracy | 0 1 | valid_acc | Accuracy | 0 2 | test_acc | Accuracy | 0 3 | model | Sequential | 25.8 K ----------------------------------------- 25.8 K Trainable params 0 Non-trainable params 25.8 K Total params 0.103 Total estimated model params size (MB)
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trainer.test(model=mnistclassifier, datamodule=mnist_dm)
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-------------------------------------------------------------------------------- DATALOADER:0 TEST RESULTS {'test_acc': 0.9600539803504944, 'test_loss': 1.498080849647522} --------------------------------------------------------------------------------
[{'test_loss': 1.498080849647522, 'test_acc': 0.9600539803504944}]
from IPython.display import Image
Image(filename='lightning-mlp-best-model_images/1.png')
# Start tensorboard
%load_ext tensorboard
%tensorboard --logdir lightning_logs/
Reusing TensorBoard on port 6006 (pid 54170), started 6:25:38 ago. (Use '!kill 54170' to kill it.)
Based on the TensorBoard output above, we can see that the best model is not the model after the final training step. We can load the best for evaluation as follows:
trainer.test(model=mnistclassifier, datamodule=mnist_dm, ckpt_path='best')
/Users/sebastian/miniforge3/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py:469: LightningDeprecationWarning: DataModule.setup has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.setup. rank_zero_deprecation( Restoring states from the checkpoint path at /Users/sebastian/Desktop/lightning_logs/version_0/checkpoints/epoch=273-step=235639.ckpt Loaded model weights from checkpoint at /Users/sebastian/Desktop/lightning_logs/version_0/checkpoints/epoch=273-step=235639.ckpt
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-------------------------------------------------------------------------------- DATALOADER:0 TEST RESULTS {'test_acc': 0.9628065228462219, 'test_loss': 1.4963043928146362} --------------------------------------------------------------------------------
/Users/sebastian/miniforge3/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py:469: LightningDeprecationWarning: DataModule.teardown has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.teardown. rank_zero_deprecation(
[{'test_loss': 1.4963043928146362, 'test_acc': 0.9628065228462219}]