From f0c065e1baa466340676c04d2be980bf080f069a Mon Sep 17 00:00:00 2001
From: Achille Mbogol Touye <achille.mbogol-touye@univ-grenoble-alpes.fr>
Date: Wed, 1 Nov 2023 11:53:40 +0100
Subject: [PATCH] Replace 02-CNN-MNIST_Lightning.ipynb
---
MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb | 145 +++++++++++++------
1 file changed, 103 insertions(+), 42 deletions(-)
diff --git a/MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb b/MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb
index f1559c5..2580dd1 100644
--- a/MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb
+++ b/MNIST.Lightning/02-CNN-MNIST_Lightning.ipynb
@@ -6,9 +6,9 @@
"metadata": {},
"source": [
"\n",
- "<img width=\"800px\" src=\"../fidle/img/header.svg\"></img>\n",
+ "<img width=\"800px\" src=\"../fidle/img/00-Fidle-header-01.svg\"></img>\n",
"\n",
- "## <!-- TITLE --> [LMNIST2] - Simple classification with CNN using Pytorch Lightning\n",
+ "## <!-- TITLE --> [MNIST2] - Simple classification with CNN using lightning\n",
"<!-- DESC --> An example of classification using a convolutional neural network for the famous MNIST dataset\n",
"<!-- AUTHOR : MBOGOL Touye Achille (AI/ML Engineer MIAI/SIMaP) -->\n",
"\n",
@@ -59,7 +59,7 @@
"import multiprocessing\n",
"import matplotlib.pyplot as plt\n",
"\n",
- "from lightning.pytorch.loggers.tensorboard import TensorBoardLogger\n",
+ "from lightning.pytorch.loggers import TensorBoardLogger\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from torchvision import datasets\n",
"from torchmetrics.functional import accuracy\n",
@@ -90,8 +90,16 @@
"# Load data sets \n",
"train_dataset = datasets.MNIST(root=\"data\", train=True, download=True, transform=None)\n",
"\n",
- "test_dataset= datasets.MNIST(root=\"data\", train=False, download=False, transform=None)\n",
- "\n",
+ "test_dataset= datasets.MNIST(root=\"data\", train=False, download=False, transform=None)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "a14d6fc2-b913-4eaa-9cde-5ca6785bfa12",
+ "metadata": {},
+ "outputs": [],
+ "source": [
"# print info for train data\n",
"print(train_dataset)\n",
"\n",
@@ -248,7 +256,9 @@
" - [Optimizer](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers)\n",
" - [Activation](https://www.tensorflow.org/api_docs/python/tf/keras/activations)\n",
" - [Loss](https://www.tensorflow.org/api_docs/python/tf/keras/losses)\n",
- " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)"
+ " - [Metrics](https://www.tensorflow.org/api_docs/python/tf/keras/metrics)\n",
+ " \n",
+ " `Note :` PyTorch provides losses such as the cross-entropy loss (`nn.CrossEntropyLoss`) usually use for classification problem. we're using the softmax function to predict class probabilities. With a softmax output, you want to use cross-entropy as the loss. To actually calculate the loss, we need to pass in the raw output of our network into the loss, not the output of the softmax function. This raw output is usually called the *logits* or *scores*. because in pytorch the cross entropy contain softmax function already."
]
},
{
@@ -264,6 +274,7 @@
" super().__init__()\n",
" self.num_class=num_class\n",
" self.model = nn.Sequential(\n",
+ " \n",
" # first convolution \n",
" nn.Conv2d(in_channels=1, out_channels=8, kernel_size=3, stride=1, padding=0),\n",
" nn.ReLU(),\n",
@@ -295,7 +306,9 @@
"cell_type": "code",
"execution_count": null,
"id": "37abf99b-f8ec-4048-a65d-f173ee18b234",
- "metadata": {},
+ "metadata": {
+ "scrolled": true
+ },
"outputs": [],
"source": [
"class LitModel(pl.LightningModule):\n",
@@ -303,16 +316,31 @@
" def __init__(self, MyNet):\n",
" super().__init__()\n",
" self.MyNet = MyNet\n",
- " \n",
- " def forward(self, x): # forward pass\n",
+ "\n",
+ " # forward pass\n",
+ " def forward(self, x): \n",
" return self.MyNet(x)\n",
" \n",
- " # defines the train loop\n",
+ "\n",
+ " # optimizer\n",
+ " def configure_optimizers(self):\n",
+ " optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)\n",
+ " return optimizer \n",
+ "\n",
+ " \n",
" def training_step(self, batch, batch_idx):\n",
- " x, y = batch\n",
- " y_hat= self.MyNet(x) # forward pass \n",
- " loss= F.cross_entropy(y_hat, y) # loss function\n",
- " acc=accuracy(y_hat, y,task=\"multiclass\",num_classes=10) # metrics \n",
+ " # defines the train loop\n",
+ " x, y = batch\n",
+ " \n",
+ " # forward pass \n",
+ " y_hat = self.MyNet(x) \n",
+ " \n",
+ " # loss function\n",
+ " loss= F.cross_entropy(y_hat, y) \n",
+ "\n",
+ " # metrics accuracy\n",
+ " acc=accuracy(y_hat, y,task=\"multiclass\",num_classes=10) \n",
+ " \n",
" metrics = {\"train_loss\": loss, \"train_acc\": acc}\n",
" \n",
" # logs metrics for each training_step\n",
@@ -320,43 +348,57 @@
" on_step=False ,\n",
" on_epoch=True, \n",
" prog_bar=True, \n",
- " logger=True) \n",
+ " logger=True)\n",
+ " \n",
" return loss\n",
"\n",
" \n",
- " # defines the valid loop.\n",
" def validation_step(self, batch, batch_idx):\n",
+ " # defines the valid loop.\n",
" x, y = batch\n",
- " y_hat= self.MyNet(x) # forward pass\n",
- " loss = F.cross_entropy(y_hat, y) # loss function MSE\n",
- " acc=accuracy(y_hat, y, task=\"multiclass\", num_classes=10) # metrics\n",
+ " \n",
+ " # forward pass\n",
+ " y_hat= self.MyNet(x) \n",
+ "\n",
+ " # loss function MSE\n",
+ " loss = F.cross_entropy(y_hat, y) \n",
+ "\n",
+ " # metrics accuracy\n",
+ " acc=accuracy(y_hat, y, task=\"multiclass\", num_classes=10) \n",
+ "\n",
+ " \n",
" metrics = {\"test_loss\": loss, \"test_acc\": acc}\n",
" \n",
" # logs metrics for each validation_step\n",
" self.log_dict(metrics,\n",
- " on_step=False,\n",
- " on_epoch=True, \n",
- " prog_bar=True, \n",
- " logger=True\n",
+ " on_step = False,\n",
+ " on_epoch = True, \n",
+ " prog_bar = True, \n",
+ " logger = True\n",
" ) \n",
" \n",
+ " \n",
" return metrics\n",
" \n",
" \n",
- " # complete prediction step\n",
+ " \n",
" def predict_step(self, batch, batch_idx):\n",
+ " # defnie the predict loop \n",
" x, y = batch\n",
- " y_hat = self.MyNet(x) # forward pass\n",
- " return y_hat\n",
- " \n",
- " \n",
- " # optimizer\n",
- " def configure_optimizers(self):\n",
- " optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)\n",
- " return optimizer\n",
"\n",
- " \n",
- " \n",
+ " # forward pass\n",
+ " y_hat = self.MyNet(x)\n",
+ " \n",
+ " return y_hat\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "489af62f-8f7c-4d1b-a6d0-5a0417e79869",
+ "metadata": {},
+ "outputs": [],
+ "source": [
"# print summary model\n",
"model=LitModel(MyNet())\n",
"print(model) "
@@ -373,15 +415,24 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "ce975c03-d05d-40c4-92ff-0cc90699c13e",
+ "id": "756d5e19-6a10-42b8-8971-411389f7d19c",
"metadata": {},
"outputs": [],
"source": [
"# loggers data\n",
- "logger = TensorBoardLogger(save_dir='MNIST2_logs',name=\"history_logs\")\n",
- "\n",
+ "logger = TensorBoardLogger(save_dir=\"MNIST2_logs\",name=\"CNN_logs\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ce975c03-d05d-40c4-92ff-0cc90699c13e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
"# train model\n",
- "trainer=pl.Trainer(accelerator='auto',max_epochs=16,logger=logger)\n",
+ "trainer = pl.Trainer(accelerator='auto',max_epochs=16,logger=logger)\n",
+ "\n",
"trainer.fit(model=model, train_dataloaders=train_loader, val_dataloaders=test_loader)"
]
},
@@ -409,6 +460,16 @@
"print('x_test / loss : {:5.4f}'.format(score[0]['test_loss']))\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "5cfe9bd6-654b-42e0-b430-5f3b816526b0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "score=trainer.validate(model=model,dataloaders=test_loader, verbose=False)"
+ ]
+ },
{
"cell_type": "markdown",
"id": "e352e48d-b473-4162-a1aa-72d6d4f7aa38",
@@ -426,7 +487,7 @@
"source": [
"# launch Tensorboard \n",
"%reload_ext tensorboard\n",
- "%tensorboard --logdir MNIST2_logs/history_logs/ "
+ "%tensorboard --logdir=MNIST2_logs/CNN_logs/ "
]
},
{
@@ -445,10 +506,10 @@
"outputs": [],
"source": [
"# logits outpout by batch size\n",
- "y_logits=trainer.predict(model=model,dataloaders=test_loader)\n",
+ "y_logits= trainer.predict(model=model,dataloaders=test_loader)\n",
"\n",
"# Concat into single tensor\n",
- "y_logits=torch.cat(y_logits)\n",
+ "y_logits= torch.cat(y_logits)\n",
"\n",
"# output probabilities values\n",
"y_pred_values=F.softmax(y_logits,dim=1)\n",
@@ -539,7 +600,7 @@
"metadata": {},
"source": [
"---\n",
- "<img width=\"80px\" src=\"../fidle/img/logo-paysage.svg\"></img>"
+ "<img width=\"80px\" src=\"../fidle/img/00-Fidle-logo-01.svg\"></img>"
]
}
],
--
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