Add files via upload

Author: Programmer-RD-AI

Diff for: wandb/run-20211016_231600-fn82rqkz/files/code/0.py

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+from torch.nn import *
+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+import torch.nn.functional as F
+import matplotlib.pyplot as plt
+import pytorch_lightning as pl
+from pytorch_lightning import Trainer
+from tqdm import tqdm
+from torch.utils.data import DataLoader,Dataset
+import wandb,os
+PROJECT_NAME = 'Intel-Image-Classification-Learning-PyTorch-Lightning'
+criterion = MSELoss()
+class Model(Module):
+    def __init__(self):
+        self.activation = ReLU()
+        self.linear1 = Linear(3*5*5,256)
+        self.linear2 = Linear(256,512)
+        self.linear3 = Linear(512,1024)
+        self.linear4 = Linear(1024,len(labels))
+    
+    def training_step(self,batch,batch_idx):
+        images,labels = batch
+        preds = self.activation(self.linear1(images))
+        preds = self.activation(self.linear2(preds))
+        preds = self.activation(self.linear3(preds))
+        preds = self.activation(self.linear4(preds))
+        loss = criterion(preds,labels)
+        wandb.log({'Loss':loss.item()})
+        return {'train_loss':loss.item()}
+
+    def validation_step(self,batch,batch_idx):
+        images,labels = batch
+        preds = self.activation(self.linear1(images))
+        preds = self.activation(self.linear2(preds))
+        preds = self.activation(self.linear3(preds))
+        preds = self.activation(self.linear4(preds))
+        loss = criterion(preds,labels)
+        wandb.log({'Val Loss':loss.item()})
+        return {'val_loss':loss.item()}
+
+    def train_dataloader(self):
+        dataset = torchvision.datasets.MNIST('./data/',download=True,)
+        data_loader = DataLoader(dataset,batch_size=32,shuffle=True)
+        return data_loader
+        
+    def val_dataloader(self):
+        dataset = torchvision.datasets.MNIST('./data/',download=True,train=False)
+        data_loader = DataLoader(dataset,batch_size=32,shuffle=True)
+        return data_loader
+
+wandb.init(project=PROJECT_NAME,name='baseline')
+trainer = Trainer()
+trainer.fit(Model())
+wandb.finish()