revert

.idea/.name

@@ -1 +1 @@
-evaluation.py
+pytorch — kopia.py

pytorch/pytorch.py

@@ -18,21 +18,10 @@ from torch.utils.data import DataLoader, TensorDataset, random_split
 import random
 import os
 import sys
-from sacred import Experiment
-from sacred.observers import FileStorageObserver
-from sacred.observers import MongoObserver
 
 
 # In[2]:
 
-ex = Experiment(save_git_info=False)
-ex.observers.append(FileStorageObserver('runs'))
-@ex.config
-def config():
-    epochs = 1500
-    
-    
-    
 
 dataframe_raw = pd.read_csv("winequality-red.csv")
 dataframe_raw.head()
@@ -142,8 +131,7 @@ def evaluate(model, val_loader):
     outputs = [model.validation_step(batch) for batch in val_loader]
     return model.validation_epoch_end(outputs)
 
-@ex.capture
-def fit(lr, model, train_loader, val_loader, opt_func=torch.optim.SGD, epochs, _run):
+def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
     history = []
     optimizer = opt_func(model.parameters(), lr)
     for epoch in range(epochs):
@@ -162,8 +150,8 @@ def fit(lr, model, train_loader, val_loader, opt_func=torch.optim.SGD, epochs, _
 
 
 #epochs = int(sys.argv[1])
-
-
+lr = 1e-6
+history5 = fit(epochs, lr, model, train_loader, val_loader)
 
 
 # In[27]:
@@ -195,10 +183,5 @@ with open("result.txt", "w+") as file:
         input_, target = val_ds[i]
         file.write(str(predict_single(input_, target, model)))
 
-@ex.main
-def main():
-    lr = 1e-6
-    history5 = fit(lr, model, train_loader, val_loader, epochs)
 
-ex.run()