src/machine_learning/neural_network/learning_neural_network.py

@@ -22,29 +22,28 @@ if __name__ == '__main__':
 		nn.Linear(input_dim, output_dim),
 		nn.LogSoftmax()
 	)
-
-
+	
+	
 	def train(model, n_iter):
 		criterion = nn.NLLLoss()
 		optimizer = optim.SGD(model.parameters(), lr=0.001)
-
+		
 		for epoch in range(n_iter):
 			for image, label in zip(train_images, train_labels):
-				print(image.shape)
 				optimizer.zero_grad()
-
+				
 				output = model(image)
 				loss = criterion(output.unsqueeze(0), label.unsqueeze(0))
 				loss.backward()
 				optimizer.step()
-
+			
 			print(f'epoch: {epoch:03}')
 	
 	
 	train(model, 100)
 	
 	# def accuracy(expected, predicted):
-	# 	return len([1 for e, p in zip(expected, predicted) if e == p]) / len(expected)
+	# 	return len([_ for e, p in zip(expected, predicted) if e == p]) / len(expected)
 	#
 	#
 	# predicted = [model(image).argmax() for image in train_images]