titanlearn v 2.0.1.000

data analysis/analysis/titanlearn.py

@@ -7,10 +7,13 @@
 #    this module learns from its mistakes far faster than 2022's captains
 # setup:
 
-__version__ = "2.0.0.001"
+__version__ = "2.0.1.000"
 
 #changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+2.0.1.000:
+    - added net, dataset, dataloader, and stdtrain template definitions
+    - added graphloss function
 2.0.0.001:
     - added clear functions
 2.0.0.000:
@@ -33,6 +36,8 @@ __all__ = [
 
 import torch
 from os import system, name
+import matplotlib.pyplot as plt
+import numpy as np
 
 def clear(): 
     if name == 'nt': 
@@ -40,7 +45,34 @@ def clear():
     else: 
         _ = system('clear') 
 
-def train(device, net, epochs, trainloader, optimizer, criterion):
+class net(torch.nn.Module): #template for standard neural net
+    def __init__(self):
+        super(Net, self).__init__()
+        
+    def forward(self, input):
+        pass
+
+class dataset(torch.utils.data.Dataset): #template for standard dataset
+    
+    def __init__(self):
+        super(torch.utils.data.Dataset).__init__()
+        
+    def __getitem__(self, index):
+        pass
+    
+    def __len__(self):
+        pass
+
+def dataloader(dataset, batch_size, num_workers, shuffle = True):
+
+    return torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, num_workers=num_workers)
+
+def train(device, net, epochs, trainloader, optimizer, criterion): #expects standard dataloader, whch returns (inputs, labels)
+
+    dataset_len = trainloader.dataset.__len__()
+    iter_count = 0
+    running_loss = 0
+    running_loss_list = []
 
     for epoch in range(epochs):  # loop over the dataset multiple times
 
@@ -57,5 +89,34 @@ def train(device, net, epochs, trainloader, optimizer, criterion):
             loss.backward()
             optimizer.step()
 
-    return net
+            # monitoring steps below
+
+            iter_count += 1
+            running_loss += loss.item()
+            running_loss_list.append(running_loss)
+            clear()
+
+            print("training on: " + device)
+            print("iteration: " + str(i) + "/" + str(int(dataset_len / trainloader.batch_size)) + " | " + "epoch: " + str(epoch) + "/" + str(epochs))
+            print("current batch loss: " + str(loss.item))
+            print("running loss: " + str(running_loss / iter_count))
+        
+    return net, running_loss_list
     print("finished training")
+
+def stdtrainer(net, criterion, optimizer, dataloader, epochs, batch_size):
+
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+    net = net.to(device)
+    criterion = criterion.to(device)
+    optimizer = optimizer.to(device)
+    trainloader = dataloader
+    
+    return train(device, net, epochs, trainloader, optimizer, criterion)
+
+def graphloss(losses):
+
+    x = range(0, len(losses))
+    plt.plot(x, losses)
+    plt.show()