moved analysis-master out of data analysis

analysis-master/analysis/titanlearn.py

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+# Titan Robotics Team 2022: ML Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#    this should be imported as a python module using 'import titanlearn'
+#    this should be included in the local directory or environment variable
+#    this module is optimized for multhreaded computing
+#    this module learns from its mistakes far faster than 2022's captains
+# setup:
+
+__version__ = "2.0.1.001"
+
+#changelog should be viewed using print(analysis.__changelog__)
+__changelog__ = """changelog:
+    2.0.1.001:
+        - removed matplotlib import
+        - removed graphloss()
+    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:
+        - complete rewrite planned
+        - depreciated 1.0.0.xxx versions
+        - added simple training loop
+    1.0.0.xxx:
+        -added generation of ANNS, basic SGD training
+"""
+
+__author__ = (
+    "Arthur Lu <arthurlu@ttic.edu>,"
+    "Jacob Levine <jlevine@ttic.edu>,"
+    )
+
+__all__ = [
+    'clear',
+    'net',
+    'dataset',
+    'dataloader',
+    'train',
+    'stdtrainer',
+    ]
+
+import torch
+from os import system, name
+import numpy as np
+
+def clear(): 
+    if name == 'nt': 
+        _ = system('cls') 
+    else: 
+        _ = system('clear') 
+
+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
+
+        for i, data in enumerate(trainloader, 0):
+
+            inputs = data[0].to(device)
+            labels = data[1].to(device)
+
+            optimizer.zero_grad()
+
+            outputs = net(inputs)
+            loss = criterion(outputs, labels.to(torch.float))
+            
+            loss.backward()
+            optimizer.step()
+
+            # 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)