cleanup

neural_network/nueralnet.py

@@ -1,103 +0,0 @@
-#imports
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import torch.nn.functional as F
-from torch.utils.data import DataLoader
-import torchvision.datasets as datasets
-import torchvision.transforms as transforms
-
-#create fully connected network
-class NN(nn.Module):
-    def __init__(self, input_size, num_classes): #1 layer (28x28 = 784 nodes)
-        super(NN,self)._init_()
-        self.fc1 = nn.Linear(input_size, 50)
-        self.fc2 = nn.Linear(50,num_classes)
-
-    def forward(self, x):
-        x = F.relu(self.fc1(x))
-        x = self.fc2(x)
-        return x
-
-# model = NN(784, 10)
-# x = torch.rand(64, 784)
-# print(model(x).shape)
-
-#set device
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-#hyperparameters
-input_size = 784
-num_classes = 10
-learning_rate = 0.001
-batch_size = 64
-num_epochs = 1
-
-#load data
-train_dataset = datasets.MNIST(root='dataset/', train = True, transform = transforms.toTensor(), download = True)
-train_loader = DataLoader(dataset= train_dataset, batch_size = batch_size, shuffle = True)
-test_dataset = datasets.MNIST(root='dataset/', train = False, transform = transforms.toTensor(), download = True)
-test_loader = DataLoader(dataset= test_dataset, batch_size = batch_size, shuffle = True)
-
-
-#initialize network 
-model = NN(input_size=input_size, num_classes=num_classes).to(device)
-
-#loss and optimizer
-criterion = nn.CrossEntropyLoss()
-optimizer = optim.Adam(model.parameters(), lr=learning_rate)
-
-#train network
-for epoch in range(num_epochs):
-    for batch_idx, (data, targets) in enumerate(train_loader):
-        #get data to cuda if possible
-        data = data.to(device = device)
-        targets = targets.to(device = device)
-
-        #get to correct shape
-        data = data.reshape(data.shape[0], -1)
-
-        # forward
-        scores = model(data)
-        loss = criterion(scores, targets)
-
-        #backward
-        optimizer.zero_grad()
-        loss.backward()
-
-        #gradient descent or adam step
-        optimizer.step()
-
-
-#check accuracy on training and test to see how good our model
-
-def check_accuracy(loader, model):
-    if loader.dataset.train: 
-        print("Checking accuracy on training data")
-    else:
-        print("Checking accuracy on test data")
-
-    num_correct = 0
-    num_samples = 0
-    model.eval()
-
-    with torch.no_grad():
-        for x,y in loader:
-            x = x.to(device = device)
-            y = y.to(device = device)
-            x = x.reshape(x.shape[0], -1)
-
-            scores = model(x)
-            _, predictions = scores.max(1)
-            num_correct += (predictions == y).sum()
-            num_samples +=  predictions.size(0)
-
-        print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}')
-             
-    model.train()
-    return acc
-  
-check_accuracy(train_loader, model) 
-check_accuracy(test_loader, model)
-
-