scratch for nn

This commit is contained in:
Aliaksei Brown 2023-06-04 10:35:05 +02:00
parent d16267826d
commit b9fba20676
2 changed files with 154 additions and 0 deletions

51
learn_tree.py Normal file
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from collections import Counter
def tree_learn(examples, attributes, default_class):
if len(examples) == 0:
return default_class
if all(examples[0][-1] == example[-1] for example in examples):
return examples[0][-1]
if len(attributes) == 0:
class_counts = Counter(example[-1] for example in examples)
majority_class = class_counts.most_common(1)[0][0]
return majority_class
# Choose the attribute A as the root of the decision tree
A = select_attribute(attributes, examples)
tree = {A: {}}
new_attributes = [attr for attr in attributes if attr != A]
new_default_class = Counter(example[-1] for example in examples).most_common(1)[0][0]
for value in get_attribute_values(A):
new_examples = [example for example in examples if example[attributes.index(A)] == value]
subtree = tree_learn(new_examples, new_attributes, new_default_class)
tree[A][value] = subtree
return tree
# Helper function: Select the best attribute based on a certain criterion (e.g., information gain)
def select_attribute(attributes, examples):
# Implement your attribute selection criterion here
pass
# Helper function: Get the possible values of an attribute from the examples
def get_attribute_values(attribute):
# Implement your code to retrieve the attribute values from the examples here
pass
# Example usage with coordinates
examples = [
[1, 2, 'A'],
[3, 4, 'A'],
[5, 6, 'B'],
[7, 8, 'B']
]
attributes = ['x', 'y']
default_class = 'unknown'
decision_tree = tree_learn(examples, attributes, default_class)
print(decision_tree)

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neural_network/nueralnet.py Normal file
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#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)