neural_network #4

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s452622 merged 34 commits from neural_network into master 2021-06-08 23:47:22 +02:00
14 changed files with 134 additions and 9 deletions
Showing only changes of commit cb4d638a0f - Show all commits

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@ -5,7 +5,7 @@ from collections import Counter
from anytree import Node, RenderTree
from machine_learning.data_set import training_set, test_set, attributes as attribs
from machine_learning.decision_tree.data_set import training_set, test_set, attributes as attribs
def calculate_entropy(p: int, n: int) -> float:

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@ -0,0 +1,25 @@
import os
import pandas as pd
from torch.utils.data import Dataset
from torchvision.io import read_image
class ImageDataset(Dataset):
def __init__(self, annotations_file, img_dir, transform=None, target_transform=None):
self.img_labels = pd.read_csv(annotations_file)
self.img_dir = img_dir
self.transform = transform
self.target_transform = target_transform
def __len__(self):
return len(self.img_labels)
def __getitem__(self, idx):
img_path = os.path.join(self.img_dir, self.img_labels.iloc[idx, 0])
image = read_image(img_path)
label = self.img_labels.iloc[idx, 1]
if self.transform:
image = self.transform(image)
if self.target_transform:
label = self.target_transform(label)
return image, label

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@ -0,0 +1,6 @@
mine1.jpg, 0
mine2.jpg, 0
mine3.jpg. 0
other1.jpg, 1
other2.jpg, 1
other3.jpg, 1
1 mine1.jpg, 0
2 mine2.jpg, 0
3 mine3.jpg. 0
4 other1.jpg, 1
5 other2.jpg, 1
6 other3.jpg, 1

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@ -0,0 +1,74 @@
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from net import NeuralNetwork
from database import create_training_data
from Dataset import ImageDataset
training_data = create_training_data()
# dataset = ImageDataset(annonation_file, img_dir)
# trainset, valset = random_split(dataset, [15593, 3898])
batch_size = 64
# Create data loaders.
train_dataloader = DataLoader(training_data, batch_size=batch_size, shuffle=True)
# test_dataloader = DataLoader(test_data, batch_size=batch_size)
# for X, y in test_dataloader:
# print("Shape of X [N, C, H, W]: ", X.shape)
# print("Shape of y: ", y.shape, y.dtype)
# break
device = "cuda" if torch.cuda.is_available() else "cpu"
print("Using {} device".format(device))
model = NeuralNetwork().to(device)
print(model)
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
for batch, (X, y) in enumerate(dataloader):
X, y = X.to(device), y.to(device)
# Compute prediction error
pred = model(X)
loss = loss_fn(pred, y)
# Backpropagation
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch % 100 == 0:
loss, current = loss.item(), batch * len(X)
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
def test(dataloader, model):
size = len(dataloader.dataset)
model.eval()
test_loss, correct = 0, 0
with torch.no_grad():
for X, y in dataloader:
X, y = X.to(device), y.to(device)
pred = model(X)
test_loss += loss_fn(pred, y).item()
correct += (pred.argmax(1) == y).type(torch.float).sum().item()
test_loss /= size
correct /= size
print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
epochs = 5
for t in range(epochs):
print(f"Epoch {t+1}\n-------------------------------")
train(train_dataloader, model, loss_fn, optimizer)
test(test_dataloader, model)
print("Done!")

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@ -30,6 +30,7 @@ if __name__ == '__main__':
for epoch in range(n_iter):
for image, label in zip(train_images, train_labels):
print(image.shape)
optimizer.zero_grad()
output = model(image)

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@ -0,0 +1,19 @@
import torch.nn as nn
class NeuralNetwork(nn.Module):
def __init__(self):
super(NeuralNetwork, self).__init__()
self.flatten = nn.Flatten()
self.linear_relu_stack = nn.Sequential(
nn.Linear(28*28, 512),
nn.ReLU(),
nn.Linear(512, 512),
nn.ReLU(),
nn.Linear(512, 10),
nn.ReLU()
)
def forward(self, x):
x = self.flatten(x)
return self.linear_relu_stack(x)

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@ -7,8 +7,8 @@ from tilesFactory import TilesFactory
from const import ICON, IMAGES, TREE_ROOT
from src.search_algoritms.a_star import a_star
from search_algoritms.BFS import breadth_first_search
from machine_learning.decision_tree import get_decision
from machine_learning.helpers import get_dataset_from_tile
from machine_learning.decision_tree.decision_tree import get_decision
from machine_learning.decision_tree.helpers import get_dataset_from_tile
def handle_keys(env: Environment, agent: Agent, game_ui: GameUi, factory: TilesFactory):

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@ -4,7 +4,7 @@ from importlib import import_module
from tile import Tile
from const import IMAGES
from machine_learning.data_set import visibility, stability, armed
from machine_learning.decision_tree.data_set import visibility, stability, armed
class TilesFactory: