Syzygy2048

{
	"FileVersion": 3,
	"EngineAssociation": "4.10",
	"Category": "",
	"Description": "",
	"Modules": [
		{
			"Name": "Asteroids",
			"Type": "Runtime",
			"LoadingPhase": "Default",

Syzygy2048

    protected boolean isAvailable() {
        if (getActivity() == null) return false;
        if (getActivity().isFinishing()) return false;
        if (!isAdded()) return false;
        if (isDetached()) return false;
        if (!isVisible()) return false;

        return true;
    }

Syzygy2048

std::string shaderCode = "";
	std::ifstream codeStream(shaderPath, std::ios::in);
	if (codeStream.is_open())
	{
		std::string line = "";
		while (getline(codeStream, line))
			shaderCode += "\n" + line;
		codeStream.close();
	}

Syzygy2048

# based  on https://medium.com/initialized-capital/we-need-to-go-deeper-a-practical-guide-to-tensorflow-and-inception-50e66281804f

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('data/mnist', one_hot=True)

from tensorflow.contrib.slim.nets import inception as nn_architecture
from tensorflow.contrib import slim

Syzygy2048

from skimage import io

def extract_annotations(path):
    image = io.imread("data/cowc/datasets/ground_truth_sets/Toronto_ISPRS/03559_Annotated_Cars.png")

    print('%d, %d' % (image.shape[0], image.shape[1]))
    i = 1;
    for x in range(0, image.shape[0]):
        for y in range(0, image.shape[1]):
            if image[x][y][0] > 0:

Syzygy2048

all my code related to loss:


loss = tf.losses.softmax_cross_entropy(onehot_labels=labels, logits=logits)
total_loss = tf.losses.get_total_loss()
...
train_step = slim.learning.create_train_op(total_loss, optimizer, variables_to_train=variables_to_train) #better than Optimizer.minimize because it prevents problems like vanishing gradient
...
#this is writing everything to tensorboard
tf.summary.scalar('total loss', total_loss)