NickShargan

import os
import Image

def mergeImages(images):
	
    if len(images) > 0:
        merged = Image.new('RGB', (images[0].size[0], images[0].size[1]), "black")
        pixels = merged.load()

NickShargan

import os
import argparse

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input", required=True, help="path to dir with input  videos")
ap.add_argument("-f", "--fps", type=int, default=5, help="FPS of output video")
args = vars(ap.parse_args())

NickShargan

from keras.applications.inception_v3 import InceptionV3
# from keras.applications.resnet50 import ResNet50
from keras.applications.mobilenet import MobileNet
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint
from keras import backend as K

NickShargan

from keras.applications.inception_v3 import InceptionV3
# from keras.applications.resnet50 import ResNet50
from keras.applications.mobilenet import MobileNet
from keras.preprocessing import image
from keras.models import Model
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint
from keras import backend as K

NickShargan

import os
import numpy as np

from keras.applications.mobilenet import MobileNet
from keras.models import Sequential, Model
from keras.layers import Input, Dense, Activation, GlobalAveragePooling2D, Reshape, Conv2D, Dropout
from keras.optimizers import adam
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, TensorBoard, ReduceLROnPlateau, EarlyStopping

NickShargan

import os

data_dir = "./data/"
train_dir = "./train/"
val_dir = "./val/"

breed_names = os.listdir(data_dir)
for breed_name in breed_names:
    if not os.path.isdir(train_dir + breed_name):
        os.mkdir(train_dir + breed_name)

NickShargan

import os
import cv2
from tqdm import tqdm
import numpy as np
import pandas as pd

from keras.applications.mobilenet import MobileNet
from keras.models import Model
from keras.layers import Activation, GlobalAveragePooling2D, Dense

NickShargan

NUM_CARDS=2
NS="/usr/bin/nvidia-settings"

while true
do
for ((i=0; i<$NUM_CARDS;i++))
{
    GPU_TEMP='nvidia-smi -i $i --query-gpu=temperature.gpu --format=csv,noheader'
    FAN_SPEED='nvidia-smi -i $i --query-gpu=fan.speed --format=csv,noheader,nounits'
    if (($GPU_TEMP > 50)): then

NickShargan

nvidia-settings -a '[gpu:0]/GPUMemoryTransferRateOffset[3]=800';
nvidia-settings -a '[gpu:0]/GPUGraphicsClockOffset[3]=180';
nvidia-settings -a '[gpu:0]/GPUPowerMizerMode=1';
nvidia-settings -a '[gpu:0]/GPUFanControlState=1';
nvidia-settings -a '[fan:0]/GPUTargetFanSpeed=40';

NickShargan

sudo nvidia-xconfig -a --cool-bits=31 --allow-empty-initial-configuration --enable-all-gpus

sudo nano /etc/X11/xorg.conf

Option         "RegistryDwords" "PerfLevelScr=0x2222"