import os
import numpy as np
import pandas as pd
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import img_to_array, load_img
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preprocessing import LabelEncoder, StandardScaler


def load_numeric_training(standardize=True):
    data = pd.read_csv('../train.csv')
    ID = data.pop('id')
    y = data.pop('species')
    y = LabelEncoder().fit(y).transform(y)
    X = StandardScaler().fit(data).transform(data) if standardize else data.values
    return ID.values, X, y


def load_numeric_test(standardize=True):
    data = pd.read_csv('../test.csv')
    ID = data.pop('id')
    test = StandardScaler().fit(data).transform(data) if standardize else data.values
    return ID.values, test


def resize_img(img, max_dim=96):
    max_axis = 
np.argmax(img.size)