shgidi

import bcolz
import pickle

def save_array(fname, arr):
    c=bcolz.carray(arr, rootdir=fname, mode='w')
    c.flush()


def load_array(fname):
    return bcolz.open(fname)[:]

shgidi

#genertor
train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True) #many other augmentations are avialbile

#flow
datagen.flow(x, batch_size=1, save_to_dir='preview', save_prefix='cat', save_format='jpeg')

shgidi

import xgboost as xgb

clf = xgb.XGBClassifier(max_depth=10, n_estimators=1500,min_child_weight=9,learning_rate=0.01,
                       nthread=8, subsample=0.80,colsample_bytree=0.80,seed=4242)


clf.fit(trn , y,eval_set=[(val, y_val)], eval_metric='mlogloss', verbose=True, early_stopping_rounds=50)
#multiclass logloss

shgidi

from matplotlib import pyplot as plt
import numpy as np
%matplotlib inline
#show and tell

fig = plt.figure(figsize=(16,12))
n_images_per_row=4
choices=np.random.choice(len(imgs),16)
#choices=range(16)
for i,id in enumerate(choices):