text_plain_output_1.png
from keras.utils import np_utils

from sklearn.model_selection import train_test_split

import numpy as np # linear algebra

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

df = pd.read_csv('../input/aerial-cactus-identification/train.csv')

train = pd.read_csv('../input/cactus-images-csv/dataset/train.csv')
test = pd.read_csv('../input/cactus-images-csv/dataset/test.csv')

train = train.drop('Unnamed: 0', axis=1)
test = test.drop('Unnamed: 0', axis=1)

from keras.utils import np_utils
from sklearn.model_selection import train_test_split
images = train.drop('label', axis=1)
images = np.asarray(images)
images = images.reshape(images.shape[0], 32, 32, 3)
label = train['label']
X_train, X_test, y_train, y_test = train_test_split(images, label.values, test_size=0.33)
Cat_test_y = np_utils.to_categorical(y_test)
y_train = np_utils.to_categorical(y_train)
print('X_train shape : ', X_train.shape)
print('y_train shape : ', y_train.shape)
print('X_test shape : ', X_test.shape)
print('y_test shape : ', y_test.shape)