text_plain_output_1.png
from keras.utils import to_categorical

from sklearn.model_selection import train_test_split

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

fashion_train = pd.read_csv('../input/fashion-mnist_train.csv')
fashion_test = pd.read_csv('../input/fashion-mnist_test.csv')

from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
img_rows, img_cols = (28, 28)
input_shape = (img_rows, img_cols, 1)
X = fashion_train.drop(['label'], axis=1).values
y = to_categorical(fashion_train['label'].values)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
X_test = fashion_test.drop(['label'], axis=1).values
y_test = to_categorical(fashion_test['label'].values)