Hi Can someone please help me to change this code so that it wont get data from keras mnist. instead it will be getting data from local folder. where do i need to make changes in it. and where in this code we can use shuffle = true.

# Credits: https://github.com/keras-team/keras/blob/master/examples/mnist_cnn.pyfrom __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 12

# input image dimensions
img_rows, img_cols = 28, 28# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()if K.image_data_format() == 'channels_first':x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)input_shape = (1, img_rows, img_cols)
else:x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model1 = Sequential()
model1.add(Conv2D(32, kernel_size=(3, 3),activation='relu',input_shape=input_shape))
model1.add(Conv2D(32, (3, 3), activation='relu'))
model1.add(MaxPooling2D(pool_size=(2, 2)))
model1.add(Conv2D(64, (3, 3), activation='relu'))
model1.add(Dropout(0.25))
model1.add(Flatten())
model1.add(Dense(128, activation='relu'))
model1.add(Dropout(0.5))
model1.add(Dense(num_classes, activation='softmax'))

model1.compile(loss=keras.losses.categorical_crossentropy,optimizer=keras.optimizers.Adadelta(),metrics=['accuracy'])

x = # some array containing image data
y = # some array containing labels(x_train, x_test), (y_train, y_test) = sklearn.model_selection.train_test_split(x, y)  # shuffle=True by default

Note the change in ordering.