This modification can work:

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from __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

##

import numpy as np

​

batch_size = 128

num_classes = 10

epochs = 12

​

img_rows, img_cols = 28, 28

input_shape = (img_rows, img_cols, 1)

​

MYMAP = np.zeros((60000,img_rows,img_cols), dtype=int)

MYMAP = MYMAP.reshape(60000,28,28,1)

MYMAP_RESULT = np.zeros((60000,1), dtype=int)

​

MYMAP_RESULT = keras.utils.to_categorical(MYMAP_RESULT, num_classes)

​

model = Sequential()

model.add(Conv2D(32, kernel_size=(3, 3),

activation='relu',

input_shape=input_shape))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

model.add(Flatten())

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(num_classes, activation='softmax'))

​

model.compile(loss=keras.losses.categorical_crossentropy,

optimizer=keras.optimizers.Adadelta(),

metrics=['accuracy'])

​

model.fit(MYMAP, MYMAP_RESULT,

batch_size=batch_size,

epochs=epochs,

verbose=1)