export const SIMPLE_MNIST_CODE = `from __future__ import print_function
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD
from keras.utils import np_utils

np.random.seed(1671) # for reproducibility

# network and training

NB_EPOCH = 200
BATCH_SIZE = 128
VERBOSE = 1
NB_CLASSES = 10 # number of outputs = number of digits
OPTIMIZER = SGD()
N_HIDDEN = 128
VALIDATION_SPLIT= 0.2 # how much TRAIN is reserved for VALIDATION

# data: shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# x_train is 60000 rows of 28x28 values --> reshaped in 60000 x 784
RESHAPED = 784

x_train = x_train.reshape(60000, RESHAPED)
x_test = x_test.reshape(10000, RESHAPED)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')

# normalize

x_train /= 255
x_test /= 255

print (x_train.shape[0], 'train samples')
print (x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = np_utils.to_categorical(y_train, NB_CLASSES)
y_test = np_utils.to_categorical(y_test, NB_CLASSES)

model = Sequential()
model.add(Dense(NB_CLASSES, input_shape=(RESHAPED,)))
model.add(Activation('softmax'))
model.summary()

model.compile(loss='categorical_crossentropy', optimizer=OPTIMIZER, metrics=['accuracy'])

history = model.fit(x_train, y_train, batch_size=BATCH_SIZE, epochs=NB_EPOCH, verbose=VERBOSE, validation_split=VALIDATION_SPLIT)
score = model.evaluate(x_test, y_test, verbose=VERBOSE)

print('Test score:', score[0])
print('Test accuracy', score[1])` ;