model.py

import numpy as np
from keras.models import Sequential
from keras.datasets import mnist
from keras.layers import Dense
from keras.utils import np_utils

n_classes = 10
batch_size = 128

(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)

y_train = np_utils.to_categorical(y_train, n_classes)
y_test = np_utils.to_categorical(y_test, n_classes)

model = Sequential()
model.add(Dense(500, input_shape=[784,1]), activation='relu')
model.add(Dense(500, activation='relu'))
model.add(Dense(n_classes, activation='softax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train, y_train, batch_size=batch_size, epochs=10)
model.evaluate(X_test, y_test, batch_size=batch_size, verbose=0)