MnistTest.py

import numpy as np
np.random.seed(1337)
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense,Activation
from keras.optimizers import RMSprop

(X_train,y_train),(X_test,y_test)=mnist.load_data()
X_train=X_train.reshape(X_train.shape[0],-1)/255
X_test=X_test.reshape(X_test.shape[0],-1)/255
y_train=np_utils.to_categorical(y_train,num_classes=10)
y_test=np_utils.to_categorical(y_test,num_classes=10)

model=Sequential([
    Dense(32,input_dim=784),
    Activation('relu'),
    Dense(10),
    Activation("softmax")
])
rmsprop=RMSprop()
model.compile(optimizer='rmsprop',
            loss='categorical_crossentropy',
            metrics=['accuracy'])

print("Training...")
model.fit(X_train,y_train,nb_epoch=2)
print("Test...")
loss,accuracy=model.evaluate(X_test,y_test)
print("test loss : ",loss)
print("test accuracy : ",accuracy)