# load data
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
X_train = X_train.astype('float32')/256
X_test = X_test.astype('float32')/256
y_train = keras.utils.to_categorical(y_train)
y_test = keras.utils.to_categorical(y_test)

# build a model
def res_unit(x):
    x_shortcut = x

    x = Conv2D(16, (1, 1), padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(16, (3, 3), padding='same')(x)
    x = BatchNormalization()(x)
    x = Activation('relu')(x)

    x = Conv2D(32, (1, 1), padding='same')(x)
    x = BatchNormalization()(x)
    x = Add()([x_shortcut, x])

    x = Activation('relu')(x)

    return x


X_input = Input((X_train.shape[1:]))

X = Conv2D(32, (3, 3), padding='same')(X_input)
X = BatchNormalization()(X)
X = Activation('relu')(X)

X = res_unit(X)
X = res_unit(X)
X = res_unit(X)

X = Flatten()(X)
X = Dense(32)(X)
X = Activation('relu')(X)
X = Dense(10)(X)

X = Activation('softmax')(X)

model = Model(inputs=X_input, outputs=X)
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# run model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=5)