cnn.py 文件源码

def build_model():
    model = Sequential()

    # ???????4????????????5*5?1??????????,????1??
    model.add(Convolution2D(4, 5, 5, border_mode='valid', dim_ordering='th', input_shape=(1, 20, 20)))
    model.add(ZeroPadding2D((1, 1)))
    model.add(BatchNormalization())
    model.add(Activation('tanh'))

    # ???????8????????????3*3?4??????????????????????
    model.add(GaussianNoise(0.001))
    model.add(UpSampling2D(size=(2, 2), dim_ordering='th'))

    model.add(AtrousConvolution2D(16, 3, 3, border_mode='valid', dim_ordering='th'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(BatchNormalization())
    model.add(Activation('tanh'))
    # model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering='th'))
    model.add(AveragePooling2D(pool_size=(2, 2), dim_ordering='th'))
    model.add(Activation('tanh'))

    # ???????16????????????4*4
    model.add(AtrousConvolution2D(8, 3, 3, border_mode='valid', dim_ordering='th'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(BatchNormalization())
    model.add(Activation('linear'))

    # ???????16????????????4*4
    model.add(GaussianNoise(0.002))
    model.add(AtrousConvolution2D(4, 3, 3, border_mode='valid', dim_ordering='th'))
    model.add(ZeroPadding2D((1, 1)))
    model.add(BatchNormalization())
    model.add(Activation('tanh'))
    model.add(Dropout(0.2))
    # model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering='th'))
    model.add(AveragePooling2D(pool_size=(2, 2), dim_ordering='th'))
    model.add(Activation('tanh'))

    # ??????????????????flatten????
    model.add(Flatten())
    model.add(Dense(8))
    model.add(BatchNormalization())
    model.add(Activation('tanh'))

    model.add(Dense(1))
    model.add(Activation('linear'))

    start = time.time()

    # ??SGD + momentum
    # model.compile????loss??????(????)
    # sgd = SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
    # model.compile(loss="mse", optimizer=sgd)
    model.compile(loss="mse", optimizer="rmsprop") # mse kld # Nadam  rmsprop
    print "Compilation Time : ", time.time() - start
    return model