如何在基于Keras的CNN中包含一个自定义过滤器？

5 人关注

我正在研究一个用于CNN的模糊卷积滤波器。我已经准备好了函数--它接收二维输入矩阵和二维内核/权重矩阵。该函数输出卷积的特征或激活图。

现在，我想用Keras来构建CNN的其余部分，它也会有标准的2D卷积过滤器。

有什么办法可以将我的自定义过滤器插入Keras模型中，使内核矩阵由Keras后端内置库更新？或者，有没有什么库可以让我在每次迭代时更新内核？

python

keras

conv-neural-network

backpropagation

gradient-descent

Rangan Das

发布于 2018-08-20

2 个回答

Uzzal Podder

发布于 2018-08-20

已采纳

0 人赞同

假设我们要应用一个 3x3 的 custom filter onto an 6x6 形象 .

Dummy example input 形象 (it is 1 channel 形象. So dimension will be 6x6x1 . Here, pixel values are random integer. Generally pixel values should be 0 to 255 or 0.0 to 1.0 .)

input_mat = np.array([
    [ [4], [9], [2], [5], [8], [3] ],
    [ [3], [6], [2], [4], [0], [3] ],
    [ [2], [4], [5], [4], [5], [2] ],
    [ [5], [6], [5], [4], [7], [8] ],
    [ [5], [7], [7], [9], [2], [1] ],
    [ [5], [8], [5], [3], [8], [4] ]
# we need to give the batch size. 
# here we will just add a dimension at the beginning which makes batch size=1
input_mat = input_mat.reshape((1, 6, 6, 1))
Dummy conv model where we will use our custom filter 
def build_model():
    input_tensor = Input(shape=(6,6,1))
    x = layers.Conv2D(filters=1, 
                      kernel_size = 3,
                      kernel_initializer=my_filter,
                      strides=2, 
                      padding='valid') (input_tensor)
    model = Model(inputs=input_tensor, outputs=x)
    return model
Testing 
model = build_model()
out = model.predict(input_mat)
print(out)
Output
[[[[ 0.]
   [-4.]]
  [[-5.]
   [ 3.]]]]