How to include a custom filter in a Keras based CNN?

问题

I am working on a fuzzy convolution filter for CNNs. I have the function ready - it takes in the 2D input matrix and the 2D kernel/weight matrix. The function outputs the convolved feature or the activation map.

Now, I want to use Keras to build the rest of the CNN that will have the standard 2D convolution filters too.

Is there any way I can insert my custom filter into the Keras model in such a way that the kernel matrix is updated by the built in libraries of the Keras backend? Alternatively is there any library that I can use to update the kernel with every iteration?

回答1:

You could write your own layer class. You can find a good documentation about it here. Furthermore, you can start by using the code of the Conv2D class. Now, you only have to change the way how the kernel is initially created. This way, your filters will be updated according to the backpropagation. If you do not want your custom filter to change you must create a new variable (which only contains your filter), calculate the convolution using K.conv(...) and concatenate this result with the usual output.

回答2:

Suppose we want to apply a 3x3 custom filter onto an 6x6 image.

Necessary import

import keras.backend as K
import numpy as np
from keras import Input, layers
from keras.models import Model

Definition of the custom filter

# custom filter
def my_filter(shape, dtype=None):

    f = np.array([
            [[[1]], [[0]], [[-1]]],
            [[[1]], [[0]], [[-1]]],
            [[[1]], [[0]], [[-1]]]
        ])
    assert f.shape == shape
    return K.variable(f, dtype='float32')

Dummy example input image (it is 1 channel image. 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.]]]]

来源：https://stackoverflow.com/questions/51930312/how-to-include-a-custom-filter-in-a-keras-based-cnn

标签

python

keras

conv-neural-network

backpropagation

gradient-descent