Keras Conv2d own filters

Question

it is possible to set as param filter array with own filters instead of number of filters in Conv2D

filters = [[[1,0,0],[1,0,0],[1,0,0]],
     [[1,0,0],[0,1,0],[0,0,1]],
     [[0,1,0],[0,1,0],[0,1,0]],
     [[0,0,1],[0,0,1],[0,0,1]]]
model = Sequential()
model.add(Conv2D(filters, (3, 3), activation='relu', input_shape=(3, 1024, 1024), data_format='channels_first'))

Answer 1

The accepted answer is right but it would certainly be more useful with a complete example, similar to the one provided in this excellent tensorflow example showing what Conv2d does.

For keras, this is,

from keras.models import Sequential
from keras.layers import Conv2D

import numpy as np

# Keras version of this example:
# https://stackoverflow.com/questions/34619177/what-does-tf-nn-conv2d-do-in-tensorflow
# Requires a custom kernel initialise to set to value from example
# kernel = [[1,0,1],[2,1,0],[0,0,1]]
# image = [[4,3,1,0],[2,1,0,1],[1,2,4,1],[3,1,0,2]]
# output = [[14, 6],[6,12]] 

#Set Image
image = [[4,3,1,0],[2,1,0,1],[1,2,4,1],[3,1,0,2]]

# Pad to "channels_last" format 
# which is [batch, width, height, channels]=[1,4,4,1]
image = np.expand_dims(np.expand_dims(np.array(image),2),0)

#Initialise to set kernel to required value
def kernel_init(shape):
    kernel = np.zeros(shape)
    kernel[:,:,0,0] = np.array([[1,0,1],[2,1,0],[0,0,1]])
    return kernel

#Build Keras model
model = Sequential()
model.add(Conv2D(1, [3,3], kernel_initializer=kernel_init, 
                 input_shape=(4,4,1), padding="valid"))
model.build()

# To apply existing filter, we use predict with no training
out = model.predict(image)
print(out[0,:,:,0])

which outputs

[[14, 6]
 [6, 12]]

as expected.