default kernels in cnn in keras? what are the default kernels used in convolution done in cnn 

for example in this code of keras there are 32 filters of size 5x5 each.

my question is 1) What will be the different default kernels in those 32 filters. 
for example in an tutorial I read that the 
\begin{bmatrix}
    1 &amp; 1 \\
    -1       &amp; -1\\
\end{bmatrix} could be taken as horizontal edge detector and 
\begin{bmatrix}
1&amp; -1\\
1 &amp; -1
\end{bmatrix}
could be taken as vertical edge detector .

def baseline_model():
    # create model
    model = Sequential()
    model.add(Conv2D(32, (5, 5), input_shape=(1, 28, 28), activation='relu'))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Conv2D(32, (5, 5), activation='relu'))
    model.add(Dropout(0.2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))
    # Compile model
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model
The initial value of the CNN kernels can be seen from the documentation found here.

The default Conv2D layer looks like

keras.layers.Conv2D(32, (5, 5), activation='relu', kernel_initializer='glorot_uniform', bias_initializer='zeros')


So we see that the kernel weights are initialized by Glorot uniform method an dthe bias is initialized as all zeros. 

Glorot Uniform

Uniform sampling from $[-limit, limit]$ where 

$limit = \sqrt{\frac{6}{fan\_in + fan\_out}}$

where $fan\_in$ is the number of input neurons to the weight tensor and $fan\_out$ is the number of output neurons of the weight tensor.