我正在调整围绕CIFAR-10数据集设计的CNN模型。
CIFAR-10中的图像为32x32。我的数据集具有不规则形状的图像，192x108。
初始卷积层有32个过滤器和3x3内核大小，在后续层上增加到64，然后增加到128.
如果图像尺寸增加，最好增加滤镜数量和/或内核大小吗？如果是这样，我应该使用哪种启发式方法？
内核是否需要保持对称？
以下是我的模型定义：
Using TensorFlow backend.
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
convolution2d_1 (Convolution2D) (None, 32, 192, 108) 896 convolution2d_input_1[0][0]
____________________________________________________________________________________________________
dropout_1 (Dropout) (None, 32, 192, 108) 0 convolution2d_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 32, 192, 108) 9248 dropout_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 32, 96, 54) 0 convolution2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D) (None, 64, 96, 54) 18496 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
dropout_2 (Dropout) (None, 64, 96, 54) 0 convolution2d_3[0][0]
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D) (None, 64, 96, 54) 36928 dropout_2[0][0]
____________________________________________________________________________________________________
maxpooling2d_2 (MaxPooling2D) (None, 64, 48, 27) 0 convolution2d_4[0][0]
____________________________________________________________________________________________________
convolution2d_5 (Convolution2D) (None, 128, 48, 27) 73856 maxpooling2d_2[0][0]
____________________________________________________________________________________________________
dropout_3 (Dropout) (None, 128, 48, 27) 0 convolution2d_5[0][0]
____________________________________________________________________________________________________
convolution2d_6 (Convolution2D) (None, 128, 48, 27) 147584 dropout_3[0][0]
____________________________________________________________________________________________________
maxpooling2d_3 (MaxPooling2D) (None, 128, 24, 13) 0 convolution2d_6[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 39936) 0 maxpooling2d_3[0][0]
____________________________________________________________________________________________________
dropout_4 (Dropout) (None, 39936) 0 flatten_1[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 1024) 40895488 dropout_4[0][0]
____________________________________________________________________________________________________
dropout_5 (Dropout) (None, 1024) 0 dense_1[0][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 512) 524800 dropout_5[0][0]
____________________________________________________________________________________________________
dropout_6 (Dropout) (None, 512) 0 dense_2[0][0]
____________________________________________________________________________________________________
dense_3 (Dense) (None, 3) 1539 dropout_6[0][0]
====================================================================================================
Total params: 41708835