I'm building CNN + Ensemble model for classify images with Tensorflow at Python. I crawled dog and cat images at google images. Then changed them to 126 * 126 pixel size and gray scale, add label 0 to dog, 1 to cat. CNN has 5 conv layer and 2 fc layer. HE, PReLU, max-pooling, drop-out, Adam are used in model. When Parameter Tuning finished, I added Early-Stopping, the model learned 65~70 epoch, finished with 92.5~92.7% accuracy. After learning finished, I want change my CNN model to VGG network, I checked my CNN parameter, shockingly, I found I didn't add Bias at conv layer. 2 fc layer had Bias but 5 conv layer didn't have Bias. So I added Bias at 5 conv layer, BUT my model could not learn. Cost increased to infinite.
Bias is not necessarily at Deep Convolution Layer?
How did you add your bias to the convolutional layer? There are two ways to do this: Tied biases which share one bias per kernel and untied biases which use one bias per kernel and output. Also read this.
Regarding your question whether or not they are necessary, the answer is no. Biases in convolutional layers increase the capacity of your model, making it theoretically able to represent more complex data. If your model however already has the capacity to do this, they are not necessary.
An example is this implementation of the 152 layer ResNet architecture where the convolution layers have no bias. Instead the bias is added in the subsequent batch normalization layers.