In the original paper, it says "The second convolutional layer takes as input the (response-normalized and pooled) output of the first convolutional layer and filters it with 256 kernels of size 5 * 5 * 48. The third, fourth, and fifth convolutional layers are connected to one another without any intervening pooling or normalization layers. The third convolutional layer has 384 kernels of size 3 * 3 * 256 connected to the (normalized, pooled) outputs of the second convolutional layer. The fourth convolutional layer has 384 kernels of size 3 * 3 * 192 , and the fifth convolutional layer has 256 kernels of size 3 * 3 * 192. The fully-connected layers have 4096 neurons each."

Therefore, the calculation of conv2, conv4 and conv5 should be:

conv2: (5 * 5 * 48) * 256 + 256

conv4: (3 * 3 * 192) * 384 + 384

conv4: (3 * 3 * 192) * 384 + 384