You need to set the shape of the kernel as (3,4). For convolution kernel, it will share the weight in the same channel but not between channels, right? Cuz no matter every channel of kernel or input, the size of every channel is 4, and the dimension is 1. So, your input will just compute with the kernel on the same channel for only one time. Finally, we will get three feature maps, cuz the number of channels is three.