How a filter in convolutional neural network can generate multiple channels?

Question

I am learning convolutional neural network with Tensorflow.

I have some doubts regarding tf.nn.conv2d. One of its parameters is filter:

Answer 1

The filter argument to the tf.nn.conv2d function, as you quoted, is a 4D tensor of dimensions [filter_height, filter_width, in_channels, out_channels]. This tensor represents a stack of out_channels filters of dimension filter_height x filter_width, to be applied over an image with in_channels channels.

The parameters, filter_height, filter_width and out_channels are defined by you, whereas input_channels is dependent on your input to tf.nn.conv2d.

In other words, a filter tensor with dimensions [2, 2, 1, 5], represents 5 different 2 x 2 filters to be applied over a 1-channel input, but you could perfectly change it to [2, 2, 1, 7], or whatever else gives you better results.

To further illustrate, in the following gif you have a [3, 3, 1, 1] tensor filter convolving over a [1, 5, 5, 1] image. This means you have only 1 filter being convolved over the image.

GIF source