TensorFlow provides a variety of methods for convolution. The canonical form is applied by the conv2d operation. Let's have a look at the usage of this operation:
conv2d( input, filter, strides, padding, use_cudnn_on_gpu=True, data_format='NHWC', dilations=[1, 1, 1, 1], name=None )
The parameters we use are as follows:
- input: The operation will be applied to this original tensor. It has a definite format of four dimensions, and the default dimension order is shown next.
- filter: This is a tensor representing a kernel or filter. It has a very generic method: (filter_height, filter_width, in_channels, and out_channels).
- strides: This is a list of four int tensor datatypes, which indicate the sliding windows ...
