Practical Convolutional Neural Networks

Convolution operations in TensorFlow

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 ...