Stride is the step size by which a convolutional or pooling window moves across input data. Larger strides reduce output size more quickly, while smaller strides preserve more spatial detail.
In a convolutional operation or pooling, the delta in each dimension of the next series of input slices. For example, the following animation demonstrates a (1,1) stride during a convolutional operation. Therefore, the next input slice starts one position to the right of the previous input slice. When the operation reaches the right edge, the next slice is all the way over to the left but one position down. !An input 5x5 matrix and a 3x3 convolutional filter. Because the stride is (1,1), a convolutional filter will be applied 9 times. The first convolutional slice evaluates the top-left 3x3 submatrix of the input matrix. The second slice evaluates the top-middle 3x3 submatrix. The third convolutional slice evaluates the top-right 3x3 submatrix. The fourth slice evaluates the middle-left 3x3 submatrix. The fifth slice evaluates the middle 3x3 submatrix. The sixth slice evaluates the middle-right 3x3 submatrix. The seventh slice evaluates the bottom-left 3x3 submatrix. The eighth slice evaluates the bottom-middle 3x3 submatrix. The ninth slice evaluates the bottom-right 3x3 submatrix. The preceding example demonstrates a two-dimensional stride. If the input matrix is three-dimensional, the stride would also be three-dimensional.