A convolutional layer is a neural network layer that applies one or more convolutional filters to input data. It produces feature maps that capture local patterns and is a core component of convolutional neural networks.
A layer of a deep neural network in which a convolutional filter passes along an input matrix. For example, consider the following 3x3 convolutional filter: The following animation shows a convolutional layer consisting of 9 convolutional operations involving the 5x5 input matrix. Notice that each convolutional operation works on a different 3x3 slice of the input matrix. The resulting 3x3 matrix (on the right) consists of the results of the 9 convolutional operations: ![An animation showing two matrixes. The first matrix is the 5x5 matrix: \\[\\[128,97,53,201,198\\], \\[35,22,25,200,195\\], \\[37,24,28,197,182\\], \\[33,28,92,195,179\\], \\[31,40,100,192,177\\]\\]. The second matrix is the 3x3 matrix: \\[\\[181,303,618\\], \\[115,338,605\\], \\[169,351,560\\]\\]. The second matrix is calculated by applying the convolutional filter \\[\\[0, 1, 0\\], \\[1, 0, 1\\], \\[0, 1, 0\\]\\] across different 3x3 subsets of the 5x5 matrix.](https://developers.google.com/static/machine-learning/glossary/images/AnimatedConvolution.gif)