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Diffstat (limited to 'docs')
-rw-r--r-- | docs/tutorial/net_layer_blob.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/docs/tutorial/net_layer_blob.md b/docs/tutorial/net_layer_blob.md index e8b7bd31..d6df7374 100644 --- a/docs/tutorial/net_layer_blob.md +++ b/docs/tutorial/net_layer_blob.md @@ -19,7 +19,7 @@ Blobs conceal the computational and mental overhead of mixed CPU/GPU operation b The conventional blob dimensions for batches of image data are number N x channel K x height H x width W. Blob memory is row-major in layout, so the last / rightmost dimension changes fastest. For example, in a 4D blob, the value at index (n, k, h, w) is physically located at index ((n * K + k) * H + h) * W + w. -- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images B = 256. +- Number / N is the batch size of the data. Batch processing achieves better throughput for communication and device processing. For an ImageNet training batch of 256 images N = 256. - Channel / K is the feature dimension e.g. for RGB images K = 3. Note that although many blobs in Caffe examples are 4D with axes for image applications, it is totally valid to use blobs for non-image applications. For example, if you simply need fully-connected networks like the conventional multi-layer perceptron, use 2D blobs (shape (N, D)) and call the InnerProductLayer (which we will cover soon). |