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| -rw-r--r-- | docs/tutorial/layers.md | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/tutorial/layers.md b/docs/tutorial/layers.md index eabc792b..7362aac2 100644 --- a/docs/tutorial/layers.md +++ b/docs/tutorial/layers.md @@ -39,7 +39,7 @@ In contrast, other layers (with few exceptions) ignore the spatial structure of - `n * c_i * h_i * w_i` * Output - `n * c_o * h_o * w_o`, where `h_o = (h_i + 2 * pad_h - kernel_h) / stride_h + 1` and `w_o` likewise. -* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`) +* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`) layer { name: "conv1" @@ -83,7 +83,7 @@ The `Convolution` layer convolves the input image with a set of learnable filter - `n * c * h_i * w_i` * Output - `n * c * h_o * w_o`, where h_o and w_o are computed in the same way as convolution. -* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`) +* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`) layer { name: "pool1" @@ -197,7 +197,7 @@ In general, activation / Neuron layers are element-wise operators, taking one bo * Parameters (`ReLUParameter relu_param`) - Optional - `negative_slope` [default 0]: specifies whether to leak the negative part by multiplying it with the slope value rather than setting it to 0. -* Sample (as seen in `./examples/imagenet/imagenet_train_val.prototxt`) +* Sample (as seen in `./models/bvlc_reference_caffenet/train_val.prototxt`) layer { name: "relu1" |