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What is the depth of a Conv layer?
Depth of CONV layer is number of filters it is using. Depth of a filter is equal to depth of image it is using as input. For Example: Let’s say you are using an image of 227*227*3. Now suppose you are using a filter of size of 11*11 (spatial size).
What are the symbols in the convolution layer?
The symbols “c,” “s,”“u,” and “o” in the parentheses of the layer tags refer to convolution, subsampling, unification, and output operations, respectively. The numbers before and after “@” refer to the number of feature maps and the dimension of a feature map in this layer.
What does depth of a convolutional neural network mean?
Since (227 – 11)/4 + 1 = 55, and since the Conv layer had a depth of K=96, the Conv layer output volume had size [55x55x96]. Here we see the depth is 96. So is depth something that i choose arbitrarily? or something i compute? Also in the example above (Krizhevsky et al) they had 96 depths. So what does it mean by its 96 depths?
How is a convolutional layer different from a pooling layer?
The convolutional layers have weights that need to be trained, while the pooling layers transform the activation using a fixed function. A convolutional layer contains a set of filters whose parameters need to be learned.
Do you apply convolution to the first and second dimensions?
You apply convolution with stride 1 and appropriate padding to the first and the second dimension: this does not change dimensions. And you apply pooling to the third dimension only (for example, set k = 8 and use three pooling layers with window size 2 × 2 ).
How many pooling layers are there in convolutional neural network?
On the convolutional neural network, there used one or more pooling layers. As far as I know many tutorials instruct you to set it either 2 or 3 for the window size. For example, in this tutorial: After some ReLU layers, programmers may choose to apply a pooling layer. It is also referred to as a downsampling layer.
How is the depth of a CNN determined?
In each layer of your CNN it learns regularities about training images. In the very first layers, the regularities are curves and edges, then when you go deeper along the layers you start learning higher levels of regularities such as colors, shapes, objects etc. This is the basic idea, but there lots of technical details.