1:Find the convolution output volume size of layer 2 (W2xH2xD2) and 3 (W3xH3xD3) 2: Find the convolution...

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Mechanical Engineering

1:Find the convolution output volume size of layer 2 (W2xH2xD2)and 3 (W3xH3xD3)
2: Find the convolution output volume size of layer 2 (W2xH2xD2)and 3 (W3xH3xD3)
3: Find the output volume size of output pooling layer(W2xH2xD2)

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1Convolution output volume size of layer 2 W2xH2xD2 and 3 W3xH3xD3 The output of a convolution layer is computed as the following the depth No of feature maps is equal to the number of filters applied in this layer the width the same for height is computed according to the following equation WWF2PS1 where f is the receptive field filter width p is the padding and s is the stride Accepts a volume of size W1H1D1W1H1D1 Requires four hyperparameters Number of filters KK their spatial extent FF the stride SS the amount of zero padding PP Produces a volume of size W2H2D2W2H2D2 where W2W1F2PS1W2W1F2PS1 H2H1F2PS1H2H1F2PS1 ie width and height are computed equally by symmetry D2KD2K With parameter sharing it introduces FFD1FFD1 weights per filter for a total of FFD1KFFD1K weights and KK biases In the output volume the ddth depth slice of size W2H2W2H2 is the result of performing a valid convolution of the ddth filter over the input volume with a stride of SS and then offset by ddth bias A common setting of the hyperparameters is F3S1P1F3S1P1 However there are common conventions and rules of thumb that motivate these hyperparameters It is common to periodically insert a Pooling layer inbetween successive Conv layers in a ConvNet architecture Its function is to progressively reduce the spatial size of the representation to reduce the amount of parameters and computation in the network and hence to also control overfitting The Pooling Layer operates independently on every depth slice of the input and resizes it spatially using the MAX operation The most common form is a pooling layer with filters of size 2x2 applied with a stride of 2 downsamples every depth slice in the input by 2 along both width and height discarding 75 of the activations Every MAX operation would in this case be taking a max over 4 numbers little 2x2 region in some depth slice The depth dimension remains unchanged More generally the pooling layer Accepts a volume of size W1H1D1W1H1D1 Requires two hyperparameters their spatial extent FF the stride SS Produces a volume of size W2H2D2W2H2D2 where W2W1FS1W2W1FS1 H2H1FS1H2H1FS1 D2D1D2D1 Introduces zero parameters since it computes a fixed function of the input Note that it is not common to use zeropadding for Pooling layers It is worth noting that there are only two commonly seen variations of the max pooling layer found in practice A pooling layer with See Answer

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