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Input encoding Given that the ANN input is to be some representation of the image, one key design choice is how to encode this image For example, we could preprocess the image to extract edges, regions of uniform intensity, or other local image features, then input these features to the network One difficulty with this design option is that it would lead to a variable number of features (eg, edges) per image, whereas the ANN has a fixed number of input units The design option chosen in this case was instead to encode the image as a fixed set of 30 x 32 pixel intensity values, with one network input per pixel The pixel intensity values ranging from 0 to 255 were linearly scaled to range from 0 to 1 so that network inputs would have values in the same interval as the hidden unit and output unit activations The 30 x 32 pixel image is, in fact, a coarse resolution summary of the original 120 x 128 captured image, with each coarse pixel intensity calculated as the mean of the corresponding high-resolution pixel intensities Using this coarse-resolution image reduces the number of inputs and network weights to a much more manageable size, thereby reducing computational demands, while maintaining sufficient resolution to correctly classify the images Recall from Figure 41 that the ALVINN system uses a similar coarse-resolution image as input to the network One interesting difference is that in ALVINN, each coarse resolution pixel intensity is obtained by selecting the intensity of a single pixel at random from the appropriate region within the high-resolution image, rather than taking the mean of all pixel intensities within this region The motivation for this ic ALVINN is that it significantly reduces the computation required to produce the coarse-resolution image from the available high-resolution image This efficiency is especially important when the network must be used to process many images per second while autonomously driving the vehicle Output encoding The ANN must output one of four values indicating the direction in which the person is looking (left, right, up, or straight) Note we could encode this four-way classification using a single output unit, assigning outputs of, say, 02,04,06, and 08 to encode these four possible values Instead, we use four distinct output units, each representing one of the four possible face directions, with the highest-valued output taken as the network prediction This is often called a 1-0f-n output encoding There are two motivations for choosing the 1-of-n output encoding over the single unit option First, it provides more degrees of freedom to the network for representing the target function (ie, there are n times as many weights available in the output layer of units) Second, in the 1-of-n encoding the difference between the highest-valued output and the second-highest can be used as a measure of the confidence in the network prediction (ambiguous classifications may result in near or exact ties) A further design choice here is "what should be the target values for these four output units ' One obvious choice would be to use the four target values (1,0,0,O) to encode a face looking to the.

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65,535 (FFFFh)

Change the button s type to Info Light in the Button Attributes Inspector (Figure 9-17)

0 Not specified 0 1 Normal audio 2 Audio for visually impaired 3 Director comments 4 Alternate director comments Two lowercase ASCII letters (ISO 639) or 65,535 (none) 65,535 (FFFFh)

left, (0,1,0,O) to encode a face looking straight, etc Instead of 0 and 1 values, we use values of 01 and 09, so that (09,O1,01,01) is the target output vector for a face looking to the left The reason for avoiding target values of 0 and 1 is that sigmoid units cannot produce these output values given finite weights If we attempt to train the network to fit target values of exactly 0 and 1, gradient descent will force the weights to grow without bound On the other hand, values of 01 and 09 are achievable using a sigmoid unit with finite weights

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7 Save and exit Interface Builder 8 Open FirstViewControllerh and FirstViewControllerm and modify both files to match

TABLE A18 cont Player System Parameters (SPRMs)

.

Listings 9-7 and 9-8

0 Not specified 0 1 Normal subtitles 2 Large subtitles 3 Subtitles for children 5 Normal captions 6 Large captions 7 Captions for children 9 Forced subtitles 13 Director comments 14 Large director comments 15 Director comments for children Read-only One bit set for Player-specific corresponding region (00000001 region 1, 00000010 region 2, etc)

(continued)

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