![]() To discover the trend of regional commercial vitality plays a very important role in many potential applications. The vitality of these entities in turn reflects the commercial condition or economic strength of their surrounding district. The commercial district is a particular area of a town or country that includes clustered business entities like shops, theaters and restaurants, etc. Experiments on public Yelp datasets from 2013 to 2018 demonstrate that STCRNN outperforms the current methods in terms of mean square error. In particular, a residual network is used to eliminate gradient vanishing and exploding, caused by the increase of depth of neural networks. Finally, long short-term memory is introduced to synthesize these two variations. Then, a 3-dimension convolution is applied to deal with both recent and periodic variations, i.e., the sequential and seasonal changes of commercial vitality. Afterwards, a local convolutional neural network is employed to capture the spatial relationship of surrounding commercial districts on the vitality map. Firstly, a commercial vitality map is built to indicate the popularity of business entities. In this paper, a Spatio-Temporal Convolutional Residual Neural Network (STCRNN) is proposed for regional commercial vitality prediction, based on public online data, such as reviews and check-ins from mobile apps. Thanks to the rapid growing of location based social networks such as Yelp and Foursquare, massive amount of online data has become available for predicting the vitality of commercial entities. Unfortunately, such figures constitute business secrets and are usually publicly unavailable. The indicators of business conditions, like revenues and profits, can be employed to make a prediction beyond any doubt. You can read the paper: "identity mappings in deep residual networks" for more information on this.The vitality of commercial entities reflects the business condition of their surrounding area, the prediction of which helps identify the trend of regional development and make investment decisions. Other factor in the success of residual networks is uninterrupted gradient flow from the first layer to the last layer. ![]() Add back x and you get your desired mapping. ![]() Just set any weight to zero and you will a zero output. > 0 # look at the last 0Īchieving the above is easy. Now, if we define the desired mapping H(x) = F(x) + x, then we just need get F(x) = 0 as follows. So, to approximate the identity mapping with all these weights and ReLUs in the middle would be difficult. Then for a direct mapping it would be difficult to learn an identity mapping as there is a stack of non-linear layers as follows. To illustrate with a simple example, assume that the ideal H(x) = x. F(x)) may be easier to optimize than H(x). The authors hypothesize that the residual mapping (i.e. So, I don't understand the second part of your question. Here F(x) is processing x with two weight layers(+ ReLu non-linear function), so the desired mapping is H(x)=F(x)? where is the residual?įirst part is correct. That means the two weight layers in the residual unit should actually be able to produce the desired F(x), then getting the ideal H(x) is guaranteed. Since H(x) = F(x) + x, obtaining the desired H(x) depends on getting the perfect F(x). Now, assume that H(x) is your ideal predicted output which matches with your ground truth. So the residual unit shown obtains F(x) by processing x with two weight layers. What does it mean by "hope the 2 weight layers fit F(x)" ?
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