官术网_书友最值得收藏!

Getting started with deep feedforward neural networks

A deep feedforward neural network is designed to approximate a function, f(), that maps some set of input variables, x, to an output variable, y. They are called feedforward neural networks because information flows from the input through each successive layer as far as the output, and there are no feedback or recursive loops (models including both forward and backward connections are referred to as recurrent neural networks).

Deep feedforward neural networks are applicable to a wide range of problems, and are particularly useful for applications such as image classification. More generally, feedforward neural networks are useful for prediction and classification where there is a clearly defined outcome (what digit an image contains, whether someone is walking upstairs or walking on a flat surface, the presence/absence of disease, and so on).

Deep feedforward neural networks can be constructed by chaining layers or functions together. For example, a network with four hidden layers is shown in the following diagram:

Figure 4.1: A d eep feedforward neural network

This diagram of the model is a directed acyclic graph. Represented as a function, the overall mapping from the input, X, to the output, Y, is a multilayered function. The first hidden layer is H1=f(1)(X, w1 a1), the second hidden layer is H2=f(2)(H1, w2 a2), and so on. These multiple layers can allow complex functions and transformations to be built up from relatively simple ones.

If sufficient hidden neurons are included in a layer, it can approximate to the desired degree of precision with many different types of functions. Feedforward neural networks can approximate non-linear functions by applying non-linear transformations between layers. These non-linear functions are known as activation functions, which we will cover in the next section.

The weights for each layer will be learned as the model is trained through forward- and backward-propagation. Another key piece of the model that must be determined is the cost, or loss, function. The two most commonly used cost functions are cross-entropy, which is used for classification tasks, and mean squared error (MSE), which is used for regression tasks.

主站蜘蛛池模板: 太和县| 成武县| 石阡县| 萨嘎县| 耒阳市| 宝兴县| 年辖:市辖区| 个旧市| 海南省| 栾川县| 鄂托克前旗| 凉城县| 六盘水市| 沂水县| 宁河县| 方城县| 栾城县| 苍山县| 绥棱县| 北京市| 德惠市| 大厂| 潢川县| 六盘水市| 双江| 潞城市| 福安市| 衡阳县| 施甸县| 游戏| 九台市| 方城县| 安吉县| 林西县| 拜泉县| 漯河市| 隆化县| 克什克腾旗| 沁源县| 都匀市| 庄浪县|