官术网_书友最值得收藏!

Towards a deep learning approach

While playing with handwritten digit recognition, we came to the conclusion that the closer we get to the accuracy of 99%, the more difficult it is to improve. If we want to have more improvements, we definitely need a new idea. What are we missing? Think about it.

The fundamental intuition is that, so far, we lost all the information related to the local spatiality of the images. In particular, this piece of code transforms the bitmap, representing each written digit into a flat vector where the spatial locality is gone:

#X_train is 60000 rows of 28x28 values --> reshaped in 60000 x 784
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)

However, this is not how our brain works. Remember that our vision is based on multiple cortex levels, each one recognizing more and more structured information, still preserving the locality. First we see single pixels, then from that, we recognize simple geometric forms and then more and more sophisticated elements such as objects, faces, human bodies, animals and so on.

In Chapter 3, Deep Learning with ConvNets, we will see that a particular type of deep learning network known as convolutional neural network (CNN) has been developed by taking into account both the idea of preserving the spatial locality in images (and, more generally, in any type of information) and the idea of learning via progressive levels of abstraction: with one layer, you can only learn simple patterns; with more than one layer, you can learn multiple patterns. Before discussing CNN, we need to discuss some aspects of Keras architecture and have a practical introduction to a few additional machine learning concepts. This will be the topic of the next chapters.

主站蜘蛛池模板: 黄浦区| 湘潭市| 张家港市| 嘉义县| 水富县| 和林格尔县| 仪征市| 克什克腾旗| 固安县| 广西| 尤溪县| 黄浦区| 稻城县| 吉林省| 定安县| 扬州市| 邓州市| 吐鲁番市| 浦江县| 五指山市| 大田县| 弥勒县| 阜宁县| 光泽县| 利川市| 怀集县| 和田县| 舒兰市| 白朗县| 遂昌县| 余姚市| 启东市| 唐河县| 长岛县| 府谷县| 乡宁县| 鄂托克前旗| 永嘉县| 宁化县| 巴林右旗| 克山县|