convolutional neural network source code

Dry Goods | The latest development of speech recognition framework--deep full sequence convolution neural network debut2016-08-05 17:03 reprinted Chenyangyingjie 1 reviewsIntroduction: At present the best speech recognition system uses two-way long-term memory network (LSTM,LONGSHORT), but the system has high training complexity, decoding Singo problems, especial

Original articleReprint please register source HTTP://BLOG.CSDN.NET/TOSTQ the previous section we introduce the forward propagation process of convolutional neural networks, this section focuses on the reverse propagation process, which reflects the learning and training process of neural networks. Error back propagati

A summary of the classic network of CNN convolutional Neural NetworkThe following image refers to the blog: http://blog.csdn.net/cyh_24/article/details/51440344Second, LeNet-5 network Input Size: 32*32 Convolution layer: 2 Reduced sampling layer (pool layer): 2 Full Connection layer: 2 x Output

', borrow=true) self.p_y_given_x = T.nnet.softmax (T.dot (Input, self. W) + self.b) self.y_pred = T.argmax (self.p_y_given_x, Axis=1) self.params = [self. W, self.b] self.input = input print ("Yantao: ***********************************") def Negative_log _likelihood (Self, y): Return-t.mean (T.log (self.p_y_given_x) [T.arange (Y.shape[0]), y]) def errors (self, y): I F Y.ndim! = Self.y_pred.ndim:raise TypeError (' Y should have the same shape as self.y_pred ' , (' Y ', y.type, ' y

convolutional Neural Network (convolutional neural network,cnn), weighted sharing (weight sharing) network structure reduces the complexity of the model and reduces the number of weight

Vggnet　Vggnet is a deep convolutional neural network developed by the computer Vision Group of Oxford University and a researcher at Google DeepMind. Vggnet explores the relationship between the depth of convolutional neural networks and their performance, and vggnet success

TensorFlow deep learning convolutional neural network CNN, tensorflowcnn I. Convolutional Neural Network Overview ConvolutionalNeural Network (CNN) was originally designed to solve imag

The mnist examples of convolutional neural networks and the neural network examples in the previous blog post are mostly the same. But CNN has more layers, and the network model needs to be built on its own.The procedure is more complicated, I will be divided into several pa

Turn from: The Heart of the machine Introduction Frankly speaking, I can't really understand deep learning for a while. I look at relevant research papers and articles and feel that deep learning is extremely complex. I try to understand neural networks and their variants, but still feel difficult. Then one day, I decided to start with a step-by-step basis. I break down the steps of technical operations and manually perform these steps (and calcula

Study, the use of convolutional neural network has been a long time, the period has been based on the Caffe framework of the Jiayanqing great God to study other people's model, or in the boring time in the same way as the fortune-telling, eyes micro-closed, bobbing, the mouth occasionally leaking a few syllables, a long time DIY out of a think of a lot of models,

, database storage of things more, a lot of things are known to know do not know what. Second, the database index is fast and complete, according to a thing can quickly associate with the principle of its occurrence. Third, the sensory ability is strong, palpation all sharp. That's what makes Sherlock Holmes.Because I know so much, so when I see a paper that blends decision-making forests with convolutional neural