convolutional neural networks for sentence classification

of the word vector effect is also possible.Channel (Channels): An image can take advantage of (R, G, B) as a different channel, while the input channel of the text is usually a different way of embedding (such as Word2vec or glove), In practice, the use of static word vectors and fine-tunning-word vectors as different channel methods are also used.One dimensional convolution (conv-1d): The image is a two-dimensional data, the word vector expression of the text is one-dimensional data, so in tex

"Recurrent convolutional neural Networks for Text classification" Paper Source: Lai, S., Xu, L., Liu, K., Zhao, J. (2015, January). Recurrent convolutional neural Networks for Text

useful when combined with a number of different random subsets of other neurons. The first two fully connected layers use dropout. Without dropout, our network would show a lot of overfitting. The dropout increases the number of iterations required for convergence by roughly one-fold.4. Image preprocessing① size NormalizationTo 256x256 all the pictures to the size of the scale, as for why not directly normalized to 224 (227), please refer to the above-mentioned expansion of the dataset operatio

ImageNet classification with deep convolutional neural Networks reading notes(after deciding to read a paper each time, the notes are recorded on the blog.) ）This article, published in NIPS2012, was Hinton and his students, in response to doubts about deep learning, used deep learning for imagenet, the largest database

ImageNet classification with deep convolutional neural Networks reading notes(2013-07-06 22:16:36) reprint Tags: deep_learning imagenet Hinton Category: machine learning (after deciding to read a paper each time, the notes are recorded on the blog.) )This article, published in NIPS2012,

alexnet Summary Notes Thesis: "Imagenet classification with Deep convolutional neural" 1 Network Structure The network uses the logic regression objective function to obtain the parameter optimization, this network structure as shown in Figure 1, a total of 8 layer network: 5 layer of convolution layer, 3 layer full connection layer, and the front is the image in

Learning Goals Understand the convolution operation Understand the pooling operation Remember the vocabulary used in convolutional neural network (padding, stride, filter, ...) Build a convolutional neural network for Image Multi-Class classification

Content Overview Word Recognition system LeNet-5 Simplified LeNet-5 System The realization of convolutional neural network Deep neural network has achieved unprecedented success in the fields of speech recognition, image recognition and so on. I have been exposed to neural

on the same feature map face are the same, the network can learn in parallel, This is also a major advantage of convolutional networks over the network of neurons connected to each other. Convolution neural network has unique superiority in speech recognition and image processing because of its local weight sharing special structure, its layout is closer to the

, get S2: Feature map width, high to the original 1/2, that is, 28/2=14, feature map size into 14x14, the number of feature maps is unchanged.Then the second convolution, using 16 convolution cores, obtained the feature map of C3:16 Zhang 10x10.Then the next sampling, get S4: The feature map width, high to the original 1/2, that is, the 10/2=5, the feature map size into 5x5, the number of feature map is unchanged.After entering the convolution layer c5,120 Zhang 1x1 full connection feature map,

As a free from the vulgar Code of the farm, the Spring Festival holiday Idle, decided to do some interesting things to kill time, happened to see this paper: A neural style of convolutional neural networks, translated convolutional neura