tensorflow neural network

This article mainly introduces Python based on numpy flexible definition of neural network structure, combined with examples of the principle of neural network structure and python implementation methods, involving Python using numpy extension for mathematical operations of the relevant operation skills, the need for f

Tutorial Content:"MATLAB Neural network principles and examples of fine solutions" accompanying the book with the source program. RAR9. Random Neural Networks-rar8. Feedback Neural Networks-rar7. Self-organizing competitive neural networks. RAR6. Radial basis function

Source: Michael Nielsen's "Neural Network and Deep learning", click the end of "read the original" To view the original English.This section translator: Hit Scir undergraduate Wang YuxuanDisclaimer: If you want to reprint please contact [email protected], without authorization not reproduced. Using neural networks to recognize handwritten numbers

This paper aims at constructing probabilistic language model of Chinese based on Fudan Chinese corpus and neural network model.A goal of the statistical language model is to find the joint distribution of different words in the sentence, that is to find the probability of the occurrence of a word sequence, a well-trained statistical language model can be used in speech recognition, Chinese input method, mac

The basic overview of neural networks and neural network models are not carefully introduced here. A detailed introduction to the introduction of the neural network and its model is presented in the details of Daniel Ng, Stanford University. This paper mainly introduces the

(Layers_inputs[-1], 1, activation_function=none) cost = Tf.reduce_mean (Tf.reduce_sum (Tf.square (Ys-predi ction), reduction_indices=[1]) Train_op = Tf.train.GradientDescentOptimizer (0.001). Minimize (cost) return [Train_op, Cost, Layers_inputs] Fix_seed (1) x_data = Np.linspace ( -7, ten, 2500) [:, Np.newaxis] #水平轴 -7~10np.random.shuffle (x_data) No ise = np.random.normal (0, 8, x_data.shape) Y_data = Np.square (x_data)-5 + Noisexs = Tf.placeholder (Tf.float32, [None, 1]) # [Num_samples, N

BP algorithm is one of the most effective multi-layer neural network learning methods, its main characteristic is the signal forward transmission, and the error after the propagation, through the constant adjustment of the network weight value, so that the final output of the network and the desired output as close as

The content of particle swarm optimization can be obtained by searching. The following are mainly personal understanding of particle swarm optimization, and the adjustment of weights in BP neural network Original in: http://baike.baidu.com/view/1531379.htm Refer to some of the contents below ===============我是引用的分界线================= 粒子根据如下的公式来更新自己的 速度和新的位置 v[] = w * v[] + c1 * rand() * (pbest[] - present

It took a week to learn about neural networks after soy sauce in the Knowledge Engineering Center. The teacher arranged a question and asked me to try it. I did a little simple. I conducted several groups of tests and wrote a summary report. I posted it here. After more than a week of experimentation, I have a simple understanding of this issue. The following is my thoughts on this issue. In the last two days, I suddenly felt that the problem was much

Source: Michael Nielsen's "Neural Network and Deep learning", click the end of "read the original" To view the original English.This section translator: Hit Scir undergraduate Wang YuxuanDisclaimer: If you want to reprint please contact [email protected], without authorization not reproduced. Using neural networks to recognize handwritten numbers

BP (Back Propagation) network is a multi-layer feed-forward Network trained by the error inverse propagation algorithm, which was proposed by a team of scientists led by Rumelhart and mccelland in 1986, it is one of the most widely used neural networks. The BP network can learn and store a large number of input/output

This paper is reproduced from http://blog.csdn.net/ironyoung/article/details/49455343 BP (backward propogation) neural networkSimple to understand, neural network is a high-end fitting technology. There are a lot of tutorials, but in fact, I think it is enough to look at Stanford's relevant learning materials, and there are better translations at home: Introdu

1Import TensorFlow asTF2Import NumPy asNP3Ts_norm=tf.random_normal ([ +])4With TF. Session () asSess:5Norm_data=Ts_norm.eval ()6Print (norm_data[:5])7Import Matplotlib.pyplot asPLT8 plt.hist (norm_data)9 plt.show ()Tendef layer_debug (output_dim,input_dim,inputs,activation=None): Onew=TF. Variable (Tf.random_normal ([Input_dim,output_dim])) AB=TF. Variable (Tf.random_normal ([1, Output_dim])) -Xwb=tf.matmul (INPUTS,W) +b - ifActivation isNone: the

Gradient Based Learning 1 Depth Feedforward network (Deep Feedforward Network), also known as feedforward neural network or multilayer perceptron (multilayer PERCEPTRON,MLP), Feedforward means that information in this neural network