dropout neural network code

(Data_config[ ' Train_label ']) global_step=training_iters*model_config[ ' N_epoch '] decay_steps=training_iters*1 #global_step = tf. Variable (0, name = ' Global_step ', Trainable=false" Lr=tf.train.exponential_decay (Learning_rate=model_config[ Learning_rate '], Global_step=global_step, decay_steps=decay_steps, Decay_rate=0.1, Staircase=false, Name=none) optimizer= Tf.train.GradientDescentOptimizer (LR). Minimize (Cost,var_list=network.all_params) 1 2 3 4 5

Case code: #建立抽象模型x = Tf.placeholder (Tf.float32, [None, 784])y = Tf.placeholder (Tf.float32, [None, ten]) #实际分布的概率值w = tf. Variable (Tf.zeros ([784, 10])b = tf. Variable (Tf.zeros (10))A = Tf.nn.softmax (Tf.matmul (x, W) + b) #基于softmax多分类得到的预测概率#定义损失函数和训练方法Cross_entropy = Tf.reduce_mean (-tf.reduce_sum (Y * tf.log (a), reduction_indices=[1])) #交叉熵Optimizer = Tf.train.GradientDescentOptimizer (0.5) #梯度下降优化算法, learning step is 0.5Train = Optimizer.mi

In the classical increment PID algorithm, the parameter that needs to be debugged is KP,KI,KD. The three parameters are adjusted by BP Neural Network, with X (i) as the input layer and the middle layer as the Simoid function: f (x) = Tanh (x) = (exp (x)-exp (-X))/(exp (x) +exp (-X)). and modify parameters by gradient descent method Key code:%output LayerFor J

outputLength. Training instances that has inputs longer than I or outputsLonger than O'll be pushed to the next bucket and padded accordingly.We assume the list is sorted, e.g., [(2, 4), (8, 16)]. Size:number of units in each layer of the model. Num_layers:number of layers in the model. Max_gradient_norm:gradients'll is clipped to maximally this norm. Batch_size:the size of the batches used during training;The model construction is independent of batch_size, so it can beChanged

We will inevitably have a variety of problems in the back propagation, when problems arise, our cost function still decreases with the number of iterations, but there are some problems in the middle, So how do we check to see if our algorithm will be vulnerable to these problems?Approximate expression of gradientsThe above is the approximate expression of the derivative, taking the left side approximation instead of the right side of the unilateral approximation, usually ξ take 10-4, if the acqu

function ff =Lambdax:xclassLinearunit (Perceptron):def __init__(self, input_num):" "initialize the linear element and set the number of input parameters" "Perceptron.__init__(self, input_num, f)defGet_training_dataset ():" "Fabricate 5 People's income data" " #Building Training Data #Enter a list of vectors, each of which is the working lifeInput_vecs = [[5], [3], [8], [1.4], [10.1]] #expected output list, monthly salary, note to correspond with input one by oneLabels = [5500, 2300, 76

This paper combines the application of deep learning, convolution neural Network for some basic applications, referring to LeCun's document 0.1 for partial expansion, and results display (in Python).Divided into the following parts:1. Convolution (convolution)2. Pooling (down sampling process)3. CNN Structure4. Run the experimentThe following are described separately.PS: This blog for the ESE machine learni

Code reference: Http://deeplearning.net/tutorial/lenet.html#lenetCode Learning: http://blog.csdn.net/u012162613/article/details/43225445Experiment code download for this section: Github2015/4/9Experiment 1: Using the tutorial recommended CNN structural Experimentlearning_rate=0.1n_cv= 20 # First-layer convolution core 20N_vc=50 #第二层卷积核50n_epochs=200batch_size=500n_hidden=500Experimental results:Experiment 2

: test_features, y: test_labes}))sess.close() 1. Define weight, biases, Conv layer, pool Layer def Weight(shape): initial = tf.truncated_normal(shape, stddev=0.1) return tf.Variable(initial, tf.float32)def biases(shape): initial = tf.constant(0.1, shape=shape) return tf.Variable(initial, tf.float32)def conv(inputs, w): return tf.nn.conv2d(inputs, w, strides=[1, 1, 1, 1], padding=‘SAME‘)def pool(inputs): return tf.nn.max_pool(inputs, ksize=[1, 1, 1, 1], strides=[1, 2, 2, 1], pa

1. Write data to the CSV file, you should be able to directly implement the Python code to write the dataset, but I read this piece of file is not very skilled, and so I succeeded, plus, here I write the dataset directly into Excel2. Then change the suffix to. csv and use Pandas to readImport Matplotlib.pyplot as Pltfile = ' bp_test.csv ' import pandas as Pddf = pd.read_csv (file, header=none) x = df.iloc[:,].v Aluesprint (x)Read results[ -1. -0.9

The parameters that need to be debugged in the classic incremental PID algorithm are KP,KI,KD. The three parameters are regulated by the BP neural Network, with X (i) as the input layer and the middle layer as the Simoid function: f (x) = Tanh (x) = (exp (x)-exp (-X))/(exp (x) +exp (-X)). and modify the parameters by gradient descent method Key code:%output L

The neural Cat Game is a game developed based on html5, jquery, typescript, and other technologies. It is very fun. If you are interested, come and watch it and try it, we will share with you how to create a neural Cat Game using html5-download the source code, if you need it, you can refer to the HTML5 neural Cat Game

This article mainly introduces the knowledge of Perceptron, uses the theory + code practice Way, and carries out the learning of perceptual device. This paper first introduces the Perceptron model, then introduces the Perceptron learning rules (Perceptron learning algorithm), finally through the Python code to achieve a single layer perceptron, so that readers a more intuitive understanding. 1. Single-layer