theano deep learning

Theano is a Python library that can run fast numerical computations on a CPU or GPU. This is a key base in python depth learning, and you can use it directly to create a deep learning model or a wrapper library that simplifies the program greatly. Pip Install Theano It's an

1. Introduction Keras is a Theano based framework for deep learning, designed to refer to torch, written in Python, and is a highly modular neural network library that supports GPU and CPU. Keras Official document Address 2. Process First, use CNN for training, use the Theano function to remove the full link of the

2 (?, 1) (False, True) Fmatrix Float32 2 (?,?) (False, False) Ftensor3 Float32 3 (?,?,?) (False, False, false) Ftensor4 Float32 4 (?,?,?,?) (False, False, false, false) Cscalar Complex64 0 () () Cvector Complex64 1 (?,) (False,) Crow Complex64 2 (1,?) (True, False)

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: Add a hidden layer on the tutorial basislea

Theano Study Record (i) logistic regressionExperiment 1:Using the recommended configurationLearning_rate = 0.01L1_reg = 0.00l2_reg=0.0001n_epoches=1000Batch_size=20n_hidden=500Experimental results:Experiment Time:Experiment 2:You add two hidden layers, 400 and 300 nodes, respectively. The experiment is configured as follows:Learning_rate = 0.01L1_reg = 0.00l2_reg=0.0001n_epoches=1000Batch_size=20n_hidden_1=500n_hidden_2=400n_hidden_3=300Experimental r

Keras Learning Notes Original address: http://blog.csdn.net/hjimce/article/details/49095199 Author: hjimce Keras and the use of Torch7 is very similar to the recent fire up the depth of the open source Library, the bottom is used Theano. Keras can be said to be a python version of Torch7, very handy for building a CNN model quickly. Also contains some of the latest literature of the algorithm, such as batch

/~kevinduh/a/deep2014/ Then deeplearning 's official website, Inside good good things found themselves:http://deeplearning.net/ About learning deep learning tools, there seems to be a lot of (MATLAB version, C + + version, Python version and so on Deep learning library), dep

Python vector: Import NumPy as np a = Np.array ([[[1,2],[3,4],[5,6]]) SUM0 = Np.sum (A, axis=0) sum1 = Np.sum (A, Axis=1) PR int SUM0 Print sum1 > Results: [9 12][3 7] Dropout In the training process of the deep Learning Network, for the Neural network unit, it is temporarily discarded from the network according to certain probability.Dropout is a big kill for CNN to prevent the effect of fitting. Output

Deep Learning notes finishing (very good) Http://www.sigvc.org/bbs/thread-2187-1-3.html Affirmation: This article is not the author original, reproduced from: http://www.sigvc.org/bbs/thread-2187-1-3.html 4.2, the primary (shallow layer) feature representation Since the pixel-level feature indicates that the method has no effect, then what kind of representation is useful. Around 1995, Bruno Olshause

matrix is calculated and then multiplied by the normal matrix operation to multiply the vector. Experimental results show that using HF Second order optimization can achieve very good results without using any pre-training.Here halfway through: There is a Python library called Theano, provides deep learning optimization related to the various building blocks, su

Deep Learning-nlplecture 2:introduction to TeanoEnter link description hereNeural Networks can be expressed as one long function of vector and matrix operations.(A neural network can be represented as a long function of a vector and a matrix operation.) )Common frameworks (Common frame) C + +If you are need maximum performance,start from scratch (and if you need the highest performance then start p

learning libraries at this stage, as these are done in step 3. Step 2: Try Now that you have enough preparatory knowledge, you can learn more about deep learning. Depending on your preferences, you can focus on: Blog: (Resource 1: "Basics of deep Learning" Resource 2: "Hack

. Therefore, we need to process the image and convert it to the vector format of x [400. As long as the pixels of the image can be read and converted. We can consider using opencv for implementation. Here, my method is to convert the image to a 20*20 pixel image after drawing a number by hand, as shown in the lower right corner, and then convert the image in the lower right corner to an array of 400, enter the result of predict.3 Method 2: Use DeepBeliefSDK Https://github.com/jetpacapp/DeepBeli

This afternoon, idle to nothing, so Baidu turned to see the recent on the pattern recognition, as well as the latest progress in target detection, there are a lot of harvest!------------------------------------AUTHOR:PKF-----------------------------------------------time:2016-1-20--------------------------------------------------------------qq:13277066461. The nature of deep learning2. The effect of deep