tensorflow

Support original, more content Welcome to the author blog:http://www.china10s.com/blog/?p=490 Machine learning This method of calculation has been known to the world in the last century, but it has not been developed because of the computer-limited computing power and network speed. With the Moore effect, the current computer performance has soared, even in the hands of the iphone, than the United States on the moon on the machine used to be stronger. Therefore, in this context, machine learning

LeNet5 convolution neural network forward propagation # TensorFlow actual combat Google Depth Learning Framework 06 image recognition and convolution neural network # WIN10 Tensorflow1.0.1 python3.5.3 # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1 # filename:LeNet5_infernece.py # LeNet5 forward propagate import TensorFlow as TF # 1. Set the parameters of the neural network Input_node = 784 Output_node = Ten im

program cuda-install-samples-7.5.sh ~ CD ~/nvidia_cuda-7.5_samples cd 1_utilities/devicequery make CD ~/ Nvidia_cuda-7.5_samples/bin/x86_64/linux/release ./devicequery CD ~/nvidia_cuda-7.5_samples/1_utilities /bandwidthtest make ./bandwidthtest1 2 3 4 5 6 7 8 9 10 11 If two test results are pass, it means that Cuda is running normally. Reference links Cuda-7.5-toolkit 2. Install TensorFlow Essential Python-pip and Python-dev. in this window, enter

Define Cell In a lot of RNN paper we see similar graphs: Each of these small rectangles represents a cell. Each cell is a slightly more complex structure, as shown in the following diagram: The context in the diagram is a cell structure, and you can see that it accepts input (T), context (t-1), and then outputs output (t), such as the Rnn cell, which we use to stack up in our task, That is, the current layer of the cell output also as the next layer of input, so can be introduced into each ce

Brief introduction This article describes the second method of data import for TensorFlow. This approach is somewhat cumbersome to maintain efficiency. There are several steps to be divided:-Write all samples to binary (execute only once)-Create tensor to read a sample from a binary file-Create tensor, randomly read a mini-batch from binary files-Mini-batchtensor the incoming network as an input node. binary files Use Tf.python_io. Tfrecordwriter crea

Tensorflow-tensor Understanding and use Flyfish How to understand the tensor in TensorFlowTensor tensorEnglish [' tensə-sɔː] beauty [' Tɛnsɚ] What is a Tensor? Tensors are simply mathematical objects that can is used to describePhysical properties, just like scalars and vectors. In fact tensorsare merely a generalisation of scalars and vectors; A scalar is a zeroRank tensor, and a vector is a-a-rank tensor. Tensor is a simple mathematical object that

Reproduced in the Daily Digest of deep learning, convlstm principle and its tensorflow realizationThis document references convolutional LSTM network:a machine learning approach forPrecipitation nowcasting Today introduced a very famous network structure--convlstm, it not only has the LSTM time series modelling ability, but also can like CNN to portray the local characteristic, can say is the spatiotemporal characteristic to have. Lstm has made great

Tf.diag (Diagonal,name=none) #生成对角矩阵 Import Tensorflowas TF; diagonal=[1,1,1,1] with TF. Session () as Sess: print (Sess.run (Tf.diag (diagonal))) #输出的结果为 [[1 0 0 0] [0 1 0 0] [0 0 1 0 ] [0 0 0 1]]Tf.diag_part (Input,name=none) #功能与tf. The Diag function, in contrast, returns the diagonal element of the diagonal array Import TensorFlow as TF; Diagonal =tf.constant ([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]]) with TF.