udacity tensorflow

TensorFlow to use, first of all, you have to get the data input. The official documentation describes several of the 1. Read the data from memory to the matrix at once, input directly; 2. The input from the edge of the file is read, and the multithreaded read-write model has been designed; 3. Convert the data in the network or in-memory into the TensorFlow special format Tfrecord, and then read it after the

Code (with detailed comments for source code) and dataset can be downloaded in github: Https://github.com/crazyyanchao/TensorFlow-HelloWorld #-*-Coding:utf-8-*-' convolution neural network test mnist data ' ######## #导入MNIST数据 ######## from Tensorflow.examples.tutorials.mnist Import input_data import TensorFlow as tf mnist = input_data.read_data_sets (' mnist_data/', one_hot=true) # Create default Intera

TensorFlow matrix multiplication, rank different error In the TensorFlow wrote such a sentence: [python] view plain copy print? Y_out = Tf.matmul (outputs, W) Y_out = Tf.matmul (outputs, W) Where the shape of the outputs [16,336,400],w shape is [400,1] The following error occurred: Shape must be rank 2 but are rank 3 for ' Matmul ' (op: ' Matmul ') with input shapes: [16,336,400], [400,1]. NumPy, there'

Learning Google's deep learning finally a little bit of the prospect, to share my tensorflow learning process. TensorFlow's official Chinese document is jerky, and the dataset has been used in the Mnist binary dataset. And not much about how to build their own picture dataset Tfrecords. First paste my conversion code to transfer the pictures under the picture folder to the Tfrecords dataset. ###########################################################

Error: TENSORFLOW-0.5.0-CP27-NONE-LINUX_X86_64.WHL is isn't a supported wheel on this platform. Solve: wget https://bootstrap.pypa.io/get-pip.py sudo python2.7 get-pip.py sudo python2.7-m pip install HTTPS://STORAGE.GOOGLEAPIS.COM/TENSORFL OW/LINUX/CPU/TENSORFLOW-0.5.0-CP27-NONE-LINUX_X86_64.WHL $ python2.7 into execution Ref:http://stackoverflow.com

@author: Huangyongye@creat_date: 2017-03-09 Preface: According to my own learning TensorFlow realize lstm experience, found that although there are many tutorials on the internet, many of which are based on the official examples, using multi-layer lstm to achieve Ptbmodel language model, such as:TensorFlow notes: Multi-layer LSTM code AnalysisBut the feeling of these examples is still too complex, so here is a relatively simple version, although not e

Just beginning to contact TensorFlow, practice a small project, also refer to other bloggers of the article, I hope you put forward valuable comments. The code and images in the article have been uploaded to GitHub (Https://github.com/Quanfita/Neural-Style). What is image style migration. Each of the following pictures is a different art style. Intuitively it's hard to find out what these different styles of pictures have to do with the exact languag

Article Address: Http://blog.csdn.net/White_Idiot/article/details/78881261#insertcode Is learning TensorFlow combat 8.4.1, Thank you landlord. I was tensorflow API pit miserable and I know Tf.nn.seq2seq.sequence_loss_by_example No, but because there's another one. Tf.contrib.seq2seq.sequence_loss Input parameters Similar but requires the dimension is not the same, I reshape in that half a day, the las

are five-ply convolution plus 3-layer full link.3 Implementation of VGG network Source: Https://github.com/boyw165/tensorflow-vgg Here you can train your own VGG model, you can also load the existing VGG model to classify the image, in which the code of the VGG19 model is as follows, it is very nice to write, put everything in this class. Import NumPy as NP import TensorFlow as tf _vgg_mean = [103.939, 11

For machine learning, especially for deep-learning DL algorithms, model training can be time-consuming, hours or days, so if the test module is out of the question, it can be wasteful to re-run every time, so if there is no problem in the training section, then it is possible to save the training model directly. Then the next run loads the model directly and then tests it very conveniently. The class that holds the (save) and load (restore) models in Tensor