tensorflow classification

Mac Python3 installation TensorFlow error:futures requires Python ' >=2.6, Correspondence Resolution Link: https://github.com/tensorflow/tensorflow/issues/16478Same problem here on Ubuntu 16.04 with Python 3.5.Clearly, Python 3 doesn ' t need futures. And with Python 2.7, using:pip2 install tensorflow-gpu==1.5.0It goes

CX10 TensorFlow Application Practice of the most fire frame based on Python play to AIThe beginning of the new year, learning to be early, drip records, learning is progress!Do not look everywhere, seize the promotion of their own.For learning difficulties do not know how to improve themselves can be added: 1225462853 get information.CX103 TensorFlow Application Practice of the most fire frame based on Pyth

Just bought the new machine and installed the latest Ubuntu system. The GPU support is not selected according to the process installed on the official website. Download the source code. Git is required, and if you don't have Git installed you need to install it first. git clone --recurse-submodules https://github.com/tensorflow/tensorflow Installing BazelInstall dependencies First

The Ndarrray in List/tuple,numpy in Python and the tensor in TensorFlow.In Python, List/tuple understands that a sequence of data is understood only from the memory point of view, not the number of mathematical bids, vectors, and tensor.From the Python memory point of view, is a numeric value, length 1, and is not a sequence;From the perspective of NumPy and TensorFlow mathematics, it is a scalar, shape is (), its axis is 0;[1,2,3,4,5,6]From the Pytho

Objective Some time ago because the subject needs to use a period of time tensorflow, feel this framework is very interesting, in addition to can build complex neural network, but also can optimize the other needs of the calculation model, so I always want to learn to write a similar diagram calculation framework. In the last few days, the group will finish the decision to implement a rough version diagram computing framework that mimics the

1. General machine learning predictive function coefficient (y=0.1x+0.3) #-*-CODING:GBK-*- import tensorflow as tf import numpy as NP #生成数据, y=0.1x+0.3 X_data=np.random.rand ( Astype (np.float32) y_data=x_data*0.1+0.3 # # #开始创建tensorflow结构 ### WEIGHT=TF. Variable (Tf.random_uniform ([1],-1.0,1.0)) BIASES=TF. Variable (Tf.zeros ([1])) y=weight*x_data+biases #误差 Loss=tf.reduce_mean (Tf.square (y-y_data)

In the deep learning network structure, the categories of each layer can be divided into these kinds: convolution layer, full connection layer, Relu layer, pool layer and reverse convolution layer. At present, in pixel-level estimation and end-to-end learning problems, full convolution network shows his advantage, there is a very important layer, the convolution of the feature map sampling (deconvolution) to the input image dimension space, is the deconvolution layer. So how does it come to be a

the cause of the problem Today, try to install the CPU version of TensorFlow (GPU is not supported) by PIP3 install TensorFlow installation. The installation went well and I ran the simplest Hello wolrd example. The results do run out and a warning pops up. 2017-11-28 09:07:17.849180:i tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instru

Today learned the next TensorFlow official website on the CIFAR10 section, found some API has not seen before, here to tidy up a bit.CIFAR10 Tutorial Address 1. The first is the initialization of some parameters FLAGS = Tf.app.flags.FLAGS # Basic model parameters. Tf.app.flags.DEFINE_integer (' batch_size ', +, "" "Number of images to process in a batch." ") Tf.app.flags.DEFINE_string (' Data_dir ', '/temp/cifar10_data ',

TensorFlow and Theano and Keras are deep learning frameworks, TensorFlow and Theano are more flexible and difficult to learn, they are actually a differentiator. Keras is actually TensorFlow and Keras interface (Keras as the front end, TensorFlow or Theano as the back end), it is also very flexible, and relatively eas

Law One: Cycle Print Templates for (x, y) in Zip (Tf.global_variables (), Sess.run (Tf.global_variables ())): print ' \ n ', X, y Example # Coding=utf-8 Import TensorFlow as tf def func (In_put, Layer_name, is_training=true): With Tf.variable_scope (layer _name, REUSE=TF. Auto_reuse): bn = Tf.contrib.layers.batch_norm (Inputs=in_put, decay=0.9, Is_training=is_training, Updates_coll Ections=none) return

TensorFlow model save/load When we use an algorithmic model on-line, we must first save the trained model. TensorFlow the way to save the model is not the same as Sklearn, Sklearn is straightforward, a sklearn.externals.joblib dump and load method can be saved and loaded using. and TensorFlow because of the graph, operation these concepts, save and load the mode

TensorFlow implements AutoEncoder self-encoder, I. Overview AutoEncoder is a learning method that compresses and downgrades the high-dimensional features of data, and then undergoes the opposite decoding process. The final result obtained by decoding is compared with the original data during the learning process. The loss function is reduced by modifying the weight offset parameter, which continuously improves the ability to restore the original data.

If TensorFlow is so great, why open source it rather than keep it proprietary? The answer is simpler than you might think:we believe, which machine learning are a key ingredient to the innovative product S and technologies of the future. Growing fast, but lacks standard tools. By sharing "What we believe to be one of the best machine learning toolboxes in the world, we hope to create an open Standa Rd for exchanging the ideas and putting machine learn

Some time ago, made a compilation of the example of CC, finally finally fix ... But to compile in the IDE is not successful, continue to explore.Now share, explore the process, welcome nagging, Exchange.http://home.cnblogs.com/u/mydebug/Prepare: inception_dec_2015 files to the Data folderConcrete Look Https://github.com/tensorflow/tensorflow/tree/master/tensorflow