best tensorflow course

Always keep abreast of the times and the pace of technology-machine learning will eventually change our lives, study and work, perhaps in the near future.Today, I am ready to try to build a learning environment based on my big Google TensorFlow, which day may be able to teach it to play games, or can assist in the game development to build some interesting levels, first do accumulate and cultivate interest it. Well, the nonsense is not much to say, ge

1. Introduction Comparison and Analysis of differences between TF. Variable/TF. get_variable | TF. name_scope/TF. variable_scope 2. Description TF. Variable: create variable; TF. get_variable: Create and obtain variable TF. Variable automatically detects and processes name conflicts. TF. get_variable reports an error when reuse is not set. TF. name_scope does not have the reuse function. TF. get_variable returns an error in variable conflict. TF. variable_scope has the reuse function, which

1. Import tensorflow Import tensorflow as TF Ii. Define a computing Diagram (1) constant Initialization Constant_name = TF. Constant (value) (2) variable Initialization Create variable: Name_variable = TF. Variable (value, name) Initialization of individual variables: Init_op = name_variable.initializer () Initialize all variables: Init_op = TF. global_variables_initializer () Note: If the variable type is

After you install TensorFlow, open a Python environment and start running and using TensorFlow.First give an instance,#先导入TensorFlowImport TensorFlow as TF# Create TensorFlow object called Hello_constantHello_constant = Tf.constant (' Hello world! ')With TF. Session () as Sess:# Run The tf.constant operation in the sessionOutput = Sess.run (hello_constant)Print (

TensorFlow implements RNN Recurrent Neural Network, tensorflowrnn RNN (recurrent neural Network) recurrent neural Network It is mainly used for natural language processing (NLP) RNN is mainly usedProcess and predict sequence data RNN is widely used in speech recognition, language model, and machine translation. The source of RNN isDepicts the current output of a sequence and the previous information that affects the output of subsequent nodes. RNN isC

1.Build Docker ImageBecause you always have problems with your build image, here is a temporary lease on a mirror on Dockerhub docker.io/mochin/tensorflow-servingPush this image to the Docker registry of the K8s cluster2. Writing YamlIn the official example, a yaml is given, but some places are wrong, or the dockerimage is not applicable (probably because of the 0.4.0 version)Made some changes.Apiversion:extensions/v1beta1kind:deploymentmetadata: nam

With so long a tensorflow, the example ran n many, the basic flow is clear. But to do a small example of their own independence has come all sorts of questions. Like your own picture data set how to do. To tell the truth, TensorFlow is really annoying, the management of the document tutorial old mnist and cifar_10 this good data set said, for us beginners, completely do not know how to enter the picture. To

Let's say we've installed the TensorFlow. Generally in the installation of good TensorFlow, will run its demo, and the most common demo is handwritten digit recognition of the demo, that is, mnist data set. However, we just ran its demo, maybe a lot of people will have the same ideas as I do, if you bring a digital picture, how to use our training network model to identify, the following we will be Mnist de

TensorFlow in a Python environment, the easiest way to do this is to use the ANACONDA3 integrated environment for installation. First of all, download the anaconda3,3.6 version or version 2.7 of the Python version in the Anaconda3 website. You do not need to install the Python direct install Anconda3 beforehand, will automatically install the corresponding Python. After the Anaconda installation is successful, we need to modify its package mana

After training a good network, it is important to retrain and predict later. So this article is mainly about if the storage of good parameters and the use of well-trained parameters.The main APIs usedHttps://www.tensorflow.org/api_docs/python/tf/train/Saver The following example illustrates that the network is to construct a convolutional neural network for handwritten numerals to identify. https://github.com/xgli/TensorFlow-Examples/blob/master/examp

This post uses TensorFlow as an exercise to infer the age and gender of photos based on the face, and there are many similar apps online. Training Data –adience data set The adience data set is from Flickr, which is used by users on the iphone or other smartphone devices, and is primarily intended for unfiltered face estimation of age and gender. At the same time, the corresponding landmark is also labeled, which contains 2,284 categories and 26580 im

Issues encountered in installing tensor flow after the PIP package and Python have been installed: First, the default is the foreign mirror source, so limited, can cause errors or download speed is very slow. So look for a domestic Python image source (address: http://blog.csdn.net/yywan1314520/article/details/51045660). Second, the following error occurred in the installation because it was not added to a trusted address. Could not find a version this satisfies the requirement

Use the graph (graphs) to represent the calculation task, the node in the graph is called Operation OP (Operation). Each diagram represents its own calculation task. The TensorFlow Python library has a default diagram, and the first step in the start diagram is to create a session object, and if there are no parameters to create, the session builder starts the default diagram. The OP constructor can add nodes to it. Most of this default diagram is eno

Integrated TensorFlow TensorFlow is Google's Open framework for machine learning, the latest official version 1.0 released, the author played a bit, about the pit has the following: When the entire package is loaded in the GitHub, we need to enter the Tensorflow/tensorflow/contrib/makefile at the terminal and we can s

disconnects the connection arcs between certain nodes, so that they do not participate in the training for the time being.2. Data preprocessingThe data used for training is read first. from Import = input_data.read_data_sets ('./data/mnist', one_hot=true)In the preceding input layer, each sample entered is one-dimensional data, and the sample data of the convolutional neural network will be multidimensional. So we need to reshape the data we read to meet the requirements.Data.reshape ([BatchSi

In deeper networks, such as multilayer CNN or very long rnn, the problem of gradient disappearance (Gradient vanishing) or gradient explosion (Gradient exploding) may occur due to the chain rule of derivation.Principlequestion : Why does a gradient explosion cause instability in training and does not converge?Gradient explosion, in fact, is a large derivative of the meaning. Recall that we used the gradient descent method to update the parameters:The value of the loss function decreases along th

First, Introduction In many machine learning and depth learning applications, we find that the most used optimizer is Adam, why? The following is the optimizer in TensorFlow: See also for details: Https://www.tensorflow.org/api_guides/python/train In the Keras also have Sgd,rmsprop,adagrad,adadelta,adam, details: https://keras.io/optimizers/ We can find that in addition to the common gradient drop, there are several adadelta,adagrad,rmsprop and other

Virtual machine Ubuntu18.04 TensorFlow CPU version virtual machine vmware Configuration: 20G capacity, expandable 2G memory, expandable The network uses NAT mode Platform: Win10 under the Ubuntu18.04 Problems that arise Network connectivity Issues After the installation of VMware, you need to open all its services, normal connection should be the upper right corner of the three square sign, if

Error valueMeasure the loss error between two tensor or one tensor and 0, which can be used in a regression task or for the purpose of regularization (weight decay).loss tf.nn.l2_loss(t, name=None) Explanation: This function uses the L2 norm to calculate the error value of the tensor, but does not prescribe and takes only half of the value of the L2 norm, as follows: output = sum(t ** 2) / 2 Input parameters: t: One Tensor . The data type must be one of the followi

Environment: virtualenv xxx_pyvirtualenv -p python3 xxx_pyEnter the environment:source xxx_py/bin/activateExit:deactivate Use Tsinghua Mirror Temporary usepip install -i https://pypi.tuna.tsinghua.edu.cn/simple some-package Set as Defaultpip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple Resources:Tsinghua PyPI Mirror Use HelpVIRTUALENV Introduction and basic useOne of the essential artifacts of Python development: virtualenvvirtualenv