tensorflow-workshop | use TensorFlow to build | Machine Learning library

by SCUACM Python Version: Current License: No License

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kandi X-RAY | tensorflow-workshop Summary

tensorflow-workshop is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. tensorflow-workshop has no bugs, it has no vulnerabilities and it has low support. However tensorflow-workshop build file is not available. You can download it from GitHub.

How to use TensorFlow to build a convolutional neural network for classifying handwritten digits (MNIST).

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tensorflow-workshop has a low active ecosystem.

It has 4 star(s) with 1 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

tensorflow-workshop has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tensorflow-workshop is current.

Quality

tensorflow-workshop has no bugs reported.

Security

tensorflow-workshop has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

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tensorflow-workshop does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

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tensorflow-workshop releases are not available. You will need to build from source code and install.

tensorflow-workshop has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed tensorflow-workshop and discovered the below as its top functions. This is intended to give you an instant insight into tensorflow-workshop implemented functionality, and help decide if they suit your requirements.

Evaluate the mnist classifier
Preprocess the data from my hand written in the data folder

Get all kandi verified functions for this library.

tensorflow-workshop Key Features

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tensorflow-workshop Examples and Code Snippets

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Community Discussions

Trending Discussions on tensorflow-workshop

ValueError: invalid literal for int() with base 10 on Alexnet

Transfer learning/ retraining with TensorFlow Estimators

What level of control is required for google cloud ml

QUESTION

ValueError: invalid literal for int() with base 10 on Alexnet

Asked 2018-Feb-28 at 11:55

Hei, I got an error when running code for Alexnet feature extraction. I createalexnet.pb file using this github link. I checked using Tensorboard and the graph went well.

I want to use this model to extract feature from fc7/relu and feed it to another model. I create the graph using this:

...

ANSWER

Answered 2018-Feb-28 at 11:55

This line:

Source https://stackoverflow.com/questions/49027781

QUESTION

Transfer learning/ retraining with TensorFlow Estimators

Asked 2018-Jan-11 at 16:13

I have been unable to figure out how to use transfer learning/last layer retraining with the new TF Estimator API.

The Estimator requires a model_fn which contains the architecture of the network, and training and eval ops, as defined in the documentation. An example of a model_fn using a CNN architecture is here.

If I want to retrain the last layer of, for example, the inception architecture, I'm not sure whether I will need to specify the whole model in this model_fn, then load the pre-trained weights, or whether there is a way to use the saved graph as is done in the 'traditional' approach (example here).

This has been brought up as an issue, but is still open and the answers are unclear to me.

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ANSWER

Answered 2018-Jan-11 at 16:13

It is possible to load the metagraph during model definition and use SessionRunHook to load the weights from a ckpt file.

Source https://stackoverflow.com/questions/47041724

QUESTION

What level of control is required for google cloud ml

Asked 2017-May-09 at 20:21

When using google cloud ML to train models:

The official examples https://github.com/GoogleCloudPlatform/cloudml-samples/blob/master/census/tensorflowcore/trainer/task.py uses hooks, is_client, MonitoredTrainingSession and some other complexity.

Is this required for cloud ml or is using this example enough: https://github.com/amygdala/tensorflow-workshop/tree/master/workshop_sections/wide_n_deep?

The documentation is a bit limited in terms of best practices and optimisation, will GCP ML handle the client/worker mode or do we need to set devices e.g. replica_device_setter and so on?

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ANSWER

Answered 2017-May-09 at 20:21

CloudML Engine is largely agnostic to how you write your TensorFlow programs. You provide a Python program, and the service executes it for you, providing it with some environment variables you can use to perform distributed training (if necessary), e.g., task index, etc.

census/tensorflowcore demonstrates how to do things with the "core" TensorFlow library -- how to do everything "from scratch", including using replica_device_setters, MonitoredTrainingSessions, etc.. This may be necessary sometimes for ultimate flexibility, but can be tedious.

Alongside the census/tensorflowcore example, you'll also see a sample called census/estimator. This example is based on a higher level library, which unfortunately is in contrib and therefore does not yet have a fully stable API (expect lots of deprecation warnings, etc.). Expect it to stabilize in a future version of TensorFlow.

That particularly library (known as Estimators) is a higher level API that takes care of a lot of the dirty work for you. It will parse TF_CONFIG for you and setup the replica_device_setter as well as handle the MonitoredTrainingSession and necessary Hooks, while remaining fairly customizable.

This is the same library that the wide and deep example you pointed to is based on and they are fully supported on the service.

Source https://stackoverflow.com/questions/43878199

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tensorflow-workshop

Before you start, clone or download this repository onto your machine. We will be running our program in Python 2.7 so we need to make sure that Python 2.7 is installed on your machine along with pip, the package manager we will be using for installing the rest of the packages we will be using in this tutorial. Windows: You will need to download Python 2.7 from this link. This installation should automatically install pip for you, so you don't have to worry about that.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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