Learn_TensorFLow | 学习 TensorFLow 线性 & 逻辑回归多层感知机神经网络自编码循环神经网络优化记录 | Machine Learning library

by Ewenwan Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Learn_TensorFLow Summary

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

学习 TensorFLow 线性&逻辑回归多层感知机神经网络自编码循环神经网络优化记录

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Security

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Support

Learn_TensorFLow has a low active ecosystem.

It has 15 star(s) with 6 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

Learn_TensorFLow has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Learn_TensorFLow is current.

Quality

Learn_TensorFLow has 0 bugs and 0 code smells.

Security

Learn_TensorFLow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Learn_TensorFLow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Learn_TensorFLow 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.

Reuse

Learn_TensorFLow releases are not available. You will need to build from source code and install.

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

Top functions reviewed by kandi - BETA

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

Return data sets
Extract the labels from a MNIST label file
Read 4 bytes from bytestream
Convert a dense tensor to one - hot representation
Download a file if it exists
Extract images from a MNIST image file
Train the neural network
Generate recurrent network
Convolutional network
Train nnist
2D convolutional layer
Simulate the model
Max pooling op
Train the model by sentence
RNN layer
Bootstrap certificates
Train the network
Compute the encoder
Computes the decoder
Generate batch data

Get all kandi verified functions for this library.

Learn_TensorFLow Key Features

No Key Features are available at this moment for Learn_TensorFLow.

Learn_TensorFLow Examples and Code Snippets

No Code Snippets are available at this moment for Learn_TensorFLow.

Community Discussions

Trending Discussions on Learn_TensorFLow

Trying to restore model, but tf.train.import_meta_graph(meta_path) raises error

How to classify the image which have a larger size than the training sample using tensorflow

QUESTION

Trying to restore model, but tf.train.import_meta_graph(meta_path) raises error

Asked 2019-May-14 at 19:37

I downloaded pretrained mobilenetV2 models from tensorflow models,and try to restore the graph,but got unexpected error.

Codes to reproduce the error is pretty concise:

...

ANSWER

Answered 2019-Jan-10 at 21:57

There are some ops not defined. from conv_blocks import * will fix this bug but I got another problem "ValueError: NodeDef expected inputs 'float, int32' do not match 1 inputs specified;". Still debugging, but hope this tip solves your problem.

Source https://stackoverflow.com/questions/51836600

QUESTION

How to classify the image which have a larger size than the training sample using tensorflow

Asked 2019-Apr-30 at 08:58

I would like to identify trees in an image with the image size 6950 x 3715 and 3 channels (R,G,B) using keras model with training images with the size 256 x 256 and 3 channels (R,G,B).However, when predicting for the image with the size (6950 x 3715), it has error "Error when checking input: expected conv2d_input to have 4 dimensions, but got array with shape (25006, 17761, 3)".

How can I predict the image using the model has been built and export these trees identified into shapefile?

...

ANSWER

Answered 2019-Apr-30 at 08:29

It looks like the problem is that you are trying to evaluate on an image which doesn't have the right size. Generally, you should apply the same preprocessing to the images you evaluate on as to the images you train on, because the underlying assumption is that the training set and test set are drawn from the same distribution. For example, this gave me a prediction:

Source https://stackoverflow.com/questions/55911610

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

Vulnerabilities

No vulnerabilities reported

Install Learn_TensorFLow

You can download it from GitHub.
You can use Learn_TensorFLow like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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