Caption-Generation | Machine generate a reasonable caption | Machine Learning library

by m516825 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | Caption-Generation Summary

Caption-Generation is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Neural Network applications. Caption-Generation has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Caption-Generation build file is not available. You can download it from GitHub.

Caption-Generation

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Security

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Support

Caption-Generation has a low active ecosystem.

It has 5 star(s) with 0 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

Caption-Generation has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Caption-Generation is current.

Quality

Caption-Generation has no bugs reported.

Security

Caption-Generation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Caption-Generation is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

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

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Train the model
Compute the number of candidates in cand_d
Calculate brevity penalty
Perform beam search
Get the path of the node
Returns the best match in ref_l
Sigmoid decay function
Generate next batch
Compute BLEU score
Run inference
Count n - gram count by ngrams
Load text data
Transform a list of tokens
Builds the w2v matrix
Build the model
Generate training data
BLEU score
Loads all files in the given directory
Load valid features

Get all kandi verified functions for this library.

Caption-Generation Key Features

No Key Features are available at this moment for Caption-Generation.

Caption-Generation Examples and Code Snippets

No Code Snippets are available at this moment for Caption-Generation.

Community Discussions

Trending Discussions on Caption-Generation

Keras LSTM use softmax on every unit

Tokenizer.word_index did not contain "START" or "END", rather contained "start" and "end"

AttributeError: 'str' object has no attribute 'ndim', unable to use model.predict()

QUESTION

Keras LSTM use softmax on every unit

Asked 2020-Feb-22 at 17:17

I am creating a model somewhat similar to the one mentioned below: model

I am using Keras to create such model but have struck a dead end as I have not been able find a way to add SoftMax to outputs of the LSTM units. So far all the tutorials and helping material provides with information about outputting a single class even like in the case of image captioning as provided in this link.

So is it possible to apply SoftMax to every unit of LSTM (where return sequence is true) or do I have to move to pytorch.

...

ANSWER

Answered 2020-Jan-27 at 06:58

The answer is: yes, it is possible to apply to each unit of LSTM and no, you do not have to move to PyTorch.

While in Keras 1.X you needed to explicitly state that you add a TimeDistributed layer, in Keras 2.X you can just write:

Source https://stackoverflow.com/questions/59158684

QUESTION

Tokenizer.word_index did not contain "START" or "END", rather contained "start" and "end"

Asked 2018-May-15 at 16:49

I was trying to make an Image Captioning model in a similar fashion as in here I used ResNet50 instead off VGG16 and also had to use progressive loading via model.fit_generator() method. I used ResNet50 from here and when I imported it by setting include_top = False, It gave me features of photo in shape of {'key': [[[[value1, value2, .... value 2048]]]]}, where "key" is the image id. Here's my code of captionGenerator function:-

...

ANSWER

Answered 2018-May-15 at 16:49

That is because by default the Tokenizer lowers the words when fitting based on the lower=True parameter. You can either use the lower case or pass lower=False when creating the tokenizer, documentation.

Source https://stackoverflow.com/questions/50355007

QUESTION

AttributeError: 'str' object has no attribute 'ndim', unable to use model.predict()

Asked 2018-May-15 at 12:34

I was trying to make an Image Captioning model in a similar fashion as in here

I used ResNet50 instead off VGG16 and also had to use progressive loading via model.fit_generator() method.

I used ResNet50 from here and when I imported it by setting include_top = False, It gave me features of photo in shape of {'key': [[[[value1, value2, .... value 2048]]]]}, where "key" is the image id.

Here's my code of caption generator function:-

...

ANSWER

Answered 2018-May-15 at 12:34

You pass inSeq = "START" to model.predict as a string:

Source https://stackoverflow.com/questions/50349213

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

Vulnerabilities

No vulnerabilities reported

Install Caption-Generation

You can download it from GitHub.
You can use Caption-Generation 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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