Dataset_make | 数据集的相关制作程序

by xiaofengShi Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Dataset_make Summary

Dataset_make is a Python library. Dataset_make has no bugs, it has no vulnerabilities and it has low support. However Dataset_make build file is not available. You can download it from GitHub.

Dataset_make

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Dataset_make has a low active ecosystem.

It has 8 star(s) with 5 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of Dataset_make is current.

Quality

Dataset_make has no bugs reported.

Security

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

License

Dataset_make 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

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

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

Mouse move event
Return the intersection point between two points
Move vertex to bounding box
Move the shape by pos
Process image files
Creates a tf train Feature
Convert to tf train Example
Get a split
Creates a list of human readable names for the image
Make a tfrecord dataset
Creates a TFRecord
Handle closing the window
Check if the item is selected
Opens the previous image
Parse the XML file
Read label file
Rename a file
Enhance image
Build a lookup map from synset to human readable
Add two images
Runs train
Build a map of bounding boxes
Create a TFRecord
Processes an XML file
Create an HDF5 file
Zoom the scrollbar
Create a new shape dialog

Get all kandi verified functions for this library.

Dataset_make Key Features

No Key Features are available at this moment for Dataset_make.

Dataset_make Examples and Code Snippets

No Code Snippets are available at this moment for Dataset_make.

Community Discussions

Trending Discussions on Dataset_make

Wrong dimension received by Tensor Sequential model for 1D CNN model

QUESTION

Wrong dimension received by Tensor Sequential model for 1D CNN model

Asked 2021-Feb-18 at 10:07

I load my dataset from Pandas dataframe and follow the instructions given by Tensorflow, but i modified it since i have my own train,validation and test dataset. This is my dataframe datatypes and I load it using tensorflow dataset (target is CLASS, RECORD_NAME and MINUTE are not included):

...

ANSWER

Answered 2021-Feb-18 at 09:46

Tensorflow Conv1D expects input shape as [batchsize, steps, input_dims], and outputs [batchsize, new_steps, filters]. Batchsize can be any integer, often depending on a machine memory. Then your batch will be ``[batchsize, 150, 1]```. That's why you got that error.

Now you use model.fit. model.fit expects params to be x = tensor of [the num of total samples, 150, 1], y=[the num of total samples, 1], and you specify batch_size also

tf.keras.layers.Conv1D
Model.fit

add input_shape at the first layer.

Source https://stackoverflow.com/questions/66252421

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

Vulnerabilities

No vulnerabilities reported

Install Dataset_make

You can download it from GitHub.
You can use Dataset_make 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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