keras-extras | Extra batteries for Keras | Machine Learning library

by kuza55 Python Version: Current License: Apache-2.0

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

kandi X-RAY | keras-extras Summary

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

Extra batteries for Keras

Support

Quality

Security

License

Reuse

Support

keras-extras has a low active ecosystem.

It has 261 star(s) with 68 fork(s). There are 17 watchers for this library.

It had no major release in the last 6 months.

There are 15 open issues and 10 have been closed. On average issues are closed in 27 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of keras-extras is current.

Quality

keras-extras has 0 bugs and 2 code smells.

Security

keras-extras has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-extras code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-extras is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

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

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

keras-extras saves you 79 person hours of effort in developing the same functionality from scratch.

It has 203 lines of code, 8 functions and 6 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Calculate the decision probability matrix .
Creates a copy of the given model .
Builds the decision graph .
Initialize the Diff forest .
Make a dense layer .
Return the output shape for a given shape .

Get all kandi verified functions for this library.

keras-extras Key Features

No Key Features are available at this moment for keras-extras.

keras-extras Examples and Code Snippets

No Code Snippets are available at this moment for keras-extras.

Community Discussions

Trending Discussions on keras-extras

Dense layer probably produces InvalidArgumentError: Incompatible shapes: [0,2] vs. [32,2]

Keras' fit_generator extra training value

Keras predict not working for multiple GPU's

QUESTION

Dense layer probably produces InvalidArgumentError: Incompatible shapes: [0,2] vs. [32,2]

Asked 2021-Jan-22 at 14:11

The setup

I am using Python 3.6, TF 2.4.0 on an Azure DSVM STANDARD_NC6 (6 Cores, 56 GB RAM, 380 GB Disk) using 1 GPU

The parameters/model

I have training data: print(xtrain.shape) with shape (4599, 124, 124, 3) and ytrain | yval as categorical.

I use a classic generator

...

ANSWER

Answered 2021-Jan-22 at 14:11

The data you are feeding to the network must have the same shape than the input of the network. You are trying to feed a network that accept an image of dimensions 224x224x3 with data that has dimensions 124x124x3.

You can either:

load the mobilenet network with a compatible input dimension

Source https://stackoverflow.com/questions/65843923

QUESTION

Keras' fit_generator extra training value

Asked 2019-Jul-20 at 06:05

train_datagen = ImageDataGenerator(
                    rescale=1./255,
                    shear_range=0.1,
                    zoom_range=0.1,
                    rotation_range=5.,
                    width_shift_range=0.1,
                    height_shift_range=0.1)

val_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
                    train_data_dir,
                    target_size = (img_width, img_height),
                    batch_size = 20,
                    shuffle = True,
                    classes = TYPES,
                class_mode = 'categorical')

validation_generator = val_datagen.flow_from_directory(
                    val_data_dir,
                    target_size=(img_width, img_height),
                    batch_size=20,
                    shuffle = True,
                    classes = TYPES,
                    class_mode = 'categorical')

model.fit_generator(
                train_generator,
                samples_per_epoch = 2000,
                nb_epoch = 20
            )

Epoch 14/50
 480/2000 [======>.......................] - ETA: 128s - loss: 0.8708

Epoch 13/50
2021/2000 [==============================] - 171s - loss: 0.7973 - acc: 0.7041

...

ANSWER

Answered 2017-Apr-22 at 00:00

try change samples_per_epoch to your_train_data.shape[0]

Source https://stackoverflow.com/questions/43542226

QUESTION

Keras predict not working for multiple GPU's

Asked 2017-May-07 at 01:18

I recently implemented this make_parallel code (https://github.com/kuza55/keras-extras/blob/master/utils/multi_gpu.py) for testing on multiple GPUs. After implementing the predict_classes() function did not work with the new model structure, after some reading I switched to using the predict function instead. This function only works using certain batch sizes, for example a batch size of 750 works, while 500, 100 and 350 fails with the following error:

...

ANSWER

Answered 2017-May-07 at 01:18

If you are using make_parallel function, you need to make sure number of samples is divisible by batch_size*N, where N is the number of GPUs you are using. For example:

Source https://stackoverflow.com/questions/43620478

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

Vulnerabilities

No vulnerabilities reported

Install keras-extras

You can download it from GitHub.
You can use keras-extras 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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