simplified-keras 0.0.1

Package includes common used code in Keras

Simplier keras

Common used actions in keras

Table of contents

• General info
• Libraries
• Setup
• Documentation
• PyPi
• TODO
• Development
• Status
• Contact

General info

This package is a set of common used actions in keras. At this moment includes:

• Fast train and validation generators creation
• Default callbacks
• Accuracy and Loss plot****
• Predictions with image plot
• Histogram with CDF and image plot

Libraries

• Keras - version 2.4.3
• Matplotlib - version 3.3.3
• NumPy - version 1.19.4
• OpenCV - version 4.4.0.46
• TensorFlow - version 2.4.0rc1

Setup

• Install from PyPi: pip install simplier-keras

Documentation

Status: in progress

Generators

from keras.preprocessing.image import ImageDataGenerator
from simplier_keras.dir_flow_generators import get_train_val_generators

img_size = (48, 48)
img_datagen = ImageDataGenerator(rescale=1/255)

train_generator, validation_generator = get_train_val_generators(img_datagen, data_dir='../data/normal/',
                                                                 color_mode='grayscale', target_size=img_size)

Signature:

def get_train_val_generators(img_datagen: ImageDataGenerator, data_dir='../data/', target_size=None, color_mode='rgb',
                             batch_size=128, class_mode='categorical')

Default callbacks

from simplier_keras.default_callbacks import get_default_callbacks

callbacks = get_default_callbacks('vgg16_calssifier')

hist = model.fit(train_features, train_labels, batch_size=128, epochs=100, validation_data=(val_features, val_labels),
                     callbacks=callbacks, verbose=2, steps_per_epoch=nr_of_train_imgs/train_batch_size)

Signature:

def get_default_callbacks(model_name):
    return [
        clb.ReduceLROnPlateau(monitor='val_acc', factor=0.5, min_lr=1e-6, patience=3, verbose=1),
        clb.EarlyStopping(monitor='val_acc', patience=7, verbose=1),
        clb.ModelCheckpoint(monitor='val_acc', filepath=f'../models/{model_name}.h5',
                            save_best_only=True, verbose=1)
    ]

Accuracy and Loss plot

from simplier_keras.plots.history_plots import plot_acc_and_loss

history = model.fit(train_generator,
                    steps_per_epoch=train_steps,
                    epochs=5,
                    validation_data=validation_generator,
                    validation_steps=valid_steps,
                    callbacks=callbacks
                    )

plot_acc_and_loss(history)

Result:

--add image--

Predictions with image plot

from keras.models import load_model
from keras.preprocessing.image import ImageDataGenerator
from simplier_keras.dir_flow_generators import get_train_val_generators
from simplier_keras.plots import plot_predictions_with_img

img_size = (48, 48)
img_datagen = ImageDataGenerator(rescale=1/255)

_, validation_generator = get_train_val_generators(img_datagen, data_dir='../data/normal/',
                                                                 color_mode='grayscale', target_size=img_size)
model = load_model('../models/standard_model.h5')

batch, labels = validation_generator.next()
preds = model.predict(batch)

plot_predictions_with_img(1, preds, labels, batch)

Result:

--add image--

Histogram with CDF and image plot

import cv2
from simplier_keras.plots import plot_gray_img_with_histogram

img = cv2.imread(f'{src_train_path}/0/241.png', 0)
plot_gray_img_with_histogram(img)
img2 = stretch_histogram(img)
plot_gray_img_with_histogram(img2)

Result:

--add image--

PyPi

simplier-keras

TODO

Development

Want to contribute? Great!

To fix a bug or enhance an existing module, follow these steps:

• Fork the repo
• Create a new branch (git checkout -b improve-feature)
• Make the appropriate changes in the files
• Verify if they are correct
• Add changes to reflect the changes made
• Commit changes
• Push to the branch (git push origin improve-feature)
• Create a Pull Request

Status

Library is: in progress

Contact

[e-mail] - feel free to contact me!