EasyCNN - Easy Creating and Visualizing CNN Model

EasyCNN it's liblary enabling quick creating, training and visualizing convolutional models (CNN).

Features

• Easy definition of CNN layers
• Training and evolution models
• Visualization of the model structure

Documentation

Documentation

Authors

• @Gabrli

Tech Stack

Languages: Python

Libraries: Tensorflow, Matplotlib, Numpy, OpenCv

License

MIT

FAQ

what are the advantages ?

• Very easy and comfortable syntax
• Full control for developer
• Automatic data preparation and visualization processes

What functionalities are under construction?

• Presets for popular models
• Exporter and Converter for files with models

Contributing

Contributions are always welcome!

See contributing.md for ways to get started.

Please adhere to this project's code of conduct.

Usage/Examples

from easycnn.core import EasyCNN
from easycnn.visualizer import TrainingVisualizer
import os
from tensorflow.keras.datasets import cifar10

class_names = ['car', 'plane', 'cat', 'dog', 'bird', 'deer', 'horse', 'frog', 'ship', 'truck']

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

my_file = os.path.join(os.path.dirname(__file__), 'car.jpg')

x_train = x_train[:2000]
y_train = y_train[:2000]
x_test = x_test[:400]
y_test = y_test[:400]
x_train = x_train / 255
x_test = x_test / 255

model = EasyCNN()
model.add_conv(32, 3)
model.add_max_pool(2)
model.add_conv(64, 3)
model.add_max_pool(2)
model.add_conv(128, 3)
model.add_max_pool(2)
model.add_flatten()
model.add_dense(10, activation='softmax')
model.compile()
history = model.train(x_train, y_train, x_test, y_test, epochs=5)
prediction = model.predict(my_file)

visualizer = TrainingVisualizer()
visualizer.plot_training(history)