An attempt to recreate the most basic features of tensorflow and keras from scratch, using the numeric python library numpy.
This framework has been created for educational purposes only.
pip install dlfs
The usage is very similar to the keras library.
from dlfs.models import Sequential
from dlfs.layers import Dense, Dropout, Conv2D, Flatten
from dlfs.optimizers import SGDMomentum
model = Sequential()
model.add(Conv2D(1, (3, 3), activation='relu', input_shape=(28, 28, 1), convolution_type='simple'))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy', optimizer=SGDMomentum(learning_rate=0.1),
metrics=['accuracy'])
history = model.fit(X_train, y_train, epochs=20, batch_size=100, verbose=2,
validation_data=(X_test, y_test))For a complete example see: dlfs_examples.pynb.
You can do this easily if needed thanks to the requirements.txt file by just running the following command:
pip install -r requirements.txt
Everything is written in Python 3.7 (to be compatible with Google Colab).
This project is licensed under the Apache 2.0 license (see LICENSE.md).
This project was inspired by the Tensorflow and Keras framework.