A Python Keras model API
Project description
aitk.keras
An implementation of the main Keras API with of layers in numpy.
UNDER DEVELOPMENT
Why?
- useful to explain deep learning
- can be used where tensorflow is not available (eg, JupterLite)
Features
- supports Keras's Sequential and functional APIs
- alternative dataset downloader for JupyterLite
Examples:
# Classic XOR
from aitk.keras.layers import Input, Dense
from aitk.keras.models import Sequential
inputs = [[0, 0], [0, 1], [1, 0], [1, 1]]
targets = [[0], [1], [1], [0]]
model = Sequential()
model.add(Input(2, name="input"))
model.add(Dense(8, activation="tanh", name="hidden"))
model.add(Dense(1, activation="sigmoid", name="output"))
model.compile(optimizer="adam", loss="mse")
outputs = model.predict(inputs)
model.fit(inputs, targets, epochs=epochs, verbose=0, shuffle=False)
See the notebook directory for additional examples.
See also the examples in the tests folder.
Development
- implement shuffle
- report metrics to logs/history
- probably lots of edge cases ar broken
- see "FIXME" items in code
To run the tests:
$ pytest -vvv tests
Please feel free to report issues and make Pull Requests!
References
Lowlevel numpy code based on numpy_ml.
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