keras-cor 0.2.0

Add a regularization if the features/columns/neurons the hidden layer or output layer should be correlated. The vector with target correlation coefficient is computed before the optimization, and compared with correlation coefficients computed across the batch examples.

keras-cor : Correlated Outputs Regularization

Add a regularization if the features/columns/neurons the hidden layer or output layer should be correlated. The vector with target correlation coefficient is computed before the optimization, and compared with correlation coefficients computed across the batch examples.

Usage

See demo notebook

from keras_cor import CorrOutputsRegularizer
import tensorflow as tf

# Simple regression NN
def build_mymodel(input_dim, target_corr, cor_rate=0.1, 
                  activation="sigmoid", output_dim=3):
    inputs = tf.keras.Input(shape=(input_dim,))
    h = tf.keras.layers.Dense(units=output_dim)(inputs)
    h = tf.keras.layers.Activation(activation)(h)
    outputs = CorrOutputsRegularizer(target_corr, cor_rate)(h)  # <= HERE
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

# Gneerate toy dataset
BATCH_SZ = 128
INPUT_DIM = 64
OUTPUT_DIM = 3

X_train = tf.random.normal([BATCH_SZ, INPUT_DIM])
y_train = tf.random.normal([BATCH_SZ, OUTPUT_DIM])

# Normally you should comput `target_corr` based on your target outputs `y_train`
# e.g., target_corr = tf.constant(y_train)
# However, you can also use subjective correlations (aka expert opinions), e.g.,
target_corr = tf.constant([.5, -.4, .9])

# Optimization
model = build_mymodel(input_dim=INPUT_DIM, target_corr=target_corr, output_dim=OUTPUT_DIM)
model.compile(optimizer=tf.keras.optimizers.Adam(), loss="mean_squared_error")
history = model.fit(X_train, y_train, verbose=1, epochs=2)

# Inference
yhat = model.predict(X_train)
rhos = pearson_vec(yhat)
rhos

Appendix

Installation

The keras-cor git repo is available as PyPi package

pip install keras-cor
pip install git+ssh://[e-mail]/ulf1/keras-cor.git

Install a virtual environment

python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt --no-cache-dir
pip install -r requirements-dev.txt --no-cache-dir
pip install -r requirements-demo.txt --no-cache-dir

(If your git repo is stored in a folder with whitespaces, then don't use the subfolder .venv. Use an absolute path without whitespaces.)

Python commands

• Jupyter for the examples: jupyter lab
• Check syntax: flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')
• Run Unit Tests: PYTHONPATH=. pytest

Publish

python setup.py sdist 
twine upload -r pypi dist/*

Clean up

find . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .pytest_cache
rm -r .venv

Support

Please open an issue for support.

Contributing

Please contribute using Github Flow. Create a branch, add commits, and open a pull request.