cwtLayerKeras

This project main goal is to produce a simple way to compute CWT (Continuous Wavelet Transformation) scalogram on signals 📡 with keras functional API.

The image above shows the performance of the layer on a TitanX GPU

Installation

pip install cwtLayerKeras

Usage

from cwtLayerKeras import Cwt as cwtt

# some code

def build_model():
    input_data = layers.Input(shape=(x_val.shape[-1],))
    cwt = cwtt(
        sample_count=x_val.shape[-1],
        scales_step=10,
        min_scale = 4,
        max_scale=224,
        output_size=(224, 224),
        depth_pad=2,
    )
    scalogram = cwt(input_data)
    base_model = EfficientNetB0(include_top=False, weights=None)(scalogram)

    x = layers.GlobalAveragePooling2D()(base_model)
    x = layers.BatchNormalization()(x)
    x = layers.Dropout(0.3)(x)

    predictions = layers.Dense(OUTPUT_SIZE, activation="softmax")(x)

    model = Model(inputs=input_data, outputs=predictions)

    model.summary()
    model.compile(
        optimizer="adam",
        loss="categorical_crossentropy",
        metrics=["accuracy"],
    )
    return model

Parameters

Paramenter	Type	Description	Why
depth_pad	int	It allows to specify the number of padding channel that the layer need to add	some standard keras model (like efficientnet) need 3 channel depth to execute, instead of just the single one of the scalogram
max_scale	int	Highest wavelet's scale
min_scale	int	Lowest wavelet's scale
output_size	tuple	Size of the returned scalograms
sample_count	int	# of Raman's shifts present in the tensor (in a tabular rapresentation,this number represent the amout of columns present in the table)
scales_step	float	Change wavelet's scale by this range step
trainable_kernels	bool	It make the wavelets that produce the scalogram trainable	interesting to see how/if the net optimizers change the wavelet kernel (research purpose)
wavelet	str	Wavelet kernel to use in the CWT. Now there are just "morl" (morlet), "mex_hat"/"rick" (mexican hat / ricker)

More insightful informations can be found in the code's docs 😉

Disclaimer:

❗ This project was built for research purpose, so there could be some errors❗

Feel free to open issues on those.

Contributers are welcome 👍